librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-27 00:42:13	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 499 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-27 00:40:00	card stringlengths 11 1.01M
mini1013/master_cate_ac8	mini1013	2024-11-25T10:14:43Z	77	0	setfit	[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]	text-classification	2024-11-25T10:14:16Z	--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: '[10%+복수620]국내생산 남자여자 최대15켤레 페이크삭스 실리콘 덧신 양말 학생 무지 25_챠밍레이스실리콘_여성_베이지(4켤레) 발장난양말' - text: W616 따뜻한 두꺼운 순면 통파일 무지 긴 양말 여자 남자 빅사이즈 수면 겨울 덧신 니삭스 W432 골지 통파일 덧신_S(225-245mm)_블랙 삭스에이 - text: '[2차 11/14 예약배송][23FW] HEMISH LEG WARMER - MELANGE GREY MELANGE GREY_FREE 주식회사 타입스(Types Co.,Ltd)' - text: 도톰한 면두올 양말 국내생산/중목/장목/스니커즈/패션/학생 25~26_26.남녀 기모덧신_여)2켤레 / 블랙 투투삭스 - text: 도톰 엄지 양말 발가락 여 타비 삭스 기모 보온 컬러 여자 두꺼운 무지 연브라운 김민주 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.7735123253257968 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1.0 \| <ul><li>'자전거 등산 골프 겨울 발 다리토시 레그워머 브라운 디플코리아 (Digital Plus Korea)'</li><li>'국산 면 탁텔 겨울 방한 팔 다리 수면 토시 발 임산부 산후용품 수족냉증 겨울 방한 보온 기본 수면토시 그레이 세자매 양말'</li><li>'세븐다스 여자 레그워머 수면 여성 발토시 겨울 보온 SD001 그레이_FREE 아이보리'</li></ul> \| \| 0.0 \| <ul><li>'[매장발송] 마리떼 11/6 배송 3PACK EMBROIDERY SOCKS multi OS 와이에스마켓'</li><li>'에브리데이 플러스 쿠션 트레이닝 크루 삭스(3켤레) SX6888-100 024 '</li><li>'[롯데백화점]언더아머(백) 유니섹스 UA 코어 쿼터 양말 - 3켤레 1358344-100 1.LG 롯데백화점_'</li></ul> \| ## Evaluation ### Metrics \| Label \| Metric \| \|:--------\|:-------\| \| all \| 0.7735 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac8") # Run inference preds = model("도톰 엄지 양말 발가락 여 타비 삭스 기모 보온 컬러 여자 두꺼운 무지 연브라운 김민주") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 4 \| 10.82 \| 24 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0.0 \| 50 \| \| 1.0 \| 50 \| ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0625 \| 1 \| 0.4226 \| - \| \| 3.125 \| 50 \| 0.0022 \| - \| \| 6.25 \| 100 \| 0.0001 \| - \| \| 9.375 \| 150 \| 0.0001 \| - \| \| 12.5 \| 200 \| 0.0001 \| - \| \| 15.625 \| 250 \| 0.0001 \| - \| \| 18.75 \| 300 \| 0.0001 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
mini1013/master_cate_ac7	mini1013	2024-11-25T10:12:50Z	137	0	setfit	[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]	text-classification	2024-11-25T10:12:28Z	--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 가스코 가죽전용염색약 소파 카시트 스니커즈 33색상 100ml 다크브라운 주식회사가스코 - text: 레인슈즈 장화 방수 부츠 수중작업 신발보호 고무 미끄럼방지 여성용 H_M 34-36 지에스 - text: 가스코 가죽전용염색약 도구 풀세트 가죽옷 100ml 브라운_무광 주식회사 가스코 - text: 엑스솔 에어슬림 인솔 기능성 신발 깔창 245mm 주식회사 영창에코 - text: 깁스 양말 싸개 발 보호 보온 방한 편한 이쁜 롱 부츠형 여성 방수커버 샤워 팔 남성용 플러시 슬리브/두꺼운 버전 높이 35_45 핑크고릴라 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.9254610935283204 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 7 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 6.0 \| <ul><li>'등산화끈 1+1 통끈_라인네이비 신세계몰'</li><li>'등산화끈 1+1 트위스트_브라운 신세계몰'</li><li>'몽벨 슈레이스 플랫 4MM YELLOW JBSXXUZZ105 신발끈 평끈 등산화끈 140 (주)코어밸류'</li></ul> \| \| 2.0 \| <ul><li>'고급 강남스타힐 구두굽/소음방지/충격완화/하이힐굽 블랙_DD-107 슈미즈'</li><li>'발 뒤꿈치 패드 쿠션 신발 구두 운동화 사이즈 클때 줄이기 패드 6-피넛_아이보리화이트_One Size(2P) 저스트에잇'</li><li>'고급 강남스타힐 구두굽/소음방지/충격완화/하이힐굽 블랙_DD-092 슈미즈'</li></ul> \| \| 5.0 \| <ul><li>'[웰럽] 시그니처 깔창 아치 운동화 등산화 군대 군인 군화 안전화 평발 기능성 키높이 [0008]그린 M(255 270) CJONSTYLE'</li><li>'[롯데백화점]에코(슈즈) 컴포트 에브리데이 인솔 멘즈 9059029-00101 블랙_EU 39 롯데백화점_'</li><li>'등산화 깔창 기능성 운동화 특수 스포츠 신발 키높이 골프화 XL275-295 마켓퀸즈'</li></ul> \| \| 0.0 \| <ul><li>'[현대백화점]금강제화 랜드로바 SHOSC0150SAM 휴대용 미니 구두헤라 [00001] 휴대용 구두칼 (주)현대홈쇼핑'</li><li>'에드가 체크 소가죽 휴대용 슈혼 navy 000 (주)트라이본즈'</li><li>'[금강제화](광주신세계) 콜렉션 휴대용 슈혼 스틸 미니 구두 헤라 N8MKA150/SHOSC0150SAM 10.5cm 신세계백화점'</li></ul> \| \| 4.0 \| <ul><li>'비오는날 남성 여성 1회용비닐덧신 S M L 비올때신발 여름필수품 신발우비 색상_레인신발커버 투명블루M 오픈리빙'</li><li>'비올때 이색적인 여성용 싱글 슈즈 가죽 신발 여성 패션 레인신발커버 멋스러운코디 13_39 스톰브랜상범'</li><li>'투명 슈즈 패션 워터 장마 여성장화 미끄럼방지 학생 XXL(43-45 적합)_블루-하이 [미끄럼방지창x2년 품질] 구룡글로벌'</li></ul> \| \| 1.0 \| <ul><li>'곰돌이 블랙 검정하트 화이트 남자 성인 커플 지비추 자비츠 심플 파츠 클로그 참 장식신발 A set (블랙-화이트) 뉴지(NYUZY)'</li><li>'슈팁 금속팁 메탈락팁 듀브레 메탈밴드 악어클립 메탈고정핀 플라스틱고정핀 골드슈팁 황동슈팁 실버슈팁 금속슈팁 굵은골드(4개) 슈레이스'</li><li>'(SAPHIR) 사피르 레노베이팅 컬러 재생크림 / 가죽 염색제 리노베이팅 미디엄브라운 제이엠컴퍼니'</li></ul> \| \| 3.0 \| <ul><li>'STRATTON 남성용 삼나무 슈트리- 미국산, m / 9 - 10 알쓰리컴퍼니'</li><li>'발볼 여자 신발 남자 제골기 발등 여성하이힐 발등 코코나라'</li><li>'슈샤이너 전기 금속제골기 경첩타입 전문가용 레지가다 업소용 여성용 js9997'</li></ul> \| ## Evaluation ### Metrics \| Label \| Metric \| \|:--------\|:-------\| \| all \| 0.9255 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac7") # Run inference preds = model("엑스솔 에어슬림 인솔 기능성 신발 깔창 245mm 주식회사 영창에코") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 3 \| 10.4257 \| 27 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0.0 \| 50 \| \| 1.0 \| 50 \| \| 2.0 \| 50 \| \| 3.0 \| 50 \| \| 4.0 \| 50 \| \| 5.0 \| 50 \| \| 6.0 \| 50 \| ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:----:\|:-------------:\|:---------------:\| \| 0.0182 \| 1 \| 0.3761 \| - \| \| 0.9091 \| 50 \| 0.2291 \| - \| \| 1.8182 \| 100 \| 0.033 \| - \| \| 2.7273 \| 150 \| 0.018 \| - \| \| 3.6364 \| 200 \| 0.0001 \| - \| \| 4.5455 \| 250 \| 0.0001 \| - \| \| 5.4545 \| 300 \| 0.0001 \| - \| \| 6.3636 \| 350 \| 0.0001 \| - \| \| 7.2727 \| 400 \| 0.0001 \| - \| \| 8.1818 \| 450 \| 0.0 \| - \| \| 9.0909 \| 500 \| 0.0 \| - \| \| 10.0 \| 550 \| 0.0 \| - \| \| 10.9091 \| 600 \| 0.0 \| - \| \| 11.8182 \| 650 \| 0.0 \| - \| \| 12.7273 \| 700 \| 0.0 \| - \| \| 13.6364 \| 750 \| 0.0 \| - \| \| 14.5455 \| 800 \| 0.0 \| - \| \| 15.4545 \| 850 \| 0.0 \| - \| \| 16.3636 \| 900 \| 0.0 \| - \| \| 17.2727 \| 950 \| 0.0001 \| - \| \| 18.1818 \| 1000 \| 0.0 \| - \| \| 19.0909 \| 1050 \| 0.0 \| - \| \| 20.0 \| 1100 \| 0.0 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
MayBashendy/Arabic_FineTuningAraBERT_AugV5_k3_task3_organization_fold1	MayBashendy	2024-11-25T10:12:48Z	161	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T10:11:10Z	--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k3_task3_organization_fold1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Arabic_FineTuningAraBERT_AugV5_k3_task3_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6023 - Qwk: 0.1852 - Mse: 0.6023 - Rmse: 0.7761 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 0.1111 \| 2 \| 3.2691 \| 0.0041 \| 3.2691 \| 1.8081 \| \| No log \| 0.2222 \| 4 \| 1.5440 \| -0.0168 \| 1.5440 \| 1.2426 \| \| No log \| 0.3333 \| 6 \| 1.0494 \| -0.4426 \| 1.0494 \| 1.0244 \| \| No log \| 0.4444 \| 8 \| 0.7627 \| 0.0 \| 0.7627 \| 0.8733 \| \| No log \| 0.5556 \| 10 \| 0.7207 \| -0.0421 \| 0.7207 \| 0.8489 \| \| No log \| 0.6667 \| 12 \| 1.1277 \| -0.1000 \| 1.1277 \| 1.0619 \| \| No log \| 0.7778 \| 14 \| 0.7978 \| -0.2791 \| 0.7978 \| 0.8932 \| \| No log \| 0.8889 \| 16 \| 0.6799 \| 0.0 \| 0.6799 \| 0.8246 \| \| No log \| 1.0 \| 18 \| 0.6912 \| 0.0 \| 0.6912 \| 0.8314 \| \| No log \| 1.1111 \| 20 \| 0.7478 \| -0.0233 \| 0.7478 \| 0.8647 \| \| No log \| 1.2222 \| 22 \| 0.7894 \| -0.2692 \| 0.7894 \| 0.8885 \| \| No log \| 1.3333 \| 24 \| 0.8841 \| -0.1074 \| 0.8841 \| 0.9403 \| \| No log \| 1.4444 \| 26 \| 0.9616 \| 0.1646 \| 0.9616 \| 0.9806 \| \| No log \| 1.5556 \| 28 \| 0.8752 \| 0.0403 \| 0.8752 \| 0.9355 \| \| No log \| 1.6667 \| 30 \| 0.7599 \| -0.2655 \| 0.7599 \| 0.8717 \| \| No log \| 1.7778 \| 32 \| 0.8963 \| 0.0571 \| 0.8963 \| 0.9467 \| \| No log \| 1.8889 \| 34 \| 1.2057 \| 0.0 \| 1.2057 \| 1.0980 \| \| No log \| 2.0 \| 36 \| 1.1424 \| 0.0 \| 1.1424 \| 1.0688 \| \| No log \| 2.1111 \| 38 \| 0.9274 \| 0.0 \| 0.9274 \| 0.9630 \| \| No log \| 2.2222 \| 40 \| 0.6915 \| 0.0 \| 0.6915 \| 0.8316 \| \| No log \| 2.3333 \| 42 \| 0.6934 \| -0.0577 \| 0.6934 \| 0.8327 \| \| No log \| 2.4444 \| 44 \| 1.0658 \| 0.0120 \| 1.0658 \| 1.0324 \| \| No log \| 2.5556 \| 46 \| 2.2327 \| -0.0267 \| 2.2327 \| 1.4942 \| \| No log \| 2.6667 \| 48 \| 1.8881 \| 0.0 \| 1.8881 \| 1.3741 \| \| No log \| 2.7778 \| 50 \| 1.3046 \| 0.0 \| 1.3046 \| 1.1422 \| \| No log \| 2.8889 \| 52 \| 0.8297 \| 0.1879 \| 0.8297 \| 0.9109 \| \| No log \| 3.0 \| 54 \| 0.6838 \| 0.0 \| 0.6838 \| 0.8269 \| \| No log \| 3.1111 \| 56 \| 0.6598 \| 0.0 \| 0.6598 \| 0.8123 \| \| No log \| 3.2222 \| 58 \| 0.6455 \| 0.0 \| 0.6455 \| 0.8034 \| \| No log \| 3.3333 \| 60 \| 0.6495 \| 0.0 \| 0.6495 \| 0.8059 \| \| No log \| 3.4444 \| 62 \| 0.6547 \| 0.0 \| 0.6547 \| 0.8092 \| \| No log \| 3.5556 \| 64 \| 0.6983 \| 0.0 \| 0.6983 \| 0.8357 \| \| No log \| 3.6667 \| 66 \| 0.8622 \| -0.1074 \| 0.8622 \| 0.9286 \| \| No log \| 3.7778 \| 68 \| 1.0773 \| 0.0 \| 1.0773 \| 1.0379 \| \| No log \| 3.8889 \| 70 \| 1.0490 \| 0.0 \| 1.0490 \| 1.0242 \| \| No log \| 4.0 \| 72 \| 1.0322 \| 0.0 \| 1.0322 \| 1.0160 \| \| No log \| 4.1111 \| 74 \| 0.9420 \| 0.0 \| 0.9420 \| 0.9706 \| \| No log \| 4.2222 \| 76 \| 0.8430 \| 0.0571 \| 0.8430 \| 0.9181 \| \| No log \| 4.3333 \| 78 \| 0.7792 \| -0.0577 \| 0.7792 \| 0.8827 \| \| No log \| 4.4444 \| 80 \| 0.7126 \| -0.0233 \| 0.7126 \| 0.8441 \| \| No log \| 4.5556 \| 82 \| 0.6694 \| 0.0 \| 0.6694 \| 0.8182 \| \| No log \| 4.6667 \| 84 \| 0.6598 \| 0.0 \| 0.6598 \| 0.8123 \| \| No log \| 4.7778 \| 86 \| 0.6827 \| 0.0 \| 0.6827 \| 0.8263 \| \| No log \| 4.8889 \| 88 \| 0.7243 \| 0.1239 \| 0.7243 \| 0.8511 \| \| No log \| 5.0 \| 90 \| 0.7197 \| 0.1239 \| 0.7197 \| 0.8484 \| \| No log \| 5.1111 \| 92 \| 0.7406 \| 0.0984 \| 0.7406 \| 0.8606 \| \| No log \| 5.2222 \| 94 \| 0.7973 \| 0.0984 \| 0.7973 \| 0.8929 \| \| No log \| 5.3333 \| 96 \| 0.7799 \| 0.0984 \| 0.7799 \| 0.8831 \| \| No log \| 5.4444 \| 98 \| 0.7187 \| 0.1239 \| 0.7187 \| 0.8478 \| \| No log \| 5.5556 \| 100 \| 0.6871 \| 0.1239 \| 0.6871 \| 0.8289 \| \| No log \| 5.6667 \| 102 \| 0.6141 \| 0.0 \| 0.6141 \| 0.7836 \| \| No log \| 5.7778 \| 104 \| 0.5736 \| 0.0 \| 0.5736 \| 0.7574 \| \| No log \| 5.8889 \| 106 \| 0.5768 \| 0.0 \| 0.5768 \| 0.7595 \| \| No log \| 6.0 \| 108 \| 0.5742 \| 0.0 \| 0.5742 \| 0.7577 \| \| No log \| 6.1111 \| 110 \| 0.5836 \| 0.0 \| 0.5836 \| 0.7640 \| \| No log \| 6.2222 \| 112 \| 0.5875 \| 0.0 \| 0.5875 \| 0.7665 \| \| No log \| 6.3333 \| 114 \| 0.5939 \| 0.0 \| 0.5939 \| 0.7706 \| \| No log \| 6.4444 \| 116 \| 0.6115 \| 0.0 \| 0.6115 \| 0.7820 \| \| No log \| 6.5556 \| 118 \| 0.6120 \| 0.0 \| 0.6120 \| 0.7823 \| \| No log \| 6.6667 \| 120 \| 0.6020 \| -0.0233 \| 0.6020 \| 0.7759 \| \| No log \| 6.7778 \| 122 \| 0.6050 \| 0.1895 \| 0.6050 \| 0.7778 \| \| No log \| 6.8889 \| 124 \| 0.6034 \| -0.0233 \| 0.6034 \| 0.7768 \| \| No log \| 7.0 \| 126 \| 0.6037 \| -0.0233 \| 0.6037 \| 0.7770 \| \| No log \| 7.1111 \| 128 \| 0.6084 \| -0.0233 \| 0.6084 \| 0.7800 \| \| No log \| 7.2222 \| 130 \| 0.6394 \| 0.0 \| 0.6394 \| 0.7996 \| \| No log \| 7.3333 \| 132 \| 0.6494 \| 0.0 \| 0.6494 \| 0.8059 \| \| No log \| 7.4444 \| 134 \| 0.6215 \| -0.0233 \| 0.6215 \| 0.7884 \| \| No log \| 7.5556 \| 136 \| 0.6098 \| 0.1895 \| 0.6098 \| 0.7809 \| \| No log \| 7.6667 \| 138 \| 0.6302 \| 0.1538 \| 0.6302 \| 0.7939 \| \| No log \| 7.7778 \| 140 \| 0.6763 \| 0.1239 \| 0.6763 \| 0.8224 \| \| No log \| 7.8889 \| 142 \| 0.6795 \| 0.1239 \| 0.6795 \| 0.8243 \| \| No log \| 8.0 \| 144 \| 0.6233 \| 0.1538 \| 0.6233 \| 0.7895 \| \| No log \| 8.1111 \| 146 \| 0.5968 \| 0.1895 \| 0.5968 \| 0.7725 \| \| No log \| 8.2222 \| 148 \| 0.6159 \| 0.0 \| 0.6159 \| 0.7848 \| \| No log \| 8.3333 \| 150 \| 0.6337 \| 0.0 \| 0.6337 \| 0.7961 \| \| No log \| 8.4444 \| 152 \| 0.6319 \| 0.0 \| 0.6319 \| 0.7949 \| \| No log \| 8.5556 \| 154 \| 0.6303 \| 0.0 \| 0.6303 \| 0.7939 \| \| No log \| 8.6667 \| 156 \| 0.6209 \| 0.0 \| 0.6209 \| 0.7880 \| \| No log \| 8.7778 \| 158 \| 0.6141 \| 0.1852 \| 0.6141 \| 0.7837 \| \| No log \| 8.8889 \| 160 \| 0.6148 \| 0.1852 \| 0.6148 \| 0.7841 \| \| No log \| 9.0 \| 162 \| 0.6149 \| 0.1852 \| 0.6149 \| 0.7842 \| \| No log \| 9.1111 \| 164 \| 0.6135 \| 0.1852 \| 0.6135 \| 0.7832 \| \| No log \| 9.2222 \| 166 \| 0.6099 \| 0.1852 \| 0.6099 \| 0.7810 \| \| No log \| 9.3333 \| 168 \| 0.6068 \| 0.1852 \| 0.6068 \| 0.7790 \| \| No log \| 9.4444 \| 170 \| 0.6060 \| 0.1852 \| 0.6060 \| 0.7784 \| \| No log \| 9.5556 \| 172 \| 0.6050 \| 0.1852 \| 0.6050 \| 0.7778 \| \| No log \| 9.6667 \| 174 \| 0.6033 \| 0.1852 \| 0.6033 \| 0.7767 \| \| No log \| 9.7778 \| 176 \| 0.6029 \| 0.1852 \| 0.6029 \| 0.7765 \| \| No log \| 9.8889 \| 178 \| 0.6023 \| 0.1852 \| 0.6023 \| 0.7761 \| \| No log \| 10.0 \| 180 \| 0.6023 \| 0.1852 \| 0.6023 \| 0.7761 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
braindao/iq-code-evmind-14b-instruct-v0.2411.1	braindao	2024-11-25T10:10:34Z	8	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T10:05:01Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
MayBashendy/Arabic_FineTuningAraBERT_AugV5_k2_task3_organization_fold1	MayBashendy	2024-11-25T10:07:49Z	162	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T10:05:33Z	--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k2_task3_organization_fold1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Arabic_FineTuningAraBERT_AugV5_k2_task3_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6027 - Qwk: 0.0222 - Mse: 0.6027 - Rmse: 0.7763 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 0.1429 \| 2 \| 3.6943 \| 0.0 \| 3.6943 \| 1.9220 \| \| No log \| 0.2857 \| 4 \| 1.8695 \| 0.0 \| 1.8695 \| 1.3673 \| \| No log \| 0.4286 \| 6 \| 1.0940 \| 0.0 \| 1.0940 \| 1.0459 \| \| No log \| 0.5714 \| 8 \| 0.7397 \| 0.3654 \| 0.7397 \| 0.8601 \| \| No log \| 0.7143 \| 10 \| 0.6617 \| -0.0233 \| 0.6617 \| 0.8135 \| \| No log \| 0.8571 \| 12 \| 0.6396 \| -0.0233 \| 0.6396 \| 0.7997 \| \| No log \| 1.0 \| 14 \| 0.7214 \| -0.0577 \| 0.7214 \| 0.8493 \| \| No log \| 1.1429 \| 16 \| 0.8774 \| 0.2143 \| 0.8774 \| 0.9367 \| \| No log \| 1.2857 \| 18 \| 0.7807 \| 0.0984 \| 0.7807 \| 0.8836 \| \| No log \| 1.4286 \| 20 \| 0.6395 \| 0.0 \| 0.6395 \| 0.7997 \| \| No log \| 1.5714 \| 22 \| 0.6691 \| 0.0 \| 0.6691 \| 0.8180 \| \| No log \| 1.7143 \| 24 \| 0.8455 \| -0.0916 \| 0.8455 \| 0.9195 \| \| No log \| 1.8571 \| 26 \| 1.2566 \| 0.0 \| 1.2566 \| 1.1210 \| \| No log \| 2.0 \| 28 \| 1.9493 \| 0.0 \| 1.9493 \| 1.3962 \| \| No log \| 2.1429 \| 30 \| 1.9458 \| 0.0 \| 1.9458 \| 1.3949 \| \| No log \| 2.2857 \| 32 \| 1.4166 \| 0.0 \| 1.4166 \| 1.1902 \| \| No log \| 2.4286 \| 34 \| 1.0109 \| 0.0 \| 1.0109 \| 1.0054 \| \| No log \| 2.5714 \| 36 \| 0.7919 \| 0.2143 \| 0.7919 \| 0.8899 \| \| No log \| 2.7143 \| 38 \| 0.8311 \| 0.1879 \| 0.8311 \| 0.9117 \| \| No log \| 2.8571 \| 40 \| 0.9516 \| 0.0 \| 0.9516 \| 0.9755 \| \| No log \| 3.0 \| 42 \| 0.9142 \| 0.0 \| 0.9142 \| 0.9562 \| \| No log \| 3.1429 \| 44 \| 0.7883 \| 0.0763 \| 0.7883 \| 0.8879 \| \| No log \| 3.2857 \| 46 \| 0.8340 \| 0.0571 \| 0.8340 \| 0.9132 \| \| No log \| 3.4286 \| 48 \| 0.8201 \| 0.0571 \| 0.8201 \| 0.9056 \| \| No log \| 3.5714 \| 50 \| 0.7335 \| 0.1239 \| 0.7335 \| 0.8565 \| \| No log \| 3.7143 \| 52 \| 0.6410 \| 0.0 \| 0.6410 \| 0.8006 \| \| No log \| 3.8571 \| 54 \| 0.6221 \| 0.0 \| 0.6221 \| 0.7887 \| \| No log \| 4.0 \| 56 \| 0.6607 \| 0.0 \| 0.6607 \| 0.8128 \| \| No log \| 4.1429 \| 58 \| 0.8841 \| -0.0820 \| 0.8841 \| 0.9403 \| \| No log \| 4.2857 \| 60 \| 0.9114 \| -0.0820 \| 0.9114 \| 0.9547 \| \| No log \| 4.4286 \| 62 \| 0.7034 \| -0.0421 \| 0.7034 \| 0.8387 \| \| No log \| 4.5714 \| 64 \| 0.6314 \| 0.0 \| 0.6314 \| 0.7946 \| \| No log \| 4.7143 \| 66 \| 0.5949 \| 0.0 \| 0.5949 \| 0.7713 \| \| No log \| 4.8571 \| 68 \| 0.5761 \| 0.0 \| 0.5761 \| 0.7590 \| \| No log \| 5.0 \| 70 \| 0.5803 \| 0.0 \| 0.5803 \| 0.7618 \| \| No log \| 5.1429 \| 72 \| 0.6058 \| 0.1895 \| 0.6058 \| 0.7783 \| \| No log \| 5.2857 \| 74 \| 0.5757 \| 0.1895 \| 0.5757 \| 0.7588 \| \| No log \| 5.4286 \| 76 \| 0.5368 \| 0.0 \| 0.5368 \| 0.7327 \| \| No log \| 5.5714 \| 78 \| 0.5330 \| 0.0 \| 0.5330 \| 0.7301 \| \| No log \| 5.7143 \| 80 \| 0.5448 \| 0.0 \| 0.5448 \| 0.7381 \| \| No log \| 5.8571 \| 82 \| 0.5658 \| 0.0 \| 0.5658 \| 0.7522 \| \| No log \| 6.0 \| 84 \| 0.6706 \| 0.0 \| 0.6706 \| 0.8189 \| \| No log \| 6.1429 \| 86 \| 0.8092 \| 0.0222 \| 0.8092 \| 0.8996 \| \| No log \| 6.2857 \| 88 \| 0.8005 \| 0.0222 \| 0.8005 \| 0.8947 \| \| No log \| 6.4286 \| 90 \| 0.6768 \| 0.0222 \| 0.6768 \| 0.8227 \| \| No log \| 6.5714 \| 92 \| 0.5966 \| 0.0 \| 0.5966 \| 0.7724 \| \| No log \| 6.7143 \| 94 \| 0.6005 \| -0.0233 \| 0.6005 \| 0.7749 \| \| No log \| 6.8571 \| 96 \| 0.5849 \| -0.0233 \| 0.5849 \| 0.7648 \| \| No log \| 7.0 \| 98 \| 0.5442 \| 0.0 \| 0.5442 \| 0.7377 \| \| No log \| 7.1429 \| 100 \| 0.5926 \| 0.0222 \| 0.5926 \| 0.7698 \| \| No log \| 7.2857 \| 102 \| 0.5723 \| 0.0222 \| 0.5723 \| 0.7565 \| \| No log \| 7.4286 \| 104 \| 0.5054 \| 0.0 \| 0.5054 \| 0.7109 \| \| No log \| 7.5714 \| 106 \| 0.4898 \| 0.0 \| 0.4898 \| 0.6999 \| \| No log \| 7.7143 \| 108 \| 0.5001 \| 0.0 \| 0.5001 \| 0.7072 \| \| No log \| 7.8571 \| 110 \| 0.5214 \| -0.0421 \| 0.5214 \| 0.7221 \| \| No log \| 8.0 \| 112 \| 0.5205 \| 0.0 \| 0.5205 \| 0.7215 \| \| No log \| 8.1429 \| 114 \| 0.5150 \| 0.0 \| 0.5150 \| 0.7177 \| \| No log \| 8.2857 \| 116 \| 0.5367 \| 0.2667 \| 0.5367 \| 0.7326 \| \| No log \| 8.4286 \| 118 \| 0.6132 \| 0.2414 \| 0.6132 \| 0.7830 \| \| No log \| 8.5714 \| 120 \| 0.6227 \| 0.2326 \| 0.6227 \| 0.7891 \| \| No log \| 8.7143 \| 122 \| 0.6027 \| 0.2524 \| 0.6027 \| 0.7764 \| \| No log \| 8.8571 \| 124 \| 0.5833 \| 0.0222 \| 0.5833 \| 0.7637 \| \| No log \| 9.0 \| 126 \| 0.5827 \| 0.0222 \| 0.5827 \| 0.7634 \| \| No log \| 9.1429 \| 128 \| 0.5799 \| 0.0 \| 0.5799 \| 0.7615 \| \| No log \| 9.2857 \| 130 \| 0.5873 \| 0.0 \| 0.5873 \| 0.7664 \| \| No log \| 9.4286 \| 132 \| 0.5931 \| 0.0222 \| 0.5931 \| 0.7701 \| \| No log \| 9.5714 \| 134 \| 0.5958 \| 0.0222 \| 0.5958 \| 0.7719 \| \| No log \| 9.7143 \| 136 \| 0.5963 \| 0.0222 \| 0.5963 \| 0.7722 \| \| No log \| 9.8571 \| 138 \| 0.6013 \| 0.0222 \| 0.6013 \| 0.7754 \| \| No log \| 10.0 \| 140 \| 0.6027 \| 0.0222 \| 0.6027 \| 0.7763 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF	QuantFactory	2024-11-25T10:05:13Z	406	2	transformers	[ "transformers", "gguf", "Llama", "Llama-Cpp", "Llama3.2", "Instruct", "3B", "bin", "Sentient", "text-generation", "en", "dataset:mlabonne/lmsys-arena-human-preference-55k-sharegpt", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:creativeml-openrail-m", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-11-22T04:56:16Z	--- license: creativeml-openrail-m datasets: - mlabonne/lmsys-arena-human-preference-55k-sharegpt language: - en base_model: - meta-llama/Llama-3.2-3B-Instruct pipeline_tag: text-generation library_name: transformers tags: - Llama - Llama-Cpp - Llama3.2 - Instruct - 3B - bin - Sentient --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF This is quantized version of [prithivMLmods/Llama-Sentient-3.2-3B-Instruct](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) created using llama.cpp # Original Model Card ## Llama-Sentient-3.2-3B-Instruct Modelfile \| File Name \| Size \| Description \| Upload Status \| \|-----------------------------------------\|--------------\|-----------------------------------------\|----------------\| \| `.gitattributes` \| 1.57 kB \| Git attributes configuration file \| Uploaded \| \| `README.md` \| 42 Bytes \| Initial commit README \| Uploaded \| \| `config.json` \| 1.04 kB \| Configuration file \| Uploaded \| \| `generation_config.json` \| 248 Bytes \| Generation configuration file \| Uploaded \| \| `pytorch_model-00001-of-00002.bin` \| 4.97 GB \| PyTorch model file (part 1) \| Uploaded (LFS) \| \| `pytorch_model-00002-of-00002.bin` \| 1.46 GB \| PyTorch model file (part 2) \| Uploaded (LFS) \| \| `pytorch_model.bin.index.json` \| 21.2 kB \| Model index file \| Uploaded \| \| `special_tokens_map.json` \| 477 Bytes \| Special tokens mapping \| Uploaded \| \| `tokenizer.json` \| 17.2 MB \| Tokenizer JSON file \| Uploaded (LFS) \| \| `tokenizer_config.json` \| 57.4 kB \| Tokenizer configuration file \| Uploaded \| \| Model Type \| Size \| Context Length \| Link \| \|------------\|------\|----------------\|------\| \| GGUF \| 3B \| - \| [🤗 Llama-Sentient-3.2-3B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct-GGUF) \| The Llama-Sentient-3.2-3B-Instruct model is a fine-tuned version of the Llama-3.2-3B-Instruct model, optimized for text generation tasks, particularly where instruction-following abilities are critical. This model is trained on the mlabonne/lmsys-arena-human-preference-55k-sharegpt dataset, which enhances its performance in conversational and advisory contexts, making it suitable for a wide range of applications. ### Key Use Cases: 1. Conversational AI: Engage in intelligent dialogue, offering coherent responses and following instructions, useful for customer support and virtual assistants. 2. Text Generation: Generate high-quality, contextually appropriate content such as articles, summaries, explanations, and other forms of written communication based on user prompts. 3. Instruction Following: Follow specific instructions with accuracy, making it ideal for tasks that require structured guidance, such as technical troubleshooting or educational assistance. The model uses a PyTorch-based architecture and includes a range of necessary files such as configuration files, tokenizer files, and model weight files for deployment. ### Intended Applications: - Chatbots for virtual assistance, customer support, or as personal digital assistants. - Content Creation Tools, aiding in the generation of written materials, blog posts, or automated responses based on user inputs. - Educational and Training Systems, providing explanations and guided learning experiences in various domains. - Human-AI Interaction platforms, where the model can follow user instructions to provide personalized assistance or perform specific tasks. With its strong foundation in instruction-following and conversational contexts, the Llama-Sentient-3.2-3B-Instruct model offers versatile applications for both general and specialized domains.
PotatoB/task_3-exp	PotatoB	2024-11-25T10:03:32Z	5	0	null	[ "safetensors", "mistral", "merge", "mergekit", "potatoB/task_2-1", "potatoB/task_1-2", "license:apache-2.0", "region:us" ]	null	2024-11-25T10:00:36Z	--- license: apache-2.0 tags: - merge - mergekit - potatoB/task_2-1 - potatoB/task_1-2 --- # task_3-exp task_3-exp is a merged model generated for Model Kinship experiments, originating from * [potatoB/task_2-1](https://huggingface.co/potatoB/task_2-1) * [potatoB/task_1-2](https://huggingface.co/potatoB/task_1-2) ## 🧩 Configuration ```yaml slices: - sources: - model: potatoB/task_2-1 layer_range: [0, 32] - model: potatoB/task_1-2 layer_range: [0, 32] merge_method: slerp base_model: potatoB/task_2-1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ```
MayBashendy/Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold1	MayBashendy	2024-11-25T10:02:13Z	181	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T10:00:46Z	--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4881 - Qwk: 0.3654 - Mse: 0.4881 - Rmse: 0.6987 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 0.2 \| 2 \| 4.3733 \| 0.0 \| 4.3733 \| 2.0912 \| \| No log \| 0.4 \| 4 \| 3.0656 \| 0.0041 \| 3.0656 \| 1.7509 \| \| No log \| 0.6 \| 6 \| 1.9187 \| 0.0 \| 1.9187 \| 1.3852 \| \| No log \| 0.8 \| 8 \| 1.2784 \| 0.0 \| 1.2784 \| 1.1307 \| \| No log \| 1.0 \| 10 \| 1.0288 \| 0.0 \| 1.0288 \| 1.0143 \| \| No log \| 1.2 \| 12 \| 0.8073 \| 0.2143 \| 0.8073 \| 0.8985 \| \| No log \| 1.4 \| 14 \| 0.8244 \| -0.0708 \| 0.8244 \| 0.9080 \| \| No log \| 1.6 \| 16 \| 0.8426 \| -0.0708 \| 0.8426 \| 0.9179 \| \| No log \| 1.8 \| 18 \| 0.8425 \| -0.2623 \| 0.8425 \| 0.9179 \| \| No log \| 2.0 \| 20 \| 0.7834 \| -0.2737 \| 0.7834 \| 0.8851 \| \| No log \| 2.2 \| 22 \| 0.7676 \| -0.0233 \| 0.7676 \| 0.8761 \| \| No log \| 2.4 \| 24 \| 0.7458 \| -0.0233 \| 0.7458 \| 0.8636 \| \| No log \| 2.6 \| 26 \| 0.7393 \| -0.0233 \| 0.7393 \| 0.8598 \| \| No log \| 2.8 \| 28 \| 0.7465 \| -0.0233 \| 0.7465 \| 0.8640 \| \| No log \| 3.0 \| 30 \| 0.7807 \| -0.0708 \| 0.7807 \| 0.8836 \| \| No log \| 3.2 \| 32 \| 0.7686 \| -0.0708 \| 0.7686 \| 0.8767 \| \| No log \| 3.4 \| 34 \| 0.7733 \| 0.1239 \| 0.7733 \| 0.8794 \| \| No log \| 3.6 \| 36 \| 0.7919 \| -0.0820 \| 0.7919 \| 0.8899 \| \| No log \| 3.8 \| 38 \| 0.8260 \| 0.0571 \| 0.8260 \| 0.9088 \| \| No log \| 4.0 \| 40 \| 0.8921 \| 0.0253 \| 0.8921 \| 0.9445 \| \| No log \| 4.2 \| 42 \| 0.9072 \| 0.0253 \| 0.9072 \| 0.9524 \| \| No log \| 4.4 \| 44 \| 0.8748 \| 0.0403 \| 0.8748 \| 0.9353 \| \| No log \| 4.6 \| 46 \| 0.7669 \| 0.1538 \| 0.7669 \| 0.8757 \| \| No log \| 4.8 \| 48 \| 0.7034 \| 0.0 \| 0.7034 \| 0.8387 \| \| No log \| 5.0 \| 50 \| 0.7061 \| 0.0 \| 0.7061 \| 0.8403 \| \| No log \| 5.2 \| 52 \| 0.7177 \| -0.0233 \| 0.7177 \| 0.8472 \| \| No log \| 5.4 \| 54 \| 0.7338 \| -0.0233 \| 0.7338 \| 0.8566 \| \| No log \| 5.6 \| 56 \| 0.7832 \| 0.4590 \| 0.7832 \| 0.8850 \| \| No log \| 5.8 \| 58 \| 0.8263 \| 0.0571 \| 0.8263 \| 0.9090 \| \| No log \| 6.0 \| 60 \| 0.9227 \| 0.0253 \| 0.9227 \| 0.9606 \| \| No log \| 6.2 \| 62 \| 0.8976 \| 0.0253 \| 0.8976 \| 0.9474 \| \| No log \| 6.4 \| 64 \| 0.8543 \| 0.0403 \| 0.8543 \| 0.9243 \| \| No log \| 6.6 \| 66 \| 0.7962 \| 0.2443 \| 0.7962 \| 0.8923 \| \| No log \| 6.8 \| 68 \| 0.7551 \| 0.1239 \| 0.7551 \| 0.8690 \| \| No log \| 7.0 \| 70 \| 0.7269 \| 0.0984 \| 0.7269 \| 0.8526 \| \| No log \| 7.2 \| 72 \| 0.6806 \| 0.0984 \| 0.6806 \| 0.8250 \| \| No log \| 7.4 \| 74 \| 0.6234 \| 0.1239 \| 0.6234 \| 0.7896 \| \| No log \| 7.6 \| 76 \| 0.5757 \| 0.0984 \| 0.5757 \| 0.7587 \| \| No log \| 7.8 \| 78 \| 0.5394 \| 0.3529 \| 0.5394 \| 0.7345 \| \| No log \| 8.0 \| 80 \| 0.4984 \| 0.1239 \| 0.4984 \| 0.7059 \| \| No log \| 8.2 \| 82 \| 0.4521 \| 0.3654 \| 0.4521 \| 0.6724 \| \| No log \| 8.4 \| 84 \| 0.4245 \| 0.4884 \| 0.4245 \| 0.6516 \| \| No log \| 8.6 \| 86 \| 0.4220 \| 0.4884 \| 0.4220 \| 0.6496 \| \| No log \| 8.8 \| 88 \| 0.4231 \| 0.4884 \| 0.4231 \| 0.6505 \| \| No log \| 9.0 \| 90 \| 0.4275 \| 0.4211 \| 0.4275 \| 0.6538 \| \| No log \| 9.2 \| 92 \| 0.4397 \| 0.4211 \| 0.4397 \| 0.6631 \| \| No log \| 9.4 \| 94 \| 0.4567 \| 0.4211 \| 0.4567 \| 0.6758 \| \| No log \| 9.6 \| 96 \| 0.4734 \| 0.4211 \| 0.4734 \| 0.6880 \| \| No log \| 9.8 \| 98 \| 0.4866 \| 0.3654 \| 0.4866 \| 0.6975 \| \| No log \| 10.0 \| 100 \| 0.4881 \| 0.3654 \| 0.4881 \| 0.6987 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
mini1013/master_cate_ac4	mini1013	2024-11-25T10:01:43Z	89	0	setfit	[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]	text-classification	2024-11-25T10:01:22Z	--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 길이조절 안경고정 밴드 코받침 패드 운동 캠핑 등산 진브라운 알리몽드 - text: 레이밴 안경테 RB3691VF 2509 남자 여자 동그란안경 아시안핏 시온아이엔티 - text: 밀착 스포츠안경줄 흔들림방지 안경스트랩 비앤비 - text: '[텐바이텐]바체타팩토리 가죽 안경 케이스 08 오렌지_추가 안 함_추가 안 함 신세계몰' - text: TUMI 투미 카본 티타늄 명품 안경테 메탈 스퀘어 남자 여자 공용 안경 04.TU10-0003-01 LFmall02 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.9104360692836626 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 6 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 5.0 \| <ul><li>'초경량 국산 안경테 베타 울템 카본 티타늄 뿔테안경 551-599_S571-2 브라운투톤 ENA아이웨어'</li><li>'B019 ORIGINAL GLASS CRYSTAL GREEN '</li><li>'니시데카즈오 BROWLINE2 하금테 근적외선 차단렌즈 아이라이크(EYE LIKE)'</li></ul> \| \| 1.0 \| <ul><li>'레더렛소가죽선글라스파우치휴대용안경케이스 이정민'</li><li>'위에 안경 쓰는 파우치 편광 끼우는 선글라스 3종 세트 선그라스 클립 에끼우는 플립 온 클립선글라스3종세트_일반블랙 홉포엘'</li><li>'휴대용 가죽 선글라스 안경 파우치 케이스 보관함 안 PU안경케이스_그레이 라이프패션'</li></ul> \| \| 3.0 \| <ul><li>'아이업꽈배기인조가죽안경줄10p세트선글라스줄 유어드림커머스'</li><li>'스트랩 캐주얼디자인줄 스토퍼줄 안경걸이 끈 B 더펭귄샵'</li><li>'천연 크리스탈 안경 선글라스 걸이 줄 원석 비즈 빈티지 에스닉 마스크 스트랩 겸용 블루 3mm 70-75CM nouville'</li></ul> \| \| 0.0 \| <ul><li>'갤러리아 NIRNIR SUNGLASS 5 COLOR GREEN 갤러리아몰'</li><li>'여자 켓아이 뿔테 선그라스 썬그라스 남자 RORGGE 2111 상품선택_2유광블랙 온달이'</li><li>'뮤즈 서클 뿔테선글라스 코코아 푸치백'</li></ul> \| \| 2.0 \| <ul><li>'로에드 안경 자국 코패드 코받침 눌림 선글라스 코 통증 방지 패드 교체 스티커 안경코패드 1.8mm（화이트）_2.8mm(화이트) 로에드'</li><li>'[힐포]국산 고급 초극세사 렌즈 안경닦이 김서림방지 클리너 크리너 악기수건 안경천 융s 05. knit 안경닦이30매 15x18cm_블루 모아텍스'</li><li>'자우버 렌즈 케어 클리닝 티슈 200매 메디위'</li></ul> \| \| 4.0 \| <ul><li>'산리오 안경정리함 안경케이스 세트 6종 안경케이스시나모롤 지에이치글로벌'</li><li>'(이거찜) 프리미엄 가죽 안경집 안경케이스 가죽안경집 스카이 제이케이'</li><li>'스트랩 안경케이스 휴대용 안경파우치 가죽안경보관집 선글라스보관케이스 No.01 스트랩 안경케이스 블랙 여선영'</li></ul> \| ## Evaluation ### Metrics \| Label \| Metric \| \|:--------\|:-------\| \| all \| 0.9104 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac4") # Run inference preds = model("밀착 스포츠안경줄 흔들림방지 안경스트랩 비앤비") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 3 \| 9.53 \| 20 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0.0 \| 50 \| \| 1.0 \| 50 \| \| 2.0 \| 50 \| \| 3.0 \| 50 \| \| 4.0 \| 50 \| \| 5.0 \| 50 \| ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:----:\|:-------------:\|:---------------:\| \| 0.0213 \| 1 \| 0.4524 \| - \| \| 1.0638 \| 50 \| 0.2583 \| - \| \| 2.1277 \| 100 \| 0.0642 \| - \| \| 3.1915 \| 150 \| 0.0781 \| - \| \| 4.2553 \| 200 \| 0.0806 \| - \| \| 5.3191 \| 250 \| 0.0391 \| - \| \| 6.3830 \| 300 \| 0.0011 \| - \| \| 7.4468 \| 350 \| 0.0003 \| - \| \| 8.5106 \| 400 \| 0.0001 \| - \| \| 9.5745 \| 450 \| 0.0001 \| - \| \| 10.6383 \| 500 \| 0.0 \| - \| \| 11.7021 \| 550 \| 0.0 \| - \| \| 12.7660 \| 600 \| 0.0 \| - \| \| 13.8298 \| 650 \| 0.0 \| - \| \| 14.8936 \| 700 \| 0.0 \| - \| \| 15.9574 \| 750 \| 0.0 \| - \| \| 17.0213 \| 800 \| 0.0 \| - \| \| 18.0851 \| 850 \| 0.0 \| - \| \| 19.1489 \| 900 \| 0.0 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
mini1013/master_cate_ac3	mini1013	2024-11-25T09:57:32Z	85	0	setfit	[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]	text-classification	2024-11-25T09:57:06Z	--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 옴므 교체용 가죽 벨트끈 벨트줄 허리띠 벨트 가죽 수동 자동용 22_수동벨트용 이태리가죽 3.3cm_카멜(42인치) 에스컴퍼니 - text: 여성 여자 패션 와이드 밴딩 벨트 패딩 코트 허리 허리띠 원피스 가디건 코디 패딩벨트 088_(SH30)_아이보리 {SH30-Ivory} 스웰swell - text: '[1 + 1]쭉쭉스판 늘어나는 밴딩 벨트 남여공용 캐쥬얼 데일리 군용 텍티컬 벨트 01. 늘어나는 벨트 1+1_05. 다크브라운_라이트브라운 스토리몰2' - text: '[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free ' - text: 모두샵 남자 가죽 청바지벨트 캐주얼벨트 허리띠 이니셜각인 7. 브라운 D107_한글(정자체)_보통길이(36까지착용가능) 모두샾 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.9649836541954232 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 3 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1.0 \| <ul><li>'고리 집게 가방 여행용 멜빵 클립 다용도 삼각버클 후크 옐로우몰'</li><li>'패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 흰색 폭 2.5cm 120cm 맴매2'</li><li>'패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 파란색 흰색 빨간색 줄무늬 폭2.5 120cm 맴매2'</li></ul> \| \| 2.0 \| <ul><li>'Basic Leather Belt 네이비_100cm 만달문화여행사'</li><li>'다이에나롤랑 러블리 여자벨트 146276 은장 브라운 FCB0012CM_L 105 네잎클로버마켓'</li><li>'[갤러리아] 헤지스핸드백HJBE2F406W2브라운 스티치장식 소가죽 여성 벨트(타임월드) 한화갤러리아(주)'</li></ul> \| \| 0.0 \| <ul><li>'(아크테릭스)(공식판매처)(23SS) 컨베이어 벨트 32mm (AENSUX5577) BLACK_SM '</li><li>'[갤러리아] 헤지스핸드백 HJBE2F775BK_ 블랙 빅로고 버클 가죽 자동벨트(타임월드) 한화갤러리아(주)'</li><li>'닥스_핸드백 (선물포장/쇼핑백동봉) 블랙 체크배색 가죽 자동벨트 DBBE3E990BK 롯데백화점2관'</li></ul> \| ## Evaluation ### Metrics \| Label \| Metric \| \|:--------\|:-------\| \| all \| 0.9650 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac3") # Run inference preds = model("[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free ") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 3 \| 9.6133 \| 17 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0.0 \| 50 \| \| 1.0 \| 50 \| \| 2.0 \| 50 \| ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:----:\|:-------------:\|:---------------:\| \| 0.0417 \| 1 \| 0.394 \| - \| \| 2.0833 \| 50 \| 0.0731 \| - \| \| 4.1667 \| 100 \| 0.0 \| - \| \| 6.25 \| 150 \| 0.0 \| - \| \| 8.3333 \| 200 \| 0.0 \| - \| \| 10.4167 \| 250 \| 0.0 \| - \| \| 12.5 \| 300 \| 0.0 \| - \| \| 14.5833 \| 350 \| 0.0 \| - \| \| 16.6667 \| 400 \| 0.0 \| - \| \| 18.75 \| 450 \| 0.0 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
omarelsayeed/LayoutReader90Small	omarelsayeed	2024-11-25T09:55:03Z	131	0	transformers	[ "transformers", "safetensors", "layoutlmv3", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-25T09:54:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mini1013/master_cate_ac2	mini1013	2024-11-25T09:55:02Z	95	0	setfit	[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]	text-classification	2024-11-25T09:54:41Z	--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: MLB [MLB] 루키 언스트럭쳐 볼캡 24종 택1 203993 선택 20) 3ACP7701N-07ORL_F 위드홀리투 - text: 남여공용 기본군모 4컬러 EVE 카키 에브리씽굿 - text: 골덴와이어버킷햇(T)7252 브라운 모티브코리아 - text: 패션울벙거지97 베이지 디플코리아 (Digital Plus Korea) - text: '[닥스](강남점)DBHE4EL01W2 브라운 체크 면 헌팅캡 신세계백화점' inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.8489339496048904 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 13 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 10.0 \| <ul><li>'밀로 [Exclusive] Holiday Signature Ball Cap (20Colors) MINT GRAY 포챌린지'</li><li>'(골라) 남녀공용 (GL)CONTRAST STITCHED CAP (3 COLOR) WW9G3SAAC101 연핑크_FRE '</li><li>'밀로 [Exclusive] Holiday Signature Ball Cap (20Colors) STONE BLACK 포챌린지'</li></ul> \| \| 4.0 \| <ul><li>'꽈배기 비니 모자 두꺼운 골무 털 뜨개 여성 겨울 캡 알파카 남자 커플 니트 주황색_S(아이 32-52 cm) 앤디일레븐'</li><li>'패션모자 방한 남자 니트 후드 겨울 장갑 가을 워머 도톰한 3종세트 기모 블랙 마이클로드'</li><li>'털모자 따뜻한 낚시 모자 아빠 중년남성 노인 겨울 옵션06 에스앤지샵'</li></ul> \| \| 7.0 \| <ul><li>'[하프클럽/구김스]구김스 모자(스포츠/등산/여행/방수) BEST 7종 균일가 763_블랙_D type 하프클럽'</li><li>'캉골 아웃도어 액티비티 버켓 4480 에크루 M AK플라자1관'</li><li>'[벤시몽](신세계센텀점)[23FW] WINTER BUCKET HAT - 2color NAVY_FREE 주식회사 에스에스지닷컴'</li></ul> \| \| 3.0 \| <ul><li>'고탄성 부드러운 메쉬 원단 운동야외활동 스카프 두건 연그레이 드림픽쳐스'</li><li>'[로스코]반다나 스카프 헤어밴드 페이즐리 손수건 OLIVE DRAB_4051/Freesize 패션플러스'</li><li>'페이즐리 반다나 헤어 머리두건 비 손수건 스카프 그린 보물삼'</li></ul> \| \| 1.0 \| <ul><li>'방한모자2종 귀달이 털모자 군밤 스키 용품 트래퍼햇 마스크 캡방한모자 01.불구덩이군방모자 제이케이 아트 갤러리'</li><li>'[MLB] 패딩 트루퍼 귀달이 햇(3AWMPH136-50BKS) 블랙-50BKS/59H 에이케이에스앤디(주) AK플라자 평택점'</li><li>'겨울 곰돌이 후드 귀달이 모자 목돌이 동물 털모자 05.브라운 석진케이 주식회사'</li></ul> \| \| 9.0 \| <ul><li>'스냅백 패션모자 snapback (투톤)그레이오렌지 루나마켓'</li><li>'스냅백 패션모자 snapback 레드 루나마켓'</li><li>'공용 메탈 원포인트 스냅백 뉴욕양키스 (32CP57111-50L) '</li></ul> \| \| 0.0 \| <ul><li>'기본 군모 버킷햇 밀리터리 여자 빈티지군모 모자 남자 버캣햇 블랙 카키 / FREE 체인지비'</li><li>'빈티지 워싱 느낌 영문 레터링 장식 포인트 엣지 군모 그레이 (주)오너클랜'</li><li>'질좋은 군모 모자(차콜/국내생산) 네이비 프리마켓'</li></ul> \| \| 2.0 \| <ul><li>'여자 겨울템 따뜻 극세사 양털곰돌이머리띠 귀마개 A24973_베이지_FREE 세븐제이스(7JS)'</li><li>'양털 곰돌이귀마개 귀도리 뽀글이 귀마개 방한귀마개 목도리 화이트 현성마켓'</li><li>'스타일 더하기-36-꽈배기방한귀마개 핑크 이미연'</li></ul> \| \| 6.0 \| <ul><li>'국내발송 MARITHE FRANCOIS GIRBAUD 마리떼 CABLE KNIT BEANIE blue 1MG23SHG112 ONE SIZE 씨이랩'</li><li>'[매장발송] 마리떼 CLASSIC LOGO BEANIE black OS 와이에스마켓'</li><li>'MARITHE FRANCOIS GIRBAUD CABLE KNIT BEANIE gray 1MG23SHG112 227185 ONE SIZE 원플렉스'</li></ul> \| \| 8.0 \| <ul><li>'비앙카 BIANCA (여성용) 누가/내추럴로고_OS '</li><li>'[롯데백화점]화이트샌즈 공용 UV 프로텍션 바이저 소니아 2.아이보리 롯데백화점_'</li><li>'화이트샌즈 소니아 UV 프로텍션 썬바이저 1종 [00003] 아이보리 현대홈쇼핑'</li></ul> \| \| 12.0 \| <ul><li>'캉골 헌팅캡 울 플렉스핏 504 K0873 심리스 울 507 K0875 3107 남녀공용 베레모 3. K3107ST (Black)_SMALL 어썸우즈'</li><li>'다용도 활용 직원 종업원 단체 패션 모자 헌팅캡 화이트 가온'</li><li>'앨리 카페 바리스타 모자 베이커 캡 마도로스햇[루즈루나주얼리] 블랙 주식회사 웹이즈'</li></ul> \| \| 11.0 \| <ul><li>'1631뉴욕 볼캡 6color / 남녀공용모자 캡모자 그린 레이어드컴퍼니'</li><li>'패션벙거지0009 벙거지 가을 모자 여성 패션 밤색 골드코스트'</li><li>'꽈배기니트벙거지모자B28016 검정 프레임바이브'</li></ul> \| \| 5.0 \| <ul><li>'니트 베레모 S1450 진주방울 핑크 지에이치글로벌'</li><li>'[박민영, 라이즈 원빈 착용] 스터드 로고 울 베레모 블랙 '</li><li>'/ 베이직 레더 뉴스보이캡 빵모자 (2color) 아이보리_one size 롭스(robs)'</li></ul> \| ## Evaluation ### Metrics \| Label \| Metric \| \|:--------\|:-------\| \| all \| 0.8489 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac2") # Run inference preds = model("남여공용 기본군모 4컬러 EVE 카키 에브리씽굿") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 3 \| 9.5523 \| 21 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0.0 \| 50 \| \| 1.0 \| 50 \| \| 2.0 \| 50 \| \| 3.0 \| 50 \| \| 4.0 \| 50 \| \| 5.0 \| 50 \| \| 6.0 \| 50 \| \| 7.0 \| 50 \| \| 8.0 \| 50 \| \| 9.0 \| 50 \| \| 10.0 \| 50 \| \| 11.0 \| 50 \| \| 12.0 \| 50 \| ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:----:\|:-------------:\|:---------------:\| \| 0.0098 \| 1 \| 0.4348 \| - \| \| 0.4902 \| 50 \| 0.3427 \| - \| \| 0.9804 \| 100 \| 0.1921 \| - \| \| 1.4706 \| 150 \| 0.1061 \| - \| \| 1.9608 \| 200 \| 0.0544 \| - \| \| 2.4510 \| 250 \| 0.0384 \| - \| \| 2.9412 \| 300 \| 0.0155 \| - \| \| 3.4314 \| 350 \| 0.0128 \| - \| \| 3.9216 \| 400 \| 0.0177 \| - \| \| 4.4118 \| 450 \| 0.0082 \| - \| \| 4.9020 \| 500 \| 0.005 \| - \| \| 5.3922 \| 550 \| 0.0007 \| - \| \| 5.8824 \| 600 \| 0.0004 \| - \| \| 6.3725 \| 650 \| 0.0003 \| - \| \| 6.8627 \| 700 \| 0.0003 \| - \| \| 7.3529 \| 750 \| 0.0003 \| - \| \| 7.8431 \| 800 \| 0.0003 \| - \| \| 8.3333 \| 850 \| 0.0003 \| - \| \| 8.8235 \| 900 \| 0.0002 \| - \| \| 9.3137 \| 950 \| 0.0002 \| - \| \| 9.8039 \| 1000 \| 0.0001 \| - \| \| 10.2941 \| 1050 \| 0.0001 \| - \| \| 10.7843 \| 1100 \| 0.0001 \| - \| \| 11.2745 \| 1150 \| 0.0001 \| - \| \| 11.7647 \| 1200 \| 0.0001 \| - \| \| 12.2549 \| 1250 \| 0.0001 \| - \| \| 12.7451 \| 1300 \| 0.0001 \| - \| \| 13.2353 \| 1350 \| 0.0001 \| - \| \| 13.7255 \| 1400 \| 0.0001 \| - \| \| 14.2157 \| 1450 \| 0.0001 \| - \| \| 14.7059 \| 1500 \| 0.0001 \| - \| \| 15.1961 \| 1550 \| 0.0001 \| - \| \| 15.6863 \| 1600 \| 0.0001 \| - \| \| 16.1765 \| 1650 \| 0.0001 \| - \| \| 16.6667 \| 1700 \| 0.0001 \| - \| \| 17.1569 \| 1750 \| 0.0001 \| - \| \| 17.6471 \| 1800 \| 0.0001 \| - \| \| 18.1373 \| 1850 \| 0.0001 \| - \| \| 18.6275 \| 1900 \| 0.0001 \| - \| \| 19.1176 \| 1950 \| 0.0001 \| - \| \| 19.6078 \| 2000 \| 0.0001 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
ClaudeRitchie/tinyllama-vels-v1	ClaudeRitchie	2024-11-25T09:52:51Z	130	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T09:50:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BoltMonkey/SuperNeuralDreadDevil-8b	BoltMonkey	2024-11-25T09:46:12Z	43	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated", "BoltMonkey/DreadMix", "conversational", "base_model:BoltMonkey/DreadMix", "base_model:merge:BoltMonkey/DreadMix", "base_model:BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated", "base_model:merge:BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-13T03:25:41Z	--- library_name: transformers base_model: - BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated - BoltMonkey/DreadMix tags: - merge - mergekit - lazymergekit - BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated - BoltMonkey/DreadMix pipeline_tag: text-generation --- # SuperNeuralDreadDevil-8b SuperNeuralDreadDevil-8b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated](https://huggingface.co/BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated) * [BoltMonkey/DreadMix](https://huggingface.co/BoltMonkey/DreadMix) ## 🧩 Configuration ```yamlmodels: - model: NousResearch/Meta-Llama-3.1-8B-Instruct - model: BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated parameters: density: 0.53 weight: 0.55 - model: BoltMonkey/DreadMix parameters: density: 0.53 weight: 0.45 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3.1-8B-Instruct parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "BoltMonkey/SuperNeuralDreadDevil-8b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
PrunaAI/AmberYifan-Mistral-7B-v0.1-sft-dpo-10k-bnb-8bit-smashed	PrunaAI	2024-11-25T09:42:42Z	6	0	null	[ "safetensors", "mistral", "pruna-ai", "base_model:AmberYifan/Mistral-7B-v0.1-sft-dpo-10k", "base_model:quantized:AmberYifan/Mistral-7B-v0.1-sft-dpo-10k", "8-bit", "bitsandbytes", "region:us" ]	null	2024-11-25T09:33:11Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: AmberYifan/Mistral-7B-v0.1-sft-dpo-10k metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer"> <img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo AmberYifan/Mistral-7B-v0.1-sft-dpo-10k installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/AmberYifan-Mistral-7B-v0.1-sft-dpo-10k-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("AmberYifan/Mistral-7B-v0.1-sft-dpo-10k") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model AmberYifan/Mistral-7B-v0.1-sft-dpo-10k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Do it by yourself [here](https://docs.pruna.ai/en/latest/setup/pip.html).
glif-loradex-trainer/fabian3000_chillguy	glif-loradex-trainer	2024-11-25T09:40:56Z	846	1	diffusers	[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]	text-to-image	2024-11-25T09:40:37Z	--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1732527571108__000001500_0.jpg text: chillguy as a spartan warrior - output: url: samples/1732527596137__000001500_1.jpg text: chillguy with text saying I AM CHILL - output: url: samples/1732527621130__000001500_2.jpg text: black and white photographic portrait of chillguy base_model: black-forest-labs/FLUX.1-dev trigger: chillguy instance_prompt: chillguy license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # chillguy Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `fabian3000`. <Gallery /> ## Trigger words You should use `chillguy` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/fabian3000_chillguy/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
huihui-ai/Llama-3.2-3B-Instruct-abliterated	huihui-ai	2024-11-25T09:39:09Z	3,315	50	transformers	[ "transformers", "safetensors", "llama", "text-generation", "abliterated", "uncensored", "conversational", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-09-28T05:20:02Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-3B-Instruct tags: - abliterated - uncensored --- # 🦙 Llama-3.2-3B-Instruct-abliterated This is an uncensored version of Llama 3.2 3B Instruct created with abliteration (see [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about it). Special thanks to [@FailSpy](https://huggingface.co/failspy) for the original code and technique. Please follow him if you're interested in abliterated models. ## ollama You can use [huihui_ai/llama3.2-abliterate:3b](https://ollama.com/huihui_ai/llama3.2-abliterate:3b) directly, ``` ollama run huihui_ai/llama3.2-abliterate ``` or create your own model using the following methods. 1. Download this model. ``` huggingface-cli download huihui-ai/Llama-3.2-3B-Instruct-abliterated --local-dir ./huihui-ai/Llama-3.2-3B-Instruct-abliterated ``` 2. Get Llama-3.2-3B-Instruct model for reference. ``` ollama pull llama3.2 ``` 3. Export Llama-3.2-3B-Instruct model parameters. ``` ollama show llama3.2 --modelfile > Modelfile ``` 4. Modify Modelfile, Remove all comment lines (indicated by #) before the "FROM" keyword. Replace the "FROM" with the following content. ``` FROM huihui-ai/Llama-3.2-3B-Instruct-abliterated ``` 5. Use ollama create to then create the quantized model. ``` ollama create --quantize q4_K_M -f Modelfile Llama-3.2-3B-Instruct-abliterated-q4_K_M ``` 6. Run model ``` ollama run Llama-3.2-3B-Instruct-abliterated-q4_K_M ``` The running architecture is llama. ## Evaluations The following data has been re-evaluated and calculated as the average for each test. \| Benchmark \| Llama-3.2-3B-Instruct \| Llama-3.2-3B-Instruct-abliterated \| \|-------------\|-----------------------\|-----------------------------------\| \| IF_Eval \| 76.55 \| 76.76 \| \| MMLU Pro \| 27.88 \| 28.00 \| \| TruthfulQA \| 50.55 \| 50.73 \| \| BBH \| 41.81 \| 41.86 \| \| GPQA \| 28.39 \| 28.41 \| The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/huihui-ai/Llama-3.2-3B-Instruct-abliterated/blob/main/eval.sh)
huihui-ai/Llama-3.2-1B-Instruct-abliterated	huihui-ai	2024-11-25T09:36:23Z	632	6	transformers	[ "transformers", "safetensors", "llama", "text-generation", "abliterated", "uncensored", "conversational", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-10-01T18:46:39Z	--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B-Instruct tags: - abliterated - uncensored --- # 🦙 Llama-3.2-1B-Instruct-abliterated This is an uncensored version of Llama 3.2 1B Instruct created with abliteration (see [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about it). Special thanks to [@FailSpy](https://huggingface.co/failspy) for the original code and technique. Please follow him if you're interested in abliterated models. ## ollama You can use [huihui_ai/llama3.2-abliterate:1b](https://ollama.com/huihui_ai/llama3.2-abliterate:1b) directly, ``` ollama run huihui_ai/llama3.2-abliterate:1b ``` ## Evaluations The following data has been re-evaluated and calculated as the average for each test. \| Benchmark \| Llama-3.2-1B-Instruct \| Llama-3.2-1B-Instruct-abliterated \| \|-------------\|-----------------------\|-----------------------------------\| \| IF_Eval \| 58.50 \| 56.88 \| \| MMLU Pro \| 16.35 \| 14.35 \| \| TruthfulQA \| 43.08 \| 38.96 \| \| BBH \| 33.75 \| 31.83 \| \| GPQA \| 25.96 \| 26.39 \| The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/huihui-ai/Llama-3.2-1B-Instruct-abliterated/blob/main/eval.sh)
mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF	mradermacher	2024-11-25T09:34:00Z	34	0	transformers	[ "transformers", "gguf", "text-generation-inference", "sft", "chocolatine", "fr", "dataset:jpacifico/french-orca-pairs-culinary-9865", "dataset:jpacifico/finetome_french_cook_definitions_v2", "base_model:jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1", "base_model:quantized:jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-25T09:10:57Z	--- base_model: jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 datasets: - jpacifico/french-orca-pairs-culinary-9865 - jpacifico/finetome_french_cook_definitions_v2 language: - fr library_name: transformers license: mit quantized_by: mradermacher tags: - text-generation-inference - transformers - sft - chocolatine --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.1 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 1.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 1.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 1.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q2_K.gguf) \| i1-Q2_K \| 1.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 1.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 1.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 1.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 1.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 1.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 2.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 2.1 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 2.3 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 2.3 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 2.3 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 2.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 2.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 2.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q6_K.gguf) \| i1-Q6_K \| 3.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mini1013/master_cate_ac0	mini1013	2024-11-25T09:33:58Z	154	0	setfit	[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]	text-classification	2024-11-25T09:33:32Z	--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: '[헤지스ACC]HJBA3F885BK[13인치 노트북 수납가능][KEVIN]블랙 참장식 크로스 겸용 미니 토트백 에이케이에스앤디 (주) AK인터넷쇼핑몰' - text: 마젤란 메신저백 크로스백 슬링백 힙색 힙쌕 학생 여성 남자 캐주얼 크로스 여행용 여권 핸드폰 보조 학원 가방 LKHS-304_B-연핑크(+키홀더) 더블유팝 - text: 마젤란 메신저백 크로스백 슬링백 힙색 힙쌕 학생 여성 남자 캐주얼 크로스 여행용 여권 핸드폰 보조 학원 가방 ML-1928_연그레이 더블유팝 - text: '[갤러리아] JUBA4E021G2 [MATEO] 그레이 로고프린트 숄더백 JUBA4E021G2 [MATEO] 그레이 로고프린트 숄더백 NS홈쇼핑_NS몰' - text: '[디스커버리](신세계강남점)[23N] 디스커버리 미니 슬링백 (DXSG0043N) IVD 다크 아이보리_F 주식회사 에스에스지닷컴' inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.8488667448221962 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 9 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 6.0 \| <ul><li>'[질스튜어트](광주신세계)블랙 클래식 클러치백 [JUWA2F392BK] 주식회사 에스에스지닷컴'</li><li>'심플 클러치백 EOCFHX257BK/에스콰이아 블랙 롯데쇼핑(주)'</li><li>'[듀퐁] 소프트그레인 파우치 베이지 CG180263CL 베이지 (주)씨제이이엔엠'</li></ul> \| \| 3.0 \| <ul><li>'엔지니어드가먼츠 블랙 나일론 토트백 23F1H034BLACK 주식회사 어도어럭스'</li><li>'[가이거] 퀼팅 레더 체인 숄더백 (+플랩지갑) 캐러멜 브라운 (주)우리홈쇼핑'</li><li>'토트 브리프 크로스백 FT8570 블랙 글로리홈'</li></ul> \| \| 4.0 \| <ul><li>'여자캔버스 가방 코디 크로스백 남자에코백 신발 BLUE 고앤런'</li><li>'여학생 에코백 아이보리 가방 남녀공용 캐주얼 쇼퍼백 엘케이엠'</li><li>'패션 에코백 데일리 가방 캐주얼 숄더백 브라운 심정'</li></ul> \| \| 7.0 \| <ul><li>'[갤러리아] 644040 2BKPI 1000 ONE SIZE 한화갤러리아(주)'</li><li>'[갤러리아] 헤지스핸드백 그린 워싱가죽 크로스 겸용 토트백 HJBA3E301E2(타임월드) 한화갤러리아(주)'</li><li>'[메종키츠네] 로고 프린트 코튼 토트백 블루 LW05102WW0008 BLUE_FREE 신세계몰'</li></ul> \| \| 2.0 \| <ul><li>'바버 가죽 코팅 서류 가방 브리프 케이스 UBA0004 NAVY 뉴욕트레이딩'</li><li>'[롯데백화점]에스콰이아 23FW 신상 경량 나일론 노트북 수납 남여 데일리 토트 크로스백 EOCFHX258BK 롯데백화점_'</li><li>'22FW 신상 뉴 포멀 슬림 스퀘어 심플 비즈니스 캐주얼 서류가방 ECBFHX227GY 롯데백화점1관'</li></ul> \| \| 1.0 \| <ul><li>'NATIONALGEOGRAPHIC N225USD340 다이브 플러스 V3 BLACK 240 맥스투'</li><li>'레스포삭 보이저 백팩 경량 나일론 보부상 복조리 가방 7839 플라워 행운샵'</li><li>'레스포삭 보이저 백팩 경량 Voyager Backpack 7839 블랙 하하대행'</li></ul> \| \| 0.0 \| <ul><li>'[갤러리아] 헤지스핸드백HJBA2F770BK_ 블랙 로고 장식 솔리드 메신져백(타임월드) 한화갤러리아(주)'</li><li>'로아드로아 허쉬 메쉬 포켓 크로스 메신저백 (아이보리) 크로스백 FREE 가방팝'</li><li>'[본사공식] 타프 메신저백 사첼 S EOCBS04 008 롯데아이몰'</li></ul> \| \| 5.0 \| <ul><li>'팩세이프 가방 GO 크로스바디 백 2.5L / PACSAFE URBAN 도난방지 유럽 해외 여행 등산 슬링백 크로스백 RFID차단 1. 제트 블랙 (JET BLACK) 시계1위팝워치'</li><li>'샨타코[Chantaco] 레더 크로스백 BB NH3271C53N 000/라코스테 롯데쇼핑(주)'</li><li>'팩세이프 가방 GO 크로스바디 백 2.5L / PACSAFE URBAN 도난방지 유럽 해외 여행 등산 슬링백 크로스백 RFID차단 2. 로즈 (ROSE) 시계1위팝워치'</li></ul> \| \| 8.0 \| <ul><li>'[기회공작소] 데일리 슬링백 크로스 힙색 허리가방 스포츠 등산 힙색 허리색 슬링백 보조가방 글로리커머스'</li><li>'구찌 GG 캔버스 투웨이 밸트백 힙색 630915 KY9KN 9886 쏠나인'</li><li>'벨트형 핸드폰 허리가방 남자 벨트백 세로형 가죽 벨트파우치 지갑 허리벨트케이스 브라운 자주구매'</li></ul> \| ## Evaluation ### Metrics \| Label \| Metric \| \|:--------\|:-------\| \| all \| 0.8489 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac0") # Run inference preds = model("[디스커버리](신세계강남점)[23N] 디스커버리 미니 슬링백 (DXSG0043N) IVD 다크 아이보리_F 주식회사 에스에스지닷컴") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:-------\|:----\| \| Word count \| 4 \| 9.2289 \| 29 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0.0 \| 50 \| \| 1.0 \| 50 \| \| 2.0 \| 50 \| \| 3.0 \| 50 \| \| 4.0 \| 50 \| \| 5.0 \| 50 \| \| 6.0 \| 50 \| \| 7.0 \| 50 \| \| 8.0 \| 50 \| ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:----:\|:-------------:\|:---------------:\| \| 0.0141 \| 1 \| 0.3958 \| - \| \| 0.7042 \| 50 \| 0.3012 \| - \| \| 1.4085 \| 100 \| 0.1811 \| - \| \| 2.1127 \| 150 \| 0.0599 \| - \| \| 2.8169 \| 200 \| 0.0333 \| - \| \| 3.5211 \| 250 \| 0.0169 \| - \| \| 4.2254 \| 300 \| 0.0005 \| - \| \| 4.9296 \| 350 \| 0.0003 \| - \| \| 5.6338 \| 400 \| 0.0002 \| - \| \| 6.3380 \| 450 \| 0.0003 \| - \| \| 7.0423 \| 500 \| 0.0001 \| - \| \| 7.7465 \| 550 \| 0.0001 \| - \| \| 8.4507 \| 600 \| 0.0001 \| - \| \| 9.1549 \| 650 \| 0.0001 \| - \| \| 9.8592 \| 700 \| 0.0001 \| - \| \| 10.5634 \| 750 \| 0.0 \| - \| \| 11.2676 \| 800 \| 0.0001 \| - \| \| 11.9718 \| 850 \| 0.0001 \| - \| \| 12.6761 \| 900 \| 0.0001 \| - \| \| 13.3803 \| 950 \| 0.0 \| - \| \| 14.0845 \| 1000 \| 0.0 \| - \| \| 14.7887 \| 1050 \| 0.0 \| - \| \| 15.4930 \| 1100 \| 0.0 \| - \| \| 16.1972 \| 1150 \| 0.0 \| - \| \| 16.9014 \| 1200 \| 0.0 \| - \| \| 17.6056 \| 1250 \| 0.0 \| - \| \| 18.3099 \| 1300 \| 0.0 \| - \| \| 19.0141 \| 1350 \| 0.0 \| - \| \| 19.7183 \| 1400 \| 0.0 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF	mradermacher	2024-11-25T09:31:34Z	83	0	transformers	[ "transformers", "gguf", "text-generation-inference", "sft", "chocolatine", "fr", "dataset:jpacifico/french-orca-pairs-culinary-9865", "dataset:jpacifico/finetome_french_cook_definitions_v2", "base_model:jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1", "base_model:quantized:jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-25T09:00:57Z	--- base_model: jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 datasets: - jpacifico/french-orca-pairs-culinary-9865 - jpacifico/finetome_french_cook_definitions_v2 language: - fr library_name: transformers license: mit quantized_by: mradermacher tags: - text-generation-inference - transformers - sft - chocolatine --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q2_K.gguf) \| Q2_K \| 1.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 2.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 2.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 2.3 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 2.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 2.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q6_K.gguf) \| Q6_K \| 3.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q8_0.gguf) \| Q8_0 \| 4.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.f16.gguf) \| f16 \| 7.7 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
leinad-deinor/Llama3.2-3b-redeIT-XML-GGUF	leinad-deinor	2024-11-25T09:22:10Z	9	0	null	[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-25T08:13:52Z	--- license: apache-2.0 ---
mini1013/master_item_ac	mini1013	2024-11-25T09:19:32Z	423	0	setfit	[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:klue/roberta-base", "base_model:finetune:klue/roberta-base", "model-index", "region:us" ]	text-classification	2024-11-25T09:19:09Z	--- base_model: klue/roberta-base library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: '[자체제작] 14k 콩사다리 체인 반지 핑크_D style(1푼 굵기)_10호 (주)제이디아이인터내셔널' - text: 실리콘 동전 지갑 심플 캐릭터 [on] 블랙캣(동전지갑) 비150 - text: 체크 남자 베레모 아빠 모자 헌팅캡 패션 빵모자 외출 베이지체크 (4JS) 포제이스 - text: TIMBERLAND 남성 앨번 6인치 워터프루프 워커부츠_TB0A1OIZC641 070(250) 비츠컴퍼니 - text: 라인댄스화 헬스화 스포츠 여성 재즈화 댄스화 볼룸 모던 미드힐 37_블랙 스트레이트 3.5cm/굽(메쉬) 사랑옵다 inference: true model-index: - name: SetFit with klue/roberta-base results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.9385943021823656 name: Metric --- # SetFit with klue/roberta-base This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [klue/roberta-base](https://huggingface.co/klue/roberta-base) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 17 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 2.0 \| <ul><li>'남녀공용 멀티스카프 목토시 반다나 헤어밴드 두건 블랙 비오는밤'</li><li>'후드 모자 귀달이 겨울 털모자 동물 목돌이 03.브라운 뿔샵'</li><li>'햇빛 뒷목가리개 메쉬 통풍 선가드 자외선차단썬캡가드 그늘모자 쿨메쉬모자_그레이 에스더블유컴퍼니'</li></ul> \| \| 9.0 \| <ul><li>'[LAP](강남점)아델라 핸들 미니 크로스백 (AP7AB208) 제트블랙(ZB)_FREE 신세계백화점'</li><li>'파스텔슬링백 힙색 미니 크로스 숄더백 그린 김후철'</li><li>'[메트로시티]봉봉백 클러치백 미듐 M233MQ3852Z 에이케이에스앤디 (주) AK인터넷쇼핑몰'</li></ul> \| \| 15.0 \| <ul><li>'크리스마스 뱃지 브로치 배지 19종 세트 및 낱개 봉제 사슴 5 구매대행 이음'</li><li>'오드스튜디오 ODDSTUDIO 베이직 니트 체크 머플러 - 21COLOR 블랙 CS스페이스'</li><li>'넥케이프 넥커프스 페이크카라 레이어드카라 셔츠카라 1-카라-화이트 행복나라'</li></ul> \| \| 13.0 \| <ul><li>'펄 쥬얼리 보석함 여행용 포켓 미니 악세사리 보관함 케이스 C타입-베이비핑크 제일사'</li><li>'[갤러리아] [비앤비골드] 14K 촘촘볼 블루큐빅 도넛링 반지 SRS39135 14K 화이트골드_1호 한화갤러리아(주)'</li><li>'미니골드 김천점 14K 18K 트레버 커플링 남자 여자 금반지 RJUC4047 RJUC4048 베이직하고 심플한 디자인 여자_14K옐로우골드 미니골드 김천점'</li></ul> \| \| 1.0 \| <ul><li>'[베어파우](신세계강남점)(BEARPAW) 남성 털 슬리퍼 MARY MENS 블랙 K814001ND-M BLACK (K814001ND)_280 주식회사 에스에스지닷컴'</li><li>'노스페이스 뮬 슬립온 브이모션 - NS93P53A 블랙_290 롯데백화점2관'</li><li>'사무실 남자 슬리퍼 가죽 남성 빅 사이즈 48 47 사무용 신입생코디실내화 blue_38 리마106'</li></ul> \| \| 7.0 \| <ul><li>'부드러운 슈트리 신발주름방지 신발모양유지 신발지탱 225 245 mm 커피와 기저귀'</li><li>'[갓성비] 꿀조합 애니비츠 세트 캐릭터 신발 악세사리 포켓몬 스누피 커비편의점SET 애니팝'</li><li>'MSMAX Jazz Dance Shoes Split Sole Men Dancing Sneakers High Top Boots for Women Silver 10.5 M Silver_11 Narrow 디아트479'</li></ul> \| \| 11.0 \| <ul><li>'캐리어 수트케이스 양면 개방형 기내용 바퀴가방 화이트_26인치 피스온트레이드'</li><li>'클래시 패스 커버여권 포트월렛 포트파우치 파우치 여행지갑 포트 케이스 (01 레모니) 주식회사유마켓'</li><li>'클래시패스커버 (안티스키밍 여권케이스) (10블랙) JTEC'</li></ul> \| \| 4.0 \| <ul><li>'고급 안경집 선글라스집 휴대용 케이스 파우치 하드 보관함 블랙 다온마켓'</li><li>'고급 올 칼라 크리스탈 다중 비즈 안경 줄 마스크 걸이 상품선택_블랙(골드) 리미몰'</li><li>'아이업꽈배기인조가죽안경줄10p세트선글라스줄 마니또야'</li></ul> \| \| 14.0 \| <ul><li>'[갤러리아] [Prada]프라다 23FW 사피아노 반지갑 블랙 2MO004 QME F0002 2MO004 QME F0002 FREE 한화갤러리아(주)'</li><li>'닥스 액세서리 [OSCAR][오스카][제네시스 전용] 네이비 프리미엄 토고 수입 가죽 차키케이스 DBHO2F573N2 XXX 주식회사 LF'</li><li>'톰브라운 23SS 남성 페블그레인 머니클립 블랙 MAW025L 00198 001 ONE SIZE 주식회사 이지겟인터내셔널'</li></ul> \| \| 0.0 \| <ul><li>'[롯데백화점]닥스ACC [선물포장/쇼핑백동봉] [GRIDⅡ] 브라운 패턴배색 소가죽 클러치백 DBBA2F266W3 롯데백화점_'</li><li>'만다리나덕 토트백 PIETRO P4T05163 은하수몰'</li><li>'내셔널지오그래픽 N245ATO510 베이직 에코백 BLACK TNSC'</li></ul> \| \| 16.0 \| <ul><li>'올림머리 메탈프레임 반머리 꼬임 집게핀 114 유광스틸 7cm 이지 아트 프로덕션 (EG ART PRODUCTION)'</li><li>'꼬임 메탈프레임 반머리 올림머리 집게핀 114 무광로즈 7cm 네오몰'</li><li>'폼폼 방울털 장식 미니 머리끈 포인트 헤어끈 퍼플 1P 은강'</li></ul> \| \| 8.0 \| <ul><li>'기모 롱 오버 니삭스 겨울 스타킹 다리 워머 롱삭스 롱양말 무릎 니하이 브라운 린이팸'</li><li>'최대12켤레 남여 국산양말 장목/니트/균일가/신상/중목/발목/수면/학생 37~38_37.여)털실 중목_4켤레 / 버건디 투투삭스'</li><li>'NY코튼클럽 5켤레 국산 극세사 기모 롱 무압박 임산부 수면양말 W8001-여성-카멜5족 GSSHOP_'</li></ul> \| \| 5.0 \| <ul><li>'[한국금거래소] 순금 카네이션 배지 1.875g 부모님 추석 명절 생신 생일 기념일 기념 축하 감사선물 주식회사 한국금거래소디지털에셋'</li><li>'[한국금거래소]한국금거래소 순금 용 37.5g [순금24K] 롯데아이몰'</li><li>'한국금거래소 실버바 1kg(1000g) 주식회사 한국금거래소디지털에셋'</li></ul> \| \| 10.0 \| <ul><li>'캠퍼 브루투스 트렉 첼시 앵클부츠 346335 EU 39 주식회사 수비르글로벌커머스(SUBIR Global Commerce)'</li><li>'슈콤마보니 워커 부츠 DG3CW22519BLK 블랙_250 롯데쇼핑(주) 프리미엄아울렛 타임빌라스'</li><li>'말랑 쿠키 거실화 실내화 거실슬리퍼 실내슬리퍼 LWS 그레이265mm 생활공작소365'</li></ul> \| \| 6.0 \| <ul><li>'BOXY 박시 워치와인더 BWS-S / BWS-F 1구 아답터1개로 쌓아서 사용가능 BWS-S(DG)아답터미포함 와치닷컴'</li><li>'지샥 GA-2100 2110 지얄오크 베젤 밴드 일체형 용두 메탈 우레탄밴드 커스텀 옵션5:실버+블랙베젤_1.일반버클_화이트 방울방울'</li><li>'스타샵 카시오 MRW-200H-2B2 남성 손목시계 c57 선택19. AW-49H-1B 스타샵'</li></ul> \| \| 3.0 \| <ul><li>'남자 멜빵 2 5CM 남성 및 여성 서스펜더 클립 사이드 홀스터 스타일 탄성 백 서스펜더 05 밝은 빨간색 헬로우스토어'</li><li>'멜빵 소형멜빵 용 멜빵 어린이멜빵 멜빵 맬빵 MinSellAmount 모루모루'</li><li>'[닥스 액세서리] [23FW] DBBE3F097BK 여성벨트DD Symbol 블랙 DD메탈릭 골드 버클 소 XXX '</li></ul> \| \| 12.0 \| <ul><li>'미니 토시 사무용 광목 자수 팔토시 레드로즈 다솜이네'</li><li>'백화점 여성 남성 천연 양가죽 장갑 스마트폰 터치 털 손가락 겨울 방한 가죽 커플 장갑 2.여성용/스웨이드/차콜 힐렉스'</li><li>'[선물포장] 울 캐시미어혼방 핑거홀 장갑 JAGV2F310G2,JAGV2F311W2,JAGV2F312E2,JAGV2F313/질스튜어트 그린 롯데쇼핑(주)'</li></ul> \| ## Evaluation ### Metrics \| Label \| Metric \| \|:--------\|:-------\| \| all \| 0.9386 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_item_ac") # Run inference preds = model("실리콘 동전 지갑 심플 캐릭터 [on] 블랙캣(동전지갑) 비150") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 3 \| 10.2537 \| 30 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0.0 \| 450 \| \| 1.0 \| 650 \| \| 2.0 \| 650 \| \| 3.0 \| 150 \| \| 4.0 \| 300 \| \| 5.0 \| 120 \| \| 6.0 \| 224 \| \| 7.0 \| 350 \| \| 8.0 \| 100 \| \| 9.0 \| 467 \| \| 10.0 \| 500 \| \| 11.0 \| 600 \| \| 12.0 \| 150 \| \| 13.0 \| 450 \| \| 14.0 \| 400 \| \| 15.0 \| 1000 \| \| 16.0 \| 250 \| ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-------:\|:-----:\|:-------------:\|:---------------:\| \| 0.0009 \| 1 \| 0.407 \| - \| \| 0.0469 \| 50 \| 0.3772 \| - \| \| 0.0939 \| 100 \| 0.3062 \| - \| \| 0.1408 \| 150 \| 0.2861 \| - \| \| 0.1878 \| 200 \| 0.2513 \| - \| \| 0.2347 \| 250 \| 0.2284 \| - \| \| 0.2817 \| 300 \| 0.1952 \| - \| \| 0.3286 \| 350 \| 0.149 \| - \| \| 0.3756 \| 400 \| 0.1154 \| - \| \| 0.4225 \| 450 \| 0.1042 \| - \| \| 0.4695 \| 500 \| 0.0802 \| - \| \| 0.5164 \| 550 \| 0.0765 \| - \| \| 0.5634 \| 600 \| 0.0767 \| - \| \| 0.6103 \| 650 \| 0.0475 \| - \| \| 0.6573 \| 700 \| 0.0535 \| - \| \| 0.7042 \| 750 \| 0.0293 \| - \| \| 0.7512 \| 800 \| 0.0388 \| - \| \| 0.7981 \| 850 \| 0.0156 \| - \| \| 0.8451 \| 900 \| 0.0348 \| - \| \| 0.8920 \| 950 \| 0.0241 \| - \| \| 0.9390 \| 1000 \| 0.023 \| - \| \| 0.9859 \| 1050 \| 0.0166 \| - \| \| 1.0329 \| 1100 \| 0.0124 \| - \| \| 1.0798 \| 1150 \| 0.0139 \| - \| \| 1.1268 \| 1200 \| 0.0122 \| - \| \| 1.1737 \| 1250 \| 0.0111 \| - \| \| 1.2207 \| 1300 \| 0.0062 \| - \| \| 1.2676 \| 1350 \| 0.0106 \| - \| \| 1.3146 \| 1400 \| 0.0112 \| - \| \| 1.3615 \| 1450 \| 0.0137 \| - \| \| 1.4085 \| 1500 \| 0.0154 \| - \| \| 1.4554 \| 1550 \| 0.0185 \| - \| \| 1.5023 \| 1600 \| 0.0248 \| - \| \| 1.5493 \| 1650 \| 0.0128 \| - \| \| 1.5962 \| 1700 \| 0.018 \| - \| \| 1.6432 \| 1750 \| 0.0013 \| - \| \| 1.6901 \| 1800 \| 0.0151 \| - \| \| 1.7371 \| 1850 \| 0.0208 \| - \| \| 1.7840 \| 1900 \| 0.0076 \| - \| \| 1.8310 \| 1950 \| 0.0138 \| - \| \| 1.8779 \| 2000 \| 0.0133 \| - \| \| 1.9249 \| 2050 \| 0.0131 \| - \| \| 1.9718 \| 2100 \| 0.0123 \| - \| \| 2.0188 \| 2150 \| 0.0165 \| - \| \| 2.0657 \| 2200 \| 0.0084 \| - \| \| 2.1127 \| 2250 \| 0.0062 \| - \| \| 2.1596 \| 2300 \| 0.0068 \| - \| \| 2.2066 \| 2350 \| 0.0023 \| - \| \| 2.2535 \| 2400 \| 0.006 \| - \| \| 2.3005 \| 2450 \| 0.0048 \| - \| \| 2.3474 \| 2500 \| 0.0016 \| - \| \| 2.3944 \| 2550 \| 0.0046 \| - \| \| 2.4413 \| 2600 \| 0.001 \| - \| \| 2.4883 \| 2650 \| 0.0022 \| - \| \| 2.5352 \| 2700 \| 0.0014 \| - \| \| 2.5822 \| 2750 \| 0.0004 \| - \| \| 2.6291 \| 2800 \| 0.0002 \| - \| \| 2.6761 \| 2850 \| 0.0004 \| - \| \| 2.7230 \| 2900 \| 0.0016 \| - \| \| 2.7700 \| 2950 \| 0.0018 \| - \| \| 2.8169 \| 3000 \| 0.0004 \| - \| \| 2.8638 \| 3050 \| 0.0001 \| - \| \| 2.9108 \| 3100 \| 0.0002 \| - \| \| 2.9577 \| 3150 \| 0.0018 \| - \| \| 3.0047 \| 3200 \| 0.0019 \| - \| \| 3.0516 \| 3250 \| 0.0001 \| - \| \| 3.0986 \| 3300 \| 0.0011 \| - \| \| 3.1455 \| 3350 \| 0.0001 \| - \| \| 3.1925 \| 3400 \| 0.0001 \| - \| \| 3.2394 \| 3450 \| 0.0002 \| - \| \| 3.2864 \| 3500 \| 0.0007 \| - \| \| 3.3333 \| 3550 \| 0.0001 \| - \| \| 3.3803 \| 3600 \| 0.0002 \| - \| \| 3.4272 \| 3650 \| 0.0001 \| - \| \| 3.4742 \| 3700 \| 0.0011 \| - \| \| 3.5211 \| 3750 \| 0.0013 \| - \| \| 3.5681 \| 3800 \| 0.0014 \| - \| \| 3.6150 \| 3850 \| 0.0001 \| - \| \| 3.6620 \| 3900 \| 0.0001 \| - \| \| 3.7089 \| 3950 \| 0.0002 \| - \| \| 3.7559 \| 4000 \| 0.0001 \| - \| \| 3.8028 \| 4050 \| 0.0014 \| - \| \| 3.8498 \| 4100 \| 0.0002 \| - \| \| 3.8967 \| 4150 \| 0.0001 \| - \| \| 3.9437 \| 4200 \| 0.0 \| - \| \| 3.9906 \| 4250 \| 0.0 \| - \| \| 4.0376 \| 4300 \| 0.0001 \| - \| \| 4.0845 \| 4350 \| 0.0002 \| - \| \| 4.1315 \| 4400 \| 0.0 \| - \| \| 4.1784 \| 4450 \| 0.0001 \| - \| \| 4.2254 \| 4500 \| 0.0 \| - \| \| 4.2723 \| 4550 \| 0.0 \| - \| \| 4.3192 \| 4600 \| 0.0003 \| - \| \| 4.3662 \| 4650 \| 0.0007 \| - \| \| 4.4131 \| 4700 \| 0.0 \| - \| \| 4.4601 \| 4750 \| 0.0001 \| - \| \| 4.5070 \| 4800 \| 0.0011 \| - \| \| 4.5540 \| 4850 \| 0.0003 \| - \| \| 4.6009 \| 4900 \| 0.0005 \| - \| \| 4.6479 \| 4950 \| 0.0001 \| - \| \| 4.6948 \| 5000 \| 0.0001 \| - \| \| 4.7418 \| 5050 \| 0.0001 \| - \| \| 4.7887 \| 5100 \| 0.0001 \| - \| \| 4.8357 \| 5150 \| 0.0 \| - \| \| 4.8826 \| 5200 \| 0.0 \| - \| \| 4.9296 \| 5250 \| 0.0 \| - \| \| 4.9765 \| 5300 \| 0.0001 \| - \| \| 5.0235 \| 5350 \| 0.0 \| - \| \| 5.0704 \| 5400 \| 0.0 \| - \| \| 5.1174 \| 5450 \| 0.0 \| - \| \| 5.1643 \| 5500 \| 0.0 \| - \| \| 5.2113 \| 5550 \| 0.0 \| - \| \| 5.2582 \| 5600 \| 0.0001 \| - \| \| 5.3052 \| 5650 \| 0.0 \| - \| \| 5.3521 \| 5700 \| 0.0 \| - \| \| 5.3991 \| 5750 \| 0.0 \| - \| \| 5.4460 \| 5800 \| 0.0 \| - \| \| 5.4930 \| 5850 \| 0.0 \| - \| \| 5.5399 \| 5900 \| 0.0 \| - \| \| 5.5869 \| 5950 \| 0.0 \| - \| \| 5.6338 \| 6000 \| 0.0 \| - \| \| 5.6808 \| 6050 \| 0.0 \| - \| \| 5.7277 \| 6100 \| 0.0 \| - \| \| 5.7746 \| 6150 \| 0.0 \| - \| \| 5.8216 \| 6200 \| 0.0 \| - \| \| 5.8685 \| 6250 \| 0.0 \| - \| \| 5.9155 \| 6300 \| 0.0001 \| - \| \| 5.9624 \| 6350 \| 0.0004 \| - \| \| 6.0094 \| 6400 \| 0.0007 \| - \| \| 6.0563 \| 6450 \| 0.0 \| - \| \| 6.1033 \| 6500 \| 0.0001 \| - \| \| 6.1502 \| 6550 \| 0.0 \| - \| \| 6.1972 \| 6600 \| 0.0001 \| - \| \| 6.2441 \| 6650 \| 0.0 \| - \| \| 6.2911 \| 6700 \| 0.0 \| - \| \| 6.3380 \| 6750 \| 0.0009 \| - \| \| 6.3850 \| 6800 \| 0.0 \| - \| \| 6.4319 \| 6850 \| 0.0001 \| - \| \| 6.4789 \| 6900 \| 0.0 \| - \| \| 6.5258 \| 6950 \| 0.0001 \| - \| \| 6.5728 \| 7000 \| 0.0 \| - \| \| 6.6197 \| 7050 \| 0.0 \| - \| \| 6.6667 \| 7100 \| 0.0 \| - \| \| 6.7136 \| 7150 \| 0.0 \| - \| \| 6.7606 \| 7200 \| 0.0001 \| - \| \| 6.8075 \| 7250 \| 0.0 \| - \| \| 6.8545 \| 7300 \| 0.0 \| - \| \| 6.9014 \| 7350 \| 0.0 \| - \| \| 6.9484 \| 7400 \| 0.0 \| - \| \| 6.9953 \| 7450 \| 0.0 \| - \| \| 7.0423 \| 7500 \| 0.0 \| - \| \| 7.0892 \| 7550 \| 0.0 \| - \| \| 7.1362 \| 7600 \| 0.0 \| - \| \| 7.1831 \| 7650 \| 0.0 \| - \| \| 7.2300 \| 7700 \| 0.0 \| - \| \| 7.2770 \| 7750 \| 0.0001 \| - \| \| 7.3239 \| 7800 \| 0.0 \| - \| \| 7.3709 \| 7850 \| 0.0 \| - \| \| 7.4178 \| 7900 \| 0.0 \| - \| \| 7.4648 \| 7950 \| 0.0 \| - \| \| 7.5117 \| 8000 \| 0.0 \| - \| \| 7.5587 \| 8050 \| 0.0 \| - \| \| 7.6056 \| 8100 \| 0.0 \| - \| \| 7.6526 \| 8150 \| 0.0024 \| - \| \| 7.6995 \| 8200 \| 0.0 \| - \| \| 7.7465 \| 8250 \| 0.0 \| - \| \| 7.7934 \| 8300 \| 0.0 \| - \| \| 7.8404 \| 8350 \| 0.0 \| - \| \| 7.8873 \| 8400 \| 0.0 \| - \| \| 7.9343 \| 8450 \| 0.0 \| - \| \| 7.9812 \| 8500 \| 0.0 \| - \| \| 8.0282 \| 8550 \| 0.0 \| - \| \| 8.0751 \| 8600 \| 0.0 \| - \| \| 8.1221 \| 8650 \| 0.0 \| - \| \| 8.1690 \| 8700 \| 0.0 \| - \| \| 8.2160 \| 8750 \| 0.0 \| - \| \| 8.2629 \| 8800 \| 0.0 \| - \| \| 8.3099 \| 8850 \| 0.0 \| - \| \| 8.3568 \| 8900 \| 0.0 \| - \| \| 8.4038 \| 8950 \| 0.0 \| - \| \| 8.4507 \| 9000 \| 0.0 \| - \| \| 8.4977 \| 9050 \| 0.0 \| - \| \| 8.5446 \| 9100 \| 0.0 \| - \| \| 8.5915 \| 9150 \| 0.0 \| - \| \| 8.6385 \| 9200 \| 0.0002 \| - \| \| 8.6854 \| 9250 \| 0.0003 \| - \| \| 8.7324 \| 9300 \| 0.0005 \| - \| \| 8.7793 \| 9350 \| 0.0001 \| - \| \| 8.8263 \| 9400 \| 0.0001 \| - \| \| 8.8732 \| 9450 \| 0.0001 \| - \| \| 8.9202 \| 9500 \| 0.0 \| - \| \| 8.9671 \| 9550 \| 0.0 \| - \| \| 9.0141 \| 9600 \| 0.0001 \| - \| \| 9.0610 \| 9650 \| 0.0001 \| - \| \| 9.1080 \| 9700 \| 0.0 \| - \| \| 9.1549 \| 9750 \| 0.0 \| - \| \| 9.2019 \| 9800 \| 0.0001 \| - \| \| 9.2488 \| 9850 \| 0.0 \| - \| \| 9.2958 \| 9900 \| 0.0 \| - \| \| 9.3427 \| 9950 \| 0.0 \| - \| \| 9.3897 \| 10000 \| 0.0 \| - \| \| 9.4366 \| 10050 \| 0.0 \| - \| \| 9.4836 \| 10100 \| 0.0 \| - \| \| 9.5305 \| 10150 \| 0.0 \| - \| \| 9.5775 \| 10200 \| 0.0 \| - \| \| 9.6244 \| 10250 \| 0.0 \| - \| \| 9.6714 \| 10300 \| 0.0 \| - \| \| 9.7183 \| 10350 \| 0.0 \| - \| \| 9.7653 \| 10400 \| 0.0 \| - \| \| 9.8122 \| 10450 \| 0.0 \| - \| \| 9.8592 \| 10500 \| 0.0016 \| - \| \| 9.9061 \| 10550 \| 0.0 \| - \| \| 9.9531 \| 10600 \| 0.0 \| - \| \| 10.0 \| 10650 \| 0.0 \| - \| \| 10.0469 \| 10700 \| 0.0003 \| - \| \| 10.0939 \| 10750 \| 0.0 \| - \| \| 10.1408 \| 10800 \| 0.0 \| - \| \| 10.1878 \| 10850 \| 0.0 \| - \| \| 10.2347 \| 10900 \| 0.0 \| - \| \| 10.2817 \| 10950 \| 0.0 \| - \| \| 10.3286 \| 11000 \| 0.0 \| - \| \| 10.3756 \| 11050 \| 0.0 \| - \| \| 10.4225 \| 11100 \| 0.0 \| - \| \| 10.4695 \| 11150 \| 0.0 \| - \| \| 10.5164 \| 11200 \| 0.0 \| - \| \| 10.5634 \| 11250 \| 0.0 \| - \| \| 10.6103 \| 11300 \| 0.0 \| - \| \| 10.6573 \| 11350 \| 0.0 \| - \| \| 10.7042 \| 11400 \| 0.0 \| - \| \| 10.7512 \| 11450 \| 0.0 \| - \| \| 10.7981 \| 11500 \| 0.0 \| - \| \| 10.8451 \| 11550 \| 0.0 \| - \| \| 10.8920 \| 11600 \| 0.0 \| - \| \| 10.9390 \| 11650 \| 0.0 \| - \| \| 10.9859 \| 11700 \| 0.0 \| - \| \| 11.0329 \| 11750 \| 0.0 \| - \| \| 11.0798 \| 11800 \| 0.0 \| - \| \| 11.1268 \| 11850 \| 0.0 \| - \| \| 11.1737 \| 11900 \| 0.0 \| - \| \| 11.2207 \| 11950 \| 0.0 \| - \| \| 11.2676 \| 12000 \| 0.0 \| - \| \| 11.3146 \| 12050 \| 0.0 \| - \| \| 11.3615 \| 12100 \| 0.0 \| - \| \| 11.4085 \| 12150 \| 0.0 \| - \| \| 11.4554 \| 12200 \| 0.0 \| - \| \| 11.5023 \| 12250 \| 0.0015 \| - \| \| 11.5493 \| 12300 \| 0.0 \| - \| \| 11.5962 \| 12350 \| 0.0 \| - \| \| 11.6432 \| 12400 \| 0.0 \| - \| \| 11.6901 \| 12450 \| 0.0 \| - \| \| 11.7371 \| 12500 \| 0.0 \| - \| \| 11.7840 \| 12550 \| 0.0002 \| - \| \| 11.8310 \| 12600 \| 0.0 \| - \| \| 11.8779 \| 12650 \| 0.0 \| - \| \| 11.9249 \| 12700 \| 0.0 \| - \| \| 11.9718 \| 12750 \| 0.0001 \| - \| \| 12.0188 \| 12800 \| 0.0 \| - \| \| 12.0657 \| 12850 \| 0.0 \| - \| \| 12.1127 \| 12900 \| 0.0 \| - \| \| 12.1596 \| 12950 \| 0.0001 \| - \| \| 12.2066 \| 13000 \| 0.0001 \| - \| \| 12.2535 \| 13050 \| 0.0 \| - \| \| 12.3005 \| 13100 \| 0.0 \| - \| \| 12.3474 \| 13150 \| 0.0001 \| - \| \| 12.3944 \| 13200 \| 0.0 \| - \| \| 12.4413 \| 13250 \| 0.0 \| - \| \| 12.4883 \| 13300 \| 0.0 \| - \| \| 12.5352 \| 13350 \| 0.0 \| - \| \| 12.5822 \| 13400 \| 0.0 \| - \| \| 12.6291 \| 13450 \| 0.0 \| - \| \| 12.6761 \| 13500 \| 0.0 \| - \| \| 12.7230 \| 13550 \| 0.0 \| - \| \| 12.7700 \| 13600 \| 0.0 \| - \| \| 12.8169 \| 13650 \| 0.0 \| - \| \| 12.8638 \| 13700 \| 0.0 \| - \| \| 12.9108 \| 13750 \| 0.0 \| - \| \| 12.9577 \| 13800 \| 0.0 \| - \| \| 13.0047 \| 13850 \| 0.0 \| - \| \| 13.0516 \| 13900 \| 0.0 \| - \| \| 13.0986 \| 13950 \| 0.0 \| - \| \| 13.1455 \| 14000 \| 0.0 \| - \| \| 13.1925 \| 14050 \| 0.0 \| - \| \| 13.2394 \| 14100 \| 0.0 \| - \| \| 13.2864 \| 14150 \| 0.0 \| - \| \| 13.3333 \| 14200 \| 0.0 \| - \| \| 13.3803 \| 14250 \| 0.0 \| - \| \| 13.4272 \| 14300 \| 0.0 \| - \| \| 13.4742 \| 14350 \| 0.0 \| - \| \| 13.5211 \| 14400 \| 0.0 \| - \| \| 13.5681 \| 14450 \| 0.0 \| - \| \| 13.6150 \| 14500 \| 0.0 \| - \| \| 13.6620 \| 14550 \| 0.0 \| - \| \| 13.7089 \| 14600 \| 0.0 \| - \| \| 13.7559 \| 14650 \| 0.0 \| - \| \| 13.8028 \| 14700 \| 0.0 \| - \| \| 13.8498 \| 14750 \| 0.0 \| - \| \| 13.8967 \| 14800 \| 0.0 \| - \| \| 13.9437 \| 14850 \| 0.0 \| - \| \| 13.9906 \| 14900 \| 0.0 \| - \| \| 14.0376 \| 14950 \| 0.0 \| - \| \| 14.0845 \| 15000 \| 0.0 \| - \| \| 14.1315 \| 15050 \| 0.0 \| - \| \| 14.1784 \| 15100 \| 0.0001 \| - \| \| 14.2254 \| 15150 \| 0.0 \| - \| \| 14.2723 \| 15200 \| 0.0 \| - \| \| 14.3192 \| 15250 \| 0.0 \| - \| \| 14.3662 \| 15300 \| 0.0 \| - \| \| 14.4131 \| 15350 \| 0.0 \| - \| \| 14.4601 \| 15400 \| 0.0 \| - \| \| 14.5070 \| 15450 \| 0.0 \| - \| \| 14.5540 \| 15500 \| 0.0 \| - \| \| 14.6009 \| 15550 \| 0.0 \| - \| \| 14.6479 \| 15600 \| 0.0 \| - \| \| 14.6948 \| 15650 \| 0.0 \| - \| \| 14.7418 \| 15700 \| 0.0 \| - \| \| 14.7887 \| 15750 \| 0.0 \| - \| \| 14.8357 \| 15800 \| 0.0 \| - \| \| 14.8826 \| 15850 \| 0.0 \| - \| \| 14.9296 \| 15900 \| 0.0 \| - \| \| 14.9765 \| 15950 \| 0.0 \| - \| \| 15.0235 \| 16000 \| 0.0 \| - \| \| 15.0704 \| 16050 \| 0.0 \| - \| \| 15.1174 \| 16100 \| 0.0 \| - \| \| 15.1643 \| 16150 \| 0.0 \| - \| \| 15.2113 \| 16200 \| 0.0 \| - \| \| 15.2582 \| 16250 \| 0.0 \| - \| \| 15.3052 \| 16300 \| 0.0 \| - \| \| 15.3521 \| 16350 \| 0.0 \| - \| \| 15.3991 \| 16400 \| 0.0 \| - \| \| 15.4460 \| 16450 \| 0.0 \| - \| \| 15.4930 \| 16500 \| 0.0 \| - \| \| 15.5399 \| 16550 \| 0.0 \| - \| \| 15.5869 \| 16600 \| 0.0 \| - \| \| 15.6338 \| 16650 \| 0.0 \| - \| \| 15.6808 \| 16700 \| 0.0 \| - \| \| 15.7277 \| 16750 \| 0.0 \| - \| \| 15.7746 \| 16800 \| 0.0 \| - \| \| 15.8216 \| 16850 \| 0.0 \| - \| \| 15.8685 \| 16900 \| 0.0 \| - \| \| 15.9155 \| 16950 \| 0.0 \| - \| \| 15.9624 \| 17000 \| 0.0 \| - \| \| 16.0094 \| 17050 \| 0.0 \| - \| \| 16.0563 \| 17100 \| 0.0 \| - \| \| 16.1033 \| 17150 \| 0.0 \| - \| \| 16.1502 \| 17200 \| 0.0 \| - \| \| 16.1972 \| 17250 \| 0.0 \| - \| \| 16.2441 \| 17300 \| 0.0 \| - \| \| 16.2911 \| 17350 \| 0.0 \| - \| \| 16.3380 \| 17400 \| 0.0 \| - \| \| 16.3850 \| 17450 \| 0.0 \| - \| \| 16.4319 \| 17500 \| 0.0 \| - \| \| 16.4789 \| 17550 \| 0.0 \| - \| \| 16.5258 \| 17600 \| 0.0 \| - \| \| 16.5728 \| 17650 \| 0.0 \| - \| \| 16.6197 \| 17700 \| 0.0 \| - \| \| 16.6667 \| 17750 \| 0.0 \| - \| \| 16.7136 \| 17800 \| 0.0 \| - \| \| 16.7606 \| 17850 \| 0.0 \| - \| \| 16.8075 \| 17900 \| 0.0 \| - \| \| 16.8545 \| 17950 \| 0.0 \| - \| \| 16.9014 \| 18000 \| 0.0 \| - \| \| 16.9484 \| 18050 \| 0.0 \| - \| \| 16.9953 \| 18100 \| 0.0 \| - \| \| 17.0423 \| 18150 \| 0.0 \| - \| \| 17.0892 \| 18200 \| 0.0 \| - \| \| 17.1362 \| 18250 \| 0.0 \| - \| \| 17.1831 \| 18300 \| 0.0 \| - \| \| 17.2300 \| 18350 \| 0.0 \| - \| \| 17.2770 \| 18400 \| 0.0 \| - \| \| 17.3239 \| 18450 \| 0.0 \| - \| \| 17.3709 \| 18500 \| 0.0 \| - \| \| 17.4178 \| 18550 \| 0.0 \| - \| \| 17.4648 \| 18600 \| 0.0 \| - \| \| 17.5117 \| 18650 \| 0.0 \| - \| \| 17.5587 \| 18700 \| 0.0 \| - \| \| 17.6056 \| 18750 \| 0.0 \| - \| \| 17.6526 \| 18800 \| 0.0 \| - \| \| 17.6995 \| 18850 \| 0.0 \| - \| \| 17.7465 \| 18900 \| 0.0 \| - \| \| 17.7934 \| 18950 \| 0.0 \| - \| \| 17.8404 \| 19000 \| 0.0 \| - \| \| 17.8873 \| 19050 \| 0.0 \| - \| \| 17.9343 \| 19100 \| 0.0 \| - \| \| 17.9812 \| 19150 \| 0.0 \| - \| \| 18.0282 \| 19200 \| 0.0 \| - \| \| 18.0751 \| 19250 \| 0.0 \| - \| \| 18.1221 \| 19300 \| 0.0 \| - \| \| 18.1690 \| 19350 \| 0.0 \| - \| \| 18.2160 \| 19400 \| 0.0 \| - \| \| 18.2629 \| 19450 \| 0.0 \| - \| \| 18.3099 \| 19500 \| 0.0 \| - \| \| 18.3568 \| 19550 \| 0.0 \| - \| \| 18.4038 \| 19600 \| 0.0 \| - \| \| 18.4507 \| 19650 \| 0.0 \| - \| \| 18.4977 \| 19700 \| 0.0 \| - \| \| 18.5446 \| 19750 \| 0.0 \| - \| \| 18.5915 \| 19800 \| 0.0 \| - \| \| 18.6385 \| 19850 \| 0.0 \| - \| \| 18.6854 \| 19900 \| 0.0 \| - \| \| 18.7324 \| 19950 \| 0.0 \| - \| \| 18.7793 \| 20000 \| 0.0 \| - \| \| 18.8263 \| 20050 \| 0.0 \| - \| \| 18.8732 \| 20100 \| 0.0 \| - \| \| 18.9202 \| 20150 \| 0.0 \| - \| \| 18.9671 \| 20200 \| 0.0 \| - \| \| 19.0141 \| 20250 \| 0.0 \| - \| \| 19.0610 \| 20300 \| 0.0 \| - \| \| 19.1080 \| 20350 \| 0.0 \| - \| \| 19.1549 \| 20400 \| 0.0 \| - \| \| 19.2019 \| 20450 \| 0.0 \| - \| \| 19.2488 \| 20500 \| 0.0 \| - \| \| 19.2958 \| 20550 \| 0.0 \| - \| \| 19.3427 \| 20600 \| 0.0 \| - \| \| 19.3897 \| 20650 \| 0.0 \| - \| \| 19.4366 \| 20700 \| 0.0 \| - \| \| 19.4836 \| 20750 \| 0.0 \| - \| \| 19.5305 \| 20800 \| 0.0 \| - \| \| 19.5775 \| 20850 \| 0.0 \| - \| \| 19.6244 \| 20900 \| 0.0 \| - \| \| 19.6714 \| 20950 \| 0.0 \| - \| \| 19.7183 \| 21000 \| 0.0 \| - \| \| 19.7653 \| 21050 \| 0.0 \| - \| \| 19.8122 \| 21100 \| 0.0 \| - \| \| 19.8592 \| 21150 \| 0.0 \| - \| \| 19.9061 \| 21200 \| 0.0 \| - \| \| 19.9531 \| 21250 \| 0.0 \| - \| \| 20.0 \| 21300 \| 0.0 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
mradermacher/Ice0.41-22.11-RP-i1-GGUF	mradermacher	2024-11-25T09:18:22Z	179	3	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:icefog72/Ice0.41-22.11-RP", "base_model:quantized:icefog72/Ice0.41-22.11-RP", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T09:21:22Z	--- base_model: icefog72/Ice0.41-22.11-RP language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/icefog72/Ice0.41-22.11-RP <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ1_S.gguf) \| i1-IQ1_S \| 1.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ1_M.gguf) \| i1-IQ1_M \| 1.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ2_M.gguf) \| i1-IQ2_M \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q2_K.gguf) \| i1-Q2_K \| 2.8 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 2.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.3 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 3.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 3.9 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q6_K.gguf) \| i1-Q6_K \| 6.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF	mradermacher	2024-11-25T09:17:42Z	263	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:bunnycore/Tulu-3.1-8B-SuperNova", "base_model:quantized:bunnycore/Tulu-3.1-8B-SuperNova", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T04:51:17Z	--- base_model: bunnycore/Tulu-3.1-8B-SuperNova language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/bunnycore/Tulu-3.1-8B-SuperNova <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.8 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.8 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.8 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF	mradermacher	2024-11-25T09:17:34Z	38	2	transformers	[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T06:30:46Z	--- base_model: ddh0/Qwen2.5-14B-Mixed-Instruct language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/ddh0/Qwen2.5-14B-Mixed-Instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 8.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
Sakalti/Kan1-2.5b	Sakalti	2024-11-25T09:17:28Z	129	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T09:15:02Z	--- base_model: - Qwen/Qwen2.5-7B-Instruct library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Qwen/Qwen2.5-7B-Instruct layer_range: [0, 6] - model: Qwen/Qwen2.5-7B-Instruct layer_range: [7, 13] - model: Qwen/Qwen2.5-7B-Instruct layer_range: [14, 20] - model: Qwen/Qwen2.5-7B-Instruct layer_range: [21, 27] merge_method: model_stock base_model: Qwen/Qwen2.5-7B-Instruct dtype: bfloat16 ```
mradermacher/NeuralDaredevil-12b-32k-GGUF	mradermacher	2024-11-25T09:17:19Z	121	2	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "mlabonne/NeuralDaredevil-7B", "en", "base_model:mvpmaster/NeuralDaredevil-12b-32k", "base_model:quantized:mvpmaster/NeuralDaredevil-12b-32k", "endpoints_compatible", "region:us" ]	null	2024-11-23T18:19:56Z	--- base_model: mvpmaster/NeuralDaredevil-12b-32k language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/mvpmaster/NeuralDaredevil-12b-32k <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q2_K.gguf) \| Q2_K \| 4.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q3_K_S.gguf) \| Q3_K_S \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q3_K_M.gguf) \| Q3_K_M \| 6.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q3_K_L.gguf) \| Q3_K_L \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.IQ4_XS.gguf) \| IQ4_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 7.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q4_K_S.gguf) \| Q4_K_S \| 7.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q4_K_M.gguf) \| Q4_K_M \| 7.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q5_K_S.gguf) \| Q5_K_S \| 8.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q5_K_M.gguf) \| Q5_K_M \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q6_K.gguf) \| Q6_K \| 10.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q8_0.gguf) \| Q8_0 \| 13.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
SeppeV/xlnet_ft_pref_10pc	SeppeV	2024-11-25T09:10:11Z	89	0	transformers	[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T15:02:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
PrunaAI/ahmedheakl-asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer-bnb-8bit-smashed	PrunaAI	2024-11-25T09:09:29Z	6	0	null	[ "safetensors", "qwen2", "pruna-ai", "base_model:ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer", "base_model:quantized:ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer", "8-bit", "bitsandbytes", "region:us" ]	null	2024-11-24T09:08:22Z	--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer"> <img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) Frequently Asked Questions - *How does the compression work?* The model is compressed with llm-int8. - *How does the model quality change?* The quality of the model output might vary compared to the base model. - *How is the model efficiency evaluated?* These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - *What is the model format?* We use safetensors. - *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data. - *What is the naming convention for Pruna Huggingface models?* We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - *What are "first" metrics?* Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - *What are "Sync" and "Async" metrics?* "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/ahmedheakl-asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Do it by yourself [here](https://docs.pruna.ai/en/latest/setup/pip.html).
July-Tokyo/xlm-roberta-base-finetuned-panx-de	July-Tokyo	2024-11-25T09:09:10Z	133	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-11-25T07:10:45Z	--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1377 - F1: 0.8627 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2592 \| 1.0 \| 525 \| 0.1594 \| 0.8243 \| \| 0.126 \| 2.0 \| 1050 \| 0.1390 \| 0.8513 \| \| 0.0802 \| 3.0 \| 1575 \| 0.1377 \| 0.8627 \| ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu118 - Datasets 3.1.0 - Tokenizers 0.20.1
MayBashendy/Arabic_FineTuningAraBERT_AugV5_k60_task2_organization_fold0	MayBashendy	2024-11-25T09:09:00Z	164	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T08:30:51Z	--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k60_task2_organization_fold0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Arabic_FineTuningAraBERT_AugV5_k60_task2_organization_fold0 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5654 - Qwk: 0.4661 - Mse: 0.5654 - Rmse: 0.7519 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 0.0049 \| 2 \| 3.7329 \| 0.0 \| 3.7329 \| 1.9321 \| \| No log \| 0.0098 \| 4 \| 3.0481 \| 0.0233 \| 3.0481 \| 1.7459 \| \| No log \| 0.0147 \| 6 \| 1.8078 \| -0.0157 \| 1.8078 \| 1.3445 \| \| No log \| 0.0196 \| 8 \| 1.2965 \| 0.0 \| 1.2965 \| 1.1386 \| \| No log \| 0.0245 \| 10 \| 1.2090 \| 0.0 \| 1.2090 \| 1.0996 \| \| No log \| 0.0294 \| 12 \| 1.2281 \| -0.0302 \| 1.2281 \| 1.1082 \| \| No log \| 0.0343 \| 14 \| 1.4012 \| -0.0302 \| 1.4012 \| 1.1837 \| \| No log \| 0.0392 \| 16 \| 1.3026 \| 0.0 \| 1.3026 \| 1.1413 \| \| No log \| 0.0441 \| 18 \| 1.2453 \| 0.0 \| 1.2453 \| 1.1159 \| \| No log \| 0.0490 \| 20 \| 1.0166 \| 0.0 \| 1.0166 \| 1.0083 \| \| No log \| 0.0539 \| 22 \| 0.7326 \| 0.1213 \| 0.7326 \| 0.8559 \| \| No log \| 0.0588 \| 24 \| 0.8443 \| 0.0 \| 0.8443 \| 0.9188 \| \| No log \| 0.0637 \| 26 \| 1.5283 \| -0.0302 \| 1.5283 \| 1.2362 \| \| No log \| 0.0686 \| 28 \| 1.8904 \| -0.1442 \| 1.8904 \| 1.3749 \| \| No log \| 0.0735 \| 30 \| 1.7874 \| -0.1282 \| 1.7874 \| 1.3369 \| \| No log \| 0.0784 \| 32 \| 1.5789 \| 0.0 \| 1.5789 \| 1.2566 \| \| No log \| 0.0833 \| 34 \| 1.5011 \| 0.0 \| 1.5011 \| 1.2252 \| \| No log \| 0.0882 \| 36 \| 1.2510 \| 0.0 \| 1.2510 \| 1.1185 \| \| No log \| 0.0931 \| 38 \| 1.0157 \| 0.0 \| 1.0157 \| 1.0078 \| \| No log \| 0.0980 \| 40 \| 1.1595 \| 0.0 \| 1.1595 \| 1.0768 \| \| No log \| 0.1029 \| 42 \| 1.1783 \| 0.0 \| 1.1783 \| 1.0855 \| \| No log \| 0.1078 \| 44 \| 1.3290 \| 0.0 \| 1.3290 \| 1.1528 \| \| No log \| 0.1127 \| 46 \| 1.7079 \| 0.1811 \| 1.7079 \| 1.3069 \| \| No log \| 0.1176 \| 48 \| 1.5984 \| 0.0577 \| 1.5984 \| 1.2643 \| \| No log \| 0.1225 \| 50 \| 1.5642 \| 0.1550 \| 1.5642 \| 1.2507 \| \| No log \| 0.1275 \| 52 \| 1.5572 \| 0.1811 \| 1.5572 \| 1.2479 \| \| No log \| 0.1324 \| 54 \| 1.3567 \| 0.0491 \| 1.3567 \| 1.1648 \| \| No log \| 0.1373 \| 56 \| 1.5691 \| 0.1811 \| 1.5691 \| 1.2526 \| \| No log \| 0.1422 \| 58 \| 1.4052 \| 0.0924 \| 1.4052 \| 1.1854 \| \| No log \| 0.1471 \| 60 \| 0.8369 \| 0.0 \| 0.8369 \| 0.9148 \| \| No log \| 0.1520 \| 62 \| 0.7136 \| 0.2452 \| 0.7136 \| 0.8448 \| \| No log \| 0.1569 \| 64 \| 0.7666 \| 0.1213 \| 0.7666 \| 0.8756 \| \| No log \| 0.1618 \| 66 \| 1.0461 \| 0.0 \| 1.0461 \| 1.0228 \| \| No log \| 0.1667 \| 68 \| 1.6175 \| 0.0641 \| 1.6175 \| 1.2718 \| \| No log \| 0.1716 \| 70 \| 1.8439 \| 0.0265 \| 1.8439 \| 1.3579 \| \| No log \| 0.1765 \| 72 \| 1.6212 \| 0.0924 \| 1.6212 \| 1.2733 \| \| No log \| 0.1814 \| 74 \| 1.2282 \| 0.0 \| 1.2282 \| 1.1082 \| \| No log \| 0.1863 \| 76 \| 1.0309 \| 0.0 \| 1.0309 \| 1.0153 \| \| No log \| 0.1912 \| 78 \| 1.2423 \| 0.0491 \| 1.2423 \| 1.1146 \| \| No log \| 0.1961 \| 80 \| 1.8788 \| 0.1170 \| 1.8788 \| 1.3707 \| \| No log \| 0.2010 \| 82 \| 2.6548 \| 0.0 \| 2.6548 \| 1.6294 \| \| No log \| 0.2059 \| 84 \| 2.5991 \| 0.0 \| 2.5991 \| 1.6122 \| \| No log \| 0.2108 \| 86 \| 2.1897 \| -0.0073 \| 2.1897 \| 1.4798 \| \| No log \| 0.2157 \| 88 \| 1.4933 \| 0.0491 \| 1.4933 \| 1.2220 \| \| No log \| 0.2206 \| 90 \| 0.9251 \| 0.0 \| 0.9251 \| 0.9618 \| \| No log \| 0.2255 \| 92 \| 0.7122 \| 0.2821 \| 0.7122 \| 0.8439 \| \| No log \| 0.2304 \| 94 \| 0.6805 \| 0.2289 \| 0.6805 \| 0.8249 \| \| No log \| 0.2353 \| 96 \| 0.7859 \| -0.0544 \| 0.7859 \| 0.8865 \| \| No log \| 0.2402 \| 98 \| 1.3142 \| 0.0 \| 1.3142 \| 1.1464 \| \| No log \| 0.2451 \| 100 \| 1.8197 \| 0.0955 \| 1.8197 \| 1.3490 \| \| No log \| 0.25 \| 102 \| 2.1004 \| 0.0499 \| 2.1004 \| 1.4493 \| \| No log \| 0.2549 \| 104 \| 1.9572 \| 0.1170 \| 1.9572 \| 1.3990 \| \| No log \| 0.2598 \| 106 \| 1.6868 \| 0.125 \| 1.6868 \| 1.2988 \| \| No log \| 0.2647 \| 108 \| 1.4735 \| 0.0 \| 1.4735 \| 1.2139 \| \| No log \| 0.2696 \| 110 \| 1.1563 \| 0.0 \| 1.1563 \| 1.0753 \| \| No log \| 0.2745 \| 112 \| 1.0011 \| 0.0 \| 1.0011 \| 1.0006 \| \| No log \| 0.2794 \| 114 \| 1.1042 \| 0.0 \| 1.1042 \| 1.0508 \| \| No log \| 0.2843 \| 116 \| 1.0354 \| 0.0 \| 1.0354 \| 1.0176 \| \| No log \| 0.2892 \| 118 \| 0.9569 \| 0.0 \| 0.9569 \| 0.9782 \| \| No log \| 0.2941 \| 120 \| 1.0087 \| 0.0 \| 1.0087 \| 1.0043 \| \| No log \| 0.2990 \| 122 \| 0.9712 \| 0.0 \| 0.9712 \| 0.9855 \| \| No log \| 0.3039 \| 124 \| 0.8387 \| -0.0544 \| 0.8387 \| 0.9158 \| \| No log \| 0.3088 \| 126 \| 0.8373 \| -0.1667 \| 0.8373 \| 0.9150 \| \| No log \| 0.3137 \| 128 \| 0.9305 \| 0.0335 \| 0.9305 \| 0.9646 \| \| No log \| 0.3186 \| 130 \| 1.0780 \| 0.0 \| 1.0780 \| 1.0383 \| \| No log \| 0.3235 \| 132 \| 1.2888 \| 0.0 \| 1.2888 \| 1.1352 \| \| No log \| 0.3284 \| 134 \| 1.3256 \| 0.0 \| 1.3256 \| 1.1513 \| \| No log \| 0.3333 \| 136 \| 1.4511 \| 0.0 \| 1.4511 \| 1.2046 \| \| No log \| 0.3382 \| 138 \| 1.4020 \| 0.0 \| 1.4020 \| 1.1841 \| \| No log \| 0.3431 \| 140 \| 1.2453 \| 0.0 \| 1.2453 \| 1.1159 \| \| No log \| 0.3480 \| 142 \| 1.0186 \| 0.0 \| 1.0186 \| 1.0093 \| \| No log \| 0.3529 \| 144 \| 0.9046 \| -0.1099 \| 0.9046 \| 0.9511 \| \| No log \| 0.3578 \| 146 \| 1.1096 \| 0.0162 \| 1.1096 \| 1.0534 \| \| No log \| 0.3627 \| 148 \| 1.9099 \| 0.0808 \| 1.9099 \| 1.3820 \| \| No log \| 0.3676 \| 150 \| 2.2249 \| 0.0538 \| 2.2249 \| 1.4916 \| \| No log \| 0.3725 \| 152 \| 1.9898 \| 0.0455 \| 1.9898 \| 1.4106 \| \| No log \| 0.3775 \| 154 \| 1.3677 \| 0.0605 \| 1.3677 \| 1.1695 \| \| No log \| 0.3824 \| 156 \| 0.8731 \| -0.0548 \| 0.8731 \| 0.9344 \| \| No log \| 0.3873 \| 158 \| 0.8028 \| 0.2150 \| 0.8028 \| 0.8960 \| \| No log \| 0.3922 \| 160 \| 0.8322 \| -0.0548 \| 0.8322 \| 0.9123 \| \| No log \| 0.3971 \| 162 \| 1.0129 \| 0.0 \| 1.0129 \| 1.0064 \| \| No log \| 0.4020 \| 164 \| 1.3319 \| 0.0491 \| 1.3319 \| 1.1541 \| \| No log \| 0.4069 \| 166 \| 1.5114 \| -0.0096 \| 1.5114 \| 1.2294 \| \| No log \| 0.4118 \| 168 \| 1.4833 \| -0.0328 \| 1.4833 \| 1.2179 \| \| No log \| 0.4167 \| 170 \| 1.3613 \| 0.0173 \| 1.3613 \| 1.1667 \| \| No log \| 0.4216 \| 172 \| 1.1971 \| 0.0491 \| 1.1971 \| 1.0941 \| \| No log \| 0.4265 \| 174 \| 1.0106 \| 0.0 \| 1.0106 \| 1.0053 \| \| No log \| 0.4314 \| 176 \| 0.8181 \| 0.1564 \| 0.8181 \| 0.9045 \| \| No log \| 0.4363 \| 178 \| 0.7188 \| -0.0312 \| 0.7188 \| 0.8478 \| \| No log \| 0.4412 \| 180 \| 0.7157 \| 0.0993 \| 0.7157 \| 0.8460 \| \| No log \| 0.4461 \| 182 \| 0.7602 \| 0.0265 \| 0.7602 \| 0.8719 \| \| No log \| 0.4510 \| 184 \| 0.8625 \| -0.0087 \| 0.8625 \| 0.9287 \| \| No log \| 0.4559 \| 186 \| 1.0581 \| 0.0 \| 1.0581 \| 1.0287 \| \| No log \| 0.4608 \| 188 \| 1.1478 \| 0.1213 \| 1.1478 \| 1.0714 \| \| No log \| 0.4657 \| 190 \| 1.1894 \| 0.1213 \| 1.1894 \| 1.0906 \| \| No log \| 0.4706 \| 192 \| 1.1746 \| 0.0436 \| 1.1746 \| 1.0838 \| \| No log \| 0.4755 \| 194 \| 1.2390 \| 0.0109 \| 1.2390 \| 1.1131 \| \| No log \| 0.4804 \| 196 \| 1.1443 \| 0.1939 \| 1.1443 \| 1.0697 \| \| No log \| 0.4853 \| 198 \| 1.0193 \| 0.1429 \| 1.0193 \| 1.0096 \| \| No log \| 0.4902 \| 200 \| 0.9713 \| -0.0185 \| 0.9713 \| 0.9855 \| \| No log \| 0.4951 \| 202 \| 0.9346 \| 0.0411 \| 0.9346 \| 0.9667 \| \| No log \| 0.5 \| 204 \| 1.0566 \| 0.1765 \| 1.0566 \| 1.0279 \| \| No log \| 0.5049 \| 206 \| 1.1853 \| 0.1600 \| 1.1853 \| 1.0887 \| \| No log \| 0.5098 \| 208 \| 1.1745 \| 0.0491 \| 1.1745 \| 1.0838 \| \| No log \| 0.5147 \| 210 \| 1.0121 \| 0.1923 \| 1.0121 \| 1.0060 \| \| No log \| 0.5196 \| 212 \| 0.7318 \| 0.2329 \| 0.7318 \| 0.8554 \| \| No log \| 0.5245 \| 214 \| 0.6922 \| 0.0957 \| 0.6922 \| 0.8320 \| \| No log \| 0.5294 \| 216 \| 0.7221 \| 0.0567 \| 0.7221 \| 0.8498 \| \| No log \| 0.5343 \| 218 \| 0.7596 \| 0.1600 \| 0.7596 \| 0.8716 \| \| No log \| 0.5392 \| 220 \| 0.7837 \| 0.1600 \| 0.7837 \| 0.8853 \| \| No log \| 0.5441 \| 222 \| 0.7752 \| 0.0567 \| 0.7752 \| 0.8804 \| \| No log \| 0.5490 \| 224 \| 0.7915 \| 0.2184 \| 0.7915 \| 0.8897 \| \| No log \| 0.5539 \| 226 \| 0.7819 \| 0.2184 \| 0.7819 \| 0.8842 \| \| No log \| 0.5588 \| 228 \| 0.7789 \| 0.1600 \| 0.7789 \| 0.8825 \| \| No log \| 0.5637 \| 230 \| 1.0059 \| 0.1800 \| 1.0059 \| 1.0030 \| \| No log \| 0.5686 \| 232 \| 0.9666 \| 0.0701 \| 0.9666 \| 0.9832 \| \| No log \| 0.5735 \| 234 \| 0.7999 \| 0.1370 \| 0.7999 \| 0.8944 \| \| No log \| 0.5784 \| 236 \| 0.7399 \| 0.1168 \| 0.7399 \| 0.8602 \| \| No log \| 0.5833 \| 238 \| 0.7587 \| 0.1168 \| 0.7587 \| 0.8711 \| \| No log \| 0.5882 \| 240 \| 0.8089 \| 0.0045 \| 0.8089 \| 0.8994 \| \| No log \| 0.5931 \| 242 \| 0.8514 \| 0.0045 \| 0.8514 \| 0.9227 \| \| No log \| 0.5980 \| 244 \| 0.9308 \| -0.0312 \| 0.9308 \| 0.9648 \| \| No log \| 0.6029 \| 246 \| 0.9141 \| -0.0312 \| 0.9141 \| 0.9561 \| \| No log \| 0.6078 \| 248 \| 0.8402 \| -0.0548 \| 0.8402 \| 0.9166 \| \| No log \| 0.6127 \| 250 \| 0.7696 \| -0.0048 \| 0.7696 \| 0.8773 \| \| No log \| 0.6176 \| 252 \| 0.7865 \| -0.0943 \| 0.7865 \| 0.8868 \| \| No log \| 0.6225 \| 254 \| 0.8473 \| 0.1356 \| 0.8473 \| 0.9205 \| \| No log \| 0.6275 \| 256 \| 0.9396 \| 0.1064 \| 0.9396 \| 0.9693 \| \| No log \| 0.6324 \| 258 \| 1.0712 \| -0.0483 \| 1.0712 \| 1.0350 \| \| No log \| 0.6373 \| 260 \| 1.1024 \| -0.0909 \| 1.1024 \| 1.0500 \| \| No log \| 0.6422 \| 262 \| 1.0499 \| 0.0297 \| 1.0499 \| 1.0246 \| \| No log \| 0.6471 \| 264 \| 1.0232 \| 0.1765 \| 1.0232 \| 1.0115 \| \| No log \| 0.6520 \| 266 \| 1.0093 \| -0.0553 \| 1.0093 \| 1.0046 \| \| No log \| 0.6569 \| 268 \| 0.9457 \| -0.0153 \| 0.9457 \| 0.9725 \| \| No log \| 0.6618 \| 270 \| 0.8648 \| -0.0153 \| 0.8648 \| 0.9300 \| \| No log \| 0.6667 \| 272 \| 0.8738 \| -0.0553 \| 0.8738 \| 0.9348 \| \| No log \| 0.6716 \| 274 \| 0.9585 \| 0.0903 \| 0.9585 \| 0.9790 \| \| No log \| 0.6765 \| 276 \| 1.0674 \| 0.1408 \| 1.0674 \| 1.0332 \| \| No log \| 0.6814 \| 278 \| 1.1280 \| 0.0455 \| 1.1280 \| 1.0621 \| \| No log \| 0.6863 \| 280 \| 1.0904 \| 0.1408 \| 1.0904 \| 1.0442 \| \| No log \| 0.6912 \| 282 \| 1.0795 \| 0.0455 \| 1.0795 \| 1.0390 \| \| No log \| 0.6961 \| 284 \| 1.0543 \| 0.0720 \| 1.0543 \| 1.0268 \| \| No log \| 0.7010 \| 286 \| 0.9815 \| 0.0473 \| 0.9815 \| 0.9907 \| \| No log \| 0.7059 \| 288 \| 0.9048 \| 0.1765 \| 0.9048 \| 0.9512 \| \| No log \| 0.7108 \| 290 \| 0.8590 \| 0.1765 \| 0.8590 \| 0.9268 \| \| No log \| 0.7157 \| 292 \| 0.8301 \| 0.1765 \| 0.8301 \| 0.9111 \| \| No log \| 0.7206 \| 294 \| 0.8156 \| 0.0567 \| 0.8156 \| 0.9031 \| \| No log \| 0.7255 \| 296 \| 0.8623 \| 0.0785 \| 0.8623 \| 0.9286 \| \| No log \| 0.7304 \| 298 \| 0.8934 \| 0.0375 \| 0.8934 \| 0.9452 \| \| No log \| 0.7353 \| 300 \| 0.9323 \| 0.0038 \| 0.9323 \| 0.9656 \| \| No log \| 0.7402 \| 302 \| 0.9764 \| 0.0038 \| 0.9764 \| 0.9882 \| \| No log \| 0.7451 \| 304 \| 0.9810 \| -0.0460 \| 0.9810 \| 0.9904 \| \| No log \| 0.75 \| 306 \| 0.9550 \| 0.0170 \| 0.9550 \| 0.9773 \| \| No log \| 0.7549 \| 308 \| 0.9805 \| 0.0375 \| 0.9805 \| 0.9902 \| \| No log \| 0.7598 \| 310 \| 0.9342 \| 0.0041 \| 0.9342 \| 0.9665 \| \| No log \| 0.7647 \| 312 \| 0.9290 \| -0.0286 \| 0.9290 \| 0.9638 \| \| No log \| 0.7696 \| 314 \| 0.8797 \| -0.0185 \| 0.8797 \| 0.9379 \| \| No log \| 0.7745 \| 316 \| 0.8227 \| -0.0794 \| 0.8227 \| 0.9070 \| \| No log \| 0.7794 \| 318 \| 0.7853 \| 0.0957 \| 0.7853 \| 0.8861 \| \| No log \| 0.7843 \| 320 \| 0.7605 \| 0.1356 \| 0.7605 \| 0.8721 \| \| No log \| 0.7892 \| 322 \| 0.7714 \| 0.1765 \| 0.7714 \| 0.8783 \| \| No log \| 0.7941 \| 324 \| 0.7959 \| 0.1765 \| 0.7959 \| 0.8921 \| \| No log \| 0.7990 \| 326 \| 0.8596 \| 0.1765 \| 0.8596 \| 0.9271 \| \| No log \| 0.8039 \| 328 \| 0.9724 \| 0.3029 \| 0.9724 \| 0.9861 \| \| No log \| 0.8088 \| 330 \| 0.9962 \| 0.3029 \| 0.9962 \| 0.9981 \| \| No log \| 0.8137 \| 332 \| 0.9977 \| 0.1635 \| 0.9977 \| 0.9988 \| \| No log \| 0.8186 \| 334 \| 1.0736 \| 0.0321 \| 1.0736 \| 1.0362 \| \| No log \| 0.8235 \| 336 \| 1.0085 \| 0.1818 \| 1.0085 \| 1.0042 \| \| No log \| 0.8284 \| 338 \| 0.9455 \| 0.0099 \| 0.9455 \| 0.9724 \| \| No log \| 0.8333 \| 340 \| 0.9222 \| 0.1356 \| 0.9222 \| 0.9603 \| \| No log \| 0.8382 \| 342 \| 0.8597 \| 0.0957 \| 0.8597 \| 0.9272 \| \| No log \| 0.8431 \| 344 \| 0.8225 \| 0.1356 \| 0.8225 \| 0.9069 \| \| No log \| 0.8480 \| 346 \| 0.7537 \| 0.1765 \| 0.7537 \| 0.8682 \| \| No log \| 0.8529 \| 348 \| 0.7236 \| 0.2184 \| 0.7236 \| 0.8506 \| \| No log \| 0.8578 \| 350 \| 0.7374 \| 0.0503 \| 0.7374 \| 0.8587 \| \| No log \| 0.8627 \| 352 \| 0.8519 \| 0.0099 \| 0.8519 \| 0.9230 \| \| No log \| 0.8676 \| 354 \| 0.9807 \| 0.1765 \| 0.9807 \| 0.9903 \| \| No log \| 0.8725 \| 356 \| 1.0893 \| 0.0516 \| 1.0893 \| 1.0437 \| \| No log \| 0.8775 \| 358 \| 1.1002 \| -0.0876 \| 1.1002 \| 1.0489 \| \| No log \| 0.8824 \| 360 \| 1.0915 \| 0.1429 \| 1.0915 \| 1.0448 \| \| No log \| 0.8873 \| 362 \| 1.0676 \| 0.1715 \| 1.0676 \| 1.0332 \| \| No log \| 0.8922 \| 364 \| 0.9708 \| 0.0833 \| 0.9708 \| 0.9853 \| \| No log \| 0.8971 \| 366 \| 0.8791 \| 0.0916 \| 0.8791 \| 0.9376 \| \| No log \| 0.9020 \| 368 \| 0.7827 \| 0.0099 \| 0.7827 \| 0.8847 \| \| No log \| 0.9069 \| 370 \| 0.7366 \| 0.1356 \| 0.7366 \| 0.8582 \| \| No log \| 0.9118 \| 372 \| 0.7425 \| 0.1168 \| 0.7425 \| 0.8617 \| \| No log \| 0.9167 \| 374 \| 0.7658 \| 0.0045 \| 0.7658 \| 0.8751 \| \| No log \| 0.9216 \| 376 \| 0.7434 \| 0.0567 \| 0.7434 \| 0.8622 \| \| No log \| 0.9265 \| 378 \| 0.7418 \| 0.1356 \| 0.7418 \| 0.8613 \| \| No log \| 0.9314 \| 380 \| 0.8042 \| 0.0099 \| 0.8042 \| 0.8968 \| \| No log \| 0.9363 \| 382 \| 0.9116 \| 0.0099 \| 0.9116 \| 0.9548 \| \| No log \| 0.9412 \| 384 \| 0.9422 \| 0.1765 \| 0.9422 \| 0.9707 \| \| No log \| 0.9461 \| 386 \| 0.9646 \| 0.1765 \| 0.9646 \| 0.9821 \| \| No log \| 0.9510 \| 388 \| 0.9366 \| 0.1765 \| 0.9366 \| 0.9678 \| \| No log \| 0.9559 \| 390 \| 0.8935 \| 0.1765 \| 0.8935 \| 0.9452 \| \| No log \| 0.9608 \| 392 \| 0.8234 \| 0.1765 \| 0.8234 \| 0.9074 \| \| No log \| 0.9657 \| 394 \| 0.7723 \| 0.1356 \| 0.7723 \| 0.8788 \| \| No log \| 0.9706 \| 396 \| 0.7751 \| 0.1356 \| 0.7751 \| 0.8804 \| \| No log \| 0.9755 \| 398 \| 0.7900 \| 0.1962 \| 0.7900 \| 0.8888 \| \| No log \| 0.9804 \| 400 \| 0.8108 \| 0.1962 \| 0.8108 \| 0.9005 \| \| No log \| 0.9853 \| 402 \| 0.8253 \| 0.1765 \| 0.8253 \| 0.9085 \| \| No log \| 0.9902 \| 404 \| 0.8073 \| 0.0099 \| 0.8073 \| 0.8985 \| \| No log \| 0.9951 \| 406 \| 0.8569 \| 0.0099 \| 0.8569 \| 0.9257 \| \| No log \| 1.0 \| 408 \| 0.9797 \| 0.0503 \| 0.9797 \| 0.9898 \| \| No log \| 1.0049 \| 410 \| 1.0478 \| 0.0503 \| 1.0478 \| 1.0236 \| \| No log \| 1.0098 \| 412 \| 1.0258 \| 0.0503 \| 1.0258 \| 1.0128 \| \| No log \| 1.0147 \| 414 \| 0.9693 \| 0.0503 \| 0.9693 \| 0.9845 \| \| No log \| 1.0196 \| 416 \| 0.8774 \| 0.0503 \| 0.8774 \| 0.9367 \| \| No log \| 1.0245 \| 418 \| 0.8861 \| 0.0099 \| 0.8861 \| 0.9413 \| \| No log \| 1.0294 \| 420 \| 0.9214 \| 0.0099 \| 0.9214 \| 0.9599 \| \| No log \| 1.0343 \| 422 \| 0.8879 \| 0.0099 \| 0.8879 \| 0.9423 \| \| No log \| 1.0392 \| 424 \| 0.8490 \| 0.1231 \| 0.8490 \| 0.9214 \| \| No log \| 1.0441 \| 426 \| 0.8146 \| 0.0870 \| 0.8146 \| 0.9025 \| \| No log \| 1.0490 \| 428 \| 0.7657 \| 0.1356 \| 0.7657 \| 0.8750 \| \| No log \| 1.0539 \| 430 \| 0.7545 \| 0.0503 \| 0.7545 \| 0.8686 \| \| No log \| 1.0588 \| 432 \| 0.7598 \| 0.0503 \| 0.7598 \| 0.8717 \| \| No log \| 1.0637 \| 434 \| 0.7718 \| -0.0825 \| 0.7718 \| 0.8785 \| \| No log \| 1.0686 \| 436 \| 0.8087 \| -0.0825 \| 0.8086 \| 0.8992 \| \| No log \| 1.0735 \| 438 \| 0.8054 \| -0.0825 \| 0.8054 \| 0.8974 \| \| No log \| 1.0784 \| 440 \| 0.8101 \| 0.0503 \| 0.8101 \| 0.9000 \| \| No log \| 1.0833 \| 442 \| 0.8115 \| 0.0503 \| 0.8115 \| 0.9008 \| \| No log \| 1.0882 \| 444 \| 0.7931 \| 0.2184 \| 0.7931 \| 0.8906 \| \| No log \| 1.0931 \| 446 \| 0.7737 \| 0.1765 \| 0.7737 \| 0.8796 \| \| No log \| 1.0980 \| 448 \| 0.7484 \| 0.1765 \| 0.7484 \| 0.8651 \| \| No log \| 1.1029 \| 450 \| 0.7459 \| 0.2184 \| 0.7459 \| 0.8637 \| \| No log \| 1.1078 \| 452 \| 0.7613 \| 0.2184 \| 0.7613 \| 0.8725 \| \| No log \| 1.1127 \| 454 \| 0.7604 \| 0.2184 \| 0.7604 \| 0.8720 \| \| No log \| 1.1176 \| 456 \| 0.7483 \| 0.2184 \| 0.7483 \| 0.8651 \| \| No log \| 1.1225 \| 458 \| 0.7503 \| 0.2613 \| 0.7503 \| 0.8662 \| \| No log \| 1.1275 \| 460 \| 0.7585 \| 0.0916 \| 0.7585 \| 0.8709 \| \| No log \| 1.1324 \| 462 \| 0.7548 \| 0.2613 \| 0.7548 \| 0.8688 \| \| No log \| 1.1373 \| 464 \| 0.7606 \| 0.1765 \| 0.7606 \| 0.8721 \| \| No log \| 1.1422 \| 466 \| 0.7849 \| 0.1765 \| 0.7849 \| 0.8859 \| \| No log \| 1.1471 \| 468 \| 0.8086 \| 0.1765 \| 0.8086 \| 0.8992 \| \| No log \| 1.1520 \| 470 \| 0.8458 \| 0.1765 \| 0.8458 \| 0.9197 \| \| No log \| 1.1569 \| 472 \| 0.8762 \| 0.1765 \| 0.8762 \| 0.9360 \| \| No log \| 1.1618 \| 474 \| 0.9001 \| 0.1356 \| 0.9001 \| 0.9487 \| \| No log \| 1.1667 \| 476 \| 0.9094 \| 0.1356 \| 0.9094 \| 0.9536 \| \| No log \| 1.1716 \| 478 \| 0.9015 \| 0.1356 \| 0.9015 \| 0.9495 \| \| No log \| 1.1765 \| 480 \| 0.8821 \| 0.1765 \| 0.8821 \| 0.9392 \| \| No log \| 1.1814 \| 482 \| 0.8844 \| 0.3029 \| 0.8844 \| 0.9404 \| \| No log \| 1.1863 \| 484 \| 0.9158 \| 0.1992 \| 0.9158 \| 0.9570 \| \| No log \| 1.1912 \| 486 \| 0.9036 \| 0.1600 \| 0.9036 \| 0.9506 \| \| No log \| 1.1961 \| 488 \| 0.8359 \| 0.0258 \| 0.8359 \| 0.9143 \| \| No log \| 1.2010 \| 490 \| 0.7704 \| 0.0258 \| 0.7704 \| 0.8777 \| \| No log \| 1.2059 \| 492 \| 0.7179 \| 0.0916 \| 0.7179 \| 0.8473 \| \| No log \| 1.2108 \| 494 \| 0.6796 \| 0.2184 \| 0.6796 \| 0.8244 \| \| No log \| 1.2157 \| 496 \| 0.6828 \| 0.1765 \| 0.6828 \| 0.8263 \| \| No log \| 1.2206 \| 498 \| 0.6992 \| 0.2184 \| 0.6992 \| 0.8362 \| \| 0.5578 \| 1.2255 \| 500 \| 0.7241 \| 0.2184 \| 0.7241 \| 0.8510 \| \| 0.5578 \| 1.2304 \| 502 \| 0.7504 \| 0.0503 \| 0.7504 \| 0.8663 \| \| 0.5578 \| 1.2353 \| 504 \| 0.8165 \| 0.0679 \| 0.8165 \| 0.9036 \| \| 0.5578 \| 1.2402 \| 506 \| 0.8493 \| 0.0679 \| 0.8493 \| 0.9216 \| \| 0.5578 \| 1.2451 \| 508 \| 0.8073 \| 0.0679 \| 0.8073 \| 0.8985 \| \| 0.5578 \| 1.25 \| 510 \| 0.7283 \| 0.0258 \| 0.7283 \| 0.8534 \| \| 0.5578 \| 1.2549 \| 512 \| 0.6871 \| 0.0503 \| 0.6871 \| 0.8289 \| \| 0.5578 \| 1.2598 \| 514 \| 0.6995 \| 0.1783 \| 0.6995 \| 0.8364 \| \| 0.5578 \| 1.2647 \| 516 \| 0.6958 \| 0.1783 \| 0.6958 \| 0.8341 \| \| 0.5578 \| 1.2696 \| 518 \| 0.6776 \| 0.1356 \| 0.6776 \| 0.8232 \| \| 0.5578 \| 1.2745 \| 520 \| 0.6641 \| 0.0503 \| 0.6641 \| 0.8149 \| \| 0.5578 \| 1.2794 \| 522 \| 0.6771 \| 0.0258 \| 0.6771 \| 0.8228 \| \| 0.5578 \| 1.2843 \| 524 \| 0.7397 \| 0.0258 \| 0.7397 \| 0.8601 \| \| 0.5578 \| 1.2892 \| 526 \| 0.7622 \| 0.0258 \| 0.7622 \| 0.8731 \| \| 0.5578 \| 1.2941 \| 528 \| 0.7170 \| 0.0258 \| 0.7170 \| 0.8468 \| \| 0.5578 \| 1.2990 \| 530 \| 0.6831 \| -0.0153 \| 0.6831 \| 0.8265 \| \| 0.5578 \| 1.3039 \| 532 \| 0.6822 \| -0.0153 \| 0.6822 \| 0.8259 \| \| 0.5578 \| 1.3088 \| 534 \| 0.6797 \| -0.0153 \| 0.6797 \| 0.8244 \| \| 0.5578 \| 1.3137 \| 536 \| 0.6996 \| -0.0153 \| 0.6996 \| 0.8364 \| \| 0.5578 \| 1.3186 \| 538 \| 0.7385 \| 0.0258 \| 0.7385 \| 0.8594 \| \| 0.5578 \| 1.3235 \| 540 \| 0.7466 \| -0.0153 \| 0.7466 \| 0.8641 \| \| 0.5578 \| 1.3284 \| 542 \| 0.7164 \| -0.0153 \| 0.7164 \| 0.8464 \| \| 0.5578 \| 1.3333 \| 544 \| 0.7053 \| -0.0153 \| 0.7053 \| 0.8398 \| \| 0.5578 \| 1.3382 \| 546 \| 0.7006 \| 0.0503 \| 0.7006 \| 0.8370 \| \| 0.5578 \| 1.3431 \| 548 \| 0.7064 \| 0.0099 \| 0.7064 \| 0.8405 \| \| 0.5578 \| 1.3480 \| 550 \| 0.7086 \| 0.1765 \| 0.7086 \| 0.8418 \| \| 0.5578 \| 1.3529 \| 552 \| 0.7189 \| 0.1765 \| 0.7189 \| 0.8479 \| \| 0.5578 \| 1.3578 \| 554 \| 0.7168 \| 0.1765 \| 0.7168 \| 0.8466 \| \| 0.5578 \| 1.3627 \| 556 \| 0.7376 \| 0.1765 \| 0.7376 \| 0.8588 \| \| 0.5578 \| 1.3676 \| 558 \| 0.7608 \| 0.0828 \| 0.7608 \| 0.8723 \| \| 0.5578 \| 1.3725 \| 560 \| 0.7547 \| 0.0503 \| 0.7547 \| 0.8688 \| \| 0.5578 \| 1.3775 \| 562 \| 0.7262 \| 0.1765 \| 0.7262 \| 0.8522 \| \| 0.5578 \| 1.3824 \| 564 \| 0.7455 \| 0.0503 \| 0.7455 \| 0.8634 \| \| 0.5578 \| 1.3873 \| 566 \| 0.8094 \| 0.1818 \| 0.8094 \| 0.8997 \| \| 0.5578 \| 1.3922 \| 568 \| 0.7910 \| 0.1209 \| 0.7910 \| 0.8894 \| \| 0.5578 \| 1.3971 \| 570 \| 0.7395 \| 0.0916 \| 0.7395 \| 0.8599 \| \| 0.5578 \| 1.4020 \| 572 \| 0.6950 \| 0.0503 \| 0.6950 \| 0.8336 \| \| 0.5578 \| 1.4069 \| 574 \| 0.6585 \| 0.1765 \| 0.6585 \| 0.8115 \| \| 0.5578 \| 1.4118 \| 576 \| 0.6510 \| 0.1765 \| 0.6510 \| 0.8068 \| \| 0.5578 \| 1.4167 \| 578 \| 0.6428 \| 0.1765 \| 0.6428 \| 0.8018 \| \| 0.5578 \| 1.4216 \| 580 \| 0.6618 \| 0.0503 \| 0.6618 \| 0.8135 \| \| 0.5578 \| 1.4265 \| 582 \| 0.6488 \| 0.0099 \| 0.6488 \| 0.8055 \| \| 0.5578 \| 1.4314 \| 584 \| 0.6235 \| -0.0294 \| 0.6235 \| 0.7896 \| \| 0.5578 \| 1.4363 \| 586 \| 0.6235 \| 0.0735 \| 0.6235 \| 0.7896 \| \| 0.5578 \| 1.4412 \| 588 \| 0.6686 \| 0.0099 \| 0.6686 \| 0.8177 \| \| 0.5578 \| 1.4461 \| 590 \| 0.7365 \| 0.0916 \| 0.7365 \| 0.8582 \| \| 0.5578 \| 1.4510 \| 592 \| 0.7768 \| 0.1793 \| 0.7768 \| 0.8814 \| \| 0.5578 \| 1.4559 \| 594 \| 0.7998 \| 0.1793 \| 0.7998 \| 0.8943 \| \| 0.5578 \| 1.4608 \| 596 \| 0.8258 \| 0.1635 \| 0.8258 \| 0.9087 \| \| 0.5578 \| 1.4657 \| 598 \| 0.8195 \| 0.1765 \| 0.8195 \| 0.9053 \| \| 0.5578 \| 1.4706 \| 600 \| 0.8337 \| 0.0455 \| 0.8337 \| 0.9131 \| \| 0.5578 \| 1.4755 \| 602 \| 0.7986 \| 0.0503 \| 0.7986 \| 0.8936 \| \| 0.5578 \| 1.4804 \| 604 \| 0.7542 \| 0.0099 \| 0.7542 \| 0.8684 \| \| 0.5578 \| 1.4853 \| 606 \| 0.7238 \| 0.0503 \| 0.7238 \| 0.8508 \| \| 0.5578 \| 1.4902 \| 608 \| 0.7202 \| 0.0503 \| 0.7202 \| 0.8486 \| \| 0.5578 \| 1.4951 \| 610 \| 0.6943 \| 0.0503 \| 0.6943 \| 0.8333 \| \| 0.5578 \| 1.5 \| 612 \| 0.7182 \| -0.0153 \| 0.7182 \| 0.8475 \| \| 0.5578 \| 1.5049 \| 614 \| 0.7282 \| 0.0258 \| 0.7282 \| 0.8534 \| \| 0.5578 \| 1.5098 \| 616 \| 0.7412 \| 0.0258 \| 0.7412 \| 0.8609 \| \| 0.5578 \| 1.5147 \| 618 \| 0.7442 \| 0.0258 \| 0.7442 \| 0.8627 \| \| 0.5578 \| 1.5196 \| 620 \| 0.7727 \| 0.0258 \| 0.7727 \| 0.8790 \| \| 0.5578 \| 1.5245 \| 622 \| 0.7509 \| -0.0153 \| 0.7509 \| 0.8666 \| \| 0.5578 \| 1.5294 \| 624 \| 0.7460 \| 0.0503 \| 0.7460 \| 0.8637 \| \| 0.5578 \| 1.5343 \| 626 \| 0.7664 \| 0.2184 \| 0.7664 \| 0.8754 \| \| 0.5578 \| 1.5392 \| 628 \| 0.7797 \| 0.0503 \| 0.7797 \| 0.8830 \| \| 0.5578 \| 1.5441 \| 630 \| 0.8092 \| 0.0503 \| 0.8092 \| 0.8996 \| \| 0.5578 \| 1.5490 \| 632 \| 0.8579 \| -0.0153 \| 0.8579 \| 0.9262 \| \| 0.5578 \| 1.5539 \| 634 \| 0.8892 \| -0.0153 \| 0.8892 \| 0.9430 \| \| 0.5578 \| 1.5588 \| 636 \| 0.8795 \| -0.0153 \| 0.8795 \| 0.9378 \| \| 0.5578 \| 1.5637 \| 638 \| 0.8237 \| 0.0503 \| 0.8237 \| 0.9076 \| \| 0.5578 \| 1.5686 \| 640 \| 0.7623 \| 0.0503 \| 0.7623 \| 0.8731 \| \| 0.5578 \| 1.5735 \| 642 \| 0.7500 \| 0.0503 \| 0.7500 \| 0.8660 \| \| 0.5578 \| 1.5784 \| 644 \| 0.7688 \| 0.0503 \| 0.7688 \| 0.8768 \| \| 0.5578 \| 1.5833 \| 646 \| 0.7593 \| 0.0503 \| 0.7593 \| 0.8714 \| \| 0.5578 \| 1.5882 \| 648 \| 0.7676 \| 0.0916 \| 0.7676 \| 0.8761 \| \| 0.5578 \| 1.5931 \| 650 \| 0.7992 \| 0.2355 \| 0.7992 \| 0.8940 \| \| 0.5578 \| 1.5980 \| 652 \| 0.7329 \| 0.0916 \| 0.7329 \| 0.8561 \| \| 0.5578 \| 1.6029 \| 654 \| 0.6586 \| 0.1765 \| 0.6586 \| 0.8116 \| \| 0.5578 \| 1.6078 \| 656 \| 0.6617 \| 0.2725 \| 0.6617 \| 0.8135 \| \| 0.5578 \| 1.6127 \| 658 \| 0.7002 \| 0.2329 \| 0.7002 \| 0.8368 \| \| 0.5578 \| 1.6176 \| 660 \| 0.7021 \| 0.2725 \| 0.7021 \| 0.8379 \| \| 0.5578 \| 1.6225 \| 662 \| 0.6746 \| 0.3131 \| 0.6746 \| 0.8214 \| \| 0.5578 \| 1.6275 \| 664 \| 0.6984 \| 0.2921 \| 0.6984 \| 0.8357 \| \| 0.5578 \| 1.6324 \| 666 \| 0.8072 \| 0.1992 \| 0.8072 \| 0.8984 \| \| 0.5578 \| 1.6373 \| 668 \| 0.8233 \| 0.1992 \| 0.8233 \| 0.9074 \| \| 0.5578 \| 1.6422 \| 670 \| 0.8055 \| 0.2355 \| 0.8055 \| 0.8975 \| \| 0.5578 \| 1.6471 \| 672 \| 0.7299 \| 0.2921 \| 0.7299 \| 0.8543 \| \| 0.5578 \| 1.6520 \| 674 \| 0.6800 \| 0.1765 \| 0.6800 \| 0.8246 \| \| 0.5578 \| 1.6569 \| 676 \| 0.7202 \| 0.1783 \| 0.7202 \| 0.8487 \| \| 0.5578 \| 1.6618 \| 678 \| 0.7445 \| 0.1797 \| 0.7445 \| 0.8629 \| \| 0.5578 \| 1.6667 \| 680 \| 0.7278 \| 0.1419 \| 0.7278 \| 0.8531 \| \| 0.5578 \| 1.6716 \| 682 \| 0.6878 \| 0.0957 \| 0.6878 \| 0.8293 \| \| 0.5578 \| 1.6765 \| 684 \| 0.7109 \| 0.1356 \| 0.7109 \| 0.8432 \| \| 0.5578 \| 1.6814 \| 686 \| 0.7583 \| 0.2921 \| 0.7583 \| 0.8708 \| \| 0.5578 \| 1.6863 \| 688 \| 0.7626 \| 0.2921 \| 0.7626 \| 0.8733 \| \| 0.5578 \| 1.6912 \| 690 \| 0.7425 \| 0.1765 \| 0.7425 \| 0.8617 \| \| 0.5578 \| 1.6961 \| 692 \| 0.7350 \| 0.1765 \| 0.7350 \| 0.8573 \| \| 0.5578 \| 1.7010 \| 694 \| 0.7285 \| 0.1765 \| 0.7285 \| 0.8535 \| \| 0.5578 \| 1.7059 \| 696 \| 0.7034 \| 0.1765 \| 0.7034 \| 0.8387 \| \| 0.5578 \| 1.7108 \| 698 \| 0.6936 \| 0.1765 \| 0.6936 \| 0.8328 \| \| 0.5578 \| 1.7157 \| 700 \| 0.6978 \| 0.1765 \| 0.6978 \| 0.8354 \| \| 0.5578 \| 1.7206 \| 702 \| 0.7344 \| 0.1765 \| 0.7344 \| 0.8570 \| \| 0.5578 \| 1.7255 \| 704 \| 0.7590 \| 0.2921 \| 0.7590 \| 0.8712 \| \| 0.5578 \| 1.7304 \| 706 \| 0.7798 \| 0.2921 \| 0.7798 \| 0.8831 \| \| 0.5578 \| 1.7353 \| 708 \| 0.7821 \| 0.1992 \| 0.7821 \| 0.8843 \| \| 0.5578 \| 1.7402 \| 710 \| 0.7444 \| 0.2921 \| 0.7444 \| 0.8628 \| \| 0.5578 \| 1.7451 \| 712 \| 0.6936 \| 0.2921 \| 0.6936 \| 0.8328 \| \| 0.5578 \| 1.75 \| 714 \| 0.6691 \| 0.1765 \| 0.6691 \| 0.8180 \| \| 0.5578 \| 1.7549 \| 716 \| 0.6563 \| 0.1356 \| 0.6563 \| 0.8101 \| \| 0.5578 \| 1.7598 \| 718 \| 0.6508 \| 0.1356 \| 0.6508 \| 0.8067 \| \| 0.5578 \| 1.7647 \| 720 \| 0.6493 \| 0.0099 \| 0.6493 \| 0.8058 \| \| 0.5578 \| 1.7696 \| 722 \| 0.6629 \| 0.0503 \| 0.6629 \| 0.8142 \| \| 0.5578 \| 1.7745 \| 724 \| 0.7317 \| 0.2186 \| 0.7317 \| 0.8554 \| \| 0.5578 \| 1.7794 \| 726 \| 0.8393 \| 0.2727 \| 0.8393 \| 0.9162 \| \| 0.5578 \| 1.7843 \| 728 \| 0.8773 \| 0.2846 \| 0.8773 \| 0.9367 \| \| 0.5578 \| 1.7892 \| 730 \| 0.7873 \| 0.2186 \| 0.7873 \| 0.8873 \| \| 0.5578 \| 1.7941 \| 732 \| 0.7080 \| 0.2184 \| 0.7080 \| 0.8414 \| \| 0.5578 \| 1.7990 \| 734 \| 0.6906 \| 0.1356 \| 0.6906 \| 0.8310 \| \| 0.5578 \| 1.8039 \| 736 \| 0.6882 \| 0.1356 \| 0.6882 \| 0.8296 \| \| 0.5578 \| 1.8088 \| 738 \| 0.6746 \| 0.1356 \| 0.6746 \| 0.8213 \| \| 0.5578 \| 1.8137 \| 740 \| 0.6798 \| 0.1765 \| 0.6798 \| 0.8245 \| \| 0.5578 \| 1.8186 \| 742 \| 0.6784 \| 0.1765 \| 0.6784 \| 0.8236 \| \| 0.5578 \| 1.8235 \| 744 \| 0.6669 \| 0.1765 \| 0.6669 \| 0.8167 \| \| 0.5578 \| 1.8284 \| 746 \| 0.6734 \| 0.2184 \| 0.6734 \| 0.8206 \| \| 0.5578 \| 1.8333 \| 748 \| 0.6727 \| 0.2184 \| 0.6727 \| 0.8202 \| \| 0.5578 \| 1.8382 \| 750 \| 0.6770 \| 0.2613 \| 0.6770 \| 0.8228 \| \| 0.5578 \| 1.8431 \| 752 \| 0.6828 \| 0.0916 \| 0.6828 \| 0.8263 \| \| 0.5578 \| 1.8480 \| 754 \| 0.6833 \| 0.0258 \| 0.6833 \| 0.8266 \| \| 0.5578 \| 1.8529 \| 756 \| 0.7135 \| 0.0258 \| 0.7135 \| 0.8447 \| \| 0.5578 \| 1.8578 \| 758 \| 0.7219 \| -0.0153 \| 0.7219 \| 0.8496 \| \| 0.5578 \| 1.8627 \| 760 \| 0.7159 \| -0.0153 \| 0.7159 \| 0.8461 \| \| 0.5578 \| 1.8676 \| 762 \| 0.7267 \| 0.0099 \| 0.7267 \| 0.8525 \| \| 0.5578 \| 1.8725 \| 764 \| 0.7224 \| 0.1356 \| 0.7224 \| 0.8499 \| \| 0.5578 \| 1.8775 \| 766 \| 0.7271 \| 0.1356 \| 0.7271 \| 0.8527 \| \| 0.5578 \| 1.8824 \| 768 \| 0.7485 \| 0.1765 \| 0.7485 \| 0.8651 \| \| 0.5578 \| 1.8873 \| 770 \| 0.7799 \| 0.1765 \| 0.7799 \| 0.8831 \| \| 0.5578 \| 1.8922 \| 772 \| 0.7954 \| 0.0099 \| 0.7954 \| 0.8919 \| \| 0.5578 \| 1.8971 \| 774 \| 0.7923 \| 0.0099 \| 0.7923 \| 0.8901 \| \| 0.5578 \| 1.9020 \| 776 \| 0.7898 \| 0.0099 \| 0.7898 \| 0.8887 \| \| 0.5578 \| 1.9069 \| 778 \| 0.8181 \| 0.1793 \| 0.8181 \| 0.9045 \| \| 0.5578 \| 1.9118 \| 780 \| 0.8152 \| 0.0503 \| 0.8152 \| 0.9029 \| \| 0.5578 \| 1.9167 \| 782 \| 0.7926 \| 0.1765 \| 0.7926 \| 0.8903 \| \| 0.5578 \| 1.9216 \| 784 \| 0.7978 \| 0.1765 \| 0.7978 \| 0.8932 \| \| 0.5578 \| 1.9265 \| 786 \| 0.8013 \| 0.2184 \| 0.8013 \| 0.8951 \| \| 0.5578 \| 1.9314 \| 788 \| 0.8023 \| 0.2184 \| 0.8023 \| 0.8957 \| \| 0.5578 \| 1.9363 \| 790 \| 0.7933 \| 0.1765 \| 0.7933 \| 0.8907 \| \| 0.5578 \| 1.9412 \| 792 \| 0.7900 \| 0.3318 \| 0.7900 \| 0.8888 \| \| 0.5578 \| 1.9461 \| 794 \| 0.7859 \| 0.3318 \| 0.7859 \| 0.8865 \| \| 0.5578 \| 1.9510 \| 796 \| 0.7713 \| 0.3318 \| 0.7713 \| 0.8782 \| \| 0.5578 \| 1.9559 \| 798 \| 0.7542 \| 0.1765 \| 0.7542 \| 0.8685 \| \| 0.5578 \| 1.9608 \| 800 \| 0.7744 \| 0.3318 \| 0.7744 \| 0.8800 \| \| 0.5578 \| 1.9657 \| 802 \| 0.8383 \| 0.2186 \| 0.8383 \| 0.9156 \| \| 0.5578 \| 1.9706 \| 804 \| 0.8534 \| 0.2186 \| 0.8534 \| 0.9238 \| \| 0.5578 \| 1.9755 \| 806 \| 0.8328 \| 0.1793 \| 0.8328 \| 0.9126 \| \| 0.5578 \| 1.9804 \| 808 \| 0.7872 \| 0.2921 \| 0.7872 \| 0.8872 \| \| 0.5578 \| 1.9853 \| 810 \| 0.7593 \| 0.1765 \| 0.7593 \| 0.8714 \| \| 0.5578 \| 1.9902 \| 812 \| 0.7483 \| 0.1765 \| 0.7483 \| 0.8651 \| \| 0.5578 \| 1.9951 \| 814 \| 0.7205 \| 0.1765 \| 0.7205 \| 0.8488 \| \| 0.5578 \| 2.0 \| 816 \| 0.7090 \| 0.0099 \| 0.7090 \| 0.8420 \| \| 0.5578 \| 2.0049 \| 818 \| 0.7199 \| 0.0503 \| 0.7199 \| 0.8485 \| \| 0.5578 \| 2.0098 \| 820 \| 0.7702 \| 0.1600 \| 0.7702 \| 0.8776 \| \| 0.5578 \| 2.0147 \| 822 \| 0.7948 \| 0.1600 \| 0.7948 \| 0.8915 \| \| 0.5578 \| 2.0196 \| 824 \| 0.7714 \| 0.1209 \| 0.7714 \| 0.8783 \| \| 0.5578 \| 2.0245 \| 826 \| 0.7442 \| 0.0503 \| 0.7442 \| 0.8626 \| \| 0.5578 \| 2.0294 \| 828 \| 0.7438 \| 0.0503 \| 0.7438 \| 0.8625 \| \| 0.5578 \| 2.0343 \| 830 \| 0.7616 \| 0.0099 \| 0.7616 \| 0.8727 \| \| 0.5578 \| 2.0392 \| 832 \| 0.7911 \| 0.0099 \| 0.7911 \| 0.8894 \| \| 0.5578 \| 2.0441 \| 834 \| 0.8279 \| 0.0099 \| 0.8279 \| 0.9099 \| \| 0.5578 \| 2.0490 \| 836 \| 0.8528 \| 0.0099 \| 0.8528 \| 0.9235 \| \| 0.5578 \| 2.0539 \| 838 \| 0.8694 \| 0.1409 \| 0.8694 \| 0.9324 \| \| 0.5578 \| 2.0588 \| 840 \| 0.8550 \| 0.0099 \| 0.8550 \| 0.9247 \| \| 0.5578 \| 2.0637 \| 842 \| 0.8447 \| 0.0099 \| 0.8447 \| 0.9191 \| \| 0.5578 \| 2.0686 \| 844 \| 0.8239 \| 0.0099 \| 0.8239 \| 0.9077 \| \| 0.5578 \| 2.0735 \| 846 \| 0.7818 \| 0.0099 \| 0.7818 \| 0.8842 \| \| 0.5578 \| 2.0784 \| 848 \| 0.7487 \| 0.0099 \| 0.7487 \| 0.8653 \| \| 0.5578 \| 2.0833 \| 850 \| 0.7326 \| 0.0099 \| 0.7326 \| 0.8559 \| \| 0.5578 \| 2.0882 \| 852 \| 0.7200 \| 0.0099 \| 0.7200 \| 0.8485 \| \| 0.5578 \| 2.0931 \| 854 \| 0.7178 \| 0.0099 \| 0.7178 \| 0.8472 \| \| 0.5578 \| 2.0980 \| 856 \| 0.7123 \| 0.0099 \| 0.7123 \| 0.8440 \| \| 0.5578 \| 2.1029 \| 858 \| 0.7103 \| 0.0503 \| 0.7103 \| 0.8428 \| \| 0.5578 \| 2.1078 \| 860 \| 0.7040 \| 0.0099 \| 0.7040 \| 0.8391 \| \| 0.5578 \| 2.1127 \| 862 \| 0.7110 \| 0.0099 \| 0.7110 \| 0.8432 \| \| 0.5578 \| 2.1176 \| 864 \| 0.7208 \| 0.0099 \| 0.7208 \| 0.8490 \| \| 0.5578 \| 2.1225 \| 866 \| 0.7310 \| 0.0099 \| 0.7310 \| 0.8550 \| \| 0.5578 \| 2.1275 \| 868 \| 0.7427 \| 0.0099 \| 0.7427 \| 0.8618 \| \| 0.5578 \| 2.1324 \| 870 \| 0.7558 \| 0.0099 \| 0.7558 \| 0.8694 \| \| 0.5578 \| 2.1373 \| 872 \| 0.7712 \| 0.0099 \| 0.7712 \| 0.8782 \| \| 0.5578 \| 2.1422 \| 874 \| 0.8072 \| 0.0099 \| 0.8072 \| 0.8984 \| \| 0.5578 \| 2.1471 \| 876 \| 0.8080 \| 0.1793 \| 0.8080 \| 0.8989 \| \| 0.5578 \| 2.1520 \| 878 \| 0.7558 \| 0.0099 \| 0.7558 \| 0.8694 \| \| 0.5578 \| 2.1569 \| 880 \| 0.7004 \| 0.0099 \| 0.7004 \| 0.8369 \| \| 0.5578 \| 2.1618 \| 882 \| 0.6875 \| 0.0099 \| 0.6875 \| 0.8292 \| \| 0.5578 \| 2.1667 \| 884 \| 0.6856 \| 0.0099 \| 0.6856 \| 0.8280 \| \| 0.5578 \| 2.1716 \| 886 \| 0.6961 \| 0.0099 \| 0.6961 \| 0.8343 \| \| 0.5578 \| 2.1765 \| 888 \| 0.7183 \| 0.0099 \| 0.7183 \| 0.8475 \| \| 0.5578 \| 2.1814 \| 890 \| 0.7322 \| 0.0099 \| 0.7322 \| 0.8557 \| \| 0.5578 \| 2.1863 \| 892 \| 0.7432 \| 0.0099 \| 0.7432 \| 0.8621 \| \| 0.5578 \| 2.1912 \| 894 \| 0.7627 \| 0.0099 \| 0.7627 \| 0.8733 \| \| 0.5578 \| 2.1961 \| 896 \| 0.7871 \| 0.0099 \| 0.7871 \| 0.8872 \| \| 0.5578 \| 2.2010 \| 898 \| 0.8083 \| 0.0099 \| 0.8083 \| 0.8990 \| \| 0.5578 \| 2.2059 \| 900 \| 0.8043 \| 0.0503 \| 0.8043 \| 0.8968 \| \| 0.5578 \| 2.2108 \| 902 \| 0.7885 \| 0.0916 \| 0.7885 \| 0.8880 \| \| 0.5578 \| 2.2157 \| 904 \| 0.7669 \| 0.2186 \| 0.7669 \| 0.8757 \| \| 0.5578 \| 2.2206 \| 906 \| 0.7204 \| 0.2186 \| 0.7204 \| 0.8488 \| \| 0.5578 \| 2.2255 \| 908 \| 0.7269 \| 0.2186 \| 0.7269 \| 0.8526 \| \| 0.5578 \| 2.2304 \| 910 \| 0.7296 \| 0.2186 \| 0.7296 \| 0.8542 \| \| 0.5578 \| 2.2353 \| 912 \| 0.7265 \| 0.1793 \| 0.7265 \| 0.8523 \| \| 0.5578 \| 2.2402 \| 914 \| 0.7163 \| 0.1793 \| 0.7163 \| 0.8464 \| \| 0.5578 \| 2.2451 \| 916 \| 0.7555 \| 0.1793 \| 0.7555 \| 0.8692 \| \| 0.5578 \| 2.25 \| 918 \| 0.7513 \| 0.1793 \| 0.7513 \| 0.8668 \| \| 0.5578 \| 2.2549 \| 920 \| 0.7326 \| 0.1793 \| 0.7326 \| 0.8559 \| \| 0.5578 \| 2.2598 \| 922 \| 0.7222 \| 0.1793 \| 0.7222 \| 0.8498 \| \| 0.5578 \| 2.2647 \| 924 \| 0.7370 \| 0.1793 \| 0.7370 \| 0.8585 \| \| 0.5578 \| 2.2696 \| 926 \| 0.7920 \| 0.1992 \| 0.7920 \| 0.8900 \| \| 0.5578 \| 2.2745 \| 928 \| 0.8360 \| 0.1992 \| 0.8360 \| 0.9143 \| \| 0.5578 \| 2.2794 \| 930 \| 0.7819 \| 0.1992 \| 0.7819 \| 0.8843 \| \| 0.5578 \| 2.2843 \| 932 \| 0.7211 \| 0.2533 \| 0.7211 \| 0.8492 \| \| 0.5578 \| 2.2892 \| 934 \| 0.6942 \| 0.1356 \| 0.6942 \| 0.8332 \| \| 0.5578 \| 2.2941 \| 936 \| 0.6928 \| 0.1356 \| 0.6928 \| 0.8323 \| \| 0.5578 \| 2.2990 \| 938 \| 0.6936 \| 0.2533 \| 0.6936 \| 0.8328 \| \| 0.5578 \| 2.3039 \| 940 \| 0.6979 \| 0.1409 \| 0.6979 \| 0.8354 \| \| 0.5578 \| 2.3088 \| 942 \| 0.6968 \| 0.1409 \| 0.6968 \| 0.8347 \| \| 0.5578 \| 2.3137 \| 944 \| 0.6995 \| 0.1409 \| 0.6995 \| 0.8363 \| \| 0.5578 \| 2.3186 \| 946 \| 0.7280 \| 0.1409 \| 0.7280 \| 0.8532 \| \| 0.5578 \| 2.3235 \| 948 \| 0.7728 \| 0.1635 \| 0.7728 \| 0.8791 \| \| 0.5578 \| 2.3284 \| 950 \| 0.7729 \| 0.1635 \| 0.7729 \| 0.8792 \| \| 0.5578 \| 2.3333 \| 952 \| 0.7663 \| 0.1635 \| 0.7663 \| 0.8754 \| \| 0.5578 \| 2.3382 \| 954 \| 0.7735 \| 0.1635 \| 0.7735 \| 0.8795 \| \| 0.5578 \| 2.3431 \| 956 \| 0.7792 \| 0.1409 \| 0.7792 \| 0.8827 \| \| 0.5578 \| 2.3480 \| 958 \| 0.7592 \| 0.2921 \| 0.7592 \| 0.8713 \| \| 0.5578 \| 2.3529 \| 960 \| 0.7620 \| 0.1409 \| 0.7620 \| 0.8729 \| \| 0.5578 \| 2.3578 \| 962 \| 0.7656 \| 0.1793 \| 0.7656 \| 0.8750 \| \| 0.5578 \| 2.3627 \| 964 \| 0.7905 \| 0.2588 \| 0.7905 \| 0.8891 \| \| 0.5578 \| 2.3676 \| 966 \| 0.7927 \| 0.2588 \| 0.7927 \| 0.8904 \| \| 0.5578 \| 2.3725 \| 968 \| 0.7444 \| 0.2588 \| 0.7444 \| 0.8628 \| \| 0.5578 \| 2.3775 \| 970 \| 0.7016 \| 0.2186 \| 0.7016 \| 0.8376 \| \| 0.5578 \| 2.3824 \| 972 \| 0.6727 \| 0.0503 \| 0.6727 \| 0.8202 \| \| 0.5578 \| 2.3873 \| 974 \| 0.6824 \| 0.0099 \| 0.6824 \| 0.8261 \| \| 0.5578 \| 2.3922 \| 976 \| 0.6995 \| 0.1409 \| 0.6995 \| 0.8364 \| \| 0.5578 \| 2.3971 \| 978 \| 0.7122 \| 0.1409 \| 0.7122 \| 0.8439 \| \| 0.5578 \| 2.4020 \| 980 \| 0.7272 \| 0.1793 \| 0.7272 \| 0.8528 \| \| 0.5578 \| 2.4069 \| 982 \| 0.7788 \| 0.1793 \| 0.7788 \| 0.8825 \| \| 0.5578 \| 2.4118 \| 984 \| 0.8050 \| 0.1793 \| 0.8050 \| 0.8972 \| \| 0.5578 \| 2.4167 \| 986 \| 0.8236 \| 0.2186 \| 0.8236 \| 0.9075 \| \| 0.5578 \| 2.4216 \| 988 \| 0.7882 \| 0.2588 \| 0.7882 \| 0.8878 \| \| 0.5578 \| 2.4265 \| 990 \| 0.7090 \| 0.1793 \| 0.7090 \| 0.8420 \| \| 0.5578 \| 2.4314 \| 992 \| 0.6612 \| 0.1793 \| 0.6612 \| 0.8132 \| \| 0.5578 \| 2.4363 \| 994 \| 0.6352 \| 0.0099 \| 0.6352 \| 0.7970 \| \| 0.5578 \| 2.4412 \| 996 \| 0.6234 \| 0.2553 \| 0.6234 \| 0.7895 \| \| 0.5578 \| 2.4461 \| 998 \| 0.6244 \| 0.2553 \| 0.6244 \| 0.7902 \| \| 0.1212 \| 2.4510 \| 1000 \| 0.6264 \| 0.2373 \| 0.6264 \| 0.7914 \| \| 0.1212 \| 2.4559 \| 1002 \| 0.6482 \| 0.1409 \| 0.6482 \| 0.8051 \| \| 0.1212 \| 2.4608 \| 1004 \| 0.6848 \| 0.1409 \| 0.6848 \| 0.8275 \| \| 0.1212 \| 2.4657 \| 1006 \| 0.7350 \| 0.1793 \| 0.7350 \| 0.8573 \| \| 0.1212 \| 2.4706 \| 1008 \| 0.7820 \| 0.1793 \| 0.7820 \| 0.8843 \| \| 0.1212 \| 2.4755 \| 1010 \| 0.8048 \| 0.1793 \| 0.8048 \| 0.8971 \| \| 0.1212 \| 2.4804 \| 1012 \| 0.8372 \| 0.2881 \| 0.8372 \| 0.9150 \| \| 0.1212 \| 2.4853 \| 1014 \| 0.8292 \| 0.2881 \| 0.8292 \| 0.9106 \| \| 0.1212 \| 2.4902 \| 1016 \| 0.7600 \| 0.1978 \| 0.7600 \| 0.8718 \| \| 0.1212 \| 2.4951 \| 1018 \| 0.7176 \| 0.1978 \| 0.7176 \| 0.8471 \| \| 0.1212 \| 2.5 \| 1020 \| 0.6963 \| 0.1978 \| 0.6963 \| 0.8344 \| \| 0.1212 \| 2.5049 \| 1022 \| 0.7406 \| 0.2364 \| 0.7406 \| 0.8606 \| \| 0.1212 \| 2.5098 \| 1024 \| 0.7410 \| 0.1793 \| 0.7410 \| 0.8608 \| \| 0.1212 \| 2.5147 \| 1026 \| 0.6967 \| 0.2364 \| 0.6967 \| 0.8347 \| \| 0.1212 \| 2.5196 \| 1028 \| 0.6256 \| 0.1978 \| 0.6256 \| 0.7910 \| \| 0.1212 \| 2.5245 \| 1030 \| 0.6013 \| 0.1978 \| 0.6013 \| 0.7754 \| \| 0.1212 \| 2.5294 \| 1032 \| 0.6069 \| 0.1141 \| 0.6069 \| 0.7790 \| \| 0.1212 \| 2.5343 \| 1034 \| 0.6189 \| 0.0503 \| 0.6189 \| 0.7867 \| \| 0.1212 \| 2.5392 \| 1036 \| 0.6228 \| 0.0503 \| 0.6228 \| 0.7892 \| \| 0.1212 \| 2.5441 \| 1038 \| 0.6630 \| 0.0099 \| 0.6630 \| 0.8143 \| \| 0.1212 \| 2.5490 \| 1040 \| 0.6965 \| 0.1765 \| 0.6965 \| 0.8346 \| \| 0.1212 \| 2.5539 \| 1042 \| 0.7601 \| 0.2921 \| 0.7601 \| 0.8718 \| \| 0.1212 \| 2.5588 \| 1044 \| 0.8109 \| 0.2921 \| 0.8109 \| 0.9005 \| \| 0.1212 \| 2.5637 \| 1046 \| 0.8554 \| 0.2921 \| 0.8554 \| 0.9249 \| \| 0.1212 \| 2.5686 \| 1048 \| 0.9004 \| 0.1503 \| 0.9004 \| 0.9489 \| \| 0.1212 \| 2.5735 \| 1050 \| 0.9211 \| 0.0928 \| 0.9211 \| 0.9597 \| \| 0.1212 \| 2.5784 \| 1052 \| 0.9303 \| 0.1463 \| 0.9303 \| 0.9645 \| \| 0.1212 \| 2.5833 \| 1054 \| 0.9738 \| 0.1463 \| 0.9738 \| 0.9868 \| \| 0.1212 \| 2.5882 \| 1056 \| 0.9299 \| 0.1463 \| 0.9299 \| 0.9643 \| \| 0.1212 \| 2.5931 \| 1058 \| 0.8688 \| 0.2186 \| 0.8688 \| 0.9321 \| \| 0.1212 \| 2.5980 \| 1060 \| 0.7843 \| 0.3318 \| 0.7843 \| 0.8856 \| \| 0.1212 \| 2.6029 \| 1062 \| 0.7275 \| 0.1765 \| 0.7275 \| 0.8529 \| \| 0.1212 \| 2.6078 \| 1064 \| 0.7032 \| 0.1356 \| 0.7032 \| 0.8386 \| \| 0.1212 \| 2.6127 \| 1066 \| 0.7016 \| 0.0957 \| 0.7016 \| 0.8376 \| \| 0.1212 \| 2.6176 \| 1068 \| 0.7012 \| 0.1356 \| 0.7012 \| 0.8374 \| \| 0.1212 \| 2.6225 \| 1070 \| 0.7049 \| 0.1765 \| 0.7049 \| 0.8396 \| \| 0.1212 \| 2.6275 \| 1072 \| 0.7225 \| 0.1765 \| 0.7225 \| 0.8500 \| \| 0.1212 \| 2.6324 \| 1074 \| 0.7325 \| 0.1765 \| 0.7325 \| 0.8558 \| \| 0.1212 \| 2.6373 \| 1076 \| 0.7354 \| 0.2921 \| 0.7354 \| 0.8575 \| \| 0.1212 \| 2.6422 \| 1078 \| 0.7511 \| 0.2921 \| 0.7511 \| 0.8667 \| \| 0.1212 \| 2.6471 \| 1080 \| 0.7452 \| 0.2921 \| 0.7452 \| 0.8633 \| \| 0.1212 \| 2.6520 \| 1082 \| 0.7244 \| 0.3318 \| 0.7244 \| 0.8511 \| \| 0.1212 \| 2.6569 \| 1084 \| 0.7004 \| 0.2921 \| 0.7004 \| 0.8369 \| \| 0.1212 \| 2.6618 \| 1086 \| 0.6903 \| 0.2533 \| 0.6903 \| 0.8308 \| \| 0.1212 \| 2.6667 \| 1088 \| 0.6843 \| 0.2533 \| 0.6843 \| 0.8272 \| \| 0.1212 \| 2.6716 \| 1090 \| 0.6789 \| 0.3318 \| 0.6789 \| 0.8240 \| \| 0.1212 \| 2.6765 \| 1092 \| 0.6671 \| 0.2533 \| 0.6671 \| 0.8168 \| \| 0.1212 \| 2.6814 \| 1094 \| 0.6698 \| 0.2921 \| 0.6698 \| 0.8184 \| \| 0.1212 \| 2.6863 \| 1096 \| 0.6922 \| 0.1793 \| 0.6922 \| 0.8320 \| \| 0.1212 \| 2.6912 \| 1098 \| 0.7097 \| 0.2186 \| 0.7097 \| 0.8424 \| \| 0.1212 \| 2.6961 \| 1100 \| 0.7115 \| 0.2588 \| 0.7115 \| 0.8435 \| \| 0.1212 \| 2.7010 \| 1102 \| 0.6823 \| 0.2588 \| 0.6823 \| 0.8260 \| \| 0.1212 \| 2.7059 \| 1104 \| 0.6424 \| 0.1793 \| 0.6424 \| 0.8015 \| \| 0.1212 \| 2.7108 \| 1106 \| 0.6226 \| 0.1765 \| 0.6226 \| 0.7891 \| \| 0.1212 \| 2.7157 \| 1108 \| 0.6197 \| 0.1765 \| 0.6197 \| 0.7872 \| \| 0.1212 \| 2.7206 \| 1110 \| 0.6313 \| 0.1765 \| 0.6313 \| 0.7946 \| \| 0.1212 \| 2.7255 \| 1112 \| 0.6564 \| 0.1793 \| 0.6564 \| 0.8102 \| \| 0.1212 \| 2.7304 \| 1114 \| 0.7129 \| 0.2588 \| 0.7129 \| 0.8443 \| \| 0.1212 \| 2.7353 \| 1116 \| 0.7700 \| 0.2588 \| 0.7700 \| 0.8775 \| \| 0.1212 \| 2.7402 \| 1118 \| 0.8459 \| 0.3636 \| 0.8459 \| 0.9197 \| \| 0.1212 \| 2.7451 \| 1120 \| 0.8834 \| 0.3687 \| 0.8834 \| 0.9399 \| \| 0.1212 \| 2.75 \| 1122 \| 0.8543 \| 0.3687 \| 0.8543 \| 0.9243 \| \| 0.1212 \| 2.7549 \| 1124 \| 0.7792 \| 0.1793 \| 0.7792 \| 0.8827 \| \| 0.1212 \| 2.7598 \| 1126 \| 0.7107 \| 0.2154 \| 0.7107 \| 0.8430 \| \| 0.1212 \| 2.7647 \| 1128 \| 0.6777 \| 0.2154 \| 0.6777 \| 0.8232 \| \| 0.1212 \| 2.7696 \| 1130 \| 0.6717 \| 0.2154 \| 0.6717 \| 0.8196 \| \| 0.1212 \| 2.7745 \| 1132 \| 0.6612 \| 0.0957 \| 0.6612 \| 0.8131 \| \| 0.1212 \| 2.7794 \| 1134 \| 0.6444 \| 0.1560 \| 0.6444 \| 0.8028 \| \| 0.1212 \| 2.7843 \| 1136 \| 0.6364 \| 0.1560 \| 0.6364 \| 0.7978 \| \| 0.1212 \| 2.7892 \| 1138 \| 0.6291 \| 0.1560 \| 0.6291 \| 0.7932 \| \| 0.1212 \| 2.7941 \| 1140 \| 0.6268 \| 0.1560 \| 0.6268 \| 0.7917 \| \| 0.1212 \| 2.7990 \| 1142 \| 0.6319 \| 0.1560 \| 0.6319 \| 0.7949 \| \| 0.1212 \| 2.8039 \| 1144 \| 0.6426 \| 0.0957 \| 0.6426 \| 0.8016 \| \| 0.1212 \| 2.8088 \| 1146 \| 0.6633 \| 0.0099 \| 0.6633 \| 0.8144 \| \| 0.1212 \| 2.8137 \| 1148 \| 0.7144 \| 0.2186 \| 0.7144 \| 0.8452 \| \| 0.1212 \| 2.8186 \| 1150 \| 0.7613 \| 0.2186 \| 0.7613 \| 0.8726 \| \| 0.1212 \| 2.8235 \| 1152 \| 0.7801 \| 0.1409 \| 0.7801 \| 0.8832 \| \| 0.1212 \| 2.8284 \| 1154 \| 0.7979 \| 0.2533 \| 0.7979 \| 0.8933 \| \| 0.1212 \| 2.8333 \| 1156 \| 0.8013 \| 0.2533 \| 0.8013 \| 0.8951 \| \| 0.1212 \| 2.8382 \| 1158 \| 0.8101 \| 0.2533 \| 0.8101 \| 0.9001 \| \| 0.1212 \| 2.8431 \| 1160 \| 0.8162 \| 0.1409 \| 0.8162 \| 0.9034 \| \| 0.1212 \| 2.8480 \| 1162 \| 0.7858 \| 0.1793 \| 0.7858 \| 0.8864 \| \| 0.1212 \| 2.8529 \| 1164 \| 0.7463 \| 0.1409 \| 0.7463 \| 0.8639 \| \| 0.1212 \| 2.8578 \| 1166 \| 0.7142 \| 0.1409 \| 0.7142 \| 0.8451 \| \| 0.1212 \| 2.8627 \| 1168 \| 0.6781 \| 0.0099 \| 0.6781 \| 0.8235 \| \| 0.1212 \| 2.8676 \| 1170 \| 0.6558 \| -0.0294 \| 0.6558 \| 0.8098 \| \| 0.1212 \| 2.8725 \| 1172 \| 0.6414 \| 0.1034 \| 0.6414 \| 0.8009 \| \| 0.1212 \| 2.8775 \| 1174 \| 0.6365 \| -0.0294 \| 0.6365 \| 0.7978 \| \| 0.1212 \| 2.8824 \| 1176 \| 0.6352 \| 0.1962 \| 0.6352 \| 0.7970 \| \| 0.1212 \| 2.8873 \| 1178 \| 0.6246 \| 0.1962 \| 0.6246 \| 0.7903 \| \| 0.1212 \| 2.8922 \| 1180 \| 0.6277 \| -0.0294 \| 0.6277 \| 0.7923 \| \| 0.1212 \| 2.8971 \| 1182 \| 0.6271 \| 0.0339 \| 0.6271 \| 0.7919 \| \| 0.1212 \| 2.9020 \| 1184 \| 0.6218 \| 0.1409 \| 0.6218 \| 0.7886 \| \| 0.1212 \| 2.9069 \| 1186 \| 0.6191 \| 0.1793 \| 0.6191 \| 0.7868 \| \| 0.1212 \| 2.9118 \| 1188 \| 0.6122 \| 0.0099 \| 0.6122 \| 0.7824 \| \| 0.1212 \| 2.9167 \| 1190 \| 0.6226 \| 0.1962 \| 0.6226 \| 0.7890 \| \| 0.1212 \| 2.9216 \| 1192 \| 0.6327 \| 0.1962 \| 0.6327 \| 0.7954 \| \| 0.1212 \| 2.9265 \| 1194 \| 0.6284 \| 0.1962 \| 0.6284 \| 0.7927 \| \| 0.1212 \| 2.9314 \| 1196 \| 0.6272 \| 0.0503 \| 0.6272 \| 0.7920 \| \| 0.1212 \| 2.9363 \| 1198 \| 0.6282 \| 0.0916 \| 0.6282 \| 0.7926 \| \| 0.1212 \| 2.9412 \| 1200 \| 0.6260 \| 0.0916 \| 0.6260 \| 0.7912 \| \| 0.1212 \| 2.9461 \| 1202 \| 0.6322 \| 0.0916 \| 0.6322 \| 0.7951 \| \| 0.1212 \| 2.9510 \| 1204 \| 0.6453 \| 0.2186 \| 0.6453 \| 0.8033 \| \| 0.1212 \| 2.9559 \| 1206 \| 0.6579 \| 0.1793 \| 0.6579 \| 0.8111 \| \| 0.1212 \| 2.9608 \| 1208 \| 0.6697 \| 0.2186 \| 0.6697 \| 0.8183 \| \| 0.1212 \| 2.9657 \| 1210 \| 0.6697 \| 0.1793 \| 0.6697 \| 0.8183 \| \| 0.1212 \| 2.9706 \| 1212 \| 0.6698 \| 0.1793 \| 0.6698 \| 0.8184 \| \| 0.1212 \| 2.9755 \| 1214 \| 0.6784 \| 0.1793 \| 0.6784 \| 0.8236 \| \| 0.1212 \| 2.9804 \| 1216 \| 0.6913 \| 0.1793 \| 0.6913 \| 0.8315 \| \| 0.1212 \| 2.9853 \| 1218 \| 0.6965 \| 0.1793 \| 0.6965 \| 0.8346 \| \| 0.1212 \| 2.9902 \| 1220 \| 0.7009 \| 0.1409 \| 0.7009 \| 0.8372 \| \| 0.1212 \| 2.9951 \| 1222 \| 0.7186 \| 0.1034 \| 0.7186 \| 0.8477 \| \| 0.1212 \| 3.0 \| 1224 \| 0.7407 \| 0.2154 \| 0.7407 \| 0.8606 \| \| 0.1212 \| 3.0049 \| 1226 \| 0.7650 \| 0.2154 \| 0.7650 \| 0.8746 \| \| 0.1212 \| 3.0098 \| 1228 \| 0.7814 \| 0.2154 \| 0.7814 \| 0.8840 \| \| 0.1212 \| 3.0147 \| 1230 \| 0.7895 \| 0.2154 \| 0.7895 \| 0.8885 \| \| 0.1212 \| 3.0196 \| 1232 \| 0.7814 \| 0.2154 \| 0.7814 \| 0.8840 \| \| 0.1212 \| 3.0245 \| 1234 \| 0.7751 \| 0.0957 \| 0.7751 \| 0.8804 \| \| 0.1212 \| 3.0294 \| 1236 \| 0.7702 \| 0.0957 \| 0.7702 \| 0.8776 \| \| 0.1212 \| 3.0343 \| 1238 \| 0.7634 \| 0.1034 \| 0.7634 \| 0.8737 \| \| 0.1212 \| 3.0392 \| 1240 \| 0.7536 \| 0.1034 \| 0.7536 \| 0.8681 \| \| 0.1212 \| 3.0441 \| 1242 \| 0.7258 \| 0.1793 \| 0.7258 \| 0.8519 \| \| 0.1212 \| 3.0490 \| 1244 \| 0.6818 \| 0.0503 \| 0.6818 \| 0.8257 \| \| 0.1212 \| 3.0539 \| 1246 \| 0.6520 \| -0.0678 \| 0.6520 \| 0.8075 \| \| 0.1212 \| 3.0588 \| 1248 \| 0.6756 \| 0.2150 \| 0.6756 \| 0.8219 \| \| 0.1212 \| 3.0637 \| 1250 \| 0.7224 \| 0.2150 \| 0.7224 \| 0.8500 \| \| 0.1212 \| 3.0686 \| 1252 \| 0.7444 \| 0.1755 \| 0.7444 \| 0.8628 \| \| 0.1212 \| 3.0735 \| 1254 \| 0.7450 \| 0.2150 \| 0.7450 \| 0.8631 \| \| 0.1212 \| 3.0784 \| 1256 \| 0.7266 \| 0.2150 \| 0.7266 \| 0.8524 \| \| 0.1212 \| 3.0833 \| 1258 \| 0.7112 \| 0.0957 \| 0.7112 \| 0.8433 \| \| 0.1212 \| 3.0882 \| 1260 \| 0.7306 \| -0.0294 \| 0.7306 \| 0.8548 \| \| 0.1212 \| 3.0931 \| 1262 \| 0.7970 \| 0.2186 \| 0.7970 \| 0.8928 \| \| 0.1212 \| 3.0980 \| 1264 \| 0.8659 \| 0.1162 \| 0.8659 \| 0.9305 \| \| 0.1212 \| 3.1029 \| 1266 \| 0.8734 \| 0.1162 \| 0.8734 \| 0.9346 \| \| 0.1212 \| 3.1078 \| 1268 \| 0.8305 \| 0.1992 \| 0.8305 \| 0.9113 \| \| 0.1212 \| 3.1127 \| 1270 \| 0.7934 \| 0.2317 \| 0.7934 \| 0.8907 \| \| 0.1212 \| 3.1176 \| 1272 \| 0.7629 \| 0.2696 \| 0.7629 \| 0.8735 \| \| 0.1212 \| 3.1225 \| 1274 \| 0.7458 \| 0.3226 \| 0.7458 \| 0.8636 \| \| 0.1212 \| 3.1275 \| 1276 \| 0.7510 \| 0.2696 \| 0.7510 \| 0.8666 \| \| 0.1212 \| 3.1324 \| 1278 \| 0.7766 \| 0.2317 \| 0.7766 \| 0.8813 \| \| 0.1212 \| 3.1373 \| 1280 \| 0.8266 \| 0.1848 \| 0.8266 \| 0.9092 \| \| 0.1212 \| 3.1422 \| 1282 \| 0.8591 \| 0.2734 \| 0.8591 \| 0.9269 \| \| 0.1212 \| 3.1471 \| 1284 \| 0.8567 \| 0.3037 \| 0.8567 \| 0.9256 \| \| 0.1212 \| 3.1520 \| 1286 \| 0.8112 \| 0.3687 \| 0.8112 \| 0.9007 \| \| 0.1212 \| 3.1569 \| 1288 \| 0.7511 \| 0.2588 \| 0.7511 \| 0.8666 \| \| 0.1212 \| 3.1618 \| 1290 \| 0.6872 \| 0.2588 \| 0.6872 \| 0.8290 \| \| 0.1212 \| 3.1667 \| 1292 \| 0.6302 \| 0.1978 \| 0.6302 \| 0.7939 \| \| 0.1212 \| 3.1716 \| 1294 \| 0.6109 \| 0.0735 \| 0.6109 \| 0.7816 \| \| 0.1212 \| 3.1765 \| 1296 \| 0.6134 \| 0.2553 \| 0.6134 \| 0.7832 \| \| 0.1212 \| 3.1814 \| 1298 \| 0.6129 \| 0.2553 \| 0.6129 \| 0.7829 \| \| 0.1212 \| 3.1863 \| 1300 \| 0.6005 \| 0.0339 \| 0.6005 \| 0.7749 \| \| 0.1212 \| 3.1912 \| 1302 \| 0.6036 \| 0.0735 \| 0.6036 \| 0.7769 \| \| 0.1212 \| 3.1961 \| 1304 \| 0.6201 \| 0.0503 \| 0.6201 \| 0.7875 \| \| 0.1212 \| 3.2010 \| 1306 \| 0.6512 \| 0.2588 \| 0.6512 \| 0.8070 \| \| 0.1212 \| 3.2059 \| 1308 \| 0.7153 \| 0.2588 \| 0.7153 \| 0.8457 \| \| 0.1212 \| 3.2108 \| 1310 \| 0.7597 \| 0.2588 \| 0.7597 \| 0.8716 \| \| 0.1212 \| 3.2157 \| 1312 \| 0.7538 \| 0.2588 \| 0.7538 \| 0.8682 \| \| 0.1212 \| 3.2206 \| 1314 \| 0.7161 \| 0.2588 \| 0.7161 \| 0.8462 \| \| 0.1212 \| 3.2255 \| 1316 \| 0.6582 \| 0.2588 \| 0.6582 \| 0.8113 \| \| 0.1212 \| 3.2304 \| 1318 \| 0.6287 \| 0.0099 \| 0.6287 \| 0.7929 \| \| 0.1212 \| 3.2353 \| 1320 \| 0.6382 \| 0.1560 \| 0.6382 \| 0.7989 \| \| 0.1212 \| 3.2402 \| 1322 \| 0.6511 \| 0.1560 \| 0.6511 \| 0.8069 \| \| 0.1212 \| 3.2451 \| 1324 \| 0.6503 \| 0.1356 \| 0.6503 \| 0.8064 \| \| 0.1212 \| 3.25 \| 1326 \| 0.6509 \| -0.0294 \| 0.6509 \| 0.8068 \| \| 0.1212 \| 3.2549 \| 1328 \| 0.6657 \| 0.1793 \| 0.6657 \| 0.8159 \| \| 0.1212 \| 3.2598 \| 1330 \| 0.6928 \| 0.2186 \| 0.6928 \| 0.8323 \| \| 0.1212 \| 3.2647 \| 1332 \| 0.7036 \| 0.2186 \| 0.7036 \| 0.8388 \| \| 0.1212 \| 3.2696 \| 1334 \| 0.7117 \| 0.2186 \| 0.7117 \| 0.8436 \| \| 0.1212 \| 3.2745 \| 1336 \| 0.6872 \| 0.1793 \| 0.6872 \| 0.8290 \| \| 0.1212 \| 3.2794 \| 1338 \| 0.6692 \| 0.1409 \| 0.6692 \| 0.8181 \| \| 0.1212 \| 3.2843 \| 1340 \| 0.6413 \| 0.0099 \| 0.6413 \| 0.8008 \| \| 0.1212 \| 3.2892 \| 1342 \| 0.6219 \| 0.0735 \| 0.6219 \| 0.7886 \| \| 0.1212 \| 3.2941 \| 1344 \| 0.6120 \| 0.1141 \| 0.6120 \| 0.7823 \| \| 0.1212 \| 3.2990 \| 1346 \| 0.6147 \| 0.1558 \| 0.6147 \| 0.7840 \| \| 0.1212 \| 3.3039 \| 1348 \| 0.6333 \| 0.1340 \| 0.6333 \| 0.7958 \| \| 0.1212 \| 3.3088 \| 1350 \| 0.6374 \| 0.1340 \| 0.6374 \| 0.7984 \| \| 0.1212 \| 3.3137 \| 1352 \| 0.6323 \| 0.1985 \| 0.6323 \| 0.7952 \| \| 0.1212 \| 3.3186 \| 1354 \| 0.6119 \| 0.1985 \| 0.6119 \| 0.7822 \| \| 0.1212 \| 3.3235 \| 1356 \| 0.5826 \| 0.1558 \| 0.5826 \| 0.7633 \| \| 0.1212 \| 3.3284 \| 1358 \| 0.5638 \| 0.1141 \| 0.5638 \| 0.7508 \| \| 0.1212 \| 3.3333 \| 1360 \| 0.5615 \| 0.2794 \| 0.5615 \| 0.7493 \| \| 0.1212 \| 3.3382 \| 1362 \| 0.5737 \| 0.2794 \| 0.5737 \| 0.7574 \| \| 0.1212 \| 3.3431 \| 1364 \| 0.5984 \| 0.2794 \| 0.5984 \| 0.7735 \| \| 0.1212 \| 3.3480 \| 1366 \| 0.6173 \| 0.2794 \| 0.6173 \| 0.7857 \| \| 0.1212 \| 3.3529 \| 1368 \| 0.6508 \| 0.3865 \| 0.6508 \| 0.8067 \| \| 0.1212 \| 3.3578 \| 1370 \| 0.7077 \| 0.3163 \| 0.7077 \| 0.8412 \| \| 0.1212 \| 3.3627 \| 1372 \| 0.7377 \| 0.3163 \| 0.7377 \| 0.8589 \| \| 0.1212 \| 3.3676 \| 1374 \| 0.7257 \| 0.3163 \| 0.7257 \| 0.8519 \| \| 0.1212 \| 3.3725 \| 1376 \| 0.6912 \| 0.3163 \| 0.6912 \| 0.8314 \| \| 0.1212 \| 3.3775 \| 1378 \| 0.6567 \| 0.3163 \| 0.6567 \| 0.8104 \| \| 0.1212 \| 3.3824 \| 1380 \| 0.6102 \| 0.3163 \| 0.6102 \| 0.7812 \| \| 0.1212 \| 3.3873 \| 1382 \| 0.6045 \| 0.3163 \| 0.6045 \| 0.7775 \| \| 0.1212 \| 3.3922 \| 1384 \| 0.6079 \| 0.3163 \| 0.6079 \| 0.7797 \| \| 0.1212 \| 3.3971 \| 1386 \| 0.6079 \| 0.3163 \| 0.6079 \| 0.7797 \| \| 0.1212 \| 3.4020 \| 1388 \| 0.6066 \| 0.3163 \| 0.6066 \| 0.7788 \| \| 0.1212 \| 3.4069 \| 1390 \| 0.5949 \| 0.3163 \| 0.5949 \| 0.7713 \| \| 0.1212 \| 3.4118 \| 1392 \| 0.5952 \| 0.3163 \| 0.5952 \| 0.7715 \| \| 0.1212 \| 3.4167 \| 1394 \| 0.6242 \| 0.2588 \| 0.6242 \| 0.7901 \| \| 0.1212 \| 3.4216 \| 1396 \| 0.6646 \| 0.2588 \| 0.6646 \| 0.8152 \| \| 0.1212 \| 3.4265 \| 1398 \| 0.6828 \| 0.2588 \| 0.6828 \| 0.8263 \| \| 0.1212 \| 3.4314 \| 1400 \| 0.6958 \| 0.2588 \| 0.6958 \| 0.8342 \| \| 0.1212 \| 3.4363 \| 1402 \| 0.6845 \| 0.2588 \| 0.6845 \| 0.8274 \| \| 0.1212 \| 3.4412 \| 1404 \| 0.6459 \| 0.2186 \| 0.6459 \| 0.8037 \| \| 0.1212 \| 3.4461 \| 1406 \| 0.6205 \| 0.2154 \| 0.6205 \| 0.7877 \| \| 0.1212 \| 3.4510 \| 1408 \| 0.6152 \| 0.2696 \| 0.6152 \| 0.7844 \| \| 0.1212 \| 3.4559 \| 1410 \| 0.6115 \| 0.1560 \| 0.6115 \| 0.7820 \| \| 0.1212 \| 3.4608 \| 1412 \| 0.5999 \| 0.2150 \| 0.5999 \| 0.7745 \| \| 0.1212 \| 3.4657 \| 1414 \| 0.5983 \| 0.1978 \| 0.5983 \| 0.7735 \| \| 0.1212 \| 3.4706 \| 1416 \| 0.6313 \| 0.3163 \| 0.6313 \| 0.7945 \| \| 0.1212 \| 3.4755 \| 1418 \| 0.6568 \| 0.2588 \| 0.6568 \| 0.8105 \| \| 0.1212 \| 3.4804 \| 1420 \| 0.7026 \| 0.2588 \| 0.7026 \| 0.8382 \| \| 0.1212 \| 3.4853 \| 1422 \| 0.7229 \| 0.2588 \| 0.7229 \| 0.8503 \| \| 0.1212 \| 3.4902 \| 1424 \| 0.7069 \| 0.2588 \| 0.7069 \| 0.8408 \| \| 0.1212 \| 3.4951 \| 1426 \| 0.6812 \| 0.2588 \| 0.6812 \| 0.8253 \| \| 0.1212 \| 3.5 \| 1428 \| 0.6720 \| 0.2588 \| 0.6720 \| 0.8197 \| \| 0.1212 \| 3.5049 \| 1430 \| 0.6791 \| 0.2588 \| 0.6791 \| 0.8241 \| \| 0.1212 \| 3.5098 \| 1432 \| 0.6596 \| 0.2588 \| 0.6596 \| 0.8121 \| \| 0.1212 \| 3.5147 \| 1434 \| 0.6479 \| 0.1340 \| 0.6479 \| 0.8049 \| \| 0.1212 \| 3.5196 \| 1436 \| 0.6485 \| -0.0048 \| 0.6485 \| 0.8053 \| \| 0.1212 \| 3.5245 \| 1438 \| 0.6534 \| 0.0503 \| 0.6534 \| 0.8083 \| \| 0.1212 \| 3.5294 \| 1440 \| 0.6638 \| 0.2588 \| 0.6638 \| 0.8147 \| \| 0.1212 \| 3.5343 \| 1442 \| 0.6916 \| 0.2588 \| 0.6916 \| 0.8316 \| \| 0.1212 \| 3.5392 \| 1444 \| 0.7170 \| 0.2588 \| 0.7170 \| 0.8468 \| \| 0.1212 \| 3.5441 \| 1446 \| 0.7221 \| 0.2588 \| 0.7221 \| 0.8498 \| \| 0.1212 \| 3.5490 \| 1448 \| 0.7272 \| 0.1793 \| 0.7272 \| 0.8528 \| \| 0.1212 \| 3.5539 \| 1450 \| 0.7159 \| 0.2154 \| 0.7159 \| 0.8461 \| \| 0.1212 \| 3.5588 \| 1452 \| 0.7029 \| 0.2696 \| 0.7029 \| 0.8384 \| \| 0.1212 \| 3.5637 \| 1454 \| 0.6966 \| 0.2696 \| 0.6966 \| 0.8346 \| \| 0.1212 \| 3.5686 \| 1456 \| 0.6862 \| 0.2696 \| 0.6862 \| 0.8284 \| \| 0.1212 \| 3.5735 \| 1458 \| 0.6811 \| 0.2696 \| 0.6811 \| 0.8253 \| \| 0.1212 \| 3.5784 \| 1460 \| 0.6799 \| 0.1978 \| 0.6799 \| 0.8246 \| \| 0.1212 \| 3.5833 \| 1462 \| 0.7066 \| 0.1793 \| 0.7066 \| 0.8406 \| \| 0.1212 \| 3.5882 \| 1464 \| 0.7253 \| 0.2588 \| 0.7253 \| 0.8517 \| \| 0.1212 \| 3.5931 \| 1466 \| 0.7126 \| 0.2588 \| 0.7126 \| 0.8442 \| \| 0.1212 \| 3.5980 \| 1468 \| 0.7129 \| 0.2588 \| 0.7129 \| 0.8443 \| \| 0.1212 \| 3.6029 \| 1470 \| 0.7152 \| 0.2588 \| 0.7152 \| 0.8457 \| \| 0.1212 \| 3.6078 \| 1472 \| 0.7071 \| 0.2588 \| 0.7071 \| 0.8409 \| \| 0.1212 \| 3.6127 \| 1474 \| 0.6837 \| 0.1793 \| 0.6837 \| 0.8269 \| \| 0.1212 \| 3.6176 \| 1476 \| 0.6841 \| 0.1978 \| 0.6841 \| 0.8271 \| \| 0.1212 \| 3.6225 \| 1478 \| 0.6879 \| 0.3077 \| 0.6879 \| 0.8294 \| \| 0.1212 \| 3.6275 \| 1480 \| 0.6937 \| 0.2696 \| 0.6937 \| 0.8329 \| \| 0.1212 \| 3.6324 \| 1482 \| 0.6995 \| 0.2696 \| 0.6995 \| 0.8364 \| \| 0.1212 \| 3.6373 \| 1484 \| 0.7072 \| 0.2696 \| 0.7072 \| 0.8409 \| \| 0.1212 \| 3.6422 \| 1486 \| 0.7205 \| 0.2533 \| 0.7205 \| 0.8488 \| \| 0.1212 \| 3.6471 \| 1488 \| 0.7153 \| 0.2533 \| 0.7153 \| 0.8457 \| \| 0.1212 \| 3.6520 \| 1490 \| 0.7201 \| 0.1409 \| 0.7201 \| 0.8486 \| \| 0.1212 \| 3.6569 \| 1492 \| 0.7354 \| 0.1793 \| 0.7354 \| 0.8576 \| \| 0.1212 \| 3.6618 \| 1494 \| 0.7457 \| 0.2186 \| 0.7457 \| 0.8635 \| \| 0.1212 \| 3.6667 \| 1496 \| 0.7241 \| 0.1793 \| 0.7241 \| 0.8510 \| \| 0.1212 \| 3.6716 \| 1498 \| 0.6961 \| 0.1409 \| 0.6961 \| 0.8343 \| \| 0.0791 \| 3.6765 \| 1500 \| 0.6778 \| 0.1356 \| 0.6778 \| 0.8233 \| \| 0.0791 \| 3.6814 \| 1502 \| 0.6761 \| 0.1356 \| 0.6761 \| 0.8222 \| \| 0.0791 \| 3.6863 \| 1504 \| 0.6859 \| 0.0957 \| 0.6859 \| 0.8282 \| \| 0.0791 \| 3.6912 \| 1506 \| 0.6944 \| 0.0957 \| 0.6944 \| 0.8333 \| \| 0.0791 \| 3.6961 \| 1508 \| 0.7040 \| 0.0957 \| 0.7040 \| 0.8390 \| \| 0.0791 \| 3.7010 \| 1510 \| 0.7152 \| 0.1356 \| 0.7152 \| 0.8457 \| \| 0.0791 \| 3.7059 \| 1512 \| 0.7406 \| 0.2588 \| 0.7406 \| 0.8606 \| \| 0.0791 \| 3.7108 \| 1514 \| 0.7760 \| 0.2588 \| 0.7760 \| 0.8809 \| \| 0.0791 \| 3.7157 \| 1516 \| 0.7728 \| 0.2588 \| 0.7728 \| 0.8791 \| \| 0.0791 \| 3.7206 \| 1518 \| 0.7336 \| 0.2588 \| 0.7336 \| 0.8565 \| \| 0.0791 \| 3.7255 \| 1520 \| 0.7011 \| 0.2588 \| 0.7011 \| 0.8373 \| \| 0.0791 \| 3.7304 \| 1522 \| 0.6835 \| 0.1793 \| 0.6835 \| 0.8267 \| \| 0.0791 \| 3.7353 \| 1524 \| 0.6768 \| 0.1409 \| 0.6768 \| 0.8227 \| \| 0.0791 \| 3.7402 \| 1526 \| 0.6799 \| 0.1409 \| 0.6799 \| 0.8245 \| \| 0.0791 \| 3.7451 \| 1528 \| 0.6861 \| 0.2921 \| 0.6861 \| 0.8283 \| \| 0.0791 \| 3.75 \| 1530 \| 0.6959 \| 0.2533 \| 0.6959 \| 0.8342 \| \| 0.0791 \| 3.7549 \| 1532 \| 0.6940 \| 0.2154 \| 0.6940 \| 0.8331 \| \| 0.0791 \| 3.7598 \| 1534 \| 0.6892 \| 0.2154 \| 0.6892 \| 0.8302 \| \| 0.0791 \| 3.7647 \| 1536 \| 0.7028 \| 0.2154 \| 0.7028 \| 0.8383 \| \| 0.0791 \| 3.7696 \| 1538 \| 0.7101 \| 0.0957 \| 0.7101 \| 0.8427 \| \| 0.0791 \| 3.7745 \| 1540 \| 0.7086 \| 0.2154 \| 0.7086 \| 0.8418 \| \| 0.0791 \| 3.7794 \| 1542 \| 0.7038 \| 0.2154 \| 0.7038 \| 0.8389 \| \| 0.0791 \| 3.7843 \| 1544 \| 0.6954 \| 0.2154 \| 0.6954 \| 0.8339 \| \| 0.0791 \| 3.7892 \| 1546 \| 0.7015 \| 0.3318 \| 0.7015 \| 0.8376 \| \| 0.0791 \| 3.7941 \| 1548 \| 0.7003 \| 0.1793 \| 0.7003 \| 0.8368 \| \| 0.0791 \| 3.7990 \| 1550 \| 0.6815 \| 0.1793 \| 0.6815 \| 0.8255 \| \| 0.0791 \| 3.8039 \| 1552 \| 0.6841 \| 0.2186 \| 0.6841 \| 0.8271 \| \| 0.0791 \| 3.8088 \| 1554 \| 0.6907 \| 0.2186 \| 0.6907 \| 0.8311 \| \| 0.0791 \| 3.8137 \| 1556 \| 0.6936 \| 0.2186 \| 0.6936 \| 0.8328 \| \| 0.0791 \| 3.8186 \| 1558 \| 0.6869 \| 0.2186 \| 0.6869 \| 0.8288 \| \| 0.0791 \| 3.8235 \| 1560 \| 0.6744 \| 0.2921 \| 0.6744 \| 0.8212 \| \| 0.0791 \| 3.8284 \| 1562 \| 0.6651 \| 0.2154 \| 0.6651 \| 0.8156 \| \| 0.0791 \| 3.8333 \| 1564 \| 0.6676 \| 0.2154 \| 0.6676 \| 0.8171 \| \| 0.0791 \| 3.8382 \| 1566 \| 0.6710 \| 0.2154 \| 0.6710 \| 0.8191 \| \| 0.0791 \| 3.8431 \| 1568 \| 0.6762 \| 0.2533 \| 0.6762 \| 0.8223 \| \| 0.0791 \| 3.8480 \| 1570 \| 0.6898 \| 0.1034 \| 0.6898 \| 0.8305 \| \| 0.0791 \| 3.8529 \| 1572 \| 0.6950 \| 0.2186 \| 0.6950 \| 0.8337 \| \| 0.0791 \| 3.8578 \| 1574 \| 0.6865 \| 0.2186 \| 0.6865 \| 0.8286 \| \| 0.0791 \| 3.8627 \| 1576 \| 0.6698 \| 0.1409 \| 0.6698 \| 0.8184 \| \| 0.0791 \| 3.8676 \| 1578 \| 0.6508 \| 0.2154 \| 0.6508 \| 0.8067 \| \| 0.0791 \| 3.8725 \| 1580 \| 0.6741 \| 0.2150 \| 0.6741 \| 0.8210 \| \| 0.0791 \| 3.8775 \| 1582 \| 0.7014 \| 0.1755 \| 0.7014 \| 0.8375 \| \| 0.0791 \| 3.8824 \| 1584 \| 0.6867 \| 0.2851 \| 0.6867 \| 0.8286 \| \| 0.0791 \| 3.8873 \| 1586 \| 0.6669 \| 0.3226 \| 0.6669 \| 0.8166 \| \| 0.0791 \| 3.8922 \| 1588 \| 0.6563 \| 0.3226 \| 0.6563 \| 0.8101 \| \| 0.0791 \| 3.8971 \| 1590 \| 0.6450 \| 0.3077 \| 0.6450 \| 0.8031 \| \| 0.0791 \| 3.9020 \| 1592 \| 0.6484 \| 0.3467 \| 0.6484 \| 0.8053 \| \| 0.0791 \| 3.9069 \| 1594 \| 0.6492 \| 0.3467 \| 0.6492 \| 0.8057 \| \| 0.0791 \| 3.9118 \| 1596 \| 0.6567 \| 0.3077 \| 0.6567 \| 0.8104 \| \| 0.0791 \| 3.9167 \| 1598 \| 0.6630 \| 0.3077 \| 0.6630 \| 0.8143 \| \| 0.0791 \| 3.9216 \| 1600 \| 0.6768 \| 0.3077 \| 0.6768 \| 0.8227 \| \| 0.0791 \| 3.9265 \| 1602 \| 0.6933 \| 0.3077 \| 0.6933 \| 0.8327 \| \| 0.0791 \| 3.9314 \| 1604 \| 0.7030 \| 0.1600 \| 0.7030 \| 0.8385 \| \| 0.0791 \| 3.9363 \| 1606 \| 0.7104 \| 0.1978 \| 0.7104 \| 0.8428 \| \| 0.0791 \| 3.9412 \| 1608 \| 0.7090 \| 0.1978 \| 0.7090 \| 0.8420 \| \| 0.0791 \| 3.9461 \| 1610 \| 0.7204 \| 0.2364 \| 0.7204 \| 0.8488 \| \| 0.0791 \| 3.9510 \| 1612 \| 0.7198 \| 0.2364 \| 0.7198 \| 0.8484 \| \| 0.0791 \| 3.9559 \| 1614 \| 0.7037 \| 0.2364 \| 0.7037 \| 0.8388 \| \| 0.0791 \| 3.9608 \| 1616 \| 0.6855 \| 0.2364 \| 0.6855 \| 0.8279 \| \| 0.0791 \| 3.9657 \| 1618 \| 0.6595 \| 0.1600 \| 0.6595 \| 0.8121 \| \| 0.0791 \| 3.9706 \| 1620 \| 0.6500 \| 0.1600 \| 0.6500 \| 0.8063 \| \| 0.0791 \| 3.9755 \| 1622 \| 0.6526 \| 0.1600 \| 0.6526 \| 0.8078 \| \| 0.0791 \| 3.9804 \| 1624 \| 0.6712 \| 0.2364 \| 0.6712 \| 0.8193 \| \| 0.0791 \| 3.9853 \| 1626 \| 0.7021 \| 0.2186 \| 0.7021 \| 0.8379 \| \| 0.0791 \| 3.9902 \| 1628 \| 0.7048 \| 0.2186 \| 0.7048 \| 0.8395 \| \| 0.0791 \| 3.9951 \| 1630 \| 0.6990 \| 0.2186 \| 0.6990 \| 0.8360 \| \| 0.0791 \| 4.0 \| 1632 \| 0.7031 \| 0.2186 \| 0.7031 \| 0.8385 \| \| 0.0791 \| 4.0049 \| 1634 \| 0.6996 \| 0.2186 \| 0.6996 \| 0.8364 \| \| 0.0791 \| 4.0098 \| 1636 \| 0.6952 \| 0.1409 \| 0.6952 \| 0.8338 \| \| 0.0791 \| 4.0147 \| 1638 \| 0.7047 \| 0.1034 \| 0.7047 \| 0.8395 \| \| 0.0791 \| 4.0196 \| 1640 \| 0.7232 \| 0.1034 \| 0.7232 \| 0.8504 \| \| 0.0791 \| 4.0245 \| 1642 \| 0.7470 \| 0.1034 \| 0.7470 \| 0.8643 \| \| 0.0791 \| 4.0294 \| 1644 \| 0.7678 \| 0.1409 \| 0.7678 \| 0.8762 \| \| 0.0791 \| 4.0343 \| 1646 \| 0.8113 \| 0.1793 \| 0.8113 \| 0.9007 \| \| 0.0791 \| 4.0392 \| 1648 \| 0.8505 \| 0.3255 \| 0.8505 \| 0.9222 \| \| 0.0791 \| 4.0441 \| 1650 \| 0.8698 \| 0.3255 \| 0.8698 \| 0.9326 \| \| 0.0791 \| 4.0490 \| 1652 \| 0.8610 \| 0.3255 \| 0.8610 \| 0.9279 \| \| 0.0791 \| 4.0539 \| 1654 \| 0.8489 \| 0.3255 \| 0.8489 \| 0.9213 \| \| 0.0791 \| 4.0588 \| 1656 \| 0.8233 \| 0.1409 \| 0.8233 \| 0.9073 \| \| 0.0791 \| 4.0637 \| 1658 \| 0.8296 \| 0.1409 \| 0.8296 \| 0.9108 \| \| 0.0791 \| 4.0686 \| 1660 \| 0.8387 \| 0.2186 \| 0.8387 \| 0.9158 \| \| 0.0791 \| 4.0735 \| 1662 \| 0.8289 \| 0.2186 \| 0.8289 \| 0.9104 \| \| 0.0791 \| 4.0784 \| 1664 \| 0.8289 \| 0.2186 \| 0.8289 \| 0.9104 \| \| 0.0791 \| 4.0833 \| 1666 \| 0.8186 \| 0.2186 \| 0.8186 \| 0.9048 \| \| 0.0791 \| 4.0882 \| 1668 \| 0.8049 \| 0.1793 \| 0.8049 \| 0.8972 \| \| 0.0791 \| 4.0931 \| 1670 \| 0.7837 \| 0.1793 \| 0.7837 \| 0.8853 \| \| 0.0791 \| 4.0980 \| 1672 \| 0.7665 \| 0.1409 \| 0.7665 \| 0.8755 \| \| 0.0791 \| 4.1029 \| 1674 \| 0.7436 \| 0.1600 \| 0.7436 \| 0.8623 \| \| 0.0791 \| 4.1078 \| 1676 \| 0.7272 \| 0.1600 \| 0.7272 \| 0.8528 \| \| 0.0791 \| 4.1127 \| 1678 \| 0.7201 \| 0.1978 \| 0.7201 \| 0.8486 \| \| 0.0791 \| 4.1176 \| 1680 \| 0.7042 \| 0.1409 \| 0.7042 \| 0.8392 \| \| 0.0791 \| 4.1225 \| 1682 \| 0.6854 \| 0.1409 \| 0.6854 \| 0.8279 \| \| 0.0791 \| 4.1275 \| 1684 \| 0.6862 \| 0.1409 \| 0.6862 \| 0.8284 \| \| 0.0791 \| 4.1324 \| 1686 \| 0.6993 \| 0.1793 \| 0.6993 \| 0.8362 \| \| 0.0791 \| 4.1373 \| 1688 \| 0.7311 \| 0.2186 \| 0.7311 \| 0.8550 \| \| 0.0791 \| 4.1422 \| 1690 \| 0.7596 \| 0.2186 \| 0.7596 \| 0.8715 \| \| 0.0791 \| 4.1471 \| 1692 \| 0.7471 \| 0.2186 \| 0.7471 \| 0.8644 \| \| 0.0791 \| 4.1520 \| 1694 \| 0.7068 \| 0.2186 \| 0.7068 \| 0.8407 \| \| 0.0791 \| 4.1569 \| 1696 \| 0.6582 \| 0.1034 \| 0.6582 \| 0.8113 \| \| 0.0791 \| 4.1618 \| 1698 \| 0.6384 \| 0.1962 \| 0.6384 \| 0.7990 \| \| 0.0791 \| 4.1667 \| 1700 \| 0.6446 \| 0.2150 \| 0.6446 \| 0.8029 \| \| 0.0791 \| 4.1716 \| 1702 \| 0.6478 \| 0.2150 \| 0.6478 \| 0.8048 \| \| 0.0791 \| 4.1765 \| 1704 \| 0.6374 \| 0.2150 \| 0.6374 \| 0.7983 \| \| 0.0791 \| 4.1814 \| 1706 \| 0.6339 \| 0.2553 \| 0.6339 \| 0.7962 \| \| 0.0791 \| 4.1863 \| 1708 \| 0.6347 \| 0.1356 \| 0.6347 \| 0.7967 \| \| 0.0791 \| 4.1912 \| 1710 \| 0.6393 \| 0.2921 \| 0.6393 \| 0.7996 \| \| 0.0791 \| 4.1961 \| 1712 \| 0.6482 \| 0.1793 \| 0.6482 \| 0.8051 \| \| 0.0791 \| 4.2010 \| 1714 \| 0.6547 \| 0.2186 \| 0.6547 \| 0.8091 \| \| 0.0791 \| 4.2059 \| 1716 \| 0.6433 \| 0.2186 \| 0.6433 \| 0.8021 \| \| 0.0791 \| 4.2108 \| 1718 \| 0.6339 \| 0.2186 \| 0.6339 \| 0.7962 \| \| 0.0791 \| 4.2157 \| 1720 \| 0.6289 \| 0.0503 \| 0.6289 \| 0.7930 \| \| 0.0791 \| 4.2206 \| 1722 \| 0.6347 \| 0.1793 \| 0.6347 \| 0.7967 \| \| 0.0791 \| 4.2255 \| 1724 \| 0.6359 \| 0.1765 \| 0.6359 \| 0.7974 \| \| 0.0791 \| 4.2304 \| 1726 \| 0.6393 \| 0.2921 \| 0.6393 \| 0.7996 \| \| 0.0791 \| 4.2353 \| 1728 \| 0.6440 \| 0.3318 \| 0.6440 \| 0.8025 \| \| 0.0791 \| 4.2402 \| 1730 \| 0.6491 \| 0.3318 \| 0.6491 \| 0.8056 \| \| 0.0791 \| 4.2451 \| 1732 \| 0.6437 \| 0.2921 \| 0.6437 \| 0.8023 \| \| 0.0791 \| 4.25 \| 1734 \| 0.6366 \| 0.1765 \| 0.6366 \| 0.7979 \| \| 0.0791 \| 4.2549 \| 1736 \| 0.6388 \| 0.1560 \| 0.6388 \| 0.7992 \| \| 0.0791 \| 4.2598 \| 1738 \| 0.6462 \| 0.2150 \| 0.6462 \| 0.8039 \| \| 0.0791 \| 4.2647 \| 1740 \| 0.6544 \| 0.2150 \| 0.6544 \| 0.8089 \| \| 0.0791 \| 4.2696 \| 1742 \| 0.6589 \| 0.2696 \| 0.6589 \| 0.8117 \| \| 0.0791 \| 4.2745 \| 1744 \| 0.6620 \| 0.2696 \| 0.6620 \| 0.8136 \| \| 0.0791 \| 4.2794 \| 1746 \| 0.6786 \| 0.3318 \| 0.6786 \| 0.8237 \| \| 0.0791 \| 4.2843 \| 1748 \| 0.7055 \| 0.2186 \| 0.7055 \| 0.8400 \| \| 0.0791 \| 4.2892 \| 1750 \| 0.7199 \| 0.2186 \| 0.7199 \| 0.8485 \| \| 0.0791 \| 4.2941 \| 1752 \| 0.7248 \| 0.2186 \| 0.7248 \| 0.8513 \| \| 0.0791 \| 4.2990 \| 1754 \| 0.7232 \| 0.2186 \| 0.7232 \| 0.8504 \| \| 0.0791 \| 4.3039 \| 1756 \| 0.7045 \| 0.1793 \| 0.7045 \| 0.8393 \| \| 0.0791 \| 4.3088 \| 1758 \| 0.6700 \| 0.2921 \| 0.6700 \| 0.8185 \| \| 0.0791 \| 4.3137 \| 1760 \| 0.6459 \| 0.3077 \| 0.6459 \| 0.8037 \| \| 0.0791 \| 4.3186 \| 1762 \| 0.6375 \| 0.2696 \| 0.6375 \| 0.7984 \| \| 0.0791 \| 4.3235 \| 1764 \| 0.6266 \| 0.3226 \| 0.6266 \| 0.7916 \| \| 0.0791 \| 4.3284 \| 1766 \| 0.6143 \| 0.3609 \| 0.6143 \| 0.7838 \| \| 0.0791 \| 4.3333 \| 1768 \| 0.6055 \| 0.3467 \| 0.6055 \| 0.7781 \| \| 0.0791 \| 4.3382 \| 1770 \| 0.6156 \| 0.1409 \| 0.6156 \| 0.7846 \| \| 0.0791 \| 4.3431 \| 1772 \| 0.6485 \| 0.2186 \| 0.6485 \| 0.8053 \| \| 0.0791 \| 4.3480 \| 1774 \| 0.6657 \| 0.2588 \| 0.6657 \| 0.8159 \| \| 0.0791 \| 4.3529 \| 1776 \| 0.6797 \| 0.2588 \| 0.6797 \| 0.8244 \| \| 0.0791 \| 4.3578 \| 1778 \| 0.6674 \| 0.2588 \| 0.6674 \| 0.8170 \| \| 0.0791 \| 4.3627 \| 1780 \| 0.6352 \| 0.2186 \| 0.6352 \| 0.7970 \| \| 0.0791 \| 4.3676 \| 1782 \| 0.6177 \| 0.1793 \| 0.6177 \| 0.7859 \| \| 0.0791 \| 4.3725 \| 1784 \| 0.6099 \| 0.2921 \| 0.6099 \| 0.7810 \| \| 0.0791 \| 4.3775 \| 1786 \| 0.6069 \| 0.3467 \| 0.6069 \| 0.7791 \| \| 0.0791 \| 4.3824 \| 1788 \| 0.6065 \| 0.2921 \| 0.6065 \| 0.7788 \| \| 0.0791 \| 4.3873 \| 1790 \| 0.6111 \| 0.3318 \| 0.6111 \| 0.7817 \| \| 0.0791 \| 4.3922 \| 1792 \| 0.6225 \| 0.2186 \| 0.6225 \| 0.7890 \| \| 0.0791 \| 4.3971 \| 1794 \| 0.6230 \| 0.2186 \| 0.6230 \| 0.7893 \| \| 0.0791 \| 4.4020 \| 1796 \| 0.6145 \| 0.2186 \| 0.6145 \| 0.7839 \| \| 0.0791 \| 4.4069 \| 1798 \| 0.6213 \| 0.2186 \| 0.6213 \| 0.7882 \| \| 0.0791 \| 4.4118 \| 1800 \| 0.6351 \| 0.2588 \| 0.6351 \| 0.7970 \| \| 0.0791 \| 4.4167 \| 1802 \| 0.6360 \| 0.2186 \| 0.6360 \| 0.7975 \| \| 0.0791 \| 4.4216 \| 1804 \| 0.6447 \| 0.2186 \| 0.6447 \| 0.8029 \| \| 0.0791 \| 4.4265 \| 1806 \| 0.6423 \| 0.2186 \| 0.6423 \| 0.8015 \| \| 0.0791 \| 4.4314 \| 1808 \| 0.6185 \| 0.3724 \| 0.6185 \| 0.7864 \| \| 0.0791 \| 4.4363 \| 1810 \| 0.6105 \| 0.3724 \| 0.6105 \| 0.7814 \| \| 0.0791 \| 4.4412 \| 1812 \| 0.6064 \| 0.3724 \| 0.6064 \| 0.7787 \| \| 0.0791 \| 4.4461 \| 1814 \| 0.6078 \| 0.3724 \| 0.6078 \| 0.7796 \| \| 0.0791 \| 4.4510 \| 1816 \| 0.6086 \| 0.3724 \| 0.6086 \| 0.7801 \| \| 0.0791 \| 4.4559 \| 1818 \| 0.5950 \| 0.3318 \| 0.5950 \| 0.7713 \| \| 0.0791 \| 4.4608 \| 1820 \| 0.5914 \| 0.1962 \| 0.5914 \| 0.7690 \| \| 0.0791 \| 4.4657 \| 1822 \| 0.5938 \| 0.1962 \| 0.5938 \| 0.7706 \| \| 0.0791 \| 4.4706 \| 1824 \| 0.5969 \| 0.1962 \| 0.5969 \| 0.7726 \| \| 0.0791 \| 4.4755 \| 1826 \| 0.6043 \| 0.1962 \| 0.6043 \| 0.7774 \| \| 0.0791 \| 4.4804 \| 1828 \| 0.6081 \| 0.1962 \| 0.6081 \| 0.7798 \| \| 0.0791 \| 4.4853 \| 1830 \| 0.5997 \| 0.1962 \| 0.5997 \| 0.7744 \| \| 0.0791 \| 4.4902 \| 1832 \| 0.5905 \| 0.1962 \| 0.5905 \| 0.7685 \| \| 0.0791 \| 4.4951 \| 1834 \| 0.5845 \| 0.2373 \| 0.5845 \| 0.7645 \| \| 0.0791 \| 4.5 \| 1836 \| 0.5791 \| 0.2373 \| 0.5791 \| 0.7610 \| \| 0.0791 \| 4.5049 \| 1838 \| 0.5741 \| 0.2373 \| 0.5741 \| 0.7577 \| \| 0.0791 \| 4.5098 \| 1840 \| 0.5715 \| 0.2373 \| 0.5715 \| 0.7560 \| \| 0.0791 \| 4.5147 \| 1842 \| 0.5695 \| 0.1765 \| 0.5695 \| 0.7547 \| \| 0.0791 \| 4.5196 \| 1844 \| 0.5697 \| 0.2184 \| 0.5697 \| 0.7548 \| \| 0.0791 \| 4.5245 \| 1846 \| 0.5658 \| 0.2184 \| 0.5658 \| 0.7522 \| \| 0.0791 \| 4.5294 \| 1848 \| 0.5707 \| 0.3724 \| 0.5707 \| 0.7555 \| \| 0.0791 \| 4.5343 \| 1850 \| 0.5879 \| 0.3724 \| 0.5879 \| 0.7667 \| \| 0.0791 \| 4.5392 \| 1852 \| 0.6251 \| 0.2186 \| 0.6251 \| 0.7907 \| \| 0.0791 \| 4.5441 \| 1854 \| 0.6718 \| 0.2186 \| 0.6718 \| 0.8197 \| \| 0.0791 \| 4.5490 \| 1856 \| 0.6709 \| 0.2186 \| 0.6709 \| 0.8191 \| \| 0.0791 \| 4.5539 \| 1858 \| 0.6502 \| 0.2186 \| 0.6502 \| 0.8064 \| \| 0.0791 \| 4.5588 \| 1860 \| 0.6372 \| 0.3724 \| 0.6372 \| 0.7982 \| \| 0.0791 \| 4.5637 \| 1862 \| 0.6120 \| 0.3467 \| 0.6120 \| 0.7823 \| \| 0.0791 \| 4.5686 \| 1864 \| 0.5944 \| 0.3077 \| 0.5944 \| 0.7710 \| \| 0.0791 \| 4.5735 \| 1866 \| 0.5952 \| 0.3077 \| 0.5952 \| 0.7715 \| \| 0.0791 \| 4.5784 \| 1868 \| 0.6035 \| 0.3077 \| 0.6035 \| 0.7769 \| \| 0.0791 \| 4.5833 \| 1870 \| 0.6174 \| 0.3077 \| 0.6174 \| 0.7857 \| \| 0.0791 \| 4.5882 \| 1872 \| 0.6202 \| 0.3077 \| 0.6202 \| 0.7875 \| \| 0.0791 \| 4.5931 \| 1874 \| 0.6141 \| 0.3077 \| 0.6141 \| 0.7836 \| \| 0.0791 \| 4.5980 \| 1876 \| 0.6143 \| 0.3077 \| 0.6143 \| 0.7838 \| \| 0.0791 \| 4.6029 \| 1878 \| 0.6190 \| 0.3077 \| 0.6190 \| 0.7868 \| \| 0.0791 \| 4.6078 \| 1880 \| 0.6174 \| 0.3609 \| 0.6174 \| 0.7858 \| \| 0.0791 \| 4.6127 \| 1882 \| 0.6118 \| 0.3609 \| 0.6118 \| 0.7822 \| \| 0.0791 \| 4.6176 \| 1884 \| 0.5976 \| 0.3077 \| 0.5976 \| 0.7731 \| \| 0.0791 \| 4.6225 \| 1886 \| 0.5875 \| 0.3609 \| 0.5875 \| 0.7665 \| \| 0.0791 \| 4.6275 \| 1888 \| 0.5813 \| 0.3609 \| 0.5813 \| 0.7624 \| \| 0.0791 \| 4.6324 \| 1890 \| 0.5859 \| 0.3609 \| 0.5859 \| 0.7655 \| \| 0.0791 \| 4.6373 \| 1892 \| 0.5935 \| 0.3787 \| 0.5935 \| 0.7704 \| \| 0.0791 \| 4.6422 \| 1894 \| 0.5933 \| 0.3609 \| 0.5933 \| 0.7703 \| \| 0.0791 \| 4.6471 \| 1896 \| 0.5921 \| 0.3609 \| 0.5921 \| 0.7695 \| \| 0.0791 \| 4.6520 \| 1898 \| 0.5860 \| 0.3077 \| 0.5860 \| 0.7655 \| \| 0.0791 \| 4.6569 \| 1900 \| 0.5861 \| 0.3467 \| 0.5861 \| 0.7655 \| \| 0.0791 \| 4.6618 \| 1902 \| 0.5949 \| 0.3865 \| 0.5949 \| 0.7713 \| \| 0.0791 \| 4.6667 \| 1904 \| 0.5916 \| 0.4273 \| 0.5916 \| 0.7692 \| \| 0.0791 \| 4.6716 \| 1906 \| 0.5844 \| 0.4273 \| 0.5844 \| 0.7645 \| \| 0.0791 \| 4.6765 \| 1908 \| 0.5682 \| 0.3163 \| 0.5682 \| 0.7538 \| \| 0.0791 \| 4.6814 \| 1910 \| 0.5449 \| 0.3163 \| 0.5449 \| 0.7382 \| \| 0.0791 \| 4.6863 \| 1912 \| 0.5272 \| 0.2759 \| 0.5272 \| 0.7261 \| \| 0.0791 \| 4.6912 \| 1914 \| 0.5094 \| 0.2364 \| 0.5094 \| 0.7137 \| \| 0.0791 \| 4.6961 \| 1916 \| 0.4956 \| 0.2794 \| 0.4956 \| 0.7040 \| \| 0.0791 \| 4.7010 \| 1918 \| 0.4901 \| 0.2794 \| 0.4901 \| 0.7001 \| \| 0.0791 \| 4.7059 \| 1920 \| 0.4917 \| 0.3390 \| 0.4917 \| 0.7012 \| \| 0.0791 \| 4.7108 \| 1922 \| 0.4968 \| 0.3390 \| 0.4968 \| 0.7048 \| \| 0.0791 \| 4.7157 \| 1924 \| 0.5044 \| 0.2967 \| 0.5044 \| 0.7102 \| \| 0.0791 \| 4.7206 \| 1926 \| 0.5157 \| 0.4400 \| 0.5157 \| 0.7181 \| \| 0.0791 \| 4.7255 \| 1928 \| 0.5382 \| 0.3865 \| 0.5382 \| 0.7336 \| \| 0.0791 \| 4.7304 \| 1930 \| 0.5782 \| 0.3318 \| 0.5782 \| 0.7604 \| \| 0.0791 \| 4.7353 \| 1932 \| 0.6183 \| 0.3318 \| 0.6183 \| 0.7863 \| \| 0.0791 \| 4.7402 \| 1934 \| 0.6317 \| 0.3318 \| 0.6317 \| 0.7948 \| \| 0.0791 \| 4.7451 \| 1936 \| 0.6400 \| 0.3318 \| 0.6400 \| 0.8000 \| \| 0.0791 \| 4.75 \| 1938 \| 0.6419 \| 0.3318 \| 0.6419 \| 0.8012 \| \| 0.0791 \| 4.7549 \| 1940 \| 0.6341 \| 0.3318 \| 0.6341 \| 0.7963 \| \| 0.0791 \| 4.7598 \| 1942 \| 0.6109 \| 0.3318 \| 0.6109 \| 0.7816 \| \| 0.0791 \| 4.7647 \| 1944 \| 0.5942 \| 0.3467 \| 0.5942 \| 0.7708 \| \| 0.0791 \| 4.7696 \| 1946 \| 0.5860 \| 0.3077 \| 0.5860 \| 0.7655 \| \| 0.0791 \| 4.7745 \| 1948 \| 0.5697 \| 0.3077 \| 0.5697 \| 0.7548 \| \| 0.0791 \| 4.7794 \| 1950 \| 0.5595 \| 0.3467 \| 0.5595 \| 0.7480 \| \| 0.0791 \| 4.7843 \| 1952 \| 0.5565 \| 0.3467 \| 0.5565 \| 0.7460 \| \| 0.0791 \| 4.7892 \| 1954 \| 0.5562 \| 0.3467 \| 0.5562 \| 0.7458 \| \| 0.0791 \| 4.7941 \| 1956 \| 0.5550 \| 0.3467 \| 0.5550 \| 0.7450 \| \| 0.0791 \| 4.7990 \| 1958 \| 0.5606 \| 0.3467 \| 0.5606 \| 0.7488 \| \| 0.0791 \| 4.8039 \| 1960 \| 0.5687 \| 0.3318 \| 0.5687 \| 0.7541 \| \| 0.0791 \| 4.8088 \| 1962 \| 0.5724 \| 0.1793 \| 0.5724 \| 0.7566 \| \| 0.0791 \| 4.8137 \| 1964 \| 0.5674 \| 0.3865 \| 0.5674 \| 0.7532 \| \| 0.0791 \| 4.8186 \| 1966 \| 0.5649 \| 0.3467 \| 0.5649 \| 0.7516 \| \| 0.0791 \| 4.8235 \| 1968 \| 0.5609 \| 0.2373 \| 0.5609 \| 0.7489 \| \| 0.0791 \| 4.8284 \| 1970 \| 0.5599 \| 0.1962 \| 0.5599 \| 0.7482 \| \| 0.0791 \| 4.8333 \| 1972 \| 0.5604 \| 0.4000 \| 0.5604 \| 0.7486 \| \| 0.0791 \| 4.8382 \| 1974 \| 0.5587 \| 0.3609 \| 0.5587 \| 0.7475 \| \| 0.0791 \| 4.8431 \| 1976 \| 0.5640 \| 0.4000 \| 0.5640 \| 0.7510 \| \| 0.0791 \| 4.8480 \| 1978 \| 0.5731 \| 0.3467 \| 0.5731 \| 0.7570 \| \| 0.0791 \| 4.8529 \| 1980 \| 0.5777 \| 0.3865 \| 0.5777 \| 0.7601 \| \| 0.0791 \| 4.8578 \| 1982 \| 0.5758 \| 0.3865 \| 0.5758 \| 0.7588 \| \| 0.0791 \| 4.8627 \| 1984 \| 0.5747 \| 0.2364 \| 0.5747 \| 0.7581 \| \| 0.0791 \| 4.8676 \| 1986 \| 0.5802 \| 0.2759 \| 0.5802 \| 0.7617 \| \| 0.0791 \| 4.8725 \| 1988 \| 0.5758 \| 0.2759 \| 0.5758 \| 0.7588 \| \| 0.0791 \| 4.8775 \| 1990 \| 0.5640 \| 0.2759 \| 0.5640 \| 0.7510 \| \| 0.0791 \| 4.8824 \| 1992 \| 0.5500 \| 0.2364 \| 0.5500 \| 0.7416 \| \| 0.0791 \| 4.8873 \| 1994 \| 0.5369 \| 0.3865 \| 0.5369 \| 0.7327 \| \| 0.0791 \| 4.8922 \| 1996 \| 0.5348 \| 0.3865 \| 0.5348 \| 0.7313 \| \| 0.0791 \| 4.8971 \| 1998 \| 0.5365 \| 0.3865 \| 0.5365 \| 0.7325 \| \| 0.0614 \| 4.9020 \| 2000 \| 0.5377 \| 0.3467 \| 0.5377 \| 0.7333 \| \| 0.0614 \| 4.9069 \| 2002 \| 0.5395 \| 0.4000 \| 0.5395 \| 0.7345 \| \| 0.0614 \| 4.9118 \| 2004 \| 0.5483 \| 0.3609 \| 0.5483 \| 0.7405 \| \| 0.0614 \| 4.9167 \| 2006 \| 0.5586 \| 0.3609 \| 0.5586 \| 0.7474 \| \| 0.0614 \| 4.9216 \| 2008 \| 0.5619 \| 0.3609 \| 0.5619 \| 0.7496 \| \| 0.0614 \| 4.9265 \| 2010 \| 0.5632 \| 0.3609 \| 0.5632 \| 0.7504 \| \| 0.0614 \| 4.9314 \| 2012 \| 0.5689 \| 0.4000 \| 0.5689 \| 0.7542 \| \| 0.0614 \| 4.9363 \| 2014 \| 0.5818 \| 0.2364 \| 0.5818 \| 0.7628 \| \| 0.0614 \| 4.9412 \| 2016 \| 0.6181 \| 0.3163 \| 0.6181 \| 0.7862 \| \| 0.0614 \| 4.9461 \| 2018 \| 0.6791 \| 0.3163 \| 0.6791 \| 0.8241 \| \| 0.0614 \| 4.9510 \| 2020 \| 0.7314 \| 0.4154 \| 0.7314 \| 0.8552 \| \| 0.0614 \| 4.9559 \| 2022 \| 0.7389 \| 0.3636 \| 0.7389 \| 0.8596 \| \| 0.0614 \| 4.9608 \| 2024 \| 0.7179 \| 0.2588 \| 0.7179 \| 0.8473 \| \| 0.0614 \| 4.9657 \| 2026 \| 0.6848 \| 0.2588 \| 0.6848 \| 0.8275 \| \| 0.0614 \| 4.9706 \| 2028 \| 0.6596 \| 0.2588 \| 0.6596 \| 0.8122 \| \| 0.0614 \| 4.9755 \| 2030 \| 0.6251 \| 0.3163 \| 0.6251 \| 0.7906 \| \| 0.0614 \| 4.9804 \| 2032 \| 0.6034 \| 0.3163 \| 0.6034 \| 0.7768 \| \| 0.0614 \| 4.9853 \| 2034 \| 0.5772 \| 0.3163 \| 0.5772 \| 0.7597 \| \| 0.0614 \| 4.9902 \| 2036 \| 0.5650 \| 0.3163 \| 0.5650 \| 0.7517 \| \| 0.0614 \| 4.9951 \| 2038 \| 0.5640 \| 0.2759 \| 0.5640 \| 0.7510 \| \| 0.0614 \| 5.0 \| 2040 \| 0.5614 \| 0.2759 \| 0.5614 \| 0.7493 \| \| 0.0614 \| 5.0049 \| 2042 \| 0.5694 \| 0.2759 \| 0.5694 \| 0.7546 \| \| 0.0614 \| 5.0098 \| 2044 \| 0.5940 \| 0.3163 \| 0.5940 \| 0.7707 \| \| 0.0614 \| 5.0147 \| 2046 \| 0.6169 \| 0.2588 \| 0.6169 \| 0.7854 \| \| 0.0614 \| 5.0196 \| 2048 \| 0.6261 \| 0.2588 \| 0.6261 \| 0.7913 \| \| 0.0614 \| 5.0245 \| 2050 \| 0.6128 \| 0.3163 \| 0.6128 \| 0.7828 \| \| 0.0614 \| 5.0294 \| 2052 \| 0.6006 \| 0.3163 \| 0.6006 \| 0.7750 \| \| 0.0614 \| 5.0343 \| 2054 \| 0.5880 \| 0.2759 \| 0.5880 \| 0.7668 \| \| 0.0614 \| 5.0392 \| 2056 \| 0.5835 \| 0.2759 \| 0.5835 \| 0.7639 \| \| 0.0614 \| 5.0441 \| 2058 \| 0.5798 \| 0.2759 \| 0.5798 \| 0.7614 \| \| 0.0614 \| 5.0490 \| 2060 \| 0.5838 \| 0.2759 \| 0.5838 \| 0.7640 \| \| 0.0614 \| 5.0539 \| 2062 \| 0.5912 \| 0.2759 \| 0.5912 \| 0.7689 \| \| 0.0614 \| 5.0588 \| 2064 \| 0.5955 \| 0.2759 \| 0.5955 \| 0.7717 \| \| 0.0614 \| 5.0637 \| 2066 \| 0.6004 \| 0.2759 \| 0.6004 \| 0.7749 \| \| 0.0614 \| 5.0686 \| 2068 \| 0.5992 \| 0.0916 \| 0.5992 \| 0.7741 \| \| 0.0614 \| 5.0735 \| 2070 \| 0.5929 \| 0.1558 \| 0.5929 \| 0.7700 \| \| 0.0614 \| 5.0784 \| 2072 \| 0.5890 \| 0.1558 \| 0.5890 \| 0.7675 \| \| 0.0614 \| 5.0833 \| 2074 \| 0.5808 \| 0.1141 \| 0.5808 \| 0.7621 \| \| 0.0614 \| 5.0882 \| 2076 \| 0.5731 \| 0.1141 \| 0.5731 \| 0.7570 \| \| 0.0614 \| 5.0931 \| 2078 \| 0.5785 \| 0.1141 \| 0.5785 \| 0.7606 \| \| 0.0614 \| 5.0980 \| 2080 \| 0.5892 \| 0.1141 \| 0.5892 \| 0.7676 \| \| 0.0614 \| 5.1029 \| 2082 \| 0.5970 \| 0.1141 \| 0.5970 \| 0.7727 \| \| 0.0614 \| 5.1078 \| 2084 \| 0.6177 \| 0.1793 \| 0.6177 \| 0.7859 \| \| 0.0614 \| 5.1127 \| 2086 \| 0.6396 \| 0.2186 \| 0.6396 \| 0.7998 \| \| 0.0614 \| 5.1176 \| 2088 \| 0.6584 \| 0.2186 \| 0.6584 \| 0.8114 \| \| 0.0614 \| 5.1225 \| 2090 \| 0.6824 \| 0.2186 \| 0.6824 \| 0.8261 \| \| 0.0614 \| 5.1275 \| 2092 \| 0.6983 \| 0.2186 \| 0.6983 \| 0.8357 \| \| 0.0614 \| 5.1324 \| 2094 \| 0.7067 \| 0.2186 \| 0.7067 \| 0.8407 \| \| 0.0614 \| 5.1373 \| 2096 \| 0.7107 \| 0.2186 \| 0.7107 \| 0.8430 \| \| 0.0614 \| 5.1422 \| 2098 \| 0.6967 \| 0.2186 \| 0.6967 \| 0.8347 \| \| 0.0614 \| 5.1471 \| 2100 \| 0.6927 \| 0.2186 \| 0.6927 \| 0.8323 \| \| 0.0614 \| 5.1520 \| 2102 \| 0.6830 \| 0.2186 \| 0.6830 \| 0.8265 \| \| 0.0614 \| 5.1569 \| 2104 \| 0.6678 \| 0.2186 \| 0.6678 \| 0.8172 \| \| 0.0614 \| 5.1618 \| 2106 \| 0.6531 \| 0.2186 \| 0.6531 \| 0.8081 \| \| 0.0614 \| 5.1667 \| 2108 \| 0.6331 \| 0.2186 \| 0.6331 \| 0.7957 \| \| 0.0614 \| 5.1716 \| 2110 \| 0.6273 \| 0.2186 \| 0.6273 \| 0.7920 \| \| 0.0614 \| 5.1765 \| 2112 \| 0.6299 \| 0.2186 \| 0.6299 \| 0.7936 \| \| 0.0614 \| 5.1814 \| 2114 \| 0.6357 \| 0.2186 \| 0.6357 \| 0.7973 \| \| 0.0614 \| 5.1863 \| 2116 \| 0.6402 \| 0.2186 \| 0.6402 \| 0.8001 \| \| 0.0614 \| 5.1912 \| 2118 \| 0.6259 \| 0.2186 \| 0.6259 \| 0.7912 \| \| 0.0614 \| 5.1961 \| 2120 \| 0.6253 \| 0.2186 \| 0.6253 \| 0.7908 \| \| 0.0614 \| 5.2010 \| 2122 \| 0.6312 \| 0.2588 \| 0.6312 \| 0.7945 \| \| 0.0614 \| 5.2059 \| 2124 \| 0.6262 \| 0.2588 \| 0.6262 \| 0.7913 \| \| 0.0614 \| 5.2108 \| 2126 \| 0.6246 \| 0.2588 \| 0.6246 \| 0.7903 \| \| 0.0614 \| 5.2157 \| 2128 \| 0.6368 \| 0.2588 \| 0.6368 \| 0.7980 \| \| 0.0614 \| 5.2206 \| 2130 \| 0.6516 \| 0.2588 \| 0.6516 \| 0.8072 \| \| 0.0614 \| 5.2255 \| 2132 \| 0.6769 \| 0.2588 \| 0.6769 \| 0.8227 \| \| 0.0614 \| 5.2304 \| 2134 \| 0.6758 \| 0.2588 \| 0.6758 \| 0.8221 \| \| 0.0614 \| 5.2353 \| 2136 \| 0.6577 \| 0.2588 \| 0.6577 \| 0.8110 \| \| 0.0614 \| 5.2402 \| 2138 \| 0.6295 \| 0.2186 \| 0.6295 \| 0.7934 \| \| 0.0614 \| 5.2451 \| 2140 \| 0.6117 \| 0.2186 \| 0.6117 \| 0.7821 \| \| 0.0614 \| 5.25 \| 2142 \| 0.6120 \| 0.2186 \| 0.6120 \| 0.7823 \| \| 0.0614 \| 5.2549 \| 2144 \| 0.6369 \| 0.2588 \| 0.6369 \| 0.7980 \| \| 0.0614 \| 5.2598 \| 2146 \| 0.6788 \| 0.2588 \| 0.6788 \| 0.8239 \| \| 0.0614 \| 5.2647 \| 2148 \| 0.7103 \| 0.3636 \| 0.7103 \| 0.8428 \| \| 0.0614 \| 5.2696 \| 2150 \| 0.7214 \| 0.3636 \| 0.7214 \| 0.8494 \| \| 0.0614 \| 5.2745 \| 2152 \| 0.6943 \| 0.3636 \| 0.6943 \| 0.8332 \| \| 0.0614 \| 5.2794 \| 2154 \| 0.6540 \| 0.2588 \| 0.6540 \| 0.8087 \| \| 0.0614 \| 5.2843 \| 2156 \| 0.6182 \| 0.2588 \| 0.6182 \| 0.7863 \| \| 0.0614 \| 5.2892 \| 2158 \| 0.5937 \| 0.2186 \| 0.5937 \| 0.7705 \| \| 0.0614 \| 5.2941 \| 2160 \| 0.5796 \| 0.2186 \| 0.5796 \| 0.7613 \| \| 0.0614 \| 5.2990 \| 2162 \| 0.5847 \| 0.2186 \| 0.5847 \| 0.7646 \| \| 0.0614 \| 5.3039 \| 2164 \| 0.6082 \| 0.2186 \| 0.6082 \| 0.7799 \| \| 0.0614 \| 5.3088 \| 2166 \| 0.6444 \| 0.2186 \| 0.6444 \| 0.8027 \| \| 0.0614 \| 5.3137 \| 2168 \| 0.6782 \| 0.3255 \| 0.6782 \| 0.8235 \| \| 0.0614 \| 5.3186 \| 2170 \| 0.6920 \| 0.3255 \| 0.6920 \| 0.8319 \| \| 0.0614 \| 5.3235 \| 2172 \| 0.7042 \| 0.3255 \| 0.7042 \| 0.8392 \| \| 0.0614 \| 5.3284 \| 2174 \| 0.7288 \| 0.3255 \| 0.7288 \| 0.8537 \| \| 0.0614 \| 5.3333 \| 2176 \| 0.7344 \| 0.3255 \| 0.7344 \| 0.8570 \| \| 0.0614 \| 5.3382 \| 2178 \| 0.7464 \| 0.3255 \| 0.7464 \| 0.8640 \| \| 0.0614 \| 5.3431 \| 2180 \| 0.7523 \| 0.3255 \| 0.7523 \| 0.8673 \| \| 0.0614 \| 5.3480 \| 2182 \| 0.7357 \| 0.3255 \| 0.7357 \| 0.8577 \| \| 0.0614 \| 5.3529 \| 2184 \| 0.7328 \| 0.3255 \| 0.7328 \| 0.8560 \| \| 0.0614 \| 5.3578 \| 2186 \| 0.7272 \| 0.2186 \| 0.7272 \| 0.8527 \| \| 0.0614 \| 5.3627 \| 2188 \| 0.7104 \| 0.2186 \| 0.7104 \| 0.8429 \| \| 0.0614 \| 5.3676 \| 2190 \| 0.6987 \| 0.1793 \| 0.6987 \| 0.8359 \| \| 0.0614 \| 5.3725 \| 2192 \| 0.6896 \| 0.1793 \| 0.6896 \| 0.8304 \| \| 0.0614 \| 5.3775 \| 2194 \| 0.6858 \| 0.1793 \| 0.6858 \| 0.8282 \| \| 0.0614 \| 5.3824 \| 2196 \| 0.6823 \| 0.1793 \| 0.6823 \| 0.8260 \| \| 0.0614 \| 5.3873 \| 2198 \| 0.6700 \| 0.1793 \| 0.6700 \| 0.8185 \| \| 0.0614 \| 5.3922 \| 2200 \| 0.6680 \| 0.1793 \| 0.6680 \| 0.8173 \| \| 0.0614 \| 5.3971 \| 2202 \| 0.6586 \| 0.1793 \| 0.6586 \| 0.8115 \| \| 0.0614 \| 5.4020 \| 2204 \| 0.6395 \| 0.1793 \| 0.6395 \| 0.7997 \| \| 0.0614 \| 5.4069 \| 2206 \| 0.6233 \| 0.1793 \| 0.6233 \| 0.7895 \| \| 0.0614 \| 5.4118 \| 2208 \| 0.6176 \| 0.1793 \| 0.6176 \| 0.7859 \| \| 0.0614 \| 5.4167 \| 2210 \| 0.6240 \| 0.1793 \| 0.6240 \| 0.7899 \| \| 0.0614 \| 5.4216 \| 2212 \| 0.6289 \| 0.1793 \| 0.6289 \| 0.7931 \| \| 0.0614 \| 5.4265 \| 2214 \| 0.6382 \| 0.2186 \| 0.6382 \| 0.7989 \| \| 0.0614 \| 5.4314 \| 2216 \| 0.6462 \| 0.2186 \| 0.6462 \| 0.8039 \| \| 0.0614 \| 5.4363 \| 2218 \| 0.6551 \| 0.2186 \| 0.6551 \| 0.8094 \| \| 0.0614 \| 5.4412 \| 2220 \| 0.6627 \| 0.2186 \| 0.6627 \| 0.8141 \| \| 0.0614 \| 5.4461 \| 2222 \| 0.6976 \| 0.2186 \| 0.6976 \| 0.8352 \| \| 0.0614 \| 5.4510 \| 2224 \| 0.7623 \| 0.3636 \| 0.7623 \| 0.8731 \| \| 0.0614 \| 5.4559 \| 2226 \| 0.7952 \| 0.3687 \| 0.7952 \| 0.8917 \| \| 0.0614 \| 5.4608 \| 2228 \| 0.7802 \| 0.3255 \| 0.7802 \| 0.8833 \| \| 0.0614 \| 5.4657 \| 2230 \| 0.7324 \| 0.2186 \| 0.7324 \| 0.8558 \| \| 0.0614 \| 5.4706 \| 2232 \| 0.6900 \| 0.2186 \| 0.6900 \| 0.8307 \| \| 0.0614 \| 5.4755 \| 2234 \| 0.6697 \| 0.2186 \| 0.6697 \| 0.8184 \| \| 0.0614 \| 5.4804 \| 2236 \| 0.6738 \| 0.2186 \| 0.6738 \| 0.8209 \| \| 0.0614 \| 5.4853 \| 2238 \| 0.6660 \| 0.2186 \| 0.6660 \| 0.8161 \| \| 0.0614 \| 5.4902 \| 2240 \| 0.6609 \| 0.2186 \| 0.6609 \| 0.8130 \| \| 0.0614 \| 5.4951 \| 2242 \| 0.6573 \| 0.2186 \| 0.6573 \| 0.8107 \| \| 0.0614 \| 5.5 \| 2244 \| 0.6559 \| 0.2186 \| 0.6559 \| 0.8099 \| \| 0.0614 \| 5.5049 \| 2246 \| 0.6564 \| 0.2186 \| 0.6564 \| 0.8102 \| \| 0.0614 \| 5.5098 \| 2248 \| 0.6626 \| 0.2186 \| 0.6626 \| 0.8140 \| \| 0.0614 \| 5.5147 \| 2250 \| 0.6437 \| 0.2186 \| 0.6437 \| 0.8023 \| \| 0.0614 \| 5.5196 \| 2252 \| 0.6196 \| 0.2186 \| 0.6196 \| 0.7871 \| \| 0.0614 \| 5.5245 \| 2254 \| 0.6034 \| 0.3724 \| 0.6034 \| 0.7768 \| \| 0.0614 \| 5.5294 \| 2256 \| 0.6037 \| 0.3724 \| 0.6037 \| 0.7770 \| \| 0.0614 \| 5.5343 \| 2258 \| 0.6203 \| 0.2186 \| 0.6203 \| 0.7876 \| \| 0.0614 \| 5.5392 \| 2260 \| 0.6469 \| 0.2186 \| 0.6469 \| 0.8043 \| \| 0.0614 \| 5.5441 \| 2262 \| 0.6694 \| 0.2186 \| 0.6694 \| 0.8182 \| \| 0.0614 \| 5.5490 \| 2264 \| 0.6919 \| 0.2186 \| 0.6919 \| 0.8318 \| \| 0.0614 \| 5.5539 \| 2266 \| 0.6978 \| 0.2186 \| 0.6978 \| 0.8354 \| \| 0.0614 \| 5.5588 \| 2268 \| 0.6986 \| 0.2186 \| 0.6986 \| 0.8358 \| \| 0.0614 \| 5.5637 \| 2270 \| 0.6926 \| 0.2186 \| 0.6926 \| 0.8323 \| \| 0.0614 \| 5.5686 \| 2272 \| 0.6569 \| 0.2186 \| 0.6569 \| 0.8105 \| \| 0.0614 \| 5.5735 \| 2274 \| 0.6151 \| 0.2186 \| 0.6151 \| 0.7843 \| \| 0.0614 \| 5.5784 \| 2276 \| 0.5745 \| 0.3318 \| 0.5745 \| 0.7580 \| \| 0.0614 \| 5.5833 \| 2278 \| 0.5459 \| 0.3467 \| 0.5459 \| 0.7388 \| \| 0.0614 \| 5.5882 \| 2280 \| 0.5401 \| 0.3467 \| 0.5401 \| 0.7349 \| \| 0.0614 \| 5.5931 \| 2282 \| 0.5416 \| 0.3467 \| 0.5416 \| 0.7359 \| \| 0.0614 \| 5.5980 \| 2284 \| 0.5397 \| 0.3467 \| 0.5397 \| 0.7347 \| \| 0.0614 \| 5.6029 \| 2286 \| 0.5499 \| 0.3865 \| 0.5499 \| 0.7416 \| \| 0.0614 \| 5.6078 \| 2288 \| 0.5791 \| 0.3724 \| 0.5791 \| 0.7610 \| \| 0.0614 \| 5.6127 \| 2290 \| 0.6159 \| 0.3724 \| 0.6159 \| 0.7848 \| \| 0.0614 \| 5.6176 \| 2292 \| 0.6454 \| 0.3255 \| 0.6454 \| 0.8033 \| \| 0.0614 \| 5.6225 \| 2294 \| 0.6487 \| 0.3255 \| 0.6487 \| 0.8054 \| \| 0.0614 \| 5.6275 \| 2296 \| 0.6381 \| 0.2186 \| 0.6381 \| 0.7988 \| \| 0.0614 \| 5.6324 \| 2298 \| 0.6163 \| 0.2186 \| 0.6163 \| 0.7851 \| \| 0.0614 \| 5.6373 \| 2300 \| 0.5910 \| 0.3724 \| 0.5910 \| 0.7688 \| \| 0.0614 \| 5.6422 \| 2302 \| 0.5704 \| 0.3724 \| 0.5704 \| 0.7552 \| \| 0.0614 \| 5.6471 \| 2304 \| 0.5536 \| 0.4273 \| 0.5536 \| 0.7440 \| \| 0.0614 \| 5.6520 \| 2306 \| 0.5563 \| 0.3865 \| 0.5563 \| 0.7458 \| \| 0.0614 \| 5.6569 \| 2308 \| 0.5666 \| 0.3865 \| 0.5666 \| 0.7527 \| \| 0.0614 \| 5.6618 \| 2310 \| 0.5782 \| 0.4273 \| 0.5782 \| 0.7604 \| \| 0.0614 \| 5.6667 \| 2312 \| 0.5814 \| 0.4273 \| 0.5814 \| 0.7625 \| \| 0.0614 \| 5.6716 \| 2314 \| 0.5907 \| 0.4273 \| 0.5907 \| 0.7686 \| \| 0.0614 \| 5.6765 \| 2316 \| 0.5889 \| 0.3865 \| 0.5889 \| 0.7674 \| \| 0.0614 \| 5.6814 \| 2318 \| 0.5885 \| 0.4273 \| 0.5885 \| 0.7672 \| \| 0.0614 \| 5.6863 \| 2320 \| 0.5983 \| 0.3724 \| 0.5983 \| 0.7735 \| \| 0.0614 \| 5.6912 \| 2322 \| 0.6071 \| 0.3724 \| 0.6071 \| 0.7791 \| \| 0.0614 \| 5.6961 \| 2324 \| 0.6186 \| 0.3724 \| 0.6186 \| 0.7865 \| \| 0.0614 \| 5.7010 \| 2326 \| 0.6283 \| 0.3724 \| 0.6283 \| 0.7927 \| \| 0.0614 \| 5.7059 \| 2328 \| 0.6276 \| 0.3724 \| 0.6276 \| 0.7922 \| \| 0.0614 \| 5.7108 \| 2330 \| 0.6149 \| 0.3724 \| 0.6149 \| 0.7842 \| \| 0.0614 \| 5.7157 \| 2332 \| 0.6086 \| 0.3724 \| 0.6086 \| 0.7801 \| \| 0.0614 \| 5.7206 \| 2334 \| 0.6033 \| 0.3724 \| 0.6033 \| 0.7767 \| \| 0.0614 \| 5.7255 \| 2336 \| 0.5917 \| 0.3724 \| 0.5917 \| 0.7692 \| \| 0.0614 \| 5.7304 \| 2338 \| 0.5824 \| 0.3318 \| 0.5824 \| 0.7632 \| \| 0.0614 \| 5.7353 \| 2340 \| 0.5792 \| 0.3318 \| 0.5792 \| 0.7610 \| \| 0.0614 \| 5.7402 \| 2342 \| 0.5761 \| 0.3318 \| 0.5761 \| 0.7590 \| \| 0.0614 \| 5.7451 \| 2344 \| 0.5759 \| 0.2533 \| 0.5759 \| 0.7589 \| \| 0.0614 \| 5.75 \| 2346 \| 0.5735 \| 0.3318 \| 0.5735 \| 0.7573 \| \| 0.0614 \| 5.7549 \| 2348 \| 0.5756 \| 0.3724 \| 0.5756 \| 0.7587 \| \| 0.0614 \| 5.7598 \| 2350 \| 0.5897 \| 0.3724 \| 0.5897 \| 0.7679 \| \| 0.0614 \| 5.7647 \| 2352 \| 0.6112 \| 0.3724 \| 0.6112 \| 0.7818 \| \| 0.0614 \| 5.7696 \| 2354 \| 0.6272 \| 0.3724 \| 0.6272 \| 0.7919 \| \| 0.0614 \| 5.7745 \| 2356 \| 0.6303 \| 0.3724 \| 0.6303 \| 0.7939 \| \| 0.0614 \| 5.7794 \| 2358 \| 0.6222 \| 0.3724 \| 0.6222 \| 0.7888 \| \| 0.0614 \| 5.7843 \| 2360 \| 0.6133 \| 0.3724 \| 0.6133 \| 0.7831 \| \| 0.0614 \| 5.7892 \| 2362 \| 0.6069 \| 0.3724 \| 0.6069 \| 0.7790 \| \| 0.0614 \| 5.7941 \| 2364 \| 0.6120 \| 0.3724 \| 0.6120 \| 0.7823 \| \| 0.0614 \| 5.7990 \| 2366 \| 0.6179 \| 0.3724 \| 0.6179 \| 0.7860 \| \| 0.0614 \| 5.8039 \| 2368 \| 0.6327 \| 0.4140 \| 0.6327 \| 0.7954 \| \| 0.0614 \| 5.8088 \| 2370 \| 0.6337 \| 0.2588 \| 0.6337 \| 0.7960 \| \| 0.0614 \| 5.8137 \| 2372 \| 0.6242 \| 0.4140 \| 0.6242 \| 0.7901 \| \| 0.0614 \| 5.8186 \| 2374 \| 0.6180 \| 0.4140 \| 0.6180 \| 0.7861 \| \| 0.0614 \| 5.8235 \| 2376 \| 0.6003 \| 0.3724 \| 0.6003 \| 0.7748 \| \| 0.0614 \| 5.8284 \| 2378 \| 0.5881 \| 0.3724 \| 0.5881 \| 0.7669 \| \| 0.0614 \| 5.8333 \| 2380 \| 0.5781 \| 0.3724 \| 0.5781 \| 0.7603 \| \| 0.0614 \| 5.8382 \| 2382 \| 0.5773 \| 0.3724 \| 0.5773 \| 0.7598 \| \| 0.0614 \| 5.8431 \| 2384 \| 0.5765 \| 0.3865 \| 0.5765 \| 0.7593 \| \| 0.0614 \| 5.8480 \| 2386 \| 0.5628 \| 0.3865 \| 0.5628 \| 0.7502 \| \| 0.0614 \| 5.8529 \| 2388 \| 0.5582 \| 0.3865 \| 0.5582 \| 0.7471 \| \| 0.0614 \| 5.8578 \| 2390 \| 0.5647 \| 0.3865 \| 0.5647 \| 0.7514 \| \| 0.0614 \| 5.8627 \| 2392 \| 0.5633 \| 0.3865 \| 0.5633 \| 0.7505 \| \| 0.0614 \| 5.8676 \| 2394 \| 0.5591 \| 0.4400 \| 0.5591 \| 0.7477 \| \| 0.0614 \| 5.8725 \| 2396 \| 0.5588 \| 0.4400 \| 0.5588 \| 0.7475 \| \| 0.0614 \| 5.8775 \| 2398 \| 0.5587 \| 0.4400 \| 0.5587 \| 0.7475 \| \| 0.0614 \| 5.8824 \| 2400 \| 0.5598 \| 0.3318 \| 0.5598 \| 0.7482 \| \| 0.0614 \| 5.8873 \| 2402 \| 0.5681 \| 0.3724 \| 0.5681 \| 0.7537 \| \| 0.0614 \| 5.8922 \| 2404 \| 0.5759 \| 0.3724 \| 0.5759 \| 0.7589 \| \| 0.0614 \| 5.8971 \| 2406 \| 0.6007 \| 0.4140 \| 0.6007 \| 0.7750 \| \| 0.0614 \| 5.9020 \| 2408 \| 0.6132 \| 0.2588 \| 0.6132 \| 0.7831 \| \| 0.0614 \| 5.9069 \| 2410 \| 0.6169 \| 0.3636 \| 0.6169 \| 0.7854 \| \| 0.0614 \| 5.9118 \| 2412 \| 0.6200 \| 0.3636 \| 0.6200 \| 0.7874 \| \| 0.0614 \| 5.9167 \| 2414 \| 0.6188 \| 0.4661 \| 0.6188 \| 0.7866 \| \| 0.0614 \| 5.9216 \| 2416 \| 0.6097 \| 0.4661 \| 0.6097 \| 0.7808 \| \| 0.0614 \| 5.9265 \| 2418 \| 0.6117 \| 0.4661 \| 0.6117 \| 0.7821 \| \| 0.0614 \| 5.9314 \| 2420 \| 0.6331 \| 0.4661 \| 0.6331 \| 0.7957 \| \| 0.0614 \| 5.9363 \| 2422 \| 0.6584 \| 0.4661 \| 0.6584 \| 0.8114 \| \| 0.0614 \| 5.9412 \| 2424 \| 0.6692 \| 0.3255 \| 0.6692 \| 0.8180 \| \| 0.0614 \| 5.9461 \| 2426 \| 0.6618 \| 0.3255 \| 0.6618 \| 0.8135 \| \| 0.0614 \| 5.9510 \| 2428 \| 0.6336 \| 0.4661 \| 0.6336 \| 0.7960 \| \| 0.0614 \| 5.9559 \| 2430 \| 0.6206 \| 0.4661 \| 0.6206 \| 0.7878 \| \| 0.0614 \| 5.9608 \| 2432 \| 0.6073 \| 0.4661 \| 0.6073 \| 0.7793 \| \| 0.0614 \| 5.9657 \| 2434 \| 0.5952 \| 0.4661 \| 0.5952 \| 0.7715 \| \| 0.0614 \| 5.9706 \| 2436 \| 0.6036 \| 0.3255 \| 0.6036 \| 0.7769 \| \| 0.0614 \| 5.9755 \| 2438 \| 0.6183 \| 0.3636 \| 0.6183 \| 0.7863 \| \| 0.0614 \| 5.9804 \| 2440 \| 0.6499 \| 0.3636 \| 0.6499 \| 0.8062 \| \| 0.0614 \| 5.9853 \| 2442 \| 0.6780 \| 0.3636 \| 0.6780 \| 0.8234 \| \| 0.0614 \| 5.9902 \| 2444 \| 0.7000 \| 0.3636 \| 0.7000 \| 0.8367 \| \| 0.0614 \| 5.9951 \| 2446 \| 0.7083 \| 0.3636 \| 0.7083 \| 0.8416 \| \| 0.0614 \| 6.0 \| 2448 \| 0.7052 \| 0.3636 \| 0.7052 \| 0.8397 \| \| 0.0614 \| 6.0049 \| 2450 \| 0.7019 \| 0.3636 \| 0.7019 \| 0.8378 \| \| 0.0614 \| 6.0098 \| 2452 \| 0.6930 \| 0.3636 \| 0.6930 \| 0.8324 \| \| 0.0614 \| 6.0147 \| 2454 \| 0.6944 \| 0.3636 \| 0.6944 \| 0.8333 \| \| 0.0614 \| 6.0196 \| 2456 \| 0.7027 \| 0.3636 \| 0.7027 \| 0.8383 \| \| 0.0614 \| 6.0245 \| 2458 \| 0.6920 \| 0.3636 \| 0.6920 \| 0.8319 \| \| 0.0614 \| 6.0294 \| 2460 \| 0.6733 \| 0.2588 \| 0.6733 \| 0.8205 \| \| 0.0614 \| 6.0343 \| 2462 \| 0.6524 \| 0.2588 \| 0.6524 \| 0.8077 \| \| 0.0614 \| 6.0392 \| 2464 \| 0.6511 \| 0.2588 \| 0.6511 \| 0.8069 \| \| 0.0614 \| 6.0441 \| 2466 \| 0.6484 \| 0.2588 \| 0.6484 \| 0.8052 \| \| 0.0614 \| 6.0490 \| 2468 \| 0.6634 \| 0.3636 \| 0.6634 \| 0.8145 \| \| 0.0614 \| 6.0539 \| 2470 \| 0.6881 \| 0.3636 \| 0.6881 \| 0.8295 \| \| 0.0614 \| 6.0588 \| 2472 \| 0.6930 \| 0.3636 \| 0.6930 \| 0.8325 \| \| 0.0614 \| 6.0637 \| 2474 \| 0.6936 \| 0.3636 \| 0.6936 \| 0.8328 \| \| 0.0614 \| 6.0686 \| 2476 \| 0.6729 \| 0.3636 \| 0.6729 \| 0.8203 \| \| 0.0614 \| 6.0735 \| 2478 \| 0.6464 \| 0.3636 \| 0.6464 \| 0.8040 \| \| 0.0614 \| 6.0784 \| 2480 \| 0.6377 \| 0.3636 \| 0.6377 \| 0.7986 \| \| 0.0614 \| 6.0833 \| 2482 \| 0.6405 \| 0.3636 \| 0.6405 \| 0.8003 \| \| 0.0614 \| 6.0882 \| 2484 \| 0.6337 \| 0.3255 \| 0.6337 \| 0.7961 \| \| 0.0614 \| 6.0931 \| 2486 \| 0.6346 \| 0.3255 \| 0.6346 \| 0.7966 \| \| 0.0614 \| 6.0980 \| 2488 \| 0.6329 \| 0.3255 \| 0.6329 \| 0.7955 \| \| 0.0614 \| 6.1029 \| 2490 \| 0.6558 \| 0.3255 \| 0.6558 \| 0.8098 \| \| 0.0614 \| 6.1078 \| 2492 \| 0.6809 \| 0.3636 \| 0.6809 \| 0.8252 \| \| 0.0614 \| 6.1127 \| 2494 \| 0.7015 \| 0.3636 \| 0.7015 \| 0.8376 \| \| 0.0614 \| 6.1176 \| 2496 \| 0.6841 \| 0.3636 \| 0.6841 \| 0.8271 \| \| 0.0614 \| 6.1225 \| 2498 \| 0.6441 \| 0.3255 \| 0.6441 \| 0.8026 \| \| 0.0484 \| 6.1275 \| 2500 \| 0.6127 \| 0.2186 \| 0.6127 \| 0.7828 \| \| 0.0484 \| 6.1324 \| 2502 \| 0.6060 \| 0.2186 \| 0.6060 \| 0.7785 \| \| 0.0484 \| 6.1373 \| 2504 \| 0.6087 \| 0.2186 \| 0.6087 \| 0.7802 \| \| 0.0484 \| 6.1422 \| 2506 \| 0.6224 \| 0.2588 \| 0.6224 \| 0.7889 \| \| 0.0484 \| 6.1471 \| 2508 \| 0.6370 \| 0.2588 \| 0.6370 \| 0.7981 \| \| 0.0484 \| 6.1520 \| 2510 \| 0.6629 \| 0.3636 \| 0.6629 \| 0.8142 \| \| 0.0484 \| 6.1569 \| 2512 \| 0.6811 \| 0.3636 \| 0.6811 \| 0.8253 \| \| 0.0484 \| 6.1618 \| 2514 \| 0.7128 \| 0.3636 \| 0.7128 \| 0.8442 \| \| 0.0484 \| 6.1667 \| 2516 \| 0.7161 \| 0.3636 \| 0.7161 \| 0.8462 \| \| 0.0484 \| 6.1716 \| 2518 \| 0.6999 \| 0.3636 \| 0.6999 \| 0.8366 \| \| 0.0484 \| 6.1765 \| 2520 \| 0.6761 \| 0.3636 \| 0.6761 \| 0.8223 \| \| 0.0484 \| 6.1814 \| 2522 \| 0.6490 \| 0.2588 \| 0.6490 \| 0.8056 \| \| 0.0484 \| 6.1863 \| 2524 \| 0.6282 \| 0.2588 \| 0.6282 \| 0.7926 \| \| 0.0484 \| 6.1912 \| 2526 \| 0.6187 \| 0.2588 \| 0.6187 \| 0.7866 \| \| 0.0484 \| 6.1961 \| 2528 \| 0.6129 \| 0.2588 \| 0.6129 \| 0.7829 \| \| 0.0484 \| 6.2010 \| 2530 \| 0.6103 \| 0.2588 \| 0.6103 \| 0.7812 \| \| 0.0484 \| 6.2059 \| 2532 \| 0.6198 \| 0.2588 \| 0.6198 \| 0.7873 \| \| 0.0484 \| 6.2108 \| 2534 \| 0.6412 \| 0.2588 \| 0.6412 \| 0.8008 \| \| 0.0484 \| 6.2157 \| 2536 \| 0.6727 \| 0.3636 \| 0.6727 \| 0.8202 \| \| 0.0484 \| 6.2206 \| 2538 \| 0.6996 \| 0.3636 \| 0.6996 \| 0.8364 \| \| 0.0484 \| 6.2255 \| 2540 \| 0.7080 \| 0.3636 \| 0.7080 \| 0.8414 \| \| 0.0484 \| 6.2304 \| 2542 \| 0.7206 \| 0.3636 \| 0.7206 \| 0.8489 \| \| 0.0484 \| 6.2353 \| 2544 \| 0.7063 \| 0.3636 \| 0.7063 \| 0.8404 \| \| 0.0484 \| 6.2402 \| 2546 \| 0.6860 \| 0.3636 \| 0.6860 \| 0.8283 \| \| 0.0484 \| 6.2451 \| 2548 \| 0.6647 \| 0.3255 \| 0.6647 \| 0.8153 \| \| 0.0484 \| 6.25 \| 2550 \| 0.6601 \| 0.3255 \| 0.6601 \| 0.8125 \| \| 0.0484 \| 6.2549 \| 2552 \| 0.6519 \| 0.3255 \| 0.6519 \| 0.8074 \| \| 0.0484 \| 6.2598 \| 2554 \| 0.6432 \| 0.3724 \| 0.6432 \| 0.8020 \| \| 0.0484 \| 6.2647 \| 2556 \| 0.6465 \| 0.3724 \| 0.6465 \| 0.8040 \| \| 0.0484 \| 6.2696 \| 2558 \| 0.6502 \| 0.3318 \| 0.6502 \| 0.8064 \| \| 0.0484 \| 6.2745 \| 2560 \| 0.6516 \| 0.2533 \| 0.6516 \| 0.8072 \| \| 0.0484 \| 6.2794 \| 2562 \| 0.6555 \| 0.2154 \| 0.6555 \| 0.8096 \| \| 0.0484 \| 6.2843 \| 2564 \| 0.6617 \| 0.2154 \| 0.6617 \| 0.8134 \| \| 0.0484 \| 6.2892 \| 2566 \| 0.6590 \| 0.2154 \| 0.6590 \| 0.8118 \| \| 0.0484 \| 6.2941 \| 2568 \| 0.6497 \| 0.2154 \| 0.6497 \| 0.8061 \| \| 0.0484 \| 6.2990 \| 2570 \| 0.6417 \| 0.2533 \| 0.6417 \| 0.8011 \| \| 0.0484 \| 6.3039 \| 2572 \| 0.6340 \| 0.3318 \| 0.6340 \| 0.7962 \| \| 0.0484 \| 6.3088 \| 2574 \| 0.6232 \| 0.3724 \| 0.6232 \| 0.7895 \| \| 0.0484 \| 6.3137 \| 2576 \| 0.6147 \| 0.3724 \| 0.6147 \| 0.7841 \| \| 0.0484 \| 6.3186 \| 2578 \| 0.6080 \| 0.3724 \| 0.6080 \| 0.7797 \| \| 0.0484 \| 6.3235 \| 2580 \| 0.6087 \| 0.3724 \| 0.6087 \| 0.7802 \| \| 0.0484 \| 6.3284 \| 2582 \| 0.6127 \| 0.3724 \| 0.6127 \| 0.7827 \| \| 0.0484 \| 6.3333 \| 2584 \| 0.6291 \| 0.2186 \| 0.6291 \| 0.7932 \| \| 0.0484 \| 6.3382 \| 2586 \| 0.6663 \| 0.3255 \| 0.6663 \| 0.8163 \| \| 0.0484 \| 6.3431 \| 2588 \| 0.7074 \| 0.3255 \| 0.7074 \| 0.8411 \| \| 0.0484 \| 6.3480 \| 2590 \| 0.7460 \| 0.3636 \| 0.7460 \| 0.8637 \| \| 0.0484 \| 6.3529 \| 2592 \| 0.7678 \| 0.3636 \| 0.7678 \| 0.8762 \| \| 0.0484 \| 6.3578 \| 2594 \| 0.7978 \| 0.2948 \| 0.7978 \| 0.8932 \| \| 0.0484 \| 6.3627 \| 2596 \| 0.8107 \| 0.2948 \| 0.8107 \| 0.9004 \| \| 0.0484 \| 6.3676 \| 2598 \| 0.7841 \| 0.2948 \| 0.7841 \| 0.8855 \| \| 0.0484 \| 6.3725 \| 2600 \| 0.7430 \| 0.3636 \| 0.7430 \| 0.8620 \| \| 0.0484 \| 6.3775 \| 2602 \| 0.6871 \| 0.3255 \| 0.6871 \| 0.8289 \| \| 0.0484 \| 6.3824 \| 2604 \| 0.6371 \| 0.2186 \| 0.6371 \| 0.7982 \| \| 0.0484 \| 6.3873 \| 2606 \| 0.6100 \| 0.2186 \| 0.6100 \| 0.7810 \| \| 0.0484 \| 6.3922 \| 2608 \| 0.5906 \| 0.3724 \| 0.5906 \| 0.7685 \| \| 0.0484 \| 6.3971 \| 2610 \| 0.5793 \| 0.2794 \| 0.5793 \| 0.7611 \| \| 0.0484 \| 6.4020 \| 2612 \| 0.5736 \| 0.2794 \| 0.5736 \| 0.7573 \| \| 0.0484 \| 6.4069 \| 2614 \| 0.5737 \| 0.2794 \| 0.5737 \| 0.7574 \| \| 0.0484 \| 6.4118 \| 2616 \| 0.5758 \| 0.2184 \| 0.5758 \| 0.7588 \| \| 0.0484 \| 6.4167 \| 2618 \| 0.5824 \| 0.0916 \| 0.5824 \| 0.7631 \| \| 0.0484 \| 6.4216 \| 2620 \| 0.6002 \| 0.2186 \| 0.6002 \| 0.7747 \| \| 0.0484 \| 6.4265 \| 2622 \| 0.6291 \| 0.2588 \| 0.6291 \| 0.7931 \| \| 0.0484 \| 6.4314 \| 2624 \| 0.6543 \| 0.2588 \| 0.6543 \| 0.8089 \| \| 0.0484 \| 6.4363 \| 2626 \| 0.6780 \| 0.3636 \| 0.6780 \| 0.8234 \| \| 0.0484 \| 6.4412 \| 2628 \| 0.6863 \| 0.3636 \| 0.6863 \| 0.8284 \| \| 0.0484 \| 6.4461 \| 2630 \| 0.6714 \| 0.3636 \| 0.6714 \| 0.8194 \| \| 0.0484 \| 6.4510 \| 2632 \| 0.6420 \| 0.2588 \| 0.6420 \| 0.8012 \| \| 0.0484 \| 6.4559 \| 2634 \| 0.6160 \| 0.2186 \| 0.6160 \| 0.7848 \| \| 0.0484 \| 6.4608 \| 2636 \| 0.6051 \| 0.2186 \| 0.6051 \| 0.7779 \| \| 0.0484 \| 6.4657 \| 2638 \| 0.5968 \| 0.2186 \| 0.5968 \| 0.7725 \| \| 0.0484 \| 6.4706 \| 2640 \| 0.5931 \| 0.2186 \| 0.5931 \| 0.7701 \| \| 0.0484 \| 6.4755 \| 2642 \| 0.5914 \| 0.1793 \| 0.5914 \| 0.7690 \| \| 0.0484 \| 6.4804 \| 2644 \| 0.5892 \| 0.1793 \| 0.5892 \| 0.7676 \| \| 0.0484 \| 6.4853 \| 2646 \| 0.5866 \| 0.1793 \| 0.5866 \| 0.7659 \| \| 0.0484 \| 6.4902 \| 2648 \| 0.5893 \| 0.1793 \| 0.5893 \| 0.7677 \| \| 0.0484 \| 6.4951 \| 2650 \| 0.5929 \| 0.1793 \| 0.5929 \| 0.7700 \| \| 0.0484 \| 6.5 \| 2652 \| 0.5972 \| 0.1793 \| 0.5972 \| 0.7728 \| \| 0.0484 \| 6.5049 \| 2654 \| 0.6033 \| 0.1793 \| 0.6033 \| 0.7768 \| \| 0.0484 \| 6.5098 \| 2656 \| 0.6103 \| 0.1793 \| 0.6103 \| 0.7812 \| \| 0.0484 \| 6.5147 \| 2658 \| 0.6120 \| 0.1793 \| 0.6120 \| 0.7823 \| \| 0.0484 \| 6.5196 \| 2660 \| 0.6065 \| 0.3318 \| 0.6065 \| 0.7788 \| \| 0.0484 \| 6.5245 \| 2662 \| 0.6071 \| 0.3865 \| 0.6071 \| 0.7792 \| \| 0.0484 \| 6.5294 \| 2664 \| 0.6065 \| 0.3865 \| 0.6065 \| 0.7788 \| \| 0.0484 \| 6.5343 \| 2666 \| 0.6056 \| 0.3865 \| 0.6056 \| 0.7782 \| \| 0.0484 \| 6.5392 \| 2668 \| 0.6064 \| 0.3865 \| 0.6064 \| 0.7787 \| \| 0.0484 \| 6.5441 \| 2670 \| 0.6086 \| 0.3865 \| 0.6086 \| 0.7801 \| \| 0.0484 \| 6.5490 \| 2672 \| 0.6113 \| 0.3865 \| 0.6113 \| 0.7819 \| \| 0.0484 \| 6.5539 \| 2674 \| 0.6099 \| 0.3318 \| 0.6099 \| 0.7809 \| \| 0.0484 \| 6.5588 \| 2676 \| 0.6092 \| 0.3318 \| 0.6092 \| 0.7805 \| \| 0.0484 \| 6.5637 \| 2678 \| 0.6099 \| 0.3318 \| 0.6099 \| 0.7810 \| \| 0.0484 \| 6.5686 \| 2680 \| 0.6109 \| 0.1793 \| 0.6109 \| 0.7816 \| \| 0.0484 \| 6.5735 \| 2682 \| 0.6145 \| 0.2186 \| 0.6145 \| 0.7839 \| \| 0.0484 \| 6.5784 \| 2684 \| 0.6220 \| 0.2588 \| 0.6220 \| 0.7887 \| \| 0.0484 \| 6.5833 \| 2686 \| 0.6172 \| 0.2588 \| 0.6172 \| 0.7856 \| \| 0.0484 \| 6.5882 \| 2688 \| 0.6112 \| 0.2588 \| 0.6112 \| 0.7818 \| \| 0.0484 \| 6.5931 \| 2690 \| 0.6046 \| 0.2588 \| 0.6046 \| 0.7776 \| \| 0.0484 \| 6.5980 \| 2692 \| 0.5943 \| 0.2588 \| 0.5943 \| 0.7709 \| \| 0.0484 \| 6.6029 \| 2694 \| 0.5916 \| 0.2588 \| 0.5916 \| 0.7692 \| \| 0.0484 \| 6.6078 \| 2696 \| 0.5931 \| 0.3636 \| 0.5931 \| 0.7701 \| \| 0.0484 \| 6.6127 \| 2698 \| 0.5978 \| 0.3255 \| 0.5978 \| 0.7732 \| \| 0.0484 \| 6.6176 \| 2700 \| 0.6067 \| 0.3255 \| 0.6067 \| 0.7789 \| \| 0.0484 \| 6.6225 \| 2702 \| 0.6134 \| 0.3255 \| 0.6134 \| 0.7832 \| \| 0.0484 \| 6.6275 \| 2704 \| 0.6143 \| 0.3255 \| 0.6143 \| 0.7838 \| \| 0.0484 \| 6.6324 \| 2706 \| 0.6157 \| 0.2186 \| 0.6157 \| 0.7847 \| \| 0.0484 \| 6.6373 \| 2708 \| 0.6146 \| 0.3724 \| 0.6146 \| 0.7840 \| \| 0.0484 \| 6.6422 \| 2710 \| 0.6124 \| 0.3724 \| 0.6124 \| 0.7826 \| \| 0.0484 \| 6.6471 \| 2712 \| 0.6135 \| 0.3724 \| 0.6135 \| 0.7832 \| \| 0.0484 \| 6.6520 \| 2714 \| 0.6120 \| 0.3724 \| 0.6120 \| 0.7823 \| \| 0.0484 \| 6.6569 \| 2716 \| 0.6095 \| 0.3318 \| 0.6095 \| 0.7807 \| \| 0.0484 \| 6.6618 \| 2718 \| 0.6086 \| 0.3865 \| 0.6086 \| 0.7801 \| \| 0.0484 \| 6.6667 \| 2720 \| 0.6084 \| 0.3077 \| 0.6084 \| 0.7800 \| \| 0.0484 \| 6.6716 \| 2722 \| 0.6088 \| 0.3077 \| 0.6088 \| 0.7802 \| \| 0.0484 \| 6.6765 \| 2724 \| 0.6084 \| 0.3077 \| 0.6084 \| 0.7800 \| \| 0.0484 \| 6.6814 \| 2726 \| 0.6100 \| 0.3865 \| 0.6100 \| 0.7810 \| \| 0.0484 \| 6.6863 \| 2728 \| 0.6099 \| 0.3865 \| 0.6099 \| 0.7810 \| \| 0.0484 \| 6.6912 \| 2730 \| 0.6075 \| 0.3865 \| 0.6075 \| 0.7795 \| \| 0.0484 \| 6.6961 \| 2732 \| 0.6094 \| 0.3865 \| 0.6094 \| 0.7806 \| \| 0.0484 \| 6.7010 \| 2734 \| 0.6067 \| 0.3865 \| 0.6067 \| 0.7789 \| \| 0.0484 \| 6.7059 \| 2736 \| 0.6002 \| 0.3865 \| 0.6002 \| 0.7747 \| \| 0.0484 \| 6.7108 \| 2738 \| 0.5945 \| 0.3865 \| 0.5945 \| 0.7711 \| \| 0.0484 \| 6.7157 \| 2740 \| 0.5854 \| 0.3865 \| 0.5854 \| 0.7651 \| \| 0.0484 \| 6.7206 \| 2742 \| 0.5839 \| 0.3724 \| 0.5839 \| 0.7641 \| \| 0.0484 \| 6.7255 \| 2744 \| 0.5930 \| 0.3724 \| 0.5930 \| 0.7701 \| \| 0.0484 \| 6.7304 \| 2746 \| 0.6021 \| 0.3724 \| 0.6021 \| 0.7760 \| \| 0.0484 \| 6.7353 \| 2748 \| 0.6130 \| 0.2186 \| 0.6130 \| 0.7830 \| \| 0.0484 \| 6.7402 \| 2750 \| 0.6345 \| 0.2588 \| 0.6345 \| 0.7966 \| \| 0.0484 \| 6.7451 \| 2752 \| 0.6582 \| 0.3636 \| 0.6582 \| 0.8113 \| \| 0.0484 \| 6.75 \| 2754 \| 0.6611 \| 0.3636 \| 0.6611 \| 0.8131 \| \| 0.0484 \| 6.7549 \| 2756 \| 0.6475 \| 0.3636 \| 0.6475 \| 0.8047 \| \| 0.0484 \| 6.7598 \| 2758 \| 0.6347 \| 0.2588 \| 0.6347 \| 0.7967 \| \| 0.0484 \| 6.7647 \| 2760 \| 0.6102 \| 0.3724 \| 0.6102 \| 0.7812 \| \| 0.0484 \| 6.7696 \| 2762 \| 0.5882 \| 0.3724 \| 0.5882 \| 0.7670 \| \| 0.0484 \| 6.7745 \| 2764 \| 0.5803 \| 0.3724 \| 0.5803 \| 0.7618 \| \| 0.0484 \| 6.7794 \| 2766 \| 0.5746 \| 0.3724 \| 0.5746 \| 0.7580 \| \| 0.0484 \| 6.7843 \| 2768 \| 0.5763 \| 0.3724 \| 0.5763 \| 0.7591 \| \| 0.0484 \| 6.7892 \| 2770 \| 0.5820 \| 0.3724 \| 0.5820 \| 0.7629 \| \| 0.0484 \| 6.7941 \| 2772 \| 0.5859 \| 0.3724 \| 0.5859 \| 0.7655 \| \| 0.0484 \| 6.7990 \| 2774 \| 0.5917 \| 0.3724 \| 0.5917 \| 0.7692 \| \| 0.0484 \| 6.8039 \| 2776 \| 0.6052 \| 0.3724 \| 0.6052 \| 0.7779 \| \| 0.0484 \| 6.8088 \| 2778 \| 0.6158 \| 0.3724 \| 0.6158 \| 0.7847 \| \| 0.0484 \| 6.8137 \| 2780 \| 0.6289 \| 0.3724 \| 0.6289 \| 0.7930 \| \| 0.0484 \| 6.8186 \| 2782 \| 0.6449 \| 0.3724 \| 0.6449 \| 0.8031 \| \| 0.0484 \| 6.8235 \| 2784 \| 0.6509 \| 0.4140 \| 0.6509 \| 0.8068 \| \| 0.0484 \| 6.8284 \| 2786 \| 0.6423 \| 0.4140 \| 0.6423 \| 0.8015 \| \| 0.0484 \| 6.8333 \| 2788 \| 0.6323 \| 0.4140 \| 0.6323 \| 0.7952 \| \| 0.0484 \| 6.8382 \| 2790 \| 0.6192 \| 0.3724 \| 0.6192 \| 0.7869 \| \| 0.0484 \| 6.8431 \| 2792 \| 0.6140 \| 0.3724 \| 0.6140 \| 0.7836 \| \| 0.0484 \| 6.8480 \| 2794 \| 0.6046 \| 0.3724 \| 0.6046 \| 0.7776 \| \| 0.0484 \| 6.8529 \| 2796 \| 0.5935 \| 0.4273 \| 0.5935 \| 0.7704 \| \| 0.0484 \| 6.8578 \| 2798 \| 0.5860 \| 0.3226 \| 0.5860 \| 0.7655 \| \| 0.0484 \| 6.8627 \| 2800 \| 0.5835 \| 0.4273 \| 0.5835 \| 0.7639 \| \| 0.0484 \| 6.8676 \| 2802 \| 0.5858 \| 0.3724 \| 0.5858 \| 0.7654 \| \| 0.0484 \| 6.8725 \| 2804 \| 0.5940 \| 0.3724 \| 0.5940 \| 0.7707 \| \| 0.0484 \| 6.8775 \| 2806 \| 0.6006 \| 0.4140 \| 0.6006 \| 0.7750 \| \| 0.0484 \| 6.8824 \| 2808 \| 0.5999 \| 0.3724 \| 0.5999 \| 0.7746 \| \| 0.0484 \| 6.8873 \| 2810 \| 0.6014 \| 0.3724 \| 0.6014 \| 0.7755 \| \| 0.0484 \| 6.8922 \| 2812 \| 0.6036 \| 0.3724 \| 0.6036 \| 0.7769 \| \| 0.0484 \| 6.8971 \| 2814 \| 0.6012 \| 0.3724 \| 0.6012 \| 0.7754 \| \| 0.0484 \| 6.9020 \| 2816 \| 0.5993 \| 0.3724 \| 0.5993 \| 0.7742 \| \| 0.0484 \| 6.9069 \| 2818 \| 0.6011 \| 0.3724 \| 0.6011 \| 0.7753 \| \| 0.0484 \| 6.9118 \| 2820 \| 0.6006 \| 0.3724 \| 0.6006 \| 0.7750 \| \| 0.0484 \| 6.9167 \| 2822 \| 0.5976 \| 0.3318 \| 0.5976 \| 0.7731 \| \| 0.0484 \| 6.9216 \| 2824 \| 0.5950 \| 0.2921 \| 0.5950 \| 0.7713 \| \| 0.0484 \| 6.9265 \| 2826 \| 0.5923 \| 0.3077 \| 0.5923 \| 0.7696 \| \| 0.0484 \| 6.9314 \| 2828 \| 0.5876 \| 0.1962 \| 0.5876 \| 0.7666 \| \| 0.0484 \| 6.9363 \| 2830 \| 0.5836 \| 0.2373 \| 0.5836 \| 0.7640 \| \| 0.0484 \| 6.9412 \| 2832 \| 0.5781 \| 0.2373 \| 0.5781 \| 0.7604 \| \| 0.0484 \| 6.9461 \| 2834 \| 0.5741 \| 0.2794 \| 0.5741 \| 0.7577 \| \| 0.0484 \| 6.9510 \| 2836 \| 0.5697 \| 0.3226 \| 0.5697 \| 0.7548 \| \| 0.0484 \| 6.9559 \| 2838 \| 0.5672 \| 0.2613 \| 0.5672 \| 0.7531 \| \| 0.0484 \| 6.9608 \| 2840 \| 0.5661 \| 0.2613 \| 0.5661 \| 0.7524 \| \| 0.0484 \| 6.9657 \| 2842 \| 0.5678 \| 0.3724 \| 0.5678 \| 0.7535 \| \| 0.0484 \| 6.9706 \| 2844 \| 0.5757 \| 0.3724 \| 0.5757 \| 0.7587 \| \| 0.0484 \| 6.9755 \| 2846 \| 0.5816 \| 0.3724 \| 0.5816 \| 0.7626 \| \| 0.0484 \| 6.9804 \| 2848 \| 0.5889 \| 0.3724 \| 0.5889 \| 0.7674 \| \| 0.0484 \| 6.9853 \| 2850 \| 0.5996 \| 0.3724 \| 0.5996 \| 0.7744 \| \| 0.0484 \| 6.9902 \| 2852 \| 0.6045 \| 0.3724 \| 0.6045 \| 0.7775 \| \| 0.0484 \| 6.9951 \| 2854 \| 0.6024 \| 0.3724 \| 0.6024 \| 0.7762 \| \| 0.0484 \| 7.0 \| 2856 \| 0.5987 \| 0.3724 \| 0.5987 \| 0.7737 \| \| 0.0484 \| 7.0049 \| 2858 \| 0.5955 \| 0.3724 \| 0.5955 \| 0.7717 \| \| 0.0484 \| 7.0098 \| 2860 \| 0.5973 \| 0.3724 \| 0.5973 \| 0.7729 \| \| 0.0484 \| 7.0147 \| 2862 \| 0.6018 \| 0.3724 \| 0.6018 \| 0.7758 \| \| 0.0484 \| 7.0196 \| 2864 \| 0.6024 \| 0.3724 \| 0.6024 \| 0.7761 \| \| 0.0484 \| 7.0245 \| 2866 \| 0.5999 \| 0.3724 \| 0.5999 \| 0.7745 \| \| 0.0484 \| 7.0294 \| 2868 \| 0.5949 \| 0.3724 \| 0.5949 \| 0.7713 \| \| 0.0484 \| 7.0343 \| 2870 \| 0.5896 \| 0.3724 \| 0.5896 \| 0.7678 \| \| 0.0484 \| 7.0392 \| 2872 \| 0.5883 \| 0.3724 \| 0.5883 \| 0.7670 \| \| 0.0484 \| 7.0441 \| 2874 \| 0.5895 \| 0.3724 \| 0.5895 \| 0.7678 \| \| 0.0484 \| 7.0490 \| 2876 \| 0.5927 \| 0.3724 \| 0.5927 \| 0.7699 \| \| 0.0484 \| 7.0539 \| 2878 \| 0.5880 \| 0.2186 \| 0.5880 \| 0.7668 \| \| 0.0484 \| 7.0588 \| 2880 \| 0.5826 \| 0.2186 \| 0.5826 \| 0.7633 \| \| 0.0484 \| 7.0637 \| 2882 \| 0.5830 \| 0.2588 \| 0.5830 \| 0.7636 \| \| 0.0484 \| 7.0686 \| 2884 \| 0.5818 \| 0.2588 \| 0.5818 \| 0.7627 \| \| 0.0484 \| 7.0735 \| 2886 \| 0.5761 \| 0.2588 \| 0.5761 \| 0.7590 \| \| 0.0484 \| 7.0784 \| 2888 \| 0.5695 \| 0.2588 \| 0.5695 \| 0.7546 \| \| 0.0484 \| 7.0833 \| 2890 \| 0.5596 \| 0.2588 \| 0.5596 \| 0.7480 \| \| 0.0484 \| 7.0882 \| 2892 \| 0.5524 \| 0.2186 \| 0.5524 \| 0.7433 \| \| 0.0484 \| 7.0931 \| 2894 \| 0.5499 \| 0.2186 \| 0.5499 \| 0.7416 \| \| 0.0484 \| 7.0980 \| 2896 \| 0.5533 \| 0.2186 \| 0.5533 \| 0.7438 \| \| 0.0484 \| 7.1029 \| 2898 \| 0.5597 \| 0.2186 \| 0.5597 \| 0.7481 \| \| 0.0484 \| 7.1078 \| 2900 \| 0.5655 \| 0.2186 \| 0.5655 \| 0.7520 \| \| 0.0484 \| 7.1127 \| 2902 \| 0.5726 \| 0.2186 \| 0.5726 \| 0.7567 \| \| 0.0484 \| 7.1176 \| 2904 \| 0.5815 \| 0.2186 \| 0.5815 \| 0.7625 \| \| 0.0484 \| 7.1225 \| 2906 \| 0.5797 \| 0.2186 \| 0.5797 \| 0.7614 \| \| 0.0484 \| 7.1275 \| 2908 \| 0.5757 \| 0.3724 \| 0.5757 \| 0.7587 \| \| 0.0484 \| 7.1324 \| 2910 \| 0.5769 \| 0.3724 \| 0.5769 \| 0.7595 \| \| 0.0484 \| 7.1373 \| 2912 \| 0.5821 \| 0.3724 \| 0.5821 \| 0.7630 \| \| 0.0484 \| 7.1422 \| 2914 \| 0.5868 \| 0.2186 \| 0.5868 \| 0.7660 \| \| 0.0484 \| 7.1471 \| 2916 \| 0.5949 \| 0.2588 \| 0.5949 \| 0.7713 \| \| 0.0484 \| 7.1520 \| 2918 \| 0.6055 \| 0.2588 \| 0.6055 \| 0.7782 \| \| 0.0484 \| 7.1569 \| 2920 \| 0.6056 \| 0.2588 \| 0.6056 \| 0.7782 \| \| 0.0484 \| 7.1618 \| 2922 \| 0.5956 \| 0.2186 \| 0.5956 \| 0.7718 \| \| 0.0484 \| 7.1667 \| 2924 \| 0.5841 \| 0.2186 \| 0.5841 \| 0.7643 \| \| 0.0484 \| 7.1716 \| 2926 \| 0.5740 \| 0.3724 \| 0.5740 \| 0.7576 \| \| 0.0484 \| 7.1765 \| 2928 \| 0.5638 \| 0.4273 \| 0.5638 \| 0.7509 \| \| 0.0484 \| 7.1814 \| 2930 \| 0.5598 \| 0.4273 \| 0.5598 \| 0.7482 \| \| 0.0484 \| 7.1863 \| 2932 \| 0.5587 \| 0.4273 \| 0.5587 \| 0.7475 \| \| 0.0484 \| 7.1912 \| 2934 \| 0.5577 \| 0.4273 \| 0.5577 \| 0.7468 \| \| 0.0484 \| 7.1961 \| 2936 \| 0.5575 \| 0.4273 \| 0.5575 \| 0.7466 \| \| 0.0484 \| 7.2010 \| 2938 \| 0.5605 \| 0.4273 \| 0.5605 \| 0.7486 \| \| 0.0484 \| 7.2059 \| 2940 \| 0.5650 \| 0.4273 \| 0.5650 \| 0.7516 \| \| 0.0484 \| 7.2108 \| 2942 \| 0.5784 \| 0.4273 \| 0.5784 \| 0.7605 \| \| 0.0484 \| 7.2157 \| 2944 \| 0.5920 \| 0.3724 \| 0.5920 \| 0.7694 \| \| 0.0484 \| 7.2206 \| 2946 \| 0.5985 \| 0.2186 \| 0.5985 \| 0.7736 \| \| 0.0484 \| 7.2255 \| 2948 \| 0.6026 \| 0.2588 \| 0.6026 \| 0.7762 \| \| 0.0484 \| 7.2304 \| 2950 \| 0.6018 \| 0.3724 \| 0.6018 \| 0.7758 \| \| 0.0484 \| 7.2353 \| 2952 \| 0.5978 \| 0.3724 \| 0.5978 \| 0.7732 \| \| 0.0484 \| 7.2402 \| 2954 \| 0.6014 \| 0.3724 \| 0.6014 \| 0.7755 \| \| 0.0484 \| 7.2451 \| 2956 \| 0.5995 \| 0.4273 \| 0.5995 \| 0.7743 \| \| 0.0484 \| 7.25 \| 2958 \| 0.5931 \| 0.4273 \| 0.5931 \| 0.7701 \| \| 0.0484 \| 7.2549 \| 2960 \| 0.5918 \| 0.4273 \| 0.5918 \| 0.7693 \| \| 0.0484 \| 7.2598 \| 2962 \| 0.5924 \| 0.4273 \| 0.5924 \| 0.7697 \| \| 0.0484 \| 7.2647 \| 2964 \| 0.5894 \| 0.4273 \| 0.5894 \| 0.7677 \| \| 0.0484 \| 7.2696 \| 2966 \| 0.5884 \| 0.3865 \| 0.5884 \| 0.7671 \| \| 0.0484 \| 7.2745 \| 2968 \| 0.5891 \| 0.3077 \| 0.5891 \| 0.7675 \| \| 0.0484 \| 7.2794 \| 2970 \| 0.5899 \| 0.3077 \| 0.5899 \| 0.7680 \| \| 0.0484 \| 7.2843 \| 2972 \| 0.5917 \| 0.1962 \| 0.5917 \| 0.7693 \| \| 0.0484 \| 7.2892 \| 2974 \| 0.5945 \| 0.1962 \| 0.5945 \| 0.7711 \| \| 0.0484 \| 7.2941 \| 2976 \| 0.5986 \| 0.1962 \| 0.5986 \| 0.7737 \| \| 0.0484 \| 7.2990 \| 2978 \| 0.5996 \| 0.1962 \| 0.5996 \| 0.7743 \| \| 0.0484 \| 7.3039 \| 2980 \| 0.5938 \| 0.1962 \| 0.5938 \| 0.7706 \| \| 0.0484 \| 7.3088 \| 2982 \| 0.5896 \| 0.1962 \| 0.5896 \| 0.7679 \| \| 0.0484 \| 7.3137 \| 2984 \| 0.5903 \| 0.4273 \| 0.5903 \| 0.7683 \| \| 0.0484 \| 7.3186 \| 2986 \| 0.6007 \| 0.4273 \| 0.6007 \| 0.7750 \| \| 0.0484 \| 7.3235 \| 2988 \| 0.6197 \| 0.2588 \| 0.6197 \| 0.7872 \| \| 0.0484 \| 7.3284 \| 2990 \| 0.6438 \| 0.2588 \| 0.6438 \| 0.8024 \| \| 0.0484 \| 7.3333 \| 2992 \| 0.6531 \| 0.2588 \| 0.6531 \| 0.8081 \| \| 0.0484 \| 7.3382 \| 2994 \| 0.6506 \| 0.2588 \| 0.6506 \| 0.8066 \| \| 0.0484 \| 7.3431 \| 2996 \| 0.6399 \| 0.2588 \| 0.6399 \| 0.8000 \| \| 0.0484 \| 7.3480 \| 2998 \| 0.6246 \| 0.2588 \| 0.6246 \| 0.7903 \| \| 0.0411 \| 7.3529 \| 3000 \| 0.6190 \| 0.2588 \| 0.6190 \| 0.7868 \| \| 0.0411 \| 7.3578 \| 3002 \| 0.6147 \| 0.2588 \| 0.6147 \| 0.7840 \| \| 0.0411 \| 7.3627 \| 3004 \| 0.6192 \| 0.2588 \| 0.6192 \| 0.7869 \| \| 0.0411 \| 7.3676 \| 3006 \| 0.6184 \| 0.2588 \| 0.6184 \| 0.7864 \| \| 0.0411 \| 7.3725 \| 3008 \| 0.6144 \| 0.2588 \| 0.6144 \| 0.7838 \| \| 0.0411 \| 7.3775 \| 3010 \| 0.6032 \| 0.2588 \| 0.6032 \| 0.7767 \| \| 0.0411 \| 7.3824 \| 3012 \| 0.5871 \| 0.3724 \| 0.5871 \| 0.7662 \| \| 0.0411 \| 7.3873 \| 3014 \| 0.5713 \| 0.4273 \| 0.5713 \| 0.7558 \| \| 0.0411 \| 7.3922 \| 3016 \| 0.5613 \| 0.4273 \| 0.5613 \| 0.7492 \| \| 0.0411 \| 7.3971 \| 3018 \| 0.5541 \| 0.4273 \| 0.5541 \| 0.7443 \| \| 0.0411 \| 7.4020 \| 3020 \| 0.5492 \| 0.3467 \| 0.5492 \| 0.7411 \| \| 0.0411 \| 7.4069 \| 3022 \| 0.5470 \| 0.3077 \| 0.5470 \| 0.7396 \| \| 0.0411 \| 7.4118 \| 3024 \| 0.5447 \| 0.3467 \| 0.5447 \| 0.7380 \| \| 0.0411 \| 7.4167 \| 3026 \| 0.5460 \| 0.4273 \| 0.5460 \| 0.7389 \| \| 0.0411 \| 7.4216 \| 3028 \| 0.5570 \| 0.4273 \| 0.5570 \| 0.7463 \| \| 0.0411 \| 7.4265 \| 3030 \| 0.5654 \| 0.3724 \| 0.5654 \| 0.7519 \| \| 0.0411 \| 7.4314 \| 3032 \| 0.5718 \| 0.4661 \| 0.5718 \| 0.7562 \| \| 0.0411 \| 7.4363 \| 3034 \| 0.5704 \| 0.4661 \| 0.5704 \| 0.7552 \| \| 0.0411 \| 7.4412 \| 3036 \| 0.5678 \| 0.4661 \| 0.5678 \| 0.7536 \| \| 0.0411 \| 7.4461 \| 3038 \| 0.5751 \| 0.4661 \| 0.5751 \| 0.7583 \| \| 0.0411 \| 7.4510 \| 3040 \| 0.5945 \| 0.4661 \| 0.5945 \| 0.7710 \| \| 0.0411 \| 7.4559 \| 3042 \| 0.6046 \| 0.4661 \| 0.6046 \| 0.7776 \| \| 0.0411 \| 7.4608 \| 3044 \| 0.6135 \| 0.3255 \| 0.6135 \| 0.7833 \| \| 0.0411 \| 7.4657 \| 3046 \| 0.6191 \| 0.3255 \| 0.6191 \| 0.7869 \| \| 0.0411 \| 7.4706 \| 3048 \| 0.6098 \| 0.3255 \| 0.6098 \| 0.7809 \| \| 0.0411 \| 7.4755 \| 3050 \| 0.5983 \| 0.3255 \| 0.5983 \| 0.7735 \| \| 0.0411 \| 7.4804 \| 3052 \| 0.5858 \| 0.3255 \| 0.5858 \| 0.7654 \| \| 0.0411 \| 7.4853 \| 3054 \| 0.5704 \| 0.2186 \| 0.5704 \| 0.7552 \| \| 0.0411 \| 7.4902 \| 3056 \| 0.5561 \| 0.3724 \| 0.5561 \| 0.7458 \| \| 0.0411 \| 7.4951 \| 3058 \| 0.5418 \| 0.3724 \| 0.5418 \| 0.7361 \| \| 0.0411 \| 7.5 \| 3060 \| 0.5366 \| 0.4273 \| 0.5366 \| 0.7325 \| \| 0.0411 \| 7.5049 \| 3062 \| 0.5382 \| 0.3724 \| 0.5382 \| 0.7336 \| \| 0.0411 \| 7.5098 \| 3064 \| 0.5390 \| 0.3724 \| 0.5390 \| 0.7341 \| \| 0.0411 \| 7.5147 \| 3066 \| 0.5369 \| 0.3724 \| 0.5369 \| 0.7327 \| \| 0.0411 \| 7.5196 \| 3068 \| 0.5412 \| 0.3724 \| 0.5412 \| 0.7357 \| \| 0.0411 \| 7.5245 \| 3070 \| 0.5541 \| 0.3724 \| 0.5541 \| 0.7444 \| \| 0.0411 \| 7.5294 \| 3072 \| 0.5643 \| 0.4661 \| 0.5643 \| 0.7512 \| \| 0.0411 \| 7.5343 \| 3074 \| 0.5738 \| 0.4661 \| 0.5738 \| 0.7575 \| \| 0.0411 \| 7.5392 \| 3076 \| 0.5723 \| 0.4661 \| 0.5723 \| 0.7565 \| \| 0.0411 \| 7.5441 \| 3078 \| 0.5720 \| 0.4661 \| 0.5720 \| 0.7563 \| \| 0.0411 \| 7.5490 \| 3080 \| 0.5743 \| 0.4661 \| 0.5743 \| 0.7578 \| \| 0.0411 \| 7.5539 \| 3082 \| 0.5861 \| 0.4661 \| 0.5861 \| 0.7656 \| \| 0.0411 \| 7.5588 \| 3084 \| 0.6017 \| 0.4661 \| 0.6017 \| 0.7757 \| \| 0.0411 \| 7.5637 \| 3086 \| 0.6255 \| 0.3255 \| 0.6255 \| 0.7909 \| \| 0.0411 \| 7.5686 \| 3088 \| 0.6465 \| 0.3255 \| 0.6465 \| 0.8041 \| \| 0.0411 \| 7.5735 \| 3090 \| 0.6455 \| 0.3255 \| 0.6455 \| 0.8034 \| \| 0.0411 \| 7.5784 \| 3092 \| 0.6340 \| 0.3255 \| 0.6340 \| 0.7962 \| \| 0.0411 \| 7.5833 \| 3094 \| 0.6106 \| 0.3255 \| 0.6106 \| 0.7814 \| \| 0.0411 \| 7.5882 \| 3096 \| 0.5799 \| 0.4661 \| 0.5799 \| 0.7615 \| \| 0.0411 \| 7.5931 \| 3098 \| 0.5602 \| 0.4661 \| 0.5602 \| 0.7484 \| \| 0.0411 \| 7.5980 \| 3100 \| 0.5480 \| 0.3724 \| 0.5480 \| 0.7402 \| \| 0.0411 \| 7.6029 \| 3102 \| 0.5363 \| 0.4273 \| 0.5363 \| 0.7324 \| \| 0.0411 \| 7.6078 \| 3104 \| 0.5275 \| 0.4273 \| 0.5275 \| 0.7263 \| \| 0.0411 \| 7.6127 \| 3106 \| 0.5241 \| 0.4273 \| 0.5241 \| 0.7240 \| \| 0.0411 \| 7.6176 \| 3108 \| 0.5264 \| 0.4273 \| 0.5264 \| 0.7255 \| \| 0.0411 \| 7.6225 \| 3110 \| 0.5288 \| 0.4273 \| 0.5288 \| 0.7272 \| \| 0.0411 \| 7.6275 \| 3112 \| 0.5320 \| 0.4273 \| 0.5320 \| 0.7294 \| \| 0.0411 \| 7.6324 \| 3114 \| 0.5335 \| 0.3865 \| 0.5335 \| 0.7304 \| \| 0.0411 \| 7.6373 \| 3116 \| 0.5385 \| 0.3865 \| 0.5385 \| 0.7338 \| \| 0.0411 \| 7.6422 \| 3118 \| 0.5465 \| 0.3467 \| 0.5465 \| 0.7393 \| \| 0.0411 \| 7.6471 \| 3120 \| 0.5531 \| 0.3467 \| 0.5531 \| 0.7437 \| \| 0.0411 \| 7.6520 \| 3122 \| 0.5552 \| 0.3467 \| 0.5552 \| 0.7451 \| \| 0.0411 \| 7.6569 \| 3124 \| 0.5537 \| 0.3865 \| 0.5537 \| 0.7441 \| \| 0.0411 \| 7.6618 \| 3126 \| 0.5555 \| 0.3865 \| 0.5555 \| 0.7453 \| \| 0.0411 \| 7.6667 \| 3128 \| 0.5601 \| 0.3865 \| 0.5601 \| 0.7484 \| \| 0.0411 \| 7.6716 \| 3130 \| 0.5727 \| 0.4273 \| 0.5727 \| 0.7568 \| \| 0.0411 \| 7.6765 \| 3132 \| 0.5882 \| 0.3724 \| 0.5882 \| 0.7669 \| \| 0.0411 \| 7.6814 \| 3134 \| 0.6032 \| 0.3724 \| 0.6032 \| 0.7767 \| \| 0.0411 \| 7.6863 \| 3136 \| 0.6175 \| 0.2186 \| 0.6175 \| 0.7858 \| \| 0.0411 \| 7.6912 \| 3138 \| 0.6188 \| 0.2186 \| 0.6188 \| 0.7867 \| \| 0.0411 \| 7.6961 \| 3140 \| 0.6089 \| 0.2186 \| 0.6089 \| 0.7803 \| \| 0.0411 \| 7.7010 \| 3142 \| 0.6012 \| 0.2186 \| 0.6012 \| 0.7754 \| \| 0.0411 \| 7.7059 \| 3144 \| 0.5923 \| 0.2186 \| 0.5923 \| 0.7696 \| \| 0.0411 \| 7.7108 \| 3146 \| 0.5804 \| 0.3724 \| 0.5804 \| 0.7618 \| \| 0.0411 \| 7.7157 \| 3148 \| 0.5662 \| 0.4273 \| 0.5662 \| 0.7525 \| \| 0.0411 \| 7.7206 \| 3150 \| 0.5564 \| 0.4273 \| 0.5564 \| 0.7459 \| \| 0.0411 \| 7.7255 \| 3152 \| 0.5497 \| 0.4273 \| 0.5497 \| 0.7414 \| \| 0.0411 \| 7.7304 \| 3154 \| 0.5447 \| 0.4273 \| 0.5447 \| 0.7380 \| \| 0.0411 \| 7.7353 \| 3156 \| 0.5450 \| 0.4273 \| 0.5450 \| 0.7382 \| \| 0.0411 \| 7.7402 \| 3158 \| 0.5508 \| 0.4273 \| 0.5508 \| 0.7422 \| \| 0.0411 \| 7.7451 \| 3160 \| 0.5564 \| 0.4273 \| 0.5564 \| 0.7459 \| \| 0.0411 \| 7.75 \| 3162 \| 0.5608 \| 0.4273 \| 0.5608 \| 0.7489 \| \| 0.0411 \| 7.7549 \| 3164 \| 0.5661 \| 0.3724 \| 0.5662 \| 0.7524 \| \| 0.0411 \| 7.7598 \| 3166 \| 0.5762 \| 0.2186 \| 0.5762 \| 0.7591 \| \| 0.0411 \| 7.7647 \| 3168 \| 0.5785 \| 0.2186 \| 0.5785 \| 0.7606 \| \| 0.0411 \| 7.7696 \| 3170 \| 0.5750 \| 0.2186 \| 0.5750 \| 0.7583 \| \| 0.0411 \| 7.7745 \| 3172 \| 0.5703 \| 0.2186 \| 0.5703 \| 0.7552 \| \| 0.0411 \| 7.7794 \| 3174 \| 0.5662 \| 0.2186 \| 0.5662 \| 0.7525 \| \| 0.0411 \| 7.7843 \| 3176 \| 0.5640 \| 0.2186 \| 0.5640 \| 0.7510 \| \| 0.0411 \| 7.7892 \| 3178 \| 0.5623 \| 0.2186 \| 0.5623 \| 0.7498 \| \| 0.0411 \| 7.7941 \| 3180 \| 0.5666 \| 0.2186 \| 0.5666 \| 0.7527 \| \| 0.0411 \| 7.7990 \| 3182 \| 0.5736 \| 0.2186 \| 0.5736 \| 0.7574 \| \| 0.0411 \| 7.8039 \| 3184 \| 0.5749 \| 0.2186 \| 0.5749 \| 0.7582 \| \| 0.0411 \| 7.8088 \| 3186 \| 0.5747 \| 0.3255 \| 0.5747 \| 0.7581 \| \| 0.0411 \| 7.8137 \| 3188 \| 0.5698 \| 0.3255 \| 0.5698 \| 0.7549 \| \| 0.0411 \| 7.8186 \| 3190 \| 0.5756 \| 0.3255 \| 0.5756 \| 0.7587 \| \| 0.0411 \| 7.8235 \| 3192 \| 0.5875 \| 0.3636 \| 0.5875 \| 0.7665 \| \| 0.0411 \| 7.8284 \| 3194 \| 0.5931 \| 0.3636 \| 0.5931 \| 0.7701 \| \| 0.0411 \| 7.8333 \| 3196 \| 0.5926 \| 0.3636 \| 0.5926 \| 0.7698 \| \| 0.0411 \| 7.8382 \| 3198 \| 0.5871 \| 0.3636 \| 0.5871 \| 0.7662 \| \| 0.0411 \| 7.8431 \| 3200 \| 0.5834 \| 0.3636 \| 0.5834 \| 0.7638 \| \| 0.0411 \| 7.8480 \| 3202 \| 0.5857 \| 0.3636 \| 0.5857 \| 0.7653 \| \| 0.0411 \| 7.8529 \| 3204 \| 0.5867 \| 0.3255 \| 0.5867 \| 0.7659 \| \| 0.0411 \| 7.8578 \| 3206 \| 0.5769 \| 0.2186 \| 0.5769 \| 0.7596 \| \| 0.0411 \| 7.8627 \| 3208 \| 0.5661 \| 0.2186 \| 0.5661 \| 0.7524 \| \| 0.0411 \| 7.8676 \| 3210 \| 0.5590 \| 0.2186 \| 0.5590 \| 0.7476 \| \| 0.0411 \| 7.8725 \| 3212 \| 0.5509 \| 0.3724 \| 0.5509 \| 0.7422 \| \| 0.0411 \| 7.8775 \| 3214 \| 0.5431 \| 0.3724 \| 0.5431 \| 0.7369 \| \| 0.0411 \| 7.8824 \| 3216 \| 0.5374 \| 0.3724 \| 0.5374 \| 0.7331 \| \| 0.0411 \| 7.8873 \| 3218 \| 0.5318 \| 0.3724 \| 0.5318 \| 0.7292 \| \| 0.0411 \| 7.8922 \| 3220 \| 0.5291 \| 0.3724 \| 0.5291 \| 0.7274 \| \| 0.0411 \| 7.8971 \| 3222 \| 0.5220 \| 0.3724 \| 0.5220 \| 0.7225 \| \| 0.0411 \| 7.9020 \| 3224 \| 0.5191 \| 0.3724 \| 0.5191 \| 0.7205 \| \| 0.0411 \| 7.9069 \| 3226 \| 0.5246 \| 0.3724 \| 0.5246 \| 0.7243 \| \| 0.0411 \| 7.9118 \| 3228 \| 0.5357 \| 0.3724 \| 0.5357 \| 0.7319 \| \| 0.0411 \| 7.9167 \| 3230 \| 0.5448 \| 0.3724 \| 0.5448 \| 0.7381 \| \| 0.0411 \| 7.9216 \| 3232 \| 0.5568 \| 0.2186 \| 0.5568 \| 0.7462 \| \| 0.0411 \| 7.9265 \| 3234 \| 0.5743 \| 0.3255 \| 0.5743 \| 0.7579 \| \| 0.0411 \| 7.9314 \| 3236 \| 0.5857 \| 0.3255 \| 0.5857 \| 0.7653 \| \| 0.0411 \| 7.9363 \| 3238 \| 0.5902 \| 0.3255 \| 0.5902 \| 0.7682 \| \| 0.0411 \| 7.9412 \| 3240 \| 0.5941 \| 0.3255 \| 0.5941 \| 0.7708 \| \| 0.0411 \| 7.9461 \| 3242 \| 0.5940 \| 0.3255 \| 0.5940 \| 0.7707 \| \| 0.0411 \| 7.9510 \| 3244 \| 0.5792 \| 0.3255 \| 0.5792 \| 0.7611 \| \| 0.0411 \| 7.9559 \| 3246 \| 0.5686 \| 0.2186 \| 0.5686 \| 0.7541 \| \| 0.0411 \| 7.9608 \| 3248 \| 0.5643 \| 0.2186 \| 0.5643 \| 0.7512 \| \| 0.0411 \| 7.9657 \| 3250 \| 0.5577 \| 0.3724 \| 0.5577 \| 0.7468 \| \| 0.0411 \| 7.9706 \| 3252 \| 0.5513 \| 0.3724 \| 0.5513 \| 0.7425 \| \| 0.0411 \| 7.9755 \| 3254 \| 0.5494 \| 0.3724 \| 0.5494 \| 0.7412 \| \| 0.0411 \| 7.9804 \| 3256 \| 0.5517 \| 0.3724 \| 0.5517 \| 0.7428 \| \| 0.0411 \| 7.9853 \| 3258 \| 0.5462 \| 0.3724 \| 0.5462 \| 0.7391 \| \| 0.0411 \| 7.9902 \| 3260 \| 0.5383 \| 0.4273 \| 0.5383 \| 0.7337 \| \| 0.0411 \| 7.9951 \| 3262 \| 0.5321 \| 0.4273 \| 0.5321 \| 0.7295 \| \| 0.0411 \| 8.0 \| 3264 \| 0.5273 \| 0.4273 \| 0.5273 \| 0.7262 \| \| 0.0411 \| 8.0049 \| 3266 \| 0.5219 \| 0.4273 \| 0.5219 \| 0.7224 \| \| 0.0411 \| 8.0098 \| 3268 \| 0.5199 \| 0.4273 \| 0.5199 \| 0.7211 \| \| 0.0411 \| 8.0147 \| 3270 \| 0.5266 \| 0.4273 \| 0.5266 \| 0.7257 \| \| 0.0411 \| 8.0196 \| 3272 \| 0.5405 \| 0.3724 \| 0.5405 \| 0.7352 \| \| 0.0411 \| 8.0245 \| 3274 \| 0.5558 \| 0.3724 \| 0.5558 \| 0.7455 \| \| 0.0411 \| 8.0294 \| 3276 \| 0.5620 \| 0.3724 \| 0.5620 \| 0.7497 \| \| 0.0411 \| 8.0343 \| 3278 \| 0.5611 \| 0.3724 \| 0.5611 \| 0.7490 \| \| 0.0411 \| 8.0392 \| 3280 \| 0.5626 \| 0.3724 \| 0.5626 \| 0.7501 \| \| 0.0411 \| 8.0441 \| 3282 \| 0.5697 \| 0.3724 \| 0.5697 \| 0.7548 \| \| 0.0411 \| 8.0490 \| 3284 \| 0.5774 \| 0.3724 \| 0.5774 \| 0.7599 \| \| 0.0411 \| 8.0539 \| 3286 \| 0.5862 \| 0.3724 \| 0.5862 \| 0.7656 \| \| 0.0411 \| 8.0588 \| 3288 \| 0.5873 \| 0.3724 \| 0.5873 \| 0.7663 \| \| 0.0411 \| 8.0637 \| 3290 \| 0.5762 \| 0.3724 \| 0.5762 \| 0.7591 \| \| 0.0411 \| 8.0686 \| 3292 \| 0.5645 \| 0.3724 \| 0.5645 \| 0.7513 \| \| 0.0411 \| 8.0735 \| 3294 \| 0.5631 \| 0.3724 \| 0.5631 \| 0.7504 \| \| 0.0411 \| 8.0784 \| 3296 \| 0.5640 \| 0.3724 \| 0.5640 \| 0.7510 \| \| 0.0411 \| 8.0833 \| 3298 \| 0.5624 \| 0.3724 \| 0.5624 \| 0.7499 \| \| 0.0411 \| 8.0882 \| 3300 \| 0.5652 \| 0.3724 \| 0.5652 \| 0.7518 \| \| 0.0411 \| 8.0931 \| 3302 \| 0.5702 \| 0.3724 \| 0.5702 \| 0.7551 \| \| 0.0411 \| 8.0980 \| 3304 \| 0.5709 \| 0.3724 \| 0.5709 \| 0.7556 \| \| 0.0411 \| 8.1029 \| 3306 \| 0.5677 \| 0.3724 \| 0.5677 \| 0.7534 \| \| 0.0411 \| 8.1078 \| 3308 \| 0.5623 \| 0.3724 \| 0.5623 \| 0.7499 \| \| 0.0411 \| 8.1127 \| 3310 \| 0.5543 \| 0.3724 \| 0.5543 \| 0.7445 \| \| 0.0411 \| 8.1176 \| 3312 \| 0.5523 \| 0.3724 \| 0.5523 \| 0.7432 \| \| 0.0411 \| 8.1225 \| 3314 \| 0.5503 \| 0.3724 \| 0.5503 \| 0.7418 \| \| 0.0411 \| 8.1275 \| 3316 \| 0.5503 \| 0.3724 \| 0.5503 \| 0.7418 \| \| 0.0411 \| 8.1324 \| 3318 \| 0.5545 \| 0.3724 \| 0.5545 \| 0.7447 \| \| 0.0411 \| 8.1373 \| 3320 \| 0.5562 \| 0.3724 \| 0.5562 \| 0.7458 \| \| 0.0411 \| 8.1422 \| 3322 \| 0.5553 \| 0.3724 \| 0.5553 \| 0.7452 \| \| 0.0411 \| 8.1471 \| 3324 \| 0.5520 \| 0.3724 \| 0.5520 \| 0.7430 \| \| 0.0411 \| 8.1520 \| 3326 \| 0.5528 \| 0.3724 \| 0.5528 \| 0.7435 \| \| 0.0411 \| 8.1569 \| 3328 \| 0.5582 \| 0.3724 \| 0.5582 \| 0.7471 \| \| 0.0411 \| 8.1618 \| 3330 \| 0.5704 \| 0.4661 \| 0.5704 \| 0.7552 \| \| 0.0411 \| 8.1667 \| 3332 \| 0.5821 \| 0.4661 \| 0.5821 \| 0.7630 \| \| 0.0411 \| 8.1716 \| 3334 \| 0.6006 \| 0.4661 \| 0.6006 \| 0.7750 \| \| 0.0411 \| 8.1765 \| 3336 \| 0.6148 \| 0.4661 \| 0.6148 \| 0.7841 \| \| 0.0411 \| 8.1814 \| 3338 \| 0.6344 \| 0.4661 \| 0.6344 \| 0.7965 \| \| 0.0411 \| 8.1863 \| 3340 \| 0.6494 \| 0.3255 \| 0.6494 \| 0.8059 \| \| 0.0411 \| 8.1912 \| 3342 \| 0.6600 \| 0.3255 \| 0.6600 \| 0.8124 \| \| 0.0411 \| 8.1961 \| 3344 \| 0.6578 \| 0.3255 \| 0.6578 \| 0.8110 \| \| 0.0411 \| 8.2010 \| 3346 \| 0.6555 \| 0.3255 \| 0.6555 \| 0.8097 \| \| 0.0411 \| 8.2059 \| 3348 \| 0.6544 \| 0.3255 \| 0.6544 \| 0.8090 \| \| 0.0411 \| 8.2108 \| 3350 \| 0.6493 \| 0.3255 \| 0.6493 \| 0.8058 \| \| 0.0411 \| 8.2157 \| 3352 \| 0.6386 \| 0.4661 \| 0.6386 \| 0.7991 \| \| 0.0411 \| 8.2206 \| 3354 \| 0.6250 \| 0.4661 \| 0.6250 \| 0.7906 \| \| 0.0411 \| 8.2255 \| 3356 \| 0.6029 \| 0.4661 \| 0.6029 \| 0.7765 \| \| 0.0411 \| 8.2304 \| 3358 \| 0.5803 \| 0.4661 \| 0.5803 \| 0.7618 \| \| 0.0411 \| 8.2353 \| 3360 \| 0.5643 \| 0.4661 \| 0.5643 \| 0.7512 \| \| 0.0411 \| 8.2402 \| 3362 \| 0.5510 \| 0.4273 \| 0.5510 \| 0.7423 \| \| 0.0411 \| 8.2451 \| 3364 \| 0.5470 \| 0.4273 \| 0.5470 \| 0.7396 \| \| 0.0411 \| 8.25 \| 3366 \| 0.5462 \| 0.4273 \| 0.5462 \| 0.7390 \| \| 0.0411 \| 8.2549 \| 3368 \| 0.5461 \| 0.4273 \| 0.5461 \| 0.7390 \| \| 0.0411 \| 8.2598 \| 3370 \| 0.5462 \| 0.4273 \| 0.5462 \| 0.7390 \| \| 0.0411 \| 8.2647 \| 3372 \| 0.5451 \| 0.4273 \| 0.5451 \| 0.7383 \| \| 0.0411 \| 8.2696 \| 3374 \| 0.5457 \| 0.4273 \| 0.5457 \| 0.7387 \| \| 0.0411 \| 8.2745 \| 3376 \| 0.5488 \| 0.4273 \| 0.5488 \| 0.7408 \| \| 0.0411 \| 8.2794 \| 3378 \| 0.5547 \| 0.4273 \| 0.5547 \| 0.7448 \| \| 0.0411 \| 8.2843 \| 3380 \| 0.5624 \| 0.4273 \| 0.5624 \| 0.7499 \| \| 0.0411 \| 8.2892 \| 3382 \| 0.5724 \| 0.4273 \| 0.5724 \| 0.7566 \| \| 0.0411 \| 8.2941 \| 3384 \| 0.5800 \| 0.4273 \| 0.5800 \| 0.7616 \| \| 0.0411 \| 8.2990 \| 3386 \| 0.5879 \| 0.4661 \| 0.5879 \| 0.7667 \| \| 0.0411 \| 8.3039 \| 3388 \| 0.5921 \| 0.4661 \| 0.5921 \| 0.7695 \| \| 0.0411 \| 8.3088 \| 3390 \| 0.5915 \| 0.4661 \| 0.5915 \| 0.7691 \| \| 0.0411 \| 8.3137 \| 3392 \| 0.5945 \| 0.4661 \| 0.5945 \| 0.7710 \| \| 0.0411 \| 8.3186 \| 3394 \| 0.5942 \| 0.4661 \| 0.5942 \| 0.7708 \| \| 0.0411 \| 8.3235 \| 3396 \| 0.5920 \| 0.4661 \| 0.5920 \| 0.7694 \| \| 0.0411 \| 8.3284 \| 3398 \| 0.5883 \| 0.4661 \| 0.5883 \| 0.7670 \| \| 0.0411 \| 8.3333 \| 3400 \| 0.5803 \| 0.5157 \| 0.5803 \| 0.7618 \| \| 0.0411 \| 8.3382 \| 3402 \| 0.5729 \| 0.4273 \| 0.5729 \| 0.7569 \| \| 0.0411 \| 8.3431 \| 3404 \| 0.5660 \| 0.4273 \| 0.5660 \| 0.7523 \| \| 0.0411 \| 8.3480 \| 3406 \| 0.5620 \| 0.4273 \| 0.5620 \| 0.7497 \| \| 0.0411 \| 8.3529 \| 3408 \| 0.5580 \| 0.4273 \| 0.5580 \| 0.7470 \| \| 0.0411 \| 8.3578 \| 3410 \| 0.5587 \| 0.4273 \| 0.5587 \| 0.7475 \| \| 0.0411 \| 8.3627 \| 3412 \| 0.5578 \| 0.4273 \| 0.5578 \| 0.7469 \| \| 0.0411 \| 8.3676 \| 3414 \| 0.5577 \| 0.4273 \| 0.5577 \| 0.7468 \| \| 0.0411 \| 8.3725 \| 3416 \| 0.5602 \| 0.4273 \| 0.5602 \| 0.7485 \| \| 0.0411 \| 8.3775 \| 3418 \| 0.5601 \| 0.4273 \| 0.5601 \| 0.7484 \| \| 0.0411 \| 8.3824 \| 3420 \| 0.5637 \| 0.4273 \| 0.5637 \| 0.7508 \| \| 0.0411 \| 8.3873 \| 3422 \| 0.5684 \| 0.4273 \| 0.5684 \| 0.7539 \| \| 0.0411 \| 8.3922 \| 3424 \| 0.5722 \| 0.4273 \| 0.5722 \| 0.7565 \| \| 0.0411 \| 8.3971 \| 3426 \| 0.5776 \| 0.3724 \| 0.5776 \| 0.7600 \| \| 0.0411 \| 8.4020 \| 3428 \| 0.5828 \| 0.3724 \| 0.5828 \| 0.7634 \| \| 0.0411 \| 8.4069 \| 3430 \| 0.5846 \| 0.3724 \| 0.5846 \| 0.7646 \| \| 0.0411 \| 8.4118 \| 3432 \| 0.5913 \| 0.3724 \| 0.5913 \| 0.7690 \| \| 0.0411 \| 8.4167 \| 3434 \| 0.6020 \| 0.4661 \| 0.6020 \| 0.7759 \| \| 0.0411 \| 8.4216 \| 3436 \| 0.6085 \| 0.3255 \| 0.6085 \| 0.7801 \| \| 0.0411 \| 8.4265 \| 3438 \| 0.6069 \| 0.3255 \| 0.6069 \| 0.7790 \| \| 0.0411 \| 8.4314 \| 3440 \| 0.6069 \| 0.3255 \| 0.6069 \| 0.7791 \| \| 0.0411 \| 8.4363 \| 3442 \| 0.6114 \| 0.3255 \| 0.6114 \| 0.7819 \| \| 0.0411 \| 8.4412 \| 3444 \| 0.6174 \| 0.3255 \| 0.6174 \| 0.7858 \| \| 0.0411 \| 8.4461 \| 3446 \| 0.6170 \| 0.3255 \| 0.6170 \| 0.7855 \| \| 0.0411 \| 8.4510 \| 3448 \| 0.6171 \| 0.3255 \| 0.6171 \| 0.7855 \| \| 0.0411 \| 8.4559 \| 3450 \| 0.6143 \| 0.3255 \| 0.6143 \| 0.7838 \| \| 0.0411 \| 8.4608 \| 3452 \| 0.6091 \| 0.3255 \| 0.6091 \| 0.7804 \| \| 0.0411 \| 8.4657 \| 3454 \| 0.6011 \| 0.3255 \| 0.6011 \| 0.7753 \| \| 0.0411 \| 8.4706 \| 3456 \| 0.5933 \| 0.4661 \| 0.5933 \| 0.7703 \| \| 0.0411 \| 8.4755 \| 3458 \| 0.5810 \| 0.4661 \| 0.5810 \| 0.7622 \| \| 0.0411 \| 8.4804 \| 3460 \| 0.5703 \| 0.4661 \| 0.5703 \| 0.7552 \| \| 0.0411 \| 8.4853 \| 3462 \| 0.5600 \| 0.3724 \| 0.5600 \| 0.7483 \| \| 0.0411 \| 8.4902 \| 3464 \| 0.5534 \| 0.4273 \| 0.5534 \| 0.7439 \| \| 0.0411 \| 8.4951 \| 3466 \| 0.5496 \| 0.4273 \| 0.5496 \| 0.7414 \| \| 0.0411 \| 8.5 \| 3468 \| 0.5488 \| 0.4273 \| 0.5488 \| 0.7408 \| \| 0.0411 \| 8.5049 \| 3470 \| 0.5537 \| 0.4273 \| 0.5537 \| 0.7441 \| \| 0.0411 \| 8.5098 \| 3472 \| 0.5573 \| 0.5157 \| 0.5573 \| 0.7465 \| \| 0.0411 \| 8.5147 \| 3474 \| 0.5669 \| 0.4661 \| 0.5669 \| 0.7529 \| \| 0.0411 \| 8.5196 \| 3476 \| 0.5744 \| 0.4661 \| 0.5744 \| 0.7579 \| \| 0.0411 \| 8.5245 \| 3478 \| 0.5785 \| 0.4661 \| 0.5785 \| 0.7606 \| \| 0.0411 \| 8.5294 \| 3480 \| 0.5774 \| 0.4661 \| 0.5774 \| 0.7599 \| \| 0.0411 \| 8.5343 \| 3482 \| 0.5785 \| 0.4661 \| 0.5785 \| 0.7606 \| \| 0.0411 \| 8.5392 \| 3484 \| 0.5789 \| 0.4661 \| 0.5789 \| 0.7609 \| \| 0.0411 \| 8.5441 \| 3486 \| 0.5788 \| 0.4661 \| 0.5788 \| 0.7608 \| \| 0.0411 \| 8.5490 \| 3488 \| 0.5818 \| 0.4661 \| 0.5818 \| 0.7628 \| \| 0.0411 \| 8.5539 \| 3490 \| 0.5864 \| 0.4661 \| 0.5864 \| 0.7658 \| \| 0.0411 \| 8.5588 \| 3492 \| 0.5885 \| 0.4661 \| 0.5885 \| 0.7671 \| \| 0.0411 \| 8.5637 \| 3494 \| 0.5878 \| 0.4661 \| 0.5878 \| 0.7667 \| \| 0.0411 \| 8.5686 \| 3496 \| 0.5867 \| 0.4661 \| 0.5867 \| 0.7660 \| \| 0.0411 \| 8.5735 \| 3498 \| 0.5905 \| 0.4661 \| 0.5905 \| 0.7684 \| \| 0.0395 \| 8.5784 \| 3500 \| 0.5949 \| 0.4661 \| 0.5949 \| 0.7713 \| \| 0.0395 \| 8.5833 \| 3502 \| 0.6033 \| 0.4661 \| 0.6033 \| 0.7767 \| \| 0.0395 \| 8.5882 \| 3504 \| 0.6088 \| 0.4661 \| 0.6088 \| 0.7803 \| \| 0.0395 \| 8.5931 \| 3506 \| 0.6136 \| 0.3255 \| 0.6136 \| 0.7834 \| \| 0.0395 \| 8.5980 \| 3508 \| 0.6195 \| 0.3255 \| 0.6195 \| 0.7871 \| \| 0.0395 \| 8.6029 \| 3510 \| 0.6209 \| 0.3255 \| 0.6209 \| 0.7880 \| \| 0.0395 \| 8.6078 \| 3512 \| 0.6172 \| 0.3255 \| 0.6172 \| 0.7856 \| \| 0.0395 \| 8.6127 \| 3514 \| 0.6131 \| 0.3255 \| 0.6131 \| 0.7830 \| \| 0.0395 \| 8.6176 \| 3516 \| 0.6108 \| 0.3255 \| 0.6108 \| 0.7815 \| \| 0.0395 \| 8.6225 \| 3518 \| 0.6101 \| 0.3255 \| 0.6101 \| 0.7811 \| \| 0.0395 \| 8.6275 \| 3520 \| 0.6114 \| 0.3255 \| 0.6114 \| 0.7819 \| \| 0.0395 \| 8.6324 \| 3522 \| 0.6152 \| 0.3255 \| 0.6152 \| 0.7843 \| \| 0.0395 \| 8.6373 \| 3524 \| 0.6157 \| 0.3255 \| 0.6157 \| 0.7847 \| \| 0.0395 \| 8.6422 \| 3526 \| 0.6097 \| 0.3255 \| 0.6097 \| 0.7809 \| \| 0.0395 \| 8.6471 \| 3528 \| 0.5981 \| 0.3255 \| 0.5981 \| 0.7734 \| \| 0.0395 \| 8.6520 \| 3530 \| 0.5852 \| 0.3255 \| 0.5852 \| 0.7650 \| \| 0.0395 \| 8.6569 \| 3532 \| 0.5769 \| 0.3255 \| 0.5769 \| 0.7595 \| \| 0.0395 \| 8.6618 \| 3534 \| 0.5712 \| 0.2186 \| 0.5712 \| 0.7558 \| \| 0.0395 \| 8.6667 \| 3536 \| 0.5646 \| 0.3724 \| 0.5646 \| 0.7514 \| \| 0.0395 \| 8.6716 \| 3538 \| 0.5561 \| 0.3724 \| 0.5561 \| 0.7457 \| \| 0.0395 \| 8.6765 \| 3540 \| 0.5544 \| 0.3724 \| 0.5544 \| 0.7446 \| \| 0.0395 \| 8.6814 \| 3542 \| 0.5542 \| 0.3724 \| 0.5542 \| 0.7445 \| \| 0.0395 \| 8.6863 \| 3544 \| 0.5583 \| 0.3724 \| 0.5583 \| 0.7472 \| \| 0.0395 \| 8.6912 \| 3546 \| 0.5577 \| 0.3724 \| 0.5577 \| 0.7468 \| \| 0.0395 \| 8.6961 \| 3548 \| 0.5577 \| 0.3724 \| 0.5577 \| 0.7468 \| \| 0.0395 \| 8.7010 \| 3550 \| 0.5605 \| 0.3724 \| 0.5605 \| 0.7486 \| \| 0.0395 \| 8.7059 \| 3552 \| 0.5652 \| 0.3724 \| 0.5652 \| 0.7518 \| \| 0.0395 \| 8.7108 \| 3554 \| 0.5696 \| 0.3724 \| 0.5696 \| 0.7547 \| \| 0.0395 \| 8.7157 \| 3556 \| 0.5758 \| 0.3724 \| 0.5758 \| 0.7588 \| \| 0.0395 \| 8.7206 \| 3558 \| 0.5789 \| 0.3724 \| 0.5789 \| 0.7609 \| \| 0.0395 \| 8.7255 \| 3560 \| 0.5809 \| 0.3724 \| 0.5809 \| 0.7622 \| \| 0.0395 \| 8.7304 \| 3562 \| 0.5822 \| 0.3724 \| 0.5822 \| 0.7630 \| \| 0.0395 \| 8.7353 \| 3564 \| 0.5844 \| 0.3724 \| 0.5844 \| 0.7645 \| \| 0.0395 \| 8.7402 \| 3566 \| 0.5884 \| 0.3724 \| 0.5884 \| 0.7671 \| \| 0.0395 \| 8.7451 \| 3568 \| 0.5900 \| 0.3724 \| 0.5900 \| 0.7681 \| \| 0.0395 \| 8.75 \| 3570 \| 0.5911 \| 0.3724 \| 0.5911 \| 0.7688 \| \| 0.0395 \| 8.7549 \| 3572 \| 0.5946 \| 0.3724 \| 0.5946 \| 0.7711 \| \| 0.0395 \| 8.7598 \| 3574 \| 0.5956 \| 0.3724 \| 0.5956 \| 0.7718 \| \| 0.0395 \| 8.7647 \| 3576 \| 0.5976 \| 0.3724 \| 0.5976 \| 0.7731 \| \| 0.0395 \| 8.7696 \| 3578 \| 0.5984 \| 0.3724 \| 0.5984 \| 0.7736 \| \| 0.0395 \| 8.7745 \| 3580 \| 0.5987 \| 0.3724 \| 0.5987 \| 0.7738 \| \| 0.0395 \| 8.7794 \| 3582 \| 0.5977 \| 0.3724 \| 0.5977 \| 0.7731 \| \| 0.0395 \| 8.7843 \| 3584 \| 0.5946 \| 0.3724 \| 0.5946 \| 0.7711 \| \| 0.0395 \| 8.7892 \| 3586 \| 0.5934 \| 0.3724 \| 0.5934 \| 0.7703 \| \| 0.0395 \| 8.7941 \| 3588 \| 0.5942 \| 0.3724 \| 0.5942 \| 0.7709 \| \| 0.0395 \| 8.7990 \| 3590 \| 0.5933 \| 0.3724 \| 0.5933 \| 0.7702 \| \| 0.0395 \| 8.8039 \| 3592 \| 0.5892 \| 0.3724 \| 0.5892 \| 0.7676 \| \| 0.0395 \| 8.8088 \| 3594 \| 0.5852 \| 0.3724 \| 0.5852 \| 0.7650 \| \| 0.0395 \| 8.8137 \| 3596 \| 0.5821 \| 0.3724 \| 0.5821 \| 0.7629 \| \| 0.0395 \| 8.8186 \| 3598 \| 0.5784 \| 0.3724 \| 0.5784 \| 0.7605 \| \| 0.0395 \| 8.8235 \| 3600 \| 0.5757 \| 0.3724 \| 0.5757 \| 0.7588 \| \| 0.0395 \| 8.8284 \| 3602 \| 0.5732 \| 0.3724 \| 0.5732 \| 0.7571 \| \| 0.0395 \| 8.8333 \| 3604 \| 0.5686 \| 0.3724 \| 0.5686 \| 0.7541 \| \| 0.0395 \| 8.8382 \| 3606 \| 0.5621 \| 0.4273 \| 0.5621 \| 0.7497 \| \| 0.0395 \| 8.8431 \| 3608 \| 0.5570 \| 0.4273 \| 0.5570 \| 0.7463 \| \| 0.0395 \| 8.8480 \| 3610 \| 0.5540 \| 0.4273 \| 0.5540 \| 0.7443 \| \| 0.0395 \| 8.8529 \| 3612 \| 0.5528 \| 0.3865 \| 0.5528 \| 0.7435 \| \| 0.0395 \| 8.8578 \| 3614 \| 0.5531 \| 0.4273 \| 0.5531 \| 0.7437 \| \| 0.0395 \| 8.8627 \| 3616 \| 0.5538 \| 0.4273 \| 0.5538 \| 0.7442 \| \| 0.0395 \| 8.8676 \| 3618 \| 0.5534 \| 0.4273 \| 0.5534 \| 0.7439 \| \| 0.0395 \| 8.8725 \| 3620 \| 0.5550 \| 0.4273 \| 0.5550 \| 0.7450 \| \| 0.0395 \| 8.8775 \| 3622 \| 0.5559 \| 0.4273 \| 0.5559 \| 0.7456 \| \| 0.0395 \| 8.8824 \| 3624 \| 0.5563 \| 0.4273 \| 0.5563 \| 0.7458 \| \| 0.0395 \| 8.8873 \| 3626 \| 0.5566 \| 0.4273 \| 0.5566 \| 0.7461 \| \| 0.0395 \| 8.8922 \| 3628 \| 0.5598 \| 0.4273 \| 0.5598 \| 0.7482 \| \| 0.0395 \| 8.8971 \| 3630 \| 0.5633 \| 0.4273 \| 0.5633 \| 0.7506 \| \| 0.0395 \| 8.9020 \| 3632 \| 0.5721 \| 0.4273 \| 0.5721 \| 0.7563 \| \| 0.0395 \| 8.9069 \| 3634 \| 0.5803 \| 0.2186 \| 0.5803 \| 0.7618 \| \| 0.0395 \| 8.9118 \| 3636 \| 0.5836 \| 0.2186 \| 0.5836 \| 0.7640 \| \| 0.0395 \| 8.9167 \| 3638 \| 0.5866 \| 0.2186 \| 0.5866 \| 0.7659 \| \| 0.0395 \| 8.9216 \| 3640 \| 0.5892 \| 0.2186 \| 0.5892 \| 0.7676 \| \| 0.0395 \| 8.9265 \| 3642 \| 0.5961 \| 0.2186 \| 0.5961 \| 0.7721 \| \| 0.0395 \| 8.9314 \| 3644 \| 0.6027 \| 0.2186 \| 0.6027 \| 0.7764 \| \| 0.0395 \| 8.9363 \| 3646 \| 0.6056 \| 0.3255 \| 0.6056 \| 0.7782 \| \| 0.0395 \| 8.9412 \| 3648 \| 0.6084 \| 0.3636 \| 0.6084 \| 0.7800 \| \| 0.0395 \| 8.9461 \| 3650 \| 0.6100 \| 0.3636 \| 0.6100 \| 0.7810 \| \| 0.0395 \| 8.9510 \| 3652 \| 0.6083 \| 0.3636 \| 0.6083 \| 0.7799 \| \| 0.0395 \| 8.9559 \| 3654 \| 0.6041 \| 0.3636 \| 0.6041 \| 0.7772 \| \| 0.0395 \| 8.9608 \| 3656 \| 0.5978 \| 0.3636 \| 0.5978 \| 0.7732 \| \| 0.0395 \| 8.9657 \| 3658 \| 0.5888 \| 0.2588 \| 0.5888 \| 0.7673 \| \| 0.0395 \| 8.9706 \| 3660 \| 0.5776 \| 0.2186 \| 0.5776 \| 0.7600 \| \| 0.0395 \| 8.9755 \| 3662 \| 0.5683 \| 0.2186 \| 0.5683 \| 0.7539 \| \| 0.0395 \| 8.9804 \| 3664 \| 0.5618 \| 0.2186 \| 0.5618 \| 0.7495 \| \| 0.0395 \| 8.9853 \| 3666 \| 0.5585 \| 0.3724 \| 0.5585 \| 0.7473 \| \| 0.0395 \| 8.9902 \| 3668 \| 0.5572 \| 0.3724 \| 0.5572 \| 0.7465 \| \| 0.0395 \| 8.9951 \| 3670 \| 0.5576 \| 0.3724 \| 0.5576 \| 0.7467 \| \| 0.0395 \| 9.0 \| 3672 \| 0.5591 \| 0.2186 \| 0.5591 \| 0.7477 \| \| 0.0395 \| 9.0049 \| 3674 \| 0.5627 \| 0.2186 \| 0.5627 \| 0.7501 \| \| 0.0395 \| 9.0098 \| 3676 \| 0.5669 \| 0.2186 \| 0.5669 \| 0.7529 \| \| 0.0395 \| 9.0147 \| 3678 \| 0.5710 \| 0.2186 \| 0.5710 \| 0.7556 \| \| 0.0395 \| 9.0196 \| 3680 \| 0.5723 \| 0.2186 \| 0.5723 \| 0.7565 \| \| 0.0395 \| 9.0245 \| 3682 \| 0.5713 \| 0.2186 \| 0.5713 \| 0.7559 \| \| 0.0395 \| 9.0294 \| 3684 \| 0.5690 \| 0.2186 \| 0.5690 \| 0.7543 \| \| 0.0395 \| 9.0343 \| 3686 \| 0.5685 \| 0.2186 \| 0.5685 \| 0.7540 \| \| 0.0395 \| 9.0392 \| 3688 \| 0.5687 \| 0.2186 \| 0.5687 \| 0.7541 \| \| 0.0395 \| 9.0441 \| 3690 \| 0.5692 \| 0.2186 \| 0.5692 \| 0.7544 \| \| 0.0395 \| 9.0490 \| 3692 \| 0.5677 \| 0.2186 \| 0.5677 \| 0.7535 \| \| 0.0395 \| 9.0539 \| 3694 \| 0.5653 \| 0.2186 \| 0.5653 \| 0.7519 \| \| 0.0395 \| 9.0588 \| 3696 \| 0.5648 \| 0.2186 \| 0.5648 \| 0.7516 \| \| 0.0395 \| 9.0637 \| 3698 \| 0.5657 \| 0.2186 \| 0.5657 \| 0.7521 \| \| 0.0395 \| 9.0686 \| 3700 \| 0.5678 \| 0.2186 \| 0.5678 \| 0.7536 \| \| 0.0395 \| 9.0735 \| 3702 \| 0.5700 \| 0.2186 \| 0.5700 \| 0.7550 \| \| 0.0395 \| 9.0784 \| 3704 \| 0.5745 \| 0.2186 \| 0.5745 \| 0.7579 \| \| 0.0395 \| 9.0833 \| 3706 \| 0.5772 \| 0.2186 \| 0.5772 \| 0.7597 \| \| 0.0395 \| 9.0882 \| 3708 \| 0.5789 \| 0.2186 \| 0.5789 \| 0.7609 \| \| 0.0395 \| 9.0931 \| 3710 \| 0.5789 \| 0.2186 \| 0.5789 \| 0.7609 \| \| 0.0395 \| 9.0980 \| 3712 \| 0.5778 \| 0.2759 \| 0.5778 \| 0.7602 \| \| 0.0395 \| 9.1029 \| 3714 \| 0.5768 \| 0.4273 \| 0.5768 \| 0.7595 \| \| 0.0395 \| 9.1078 \| 3716 \| 0.5758 \| 0.4273 \| 0.5758 \| 0.7588 \| \| 0.0395 \| 9.1127 \| 3718 \| 0.5743 \| 0.4273 \| 0.5743 \| 0.7579 \| \| 0.0395 \| 9.1176 \| 3720 \| 0.5733 \| 0.4273 \| 0.5733 \| 0.7571 \| \| 0.0395 \| 9.1225 \| 3722 \| 0.5704 \| 0.4273 \| 0.5704 \| 0.7553 \| \| 0.0395 \| 9.1275 \| 3724 \| 0.5683 \| 0.4273 \| 0.5683 \| 0.7539 \| \| 0.0395 \| 9.1324 \| 3726 \| 0.5648 \| 0.4273 \| 0.5648 \| 0.7515 \| \| 0.0395 \| 9.1373 \| 3728 \| 0.5603 \| 0.4273 \| 0.5603 \| 0.7485 \| \| 0.0395 \| 9.1422 \| 3730 \| 0.5565 \| 0.4273 \| 0.5565 \| 0.7460 \| \| 0.0395 \| 9.1471 \| 3732 \| 0.5521 \| 0.3865 \| 0.5521 \| 0.7431 \| \| 0.0395 \| 9.1520 \| 3734 \| 0.5487 \| 0.3865 \| 0.5487 \| 0.7407 \| \| 0.0395 \| 9.1569 \| 3736 \| 0.5448 \| 0.3865 \| 0.5448 \| 0.7381 \| \| 0.0395 \| 9.1618 \| 3738 \| 0.5402 \| 0.3865 \| 0.5402 \| 0.7350 \| \| 0.0395 \| 9.1667 \| 3740 \| 0.5361 \| 0.3865 \| 0.5361 \| 0.7322 \| \| 0.0395 \| 9.1716 \| 3742 \| 0.5336 \| 0.3865 \| 0.5336 \| 0.7305 \| \| 0.0395 \| 9.1765 \| 3744 \| 0.5327 \| 0.3865 \| 0.5327 \| 0.7299 \| \| 0.0395 \| 9.1814 \| 3746 \| 0.5325 \| 0.3865 \| 0.5325 \| 0.7297 \| \| 0.0395 \| 9.1863 \| 3748 \| 0.5332 \| 0.3865 \| 0.5332 \| 0.7302 \| \| 0.0395 \| 9.1912 \| 3750 \| 0.5334 \| 0.3865 \| 0.5334 \| 0.7304 \| \| 0.0395 \| 9.1961 \| 3752 \| 0.5338 \| 0.3865 \| 0.5338 \| 0.7306 \| \| 0.0395 \| 9.2010 \| 3754 \| 0.5335 \| 0.3865 \| 0.5335 \| 0.7304 \| \| 0.0395 \| 9.2059 \| 3756 \| 0.5345 \| 0.3865 \| 0.5345 \| 0.7311 \| \| 0.0395 \| 9.2108 \| 3758 \| 0.5365 \| 0.3865 \| 0.5365 \| 0.7325 \| \| 0.0395 \| 9.2157 \| 3760 \| 0.5391 \| 0.3865 \| 0.5391 \| 0.7342 \| \| 0.0395 \| 9.2206 \| 3762 \| 0.5403 \| 0.4273 \| 0.5403 \| 0.7350 \| \| 0.0395 \| 9.2255 \| 3764 \| 0.5400 \| 0.4273 \| 0.5400 \| 0.7349 \| \| 0.0395 \| 9.2304 \| 3766 \| 0.5395 \| 0.4273 \| 0.5395 \| 0.7345 \| \| 0.0395 \| 9.2353 \| 3768 \| 0.5383 \| 0.3865 \| 0.5383 \| 0.7337 \| \| 0.0395 \| 9.2402 \| 3770 \| 0.5370 \| 0.3865 \| 0.5370 \| 0.7328 \| \| 0.0395 \| 9.2451 \| 3772 \| 0.5368 \| 0.3865 \| 0.5368 \| 0.7327 \| \| 0.0395 \| 9.25 \| 3774 \| 0.5364 \| 0.4273 \| 0.5364 \| 0.7324 \| \| 0.0395 \| 9.2549 \| 3776 \| 0.5382 \| 0.4273 \| 0.5382 \| 0.7336 \| \| 0.0395 \| 9.2598 \| 3778 \| 0.5408 \| 0.4273 \| 0.5408 \| 0.7354 \| \| 0.0395 \| 9.2647 \| 3780 \| 0.5430 \| 0.4273 \| 0.5430 \| 0.7369 \| \| 0.0395 \| 9.2696 \| 3782 \| 0.5467 \| 0.4273 \| 0.5467 \| 0.7394 \| \| 0.0395 \| 9.2745 \| 3784 \| 0.5521 \| 0.4273 \| 0.5521 \| 0.7430 \| \| 0.0395 \| 9.2794 \| 3786 \| 0.5571 \| 0.3724 \| 0.5571 \| 0.7464 \| \| 0.0395 \| 9.2843 \| 3788 \| 0.5607 \| 0.2186 \| 0.5607 \| 0.7488 \| \| 0.0395 \| 9.2892 \| 3790 \| 0.5620 \| 0.2186 \| 0.5620 \| 0.7496 \| \| 0.0395 \| 9.2941 \| 3792 \| 0.5625 \| 0.3724 \| 0.5625 \| 0.7500 \| \| 0.0395 \| 9.2990 \| 3794 \| 0.5618 \| 0.3724 \| 0.5618 \| 0.7496 \| \| 0.0395 \| 9.3039 \| 3796 \| 0.5623 \| 0.3724 \| 0.5623 \| 0.7499 \| \| 0.0395 \| 9.3088 \| 3798 \| 0.5630 \| 0.3724 \| 0.5630 \| 0.7503 \| \| 0.0395 \| 9.3137 \| 3800 \| 0.5649 \| 0.3724 \| 0.5649 \| 0.7516 \| \| 0.0395 \| 9.3186 \| 3802 \| 0.5654 \| 0.3724 \| 0.5654 \| 0.7519 \| \| 0.0395 \| 9.3235 \| 3804 \| 0.5646 \| 0.3724 \| 0.5646 \| 0.7514 \| \| 0.0395 \| 9.3284 \| 3806 \| 0.5633 \| 0.3724 \| 0.5633 \| 0.7506 \| \| 0.0395 \| 9.3333 \| 3808 \| 0.5630 \| 0.3724 \| 0.5630 \| 0.7503 \| \| 0.0395 \| 9.3382 \| 3810 \| 0.5640 \| 0.3724 \| 0.5640 \| 0.7510 \| \| 0.0395 \| 9.3431 \| 3812 \| 0.5662 \| 0.3724 \| 0.5662 \| 0.7525 \| \| 0.0395 \| 9.3480 \| 3814 \| 0.5707 \| 0.4661 \| 0.5707 \| 0.7555 \| \| 0.0395 \| 9.3529 \| 3816 \| 0.5756 \| 0.4661 \| 0.5756 \| 0.7587 \| \| 0.0395 \| 9.3578 \| 3818 \| 0.5784 \| 0.4661 \| 0.5784 \| 0.7605 \| \| 0.0395 \| 9.3627 \| 3820 \| 0.5805 \| 0.4661 \| 0.5805 \| 0.7619 \| \| 0.0395 \| 9.3676 \| 3822 \| 0.5831 \| 0.4661 \| 0.5831 \| 0.7636 \| \| 0.0395 \| 9.3725 \| 3824 \| 0.5853 \| 0.4661 \| 0.5853 \| 0.7651 \| \| 0.0395 \| 9.3775 \| 3826 \| 0.5859 \| 0.3255 \| 0.5859 \| 0.7654 \| \| 0.0395 \| 9.3824 \| 3828 \| 0.5855 \| 0.3255 \| 0.5855 \| 0.7652 \| \| 0.0395 \| 9.3873 \| 3830 \| 0.5837 \| 0.4661 \| 0.5837 \| 0.7640 \| \| 0.0395 \| 9.3922 \| 3832 \| 0.5815 \| 0.4661 \| 0.5815 \| 0.7626 \| \| 0.0395 \| 9.3971 \| 3834 \| 0.5779 \| 0.4661 \| 0.5779 \| 0.7602 \| \| 0.0395 \| 9.4020 \| 3836 \| 0.5757 \| 0.4661 \| 0.5757 \| 0.7587 \| \| 0.0395 \| 9.4069 \| 3838 \| 0.5724 \| 0.4661 \| 0.5724 \| 0.7566 \| \| 0.0395 \| 9.4118 \| 3840 \| 0.5706 \| 0.4661 \| 0.5706 \| 0.7554 \| \| 0.0395 \| 9.4167 \| 3842 \| 0.5696 \| 0.4661 \| 0.5696 \| 0.7547 \| \| 0.0395 \| 9.4216 \| 3844 \| 0.5677 \| 0.4661 \| 0.5677 \| 0.7535 \| \| 0.0395 \| 9.4265 \| 3846 \| 0.5645 \| 0.3724 \| 0.5645 \| 0.7514 \| \| 0.0395 \| 9.4314 \| 3848 \| 0.5597 \| 0.3724 \| 0.5597 \| 0.7481 \| \| 0.0395 \| 9.4363 \| 3850 \| 0.5552 \| 0.4273 \| 0.5552 \| 0.7451 \| \| 0.0395 \| 9.4412 \| 3852 \| 0.5515 \| 0.4273 \| 0.5515 \| 0.7426 \| \| 0.0395 \| 9.4461 \| 3854 \| 0.5495 \| 0.4273 \| 0.5495 \| 0.7413 \| \| 0.0395 \| 9.4510 \| 3856 \| 0.5481 \| 0.4273 \| 0.5481 \| 0.7403 \| \| 0.0395 \| 9.4559 \| 3858 \| 0.5491 \| 0.4273 \| 0.5491 \| 0.7410 \| \| 0.0395 \| 9.4608 \| 3860 \| 0.5512 \| 0.4273 \| 0.5512 \| 0.7424 \| \| 0.0395 \| 9.4657 \| 3862 \| 0.5539 \| 0.4273 \| 0.5539 \| 0.7443 \| \| 0.0395 \| 9.4706 \| 3864 \| 0.5568 \| 0.4273 \| 0.5568 \| 0.7462 \| \| 0.0395 \| 9.4755 \| 3866 \| 0.5586 \| 0.4273 \| 0.5586 \| 0.7474 \| \| 0.0395 \| 9.4804 \| 3868 \| 0.5591 \| 0.4273 \| 0.5591 \| 0.7477 \| \| 0.0395 \| 9.4853 \| 3870 \| 0.5600 \| 0.3724 \| 0.5600 \| 0.7483 \| \| 0.0395 \| 9.4902 \| 3872 \| 0.5613 \| 0.3724 \| 0.5613 \| 0.7492 \| \| 0.0395 \| 9.4951 \| 3874 \| 0.5638 \| 0.3724 \| 0.5638 \| 0.7509 \| \| 0.0395 \| 9.5 \| 3876 \| 0.5661 \| 0.3724 \| 0.5661 \| 0.7524 \| \| 0.0395 \| 9.5049 \| 3878 \| 0.5688 \| 0.4661 \| 0.5688 \| 0.7542 \| \| 0.0395 \| 9.5098 \| 3880 \| 0.5699 \| 0.4661 \| 0.5699 \| 0.7549 \| \| 0.0395 \| 9.5147 \| 3882 \| 0.5689 \| 0.4661 \| 0.5689 \| 0.7543 \| \| 0.0395 \| 9.5196 \| 3884 \| 0.5698 \| 0.4661 \| 0.5698 \| 0.7549 \| \| 0.0395 \| 9.5245 \| 3886 \| 0.5715 \| 0.4661 \| 0.5715 \| 0.7560 \| \| 0.0395 \| 9.5294 \| 3888 \| 0.5728 \| 0.4661 \| 0.5728 \| 0.7569 \| \| 0.0395 \| 9.5343 \| 3890 \| 0.5726 \| 0.4661 \| 0.5726 \| 0.7567 \| \| 0.0395 \| 9.5392 \| 3892 \| 0.5726 \| 0.4661 \| 0.5726 \| 0.7567 \| \| 0.0395 \| 9.5441 \| 3894 \| 0.5726 \| 0.4661 \| 0.5726 \| 0.7567 \| \| 0.0395 \| 9.5490 \| 3896 \| 0.5733 \| 0.4661 \| 0.5733 \| 0.7572 \| \| 0.0395 \| 9.5539 \| 3898 \| 0.5744 \| 0.4661 \| 0.5744 \| 0.7579 \| \| 0.0395 \| 9.5588 \| 3900 \| 0.5754 \| 0.4661 \| 0.5754 \| 0.7585 \| \| 0.0395 \| 9.5637 \| 3902 \| 0.5769 \| 0.4661 \| 0.5769 \| 0.7595 \| \| 0.0395 \| 9.5686 \| 3904 \| 0.5777 \| 0.4661 \| 0.5777 \| 0.7601 \| \| 0.0395 \| 9.5735 \| 3906 \| 0.5774 \| 0.4661 \| 0.5774 \| 0.7599 \| \| 0.0395 \| 9.5784 \| 3908 \| 0.5773 \| 0.4661 \| 0.5773 \| 0.7598 \| \| 0.0395 \| 9.5833 \| 3910 \| 0.5783 \| 0.4661 \| 0.5783 \| 0.7604 \| \| 0.0395 \| 9.5882 \| 3912 \| 0.5782 \| 0.4661 \| 0.5782 \| 0.7604 \| \| 0.0395 \| 9.5931 \| 3914 \| 0.5770 \| 0.4661 \| 0.5770 \| 0.7596 \| \| 0.0395 \| 9.5980 \| 3916 \| 0.5759 \| 0.4661 \| 0.5759 \| 0.7589 \| \| 0.0395 \| 9.6029 \| 3918 \| 0.5747 \| 0.4661 \| 0.5747 \| 0.7581 \| \| 0.0395 \| 9.6078 \| 3920 \| 0.5728 \| 0.4661 \| 0.5728 \| 0.7568 \| \| 0.0395 \| 9.6127 \| 3922 \| 0.5706 \| 0.3724 \| 0.5706 \| 0.7554 \| \| 0.0395 \| 9.6176 \| 3924 \| 0.5685 \| 0.3724 \| 0.5685 \| 0.7540 \| \| 0.0395 \| 9.6225 \| 3926 \| 0.5657 \| 0.3724 \| 0.5657 \| 0.7521 \| \| 0.0395 \| 9.6275 \| 3928 \| 0.5639 \| 0.3724 \| 0.5639 \| 0.7509 \| \| 0.0395 \| 9.6324 \| 3930 \| 0.5626 \| 0.3724 \| 0.5626 \| 0.7501 \| \| 0.0395 \| 9.6373 \| 3932 \| 0.5616 \| 0.3724 \| 0.5616 \| 0.7494 \| \| 0.0395 \| 9.6422 \| 3934 \| 0.5607 \| 0.3724 \| 0.5607 \| 0.7488 \| \| 0.0395 \| 9.6471 \| 3936 \| 0.5582 \| 0.3724 \| 0.5582 \| 0.7471 \| \| 0.0395 \| 9.6520 \| 3938 \| 0.5567 \| 0.3724 \| 0.5567 \| 0.7461 \| \| 0.0395 \| 9.6569 \| 3940 \| 0.5553 \| 0.3724 \| 0.5553 \| 0.7452 \| \| 0.0395 \| 9.6618 \| 3942 \| 0.5543 \| 0.3724 \| 0.5543 \| 0.7445 \| \| 0.0395 \| 9.6667 \| 3944 \| 0.5538 \| 0.3724 \| 0.5538 \| 0.7441 \| \| 0.0395 \| 9.6716 \| 3946 \| 0.5537 \| 0.3724 \| 0.5537 \| 0.7441 \| \| 0.0395 \| 9.6765 \| 3948 \| 0.5541 \| 0.3724 \| 0.5541 \| 0.7444 \| \| 0.0395 \| 9.6814 \| 3950 \| 0.5545 \| 0.3724 \| 0.5545 \| 0.7447 \| \| 0.0395 \| 9.6863 \| 3952 \| 0.5553 \| 0.3724 \| 0.5553 \| 0.7452 \| \| 0.0395 \| 9.6912 \| 3954 \| 0.5557 \| 0.3724 \| 0.5557 \| 0.7455 \| \| 0.0395 \| 9.6961 \| 3956 \| 0.5560 \| 0.3724 \| 0.5560 \| 0.7456 \| \| 0.0395 \| 9.7010 \| 3958 \| 0.5553 \| 0.3724 \| 0.5553 \| 0.7452 \| \| 0.0395 \| 9.7059 \| 3960 \| 0.5555 \| 0.3724 \| 0.5555 \| 0.7453 \| \| 0.0395 \| 9.7108 \| 3962 \| 0.5563 \| 0.3724 \| 0.5563 \| 0.7459 \| \| 0.0395 \| 9.7157 \| 3964 \| 0.5571 \| 0.3724 \| 0.5571 \| 0.7464 \| \| 0.0395 \| 9.7206 \| 3966 \| 0.5581 \| 0.3724 \| 0.5581 \| 0.7471 \| \| 0.0395 \| 9.7255 \| 3968 \| 0.5588 \| 0.3724 \| 0.5588 \| 0.7475 \| \| 0.0395 \| 9.7304 \| 3970 \| 0.5593 \| 0.3724 \| 0.5593 \| 0.7478 \| \| 0.0395 \| 9.7353 \| 3972 \| 0.5601 \| 0.3724 \| 0.5601 \| 0.7484 \| \| 0.0395 \| 9.7402 \| 3974 \| 0.5604 \| 0.3724 \| 0.5604 \| 0.7486 \| \| 0.0395 \| 9.7451 \| 3976 \| 0.5609 \| 0.3724 \| 0.5609 \| 0.7490 \| \| 0.0395 \| 9.75 \| 3978 \| 0.5611 \| 0.3724 \| 0.5611 \| 0.7490 \| \| 0.0395 \| 9.7549 \| 3980 \| 0.5616 \| 0.4661 \| 0.5616 \| 0.7494 \| \| 0.0395 \| 9.7598 \| 3982 \| 0.5622 \| 0.4661 \| 0.5622 \| 0.7498 \| \| 0.0395 \| 9.7647 \| 3984 \| 0.5627 \| 0.4661 \| 0.5627 \| 0.7501 \| \| 0.0395 \| 9.7696 \| 3986 \| 0.5633 \| 0.4661 \| 0.5633 \| 0.7505 \| \| 0.0395 \| 9.7745 \| 3988 \| 0.5643 \| 0.4661 \| 0.5643 \| 0.7512 \| \| 0.0395 \| 9.7794 \| 3990 \| 0.5650 \| 0.4661 \| 0.5650 \| 0.7517 \| \| 0.0395 \| 9.7843 \| 3992 \| 0.5657 \| 0.4661 \| 0.5657 \| 0.7522 \| \| 0.0395 \| 9.7892 \| 3994 \| 0.5667 \| 0.3255 \| 0.5667 \| 0.7528 \| \| 0.0395 \| 9.7941 \| 3996 \| 0.5677 \| 0.3255 \| 0.5677 \| 0.7535 \| \| 0.0395 \| 9.7990 \| 3998 \| 0.5690 \| 0.3255 \| 0.5690 \| 0.7543 \| \| 0.0358 \| 9.8039 \| 4000 \| 0.5703 \| 0.3255 \| 0.5703 \| 0.7552 \| \| 0.0358 \| 9.8088 \| 4002 \| 0.5722 \| 0.3255 \| 0.5722 \| 0.7564 \| \| 0.0358 \| 9.8137 \| 4004 \| 0.5739 \| 0.3255 \| 0.5739 \| 0.7576 \| \| 0.0358 \| 9.8186 \| 4006 \| 0.5745 \| 0.3255 \| 0.5745 \| 0.7580 \| \| 0.0358 \| 9.8235 \| 4008 \| 0.5747 \| 0.3255 \| 0.5747 \| 0.7581 \| \| 0.0358 \| 9.8284 \| 4010 \| 0.5741 \| 0.3255 \| 0.5741 \| 0.7577 \| \| 0.0358 \| 9.8333 \| 4012 \| 0.5736 \| 0.3255 \| 0.5736 \| 0.7574 \| \| 0.0358 \| 9.8382 \| 4014 \| 0.5741 \| 0.3255 \| 0.5741 \| 0.7577 \| \| 0.0358 \| 9.8431 \| 4016 \| 0.5744 \| 0.3255 \| 0.5744 \| 0.7579 \| \| 0.0358 \| 9.8480 \| 4018 \| 0.5741 \| 0.3255 \| 0.5741 \| 0.7577 \| \| 0.0358 \| 9.8529 \| 4020 \| 0.5739 \| 0.3255 \| 0.5739 \| 0.7576 \| \| 0.0358 \| 9.8578 \| 4022 \| 0.5734 \| 0.3255 \| 0.5734 \| 0.7572 \| \| 0.0358 \| 9.8627 \| 4024 \| 0.5731 \| 0.3255 \| 0.5731 \| 0.7570 \| \| 0.0358 \| 9.8676 \| 4026 \| 0.5723 \| 0.3255 \| 0.5723 \| 0.7565 \| \| 0.0358 \| 9.8725 \| 4028 \| 0.5719 \| 0.3255 \| 0.5719 \| 0.7562 \| \| 0.0358 \| 9.8775 \| 4030 \| 0.5717 \| 0.3255 \| 0.5717 \| 0.7561 \| \| 0.0358 \| 9.8824 \| 4032 \| 0.5713 \| 0.3255 \| 0.5713 \| 0.7558 \| \| 0.0358 \| 9.8873 \| 4034 \| 0.5709 \| 0.3255 \| 0.5709 \| 0.7556 \| \| 0.0358 \| 9.8922 \| 4036 \| 0.5706 \| 0.3255 \| 0.5706 \| 0.7554 \| \| 0.0358 \| 9.8971 \| 4038 \| 0.5701 \| 0.3255 \| 0.5701 \| 0.7550 \| \| 0.0358 \| 9.9020 \| 4040 \| 0.5696 \| 0.3255 \| 0.5696 \| 0.7547 \| \| 0.0358 \| 9.9069 \| 4042 \| 0.5691 \| 0.3255 \| 0.5691 \| 0.7544 \| \| 0.0358 \| 9.9118 \| 4044 \| 0.5685 \| 0.3255 \| 0.5685 \| 0.7540 \| \| 0.0358 \| 9.9167 \| 4046 \| 0.5679 \| 0.3255 \| 0.5679 \| 0.7536 \| \| 0.0358 \| 9.9216 \| 4048 \| 0.5674 \| 0.3255 \| 0.5674 \| 0.7533 \| \| 0.0358 \| 9.9265 \| 4050 \| 0.5670 \| 0.3255 \| 0.5670 \| 0.7530 \| \| 0.0358 \| 9.9314 \| 4052 \| 0.5666 \| 0.3255 \| 0.5666 \| 0.7527 \| \| 0.0358 \| 9.9363 \| 4054 \| 0.5663 \| 0.4661 \| 0.5663 \| 0.7525 \| \| 0.0358 \| 9.9412 \| 4056 \| 0.5662 \| 0.4661 \| 0.5662 \| 0.7524 \| \| 0.0358 \| 9.9461 \| 4058 \| 0.5659 \| 0.4661 \| 0.5659 \| 0.7523 \| \| 0.0358 \| 9.9510 \| 4060 \| 0.5657 \| 0.4661 \| 0.5657 \| 0.7521 \| \| 0.0358 \| 9.9559 \| 4062 \| 0.5656 \| 0.4661 \| 0.5656 \| 0.7521 \| \| 0.0358 \| 9.9608 \| 4064 \| 0.5656 \| 0.4661 \| 0.5656 \| 0.7521 \| \| 0.0358 \| 9.9657 \| 4066 \| 0.5656 \| 0.4661 \| 0.5656 \| 0.7520 \| \| 0.0358 \| 9.9706 \| 4068 \| 0.5655 \| 0.4661 \| 0.5655 \| 0.7520 \| \| 0.0358 \| 9.9755 \| 4070 \| 0.5654 \| 0.4661 \| 0.5654 \| 0.7519 \| \| 0.0358 \| 9.9804 \| 4072 \| 0.5654 \| 0.4661 \| 0.5654 \| 0.7519 \| \| 0.0358 \| 9.9853 \| 4074 \| 0.5654 \| 0.4661 \| 0.5654 \| 0.7519 \| \| 0.0358 \| 9.9902 \| 4076 \| 0.5654 \| 0.4661 \| 0.5654 \| 0.7519 \| \| 0.0358 \| 9.9951 \| 4078 \| 0.5654 \| 0.4661 \| 0.5654 \| 0.7519 \| \| 0.0358 \| 10.0 \| 4080 \| 0.5654 \| 0.4661 \| 0.5654 \| 0.7519 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
SeppeV/ernie2p0_ft_pref_10pc	SeppeV	2024-11-25T09:09:00Z	89	0	transformers	[ "transformers", "safetensors", "ernie", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T15:34:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
allknowingroger/Marco-01-slerp7-7B	allknowingroger	2024-11-25T09:08:36Z	10	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "base_model:AIDC-AI/Marco-o1", "base_model:merge:AIDC-AI/Marco-o1", "base_model:ZeroXClem/Qwen2.5-7B-HomerCreative-Mix", "base_model:merge:ZeroXClem/Qwen2.5-7B-HomerCreative-Mix", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T08:54:00Z	--- base_model: - ZeroXClem/Qwen2.5-7B-HomerCreative-Mix - AIDC-AI/Marco-o1 library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [ZeroXClem/Qwen2.5-7B-HomerCreative-Mix](https://huggingface.co/ZeroXClem/Qwen2.5-7B-HomerCreative-Mix) * [AIDC-AI/Marco-o1](https://huggingface.co/AIDC-AI/Marco-o1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: AIDC-AI/Marco-o1 - model: ZeroXClem/Qwen2.5-7B-HomerCreative-Mix merge_method: slerp base_model: AIDC-AI/Marco-o1 dtype: bfloat16 parameters: t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers ```
jebish7/cde-small-v1_MNR_3	jebish7	2024-11-25T09:06:55Z	6	0	sentence-transformers	[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:29545", "loss:MultipleNegativesRankingLoss", "custom_code", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:jxm/cde-small-v1", "base_model:finetune:jxm/cde-small-v1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-11-25T09:06:28Z	--- base_model: jxm/cde-small-v1 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:29545 - loss:MultipleNegativesRankingLoss widget: - source_sentence: In terms of audited accounts submission for an Applicant, could you clarify the scenarios in which the Regulator might agree that a reviewed pro forma statement of financial position is not needed, and what factors would be considered in making that determination? sentences: - "DocumentID: 1 \| PassageID: 4.2.1.(3) \| Passage: Where the regulator in another\ \ jurisdiction does not permit the implementation of policies, procedures, systems\ \ and controls consistent with these Rules, the Relevant Person must:\n(a)\tinform\ \ the Regulator in writing immediately; and\n(b)\tapply appropriate additional\ \ measures to manage the money laundering risks posed by the relevant branch or\ \ subsidiary." - "DocumentID: 11 \| PassageID: 2.3.15.(4) \| Passage: The Applicant must submit to\ \ the Regulator the following records, as applicable:\n(a)\tAudited accounts,\ \ for the purposes of this Rule and Rule 2.3.2(1), for the last three full financial\ \ years, noting that:\n(i)\tif the Applicant applies for admission less than ninety\ \ days after the end of its last financial year, unless the Applicant has audited\ \ accounts for its latest full financial year, the accounts may be for the three\ \ years to the end of the previous financial year, but must also include audited\ \ or reviewed accounts for its most recent semi-annual financial reporting period;\ \ and\n(ii)\tif the Applicant applies for admission more than six months and seventy-five\ \ days after the end of its last financial year, audited or reviewed accounts\ \ for its most recent semi-annual financial reporting period (or longer period\ \ if available).\n(b)\tUnless the Regulator agrees it is not needed, a reviewed\ \ pro forma statement of financial position. The review must be conducted by an\ \ accredited professional auditor of the company or an independent accountant." - 'DocumentID: 36 \| PassageID: D.1.3. \| Passage: Principle 1 – Oversight and responsibility of climate-related financial risk exposures.Certain functions related to the management of climate-related financial risks may be delegated, but, as with other risks, the board is ultimately responsible and accountable for monitoring, managing and overseeing climate-related risks for the financial firm. ' - source_sentence: A financial institution is interested in multiple designations, including the ADGM Green Fund and ADGM Green Bond. For each application, what fee will the institution incur? sentences: - 'DocumentID: 31 \| PassageID: 63) \| Passage: INITIAL DISCLOSURE OF MATERIAL ESTIMATES. Disclosure of material estimates of Contingent Resources Section 2.3 of the PRMS Guidelines states that Contingent Resources may be assigned for Petroleum Projects that are dependent on ‘technology under development’, and further recommended that a number of guidelines are followed in order to distinguish these estimates from those that should be classified as Unrecoverable Petroleum. By way of Rule 12.10.1(3), the FSRA fully supports and requires compliance with what is set out in the PRMS Guidelines. ' - 'DocumentID: 19 \| PassageID: 40) \| Passage: REGULATORY REQUIREMENTS FOR AUTHORISED PERSONS ENGAGED IN REGULATED ACTIVITIES IN RELATION TO VIRTUAL ASSETS Anti-Money Laundering and Countering Financing of Terrorism On 21 June 2019, FATF released a revised Guidance for a Risk-Based Approach (RBA) for VAs and VASPs, as well as an Interpretative Note for Recommendation 15. This built upon previous FATF statements by clarifying a RBA for Anti-Money Laundering and Countering the Financing of Terrorism (“AML/CFT”) purposes. The basic principle underlying the FATF Guidelines is that VASPs are expected to “identify, assess, and take effective action to mitigate their ML/TF risks” with respect to VAs. ' - "DocumentID: 4 \| PassageID: 10.1.1 \| Passage: A Person applying to the Regulator\ \ for any of the following designations:\n(a)\tADGM Green Fund;\n(b)\tADGM Climate\ \ Transition Fund;\n(c)\tADGM Green Portfolio;\n(d)\tADGM Climate Transition Portfolio;\n\ (e)\tADGM Green Bond; or\n(f)\tADGM Sustainability Linked Bond\nmust pay to the\ \ Regulator an application fee of $2,000." - source_sentence: How does the ADGM expect Authorised Persons to incorporate the eligibility of collateral types into their overall risk management framework, particularly concerning Islamic finance principles? sentences: - 'DocumentID: 17 \| PassageID: Schedule 1.Part 2.Chapter 5.42.(2) \| Passage: In determining for the purposes of sub-paragraph ‎(1)‎(b) whether Deposits are accepted only on particular occasions, regard is to be had to the frequency of those occasions and to any characteristics distinguishing them from each other.' - "DocumentID: 9 \| PassageID: 6.8.5 \| Passage: \n(a)\tA Fund Manager of an Islamic\ \ REIT may obtain financing either directly or through its Special Purpose Vehicle\ \ up to 65% of the total gross asset value of the Fund provided that such financing\ \ is provided in a Shari'a-compliant manner.\n(b)\tUpon becoming aware that the\ \ borrowing limit set out in 6.8.5(a) has been exceeded, the Fund Manager shall:\n\ (c)\timmediately inform Unitholders and the Regulator of the details of the breach\ \ and the proposed remedial action;\n(d)\tuse its best endeavours to reduce the\ \ excess borrowings;\n(e)\tnot permit the Fund to engage in additional borrowing;\ \ and\n(f)\tinform Unitholders and the Regulator on a regular basis as to the\ \ progress of the remedial action." - 'DocumentID: 9 \| PassageID: 5.1.1.Guidance.(ii) \| Passage: The prudential Category for Islamic Financial Institutions and other Authorised Persons (acting through an Islamic Window) undertaking the Regulated Activity of Managing PSIAs (which may be either a Restricted PSIA or an Unrestricted PSIA) is determined in accordance with PRU Rule 1.3. An Authorised Person which Manages PSIAs (whether as an Islamic Financial Institution or through an Islamic Window) must comply with the requirements in PRU in relation to specific prudential requirements relating to Trading Book and Non-Trading Book activities, including Credit Risk, Market Risk, Liquidity Risk and Group Risk.' - source_sentence: Can you please detail the specific Anti-Money Laundering (AML) and Countering Financing of Terrorism (CFT) measures and controls that our firm must have in place when dealing with Spot Commodities as per the FSRA's requirements? sentences: - 'DocumentID: 34 \| PassageID: 65) \| Passage: REGULATORY REQUIREMENTS - SPOT COMMODITY ACTIVITIES Sanctions Pursuant to AML Rule 11.2.1(1), an Authorised Person must have arrangements in place to ensure that only Spot Commodities that are not subject to sanctions or associated with an entity in the supply chain that is itself subject to a sanction, are used as part of its Regulated Activities, or utilised as part of a delivery and/or storage facility operated by itself (or by any third parties it uses). In demonstrating compliance with the Rule, an Authorised Person must have powers to resolve any breach in a timely fashion, such as taking emergency action itself or by compelling the delivery and/or storage facility to take appropriate action. The FSRA expects this to include the Authorised Person having the ability to sanction a Member, market participant or the delivery and/or storage facility for acts or omissions that compromise compliance with applicable sanctions. ' - "DocumentID: 18 \| PassageID: 3.2 \| Passage: Financial Services Permissions. VC\ \ Managers operating in ADGM require a Financial Services Permission (“FSP”) to\ \ undertake any Regulated Activity pertaining to VC Funds and/or co-investments\ \ by third parties in VC Funds. The Regulated Activities covered by the FSP will\ \ be dependent on the VC Managers’ investment strategy and business model.\n(a)\t\ Managing a Collective Investment Fund: this includes carrying out fund management\ \ activities in respect of a VC Fund.\n(b)\tAdvising on Investments or Credit\ \ : for VC Managers these activities will be restricted to activities related\ \ to co-investment alongside a VC Fund which the VC Manager manages, such as recommending\ \ that a client invest in an investee company alongside the VC Fund and on the\ \ strategy and structure required to make the investment.\n(c)\tArranging Deals\ \ in Investments: VC Managers may also wish to make arrangements to facilitate\ \ co-investments in the investee company.\nAuthorisation fees and supervision\ \ fees for a VC Manager are capped at USD 10,000 regardless of whether one or\ \ both of the additional Regulated Activities in b) and c) above in relation to\ \ co-investments are included in its FSP. The FSP will include restrictions appropriate\ \ to the business model of a VC Manager." - 'DocumentID: 24 \| PassageID: 3.9 \| Passage: Principle 2 – High Standards for Authorisation. This discerning approach is shown by the FSRA’s power to only permit VAs that it deems ‘acceptable’, as determined by risk factors such as security and traceability, in order to prevent the build-up of risk from illiquid or immature assets. Additionally, we do not permit stablecoins based on the algorithmic model of valuation to the underlying fiat currency.' - source_sentence: What are the common scenarios or instances where assets and liabilities are not covered by the bases of accounting in Rule 5.3.2, and how should an Insurer address these in their reporting? sentences: - 'DocumentID: 1 \| PassageID: 14.4.1.Guidance.1. \| Passage: Relevant Persons are reminded that in accordance with Federal AML Legislation, Relevant Persons or any of their Employees must not tip off any Person, that is, inform any Person that he is being scrutinised, or investigated by any other competent authority, for possible involvement in suspicious Transactions or activity related to money laundering or terrorist financing.' - "DocumentID: 12 \| PassageID: 5.3.1.Guidance \| Passage: \nThe exceptions provided\ \ in this Chapter relate to the following:\na.\tspecific Rules in respect of certain\ \ assets and liabilities, intended to achieve a regulatory objective not achieved\ \ by application of either or both of the bases of accounting set out in Rule\ \ ‎5.3.2;\nb.\tassets and liabilities that are not dealt with in either or both\ \ of the bases of accounting set out in Rule ‎5.3.2; and\nc.\tthe overriding power\ \ of the Regulator, set out in Rule ‎5.1.6, to require an Insurer to adopt a particular\ \ measurement for a specific asset or liability." - 'DocumentID: 1 \| PassageID: 6.2.1.Guidance.2. \| Passage: The risk assessment under Rule ‎6.2.1(c) should identify actions to mitigate risks associated with undertaking NFTF business generally, and the use of eKYC specifically. This is because distinct risks are often likely to arise where business is conducted entirely in an NFTF manner, compared to when the business relationship includes a mix of face-to-face and NFTF interactions. The assessment should make reference to risk mitigation measures recommended by the Regulator, a competent authority of the U.A.E., FATF, and other relevant bodies. ' --- # SentenceTransformer based on jxm/cde-small-v1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jxm/cde-small-v1](https://huggingface.co/jxm/cde-small-v1) on the csv dataset. It maps sentences & paragraphs to a None-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [jxm/cde-small-v1](https://huggingface.co/jxm/cde-small-v1) <!-- at revision 9e2ed1d8d569d34458913d2d246935c1b2324d11 --> - Maximum Sequence Length: None tokens - Output Dimensionality: None tokens - Similarity Function: Cosine Similarity - Training Dataset: - csv <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({}) with Transformer model: DatasetTransformer ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("jebish7/cde-small-v1_MNR_3") # Run inference sentences = [ 'What are the common scenarios or instances where assets and liabilities are not covered by the bases of accounting in Rule 5.3.2, and how should an Insurer address these in their reporting?', 'DocumentID: 12 \| PassageID: 5.3.1.Guidance \| Passage: \nThe exceptions provided in this Chapter relate to the following:\na.\tspecific Rules in respect of certain assets and liabilities, intended to achieve a regulatory objective not achieved by application of either or both of the bases of accounting set out in Rule \u200e5.3.2;\nb.\tassets and liabilities that are not dealt with in either or both of the bases of accounting set out in Rule \u200e5.3.2; and\nc.\tthe overriding power of the Regulator, set out in Rule \u200e5.1.6, to require an Insurer to adopt a particular measurement for a specific asset or liability.', 'DocumentID: 1 \| PassageID: 14.4.1.Guidance.1. \| Passage: Relevant Persons are reminded that in accordance with Federal AML Legislation, Relevant Persons or any of their Employees must not tip off any Person, that is, inform any Person that he is being scrutinised, or investigated by any other competent authority, for possible involvement in suspicious Transactions or activity related to money laundering or terrorist financing.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 29,545 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: \| \| anchor \| positive \| \|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 16 tokens</li><li>mean: 34.95 tokens</li><li>max: 68 tokens</li></ul> \| <ul><li>min: 35 tokens</li><li>mean: 132.0 tokens</li><li>max: 512 tokens</li></ul> \| * Samples: \| anchor \| positive \| \|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>If a financial institution offers Money Remittance as one of its services, under what circumstances is it deemed to be holding Relevant Money and therefore subject to regulatory compliance (a)?</code> \| <code>DocumentID: 13 \| PassageID: 3.7.1.Guidance.1. \| Passage: An Authorised Person is considered to be holding Relevant Money and subject to (a) where it offers Payment Services alongside currency exchange or Money Remittance.<br></code> \| \| <code>What are the consequences for a Recognised Body or Authorised Person if they fail to comply with ADGM's requirements regarding severance payments?</code> \| <code>DocumentID: 7 \| PassageID: APP1.A1.2.Guidance.9. \| Passage: Severance payments. Where an Authorised Person or Recognised Body provides discretionary payouts on termination of employment ("severance payments", also called "golden parachutes"), such payment should generally be subject to appropriate limits or shareholder approval. In any case, such payouts should be aligned with the Authorised Person or Recognised Body's overall financial condition and performance over an appropriate time horizon and should not be payable in the case of failure or threatened failure of the Authorised Person or Recognised Body, particularly to an individual whose actions may have contributed to the failure or potential failure of the Authorised Person or Recognised Body.<br></code> \| \| <code>If a Public Fund is structured as an Investment Trust, to whom should the Fund Manager report the review findings regarding delegated Regulated Activities or outsourced functions?</code> \| <code>DocumentID: 6 \| PassageID: PART 5.12.12.8.(1) \| Passage: A Fund Manager or the Trustee of a Public Fund, which has delegated any Regulated Activities or outsourced any functions, must conduct a review of the carrying out of the relevant activities or functions by the Service Provider and present the findings of the review to either:<br>(a) the Fund's Governing Body every 6 months at the Fund's board meeting; or<br>(b) in the case of a Fund structured as an Investment Trust, to the Trustee.</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `learning_rate`: 2e-05 - `warmup_ratio`: 0.1 - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| \|:------:\|:----:\|:-------------:\| \| 0.1082 \| 100 \| 1.9962 \| \| 0.2165 \| 200 \| 1.1626 \| \| 0.3247 \| 300 \| 0.9907 \| \| 0.4329 \| 400 \| 0.8196 \| \| 0.5411 \| 500 \| 0.8082 \| \| 0.6494 \| 600 \| 0.6944 \| \| 0.7576 \| 700 \| 0.6559 \| \| 0.8658 \| 800 \| 0.6242 \| \| 0.9740 \| 900 \| 0.6299 \| \| 1.0823 \| 1000 \| 0.6051 \| \| 1.1905 \| 1100 \| 0.567 \| \| 1.2987 \| 1200 \| 0.4679 \| \| 1.4069 \| 1300 \| 0.3443 \| \| 1.5152 \| 1400 \| 0.3356 \| \| 1.6234 \| 1500 \| 0.2958 \| \| 1.7316 \| 1600 \| 0.254 \| \| 1.8398 \| 1700 \| 0.2694 \| \| 1.9481 \| 1800 \| 0.2497 \| \| 2.0563 \| 1900 \| 0.2671 \| \| 2.1645 \| 2000 \| 0.2558 \| \| 2.2727 \| 2100 \| 0.1943 \| \| 2.3810 \| 2200 \| 0.1242 \| \| 2.4892 \| 2300 \| 0.116 \| \| 2.5974 \| 2400 \| 0.1081 \| \| 2.7056 \| 2500 \| 0.1056 \| \| 2.8139 \| 2600 \| 0.107 \| \| 2.9221 \| 2700 \| 0.1154 \| ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.1.1 - Transformers: 4.45.2 - PyTorch: 2.4.0 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
nuyyep81/results	nuyyep81	2024-11-25T09:04:27Z	176	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-25T08:38:06Z	--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4484 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 352 \| 1.4879 \| \| 1.8923 \| 2.0 \| 704 \| 1.4566 \| \| 1.5369 \| 3.0 \| 1056 \| 1.4484 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
mradermacher/BioinspiredLLM-GGUF	mradermacher	2024-11-25T09:00:52Z	138	1	transformers	[ "transformers", "gguf", "biology", "materials science", "code", "scientific AI", "biological materials", "bioinspiration", "machine learning", "generative", "en", "base_model:lamm-mit/BioinspiredLLM", "base_model:quantized:lamm-mit/BioinspiredLLM", "endpoints_compatible", "region:us" ]	null	2024-11-22T23:58:59Z	--- base_model: lamm-mit/BioinspiredLLM language: - en library_name: transformers quantized_by: mradermacher tags: - biology - materials science - code - scientific AI - biological materials - bioinspiration - machine learning - generative --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/lamm-mit/BioinspiredLLM <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_M.gguf) \| Q3_K_M \| 6.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 7.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_K_S.gguf) \| Q4_K_S \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q5_K_M.gguf) \| Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q8_0.gguf) \| Q8_0 \| 13.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF	mradermacher	2024-11-25T09:00:29Z	9	1	transformers	[ "transformers", "gguf", "chocolatine", "fr", "en", "dataset:jpacifico/french-orca-dpo-pairs-revised", "base_model:jpacifico/Chocolatine-32B-Instruct-DPO-v1.2", "base_model:quantized:jpacifico/Chocolatine-32B-Instruct-DPO-v1.2", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T00:20:45Z	--- base_model: jpacifico/Chocolatine-32B-Instruct-DPO-v1.2 datasets: - jpacifico/french-orca-dpo-pairs-revised language: - fr - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - chocolatine --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/jpacifico/Chocolatine-32B-Instruct-DPO-v1.2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.4 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q2_K.gguf) \| i1-Q2_K \| 12.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 12.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 13.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 14.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 14.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 14.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.0 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 17.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q4_0.gguf) \| i1-Q4_0 \| 18.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 18.9 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 22.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 23.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q6_K.gguf) \| i1-Q6_K \| 27.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/ECE-TW3-JRGL-V5-GGUF	mradermacher	2024-11-25T09:00:23Z	28	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "davidkim205/Rhea-72b-v0.5", "abacusai/Smaug-72B-v0.1", "en", "base_model:MatthieuJ/ECE-TW3-JRGL-V5", "base_model:quantized:MatthieuJ/ECE-TW3-JRGL-V5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-11-23T01:16:48Z	--- base_model: MatthieuJ/ECE-TW3-JRGL-V5 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - davidkim205/Rhea-72b-v0.5 - abacusai/Smaug-72B-v0.1 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/MatthieuJ/ECE-TW3-JRGL-V5 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q2_K.gguf) \| Q2_K \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q3_K_S.gguf) \| Q3_K_S \| 31.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q3_K_M.gguf) \| Q3_K_M \| 35.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q3_K_L.gguf) \| Q3_K_L \| 38.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.IQ4_XS.gguf) \| IQ4_XS \| 39.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q4_K_S.gguf) \| Q4_K_S \| 41.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q4_K_M.gguf) \| Q4_K_M \| 43.9 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q5_K_S.gguf) \| Q5_K_S \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 51.4 \| \| \| [PART 1](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q6_K.gguf.part2of2) \| Q6_K \| 59.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q8_0.gguf.part2of2) \| Q8_0 \| 76.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF	mradermacher	2024-11-25T09:00:17Z	38	1	transformers	[ "transformers", "gguf", "en", "base_model:Deev124/hermes-llama3-roleplay-3500-v1", "base_model:quantized:Deev124/hermes-llama3-roleplay-3500-v1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T01:28:41Z	--- base_model: Deev124/hermes-llama3-roleplay-3500-v1 language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Deev124/hermes-llama3-roleplay-3500-v1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.1 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 2.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 2.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 3.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 4.1 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 4.4 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 4.8 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 4.8 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 4.8 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_0.gguf) \| i1-Q4_0 \| 4.8 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q6_K.gguf) \| i1-Q6_K \| 6.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/blossom-v5.1-34b-i1-GGUF	mradermacher	2024-11-25T08:59:58Z	347	1	transformers	[ "transformers", "gguf", "zh", "en", "dataset:Azure99/blossom-chat-v3", "dataset:Azure99/blossom-math-v4", "dataset:Azure99/blossom-wizard-v3", "dataset:Azure99/blossom-orca-v3", "base_model:Azure99/blossom-v5.1-34b", "base_model:quantized:Azure99/blossom-v5.1-34b", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T03:42:15Z	--- base_model: Azure99/blossom-v5.1-34b datasets: - Azure99/blossom-chat-v3 - Azure99/blossom-math-v4 - Azure99/blossom-wizard-v3 - Azure99/blossom-orca-v3 language: - zh - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Azure99/blossom-v5.1-34b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/blossom-v5.1-34b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ1_S.gguf) \| i1-IQ1_S \| 7.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ1_M.gguf) \| i1-IQ1_M \| 8.3 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ2_S.gguf) \| i1-IQ2_S \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ2_M.gguf) \| i1-IQ2_M \| 11.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q2_K.gguf) \| i1-Q2_K \| 12.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 13.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 14.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 15.1 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ3_S.gguf) \| i1-IQ3_S \| 15.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ3_M.gguf) \| i1-IQ3_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 16.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 18.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 18.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q4_0.gguf) \| i1-Q4_0 \| 19.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 19.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 20.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 23.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 24.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q6_K.gguf) \| i1-Q6_K \| 28.3 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Ice0.41-22.11-RP-GGUF	mradermacher	2024-11-25T08:59:30Z	24	2	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:icefog72/Ice0.41-22.11-RP", "base_model:quantized:icefog72/Ice0.41-22.11-RP", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T04:28:09Z	--- base_model: icefog72/Ice0.41-22.11-RP language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/icefog72/Ice0.41-22.11-RP <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF	mradermacher	2024-11-25T08:59:05Z	13	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:win10/EVA-Meissa-Coder-14B-Instruct", "base_model:quantized:win10/EVA-Meissa-Coder-14B-Instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T05:10:49Z	--- base_model: win10/EVA-Meissa-Coder-14B-Instruct language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/win10/EVA-Meissa-Coder-14B-Instruct <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q2_K.gguf) \| Q2_K \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 6.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 7.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 8.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 8.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 8.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q6_K.gguf) \| Q6_K \| 12.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q8_0.gguf) \| Q8_0 \| 15.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
MakiAi/Llama-3-2-3B-Instruct-bnb-4bit-OKU-v1-1epochs_GGUF	MakiAi	2024-11-25T08:58:44Z	13	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-25T08:58:01Z	--- base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: MakiAi - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-3B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF	mradermacher	2024-11-25T08:58:36Z	13	1	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:Nopm/Opus_WritingStruct", "dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned", "dataset:Gryphe/Sonnet3.5-Charcard-Roleplay", "dataset:Gryphe/ChatGPT-4o-Writing-Prompts", "dataset:Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned", "dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned", "dataset:nothingiisreal/Reddit-Dirty-And-WritingPrompts", "dataset:allura-org/Celeste-1.x-data-mixture", "dataset:cognitivecomputations/dolphin-2.9.3", "base_model:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "base_model:quantized:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T07:28:40Z	--- base_model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 datasets: - anthracite-org/kalo-opus-instruct-22k-no-refusal - Nopm/Opus_WritingStruct - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - Gryphe/Sonnet3.5-Charcard-Roleplay - Gryphe/ChatGPT-4o-Writing-Prompts - Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - nothingiisreal/Reddit-Dirty-And-WritingPrompts - allura-org/Celeste-1.x-data-mixture - cognitivecomputations/dolphin-2.9.3 language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE license_name: qwen quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q2_K.gguf) \| Q2_K \| 29.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 34.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 37.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 39.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 40.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 44.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q5_K_S.gguf.part2of2) \| Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q5_K_M.gguf.part2of2) \| Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q6_K.gguf.part2of2) \| Q6_K \| 64.4 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q8_0.gguf.part2of2) \| Q8_0 \| 77.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF	mradermacher	2024-11-25T08:58:23Z	42	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:win10/EVA-Meissa-big-pro-v2", "base_model:quantized:win10/EVA-Meissa-big-pro-v2", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T08:16:24Z	--- base_model: win10/EVA-Meissa-big-pro-v2 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/win10/EVA-Meissa-big-pro-v2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 5.2 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 5.6 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 6.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 7.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q2_K.gguf) \| i1-Q2_K \| 8.5 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 8.8 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 9.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 9.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 10.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 10.9 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 11.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 12.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q4_0.gguf) \| i1-Q4_0 \| 12.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 12.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 13.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 15.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 15.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q6_K.gguf) \| i1-Q6_K \| 18.1 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/BioinspiredLLM-i1-GGUF	mradermacher	2024-11-25T08:57:49Z	202	1	transformers	[ "transformers", "gguf", "biology", "materials science", "code", "scientific AI", "biological materials", "bioinspiration", "machine learning", "generative", "en", "base_model:lamm-mit/BioinspiredLLM", "base_model:quantized:lamm-mit/BioinspiredLLM", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-23T08:42:52Z	--- base_model: lamm-mit/BioinspiredLLM language: - en library_name: transformers quantized_by: mradermacher tags: - biology - materials science - code - scientific AI - biological materials - bioinspiration - machine learning - generative --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/lamm-mit/BioinspiredLLM <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/BioinspiredLLM-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.0 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ1_M.gguf) \| i1-IQ1_M \| 3.2 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ2_S.gguf) \| i1-IQ2_S \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ2_M.gguf) \| i1-IQ2_M \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q2_K.gguf) \| i1-Q2_K \| 5.0 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 5.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ3_S.gguf) \| i1-IQ3_S \| 5.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 5.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ3_M.gguf) \| i1-IQ3_M \| 6.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 6.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 7.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 7.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 7.5 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 7.5 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_0.gguf) \| i1-Q4_0 \| 7.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 7.5 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q6_K.gguf) \| i1-Q6_K \| 10.8 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF	mradermacher	2024-11-25T08:57:45Z	29	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "ChaoticNeutrals/Eris_Remix_7B", "Virt-io/Erebus-Holodeck-7B", "jeiku/Eros_Prodigadigm_7B", "Epiculous/Mika-7B", "en", "base_model:weezywitasneezy/OxytocinErosEngineering_v0-4x7B-passthrough", "base_model:quantized:weezywitasneezy/OxytocinErosEngineering_v0-4x7B-passthrough", "endpoints_compatible", "region:us" ]	null	2024-11-23T08:43:07Z	--- base_model: weezywitasneezy/OxytocinErosEngineering_v0-4x7B-passthrough language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - ChaoticNeutrals/Eris_Remix_7B - Virt-io/Erebus-Holodeck-7B - jeiku/Eros_Prodigadigm_7B - Epiculous/Mika-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/weezywitasneezy/OxytocinErosEngineering_v0-4x7B-passthrough <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q2_K.gguf) \| Q2_K \| 6.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q3_K_S.gguf) \| Q3_K_S \| 7.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q3_K_M.gguf) \| Q3_K_M \| 8.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q3_K_L.gguf) \| Q3_K_L \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.IQ4_XS.gguf) \| IQ4_XS \| 9.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q4_K_S.gguf) \| Q4_K_S \| 10.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q4_K_M.gguf) \| Q4_K_M \| 10.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q5_K_S.gguf) \| Q5_K_S \| 12.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q5_K_M.gguf) \| Q5_K_M \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q6_K.gguf) \| Q6_K \| 14.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q8_0.gguf) \| Q8_0 \| 18.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF	mradermacher	2024-11-25T08:57:36Z	67	1	transformers	[ "transformers", "gguf", "ko", "en", "base_model:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T08:43:54Z	--- base_model: gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q8_0.gguf) \| Q8_0 \| 11.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.f16.gguf) \| f16 \| 21.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF	mradermacher	2024-11-25T08:57:30Z	123	2	transformers	[ "transformers", "gguf", "generated_from_trainer", "en", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:Nopm/Opus_WritingStruct", "dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned", "dataset:Gryphe/Sonnet3.5-Charcard-Roleplay", "dataset:Gryphe/ChatGPT-4o-Writing-Prompts", "dataset:Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned", "dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned", "dataset:nothingiisreal/Reddit-Dirty-And-WritingPrompts", "dataset:allura-org/Celeste-1.x-data-mixture", "dataset:cognitivecomputations/dolphin-2.9.3", "base_model:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "base_model:quantized:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T09:13:55Z	--- base_model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 datasets: - anthracite-org/kalo-opus-instruct-22k-no-refusal - Nopm/Opus_WritingStruct - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - Gryphe/Sonnet3.5-Charcard-Roleplay - Gryphe/ChatGPT-4o-Writing-Prompts - Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - nothingiisreal/Reddit-Dirty-And-WritingPrompts - allura-org/Celeste-1.x-data-mixture - cognitivecomputations/dolphin-2.9.3 language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE license_name: qwen quantized_by: mradermacher tags: - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 22.8 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 23.8 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 25.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 27.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 28.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q2_K.gguf) \| i1-Q2_K \| 29.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 31.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 32.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 34.6 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 34.6 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 35.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 37.8 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 39.6 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 39.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q4_0.gguf) \| i1-Q4_0 \| 41.5 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 44.0 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 47.5 \| fast, recommended \| \| [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q5_K_S.gguf.part2of2) \| i1-Q5_K_S \| 51.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q5_K_M.gguf.part2of2) \| i1-Q5_K_M \| 54.5 \| \| \| [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 64.4 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/mixtral-4x7b_slerp-GGUF	mradermacher	2024-11-25T08:57:19Z	14	1	transformers	[ "transformers", "gguf", "en", "base_model:isemmanuelolowe/mixtral-4x7b_slerp", "base_model:quantized:isemmanuelolowe/mixtral-4x7b_slerp", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-11-23T10:34:25Z	--- base_model: isemmanuelolowe/mixtral-4x7b_slerp language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/isemmanuelolowe/mixtral-4x7b_slerp <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q2_K.gguf) \| Q2_K \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q3_K_S.gguf) \| Q3_K_S \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q3_K_M.gguf) \| Q3_K_M \| 11.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q3_K_L.gguf) \| Q3_K_L \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.IQ4_XS.gguf) \| IQ4_XS \| 13.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q4_K_S.gguf) \| Q4_K_S \| 13.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q4_K_M.gguf) \| Q4_K_M \| 14.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q5_K_S.gguf) \| Q5_K_S \| 16.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q5_K_M.gguf) \| Q5_K_M \| 17.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q6_K.gguf) \| Q6_K \| 19.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q8_0.gguf) \| Q8_0 \| 25.8 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/llama-2-13b-cf-GGUF	mradermacher	2024-11-25T08:56:53Z	5	1	transformers	[ "transformers", "gguf", "en", "base_model:iestynmullinor/llama-2-13b-cf", "base_model:quantized:iestynmullinor/llama-2-13b-cf", "endpoints_compatible", "region:us" ]	null	2024-11-23T12:50:07Z	--- base_model: iestynmullinor/llama-2-13b-cf language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/iestynmullinor/llama-2-13b-cf <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q3_K_M.gguf) \| Q3_K_M \| 6.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 7.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q4_K_S.gguf) \| Q4_K_S \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q5_K_M.gguf) \| Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q8_0.gguf) \| Q8_0 \| 13.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF	mradermacher	2024-11-25T08:56:46Z	24	1	transformers	[ "transformers", "gguf", "ko", "en", "base_model:KBNIT/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0", "base_model:quantized:KBNIT/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T13:25:35Z	--- base_model: KBNIT/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/KBNIT/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q2_K.gguf) \| i1-Q2_K \| 4.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 4.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 5.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 6.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 6.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_0.gguf) \| i1-Q4_0 \| 6.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 6.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q6_K.gguf) \| i1-Q6_K \| 9.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/llama-2-13b-cf-ds-GGUF	mradermacher	2024-11-25T08:56:41Z	8	1	transformers	[ "transformers", "gguf", "en", "base_model:iestynmullinor/llama-2-13b-cf-ds", "base_model:quantized:iestynmullinor/llama-2-13b-cf-ds", "endpoints_compatible", "region:us" ]	null	2024-11-23T13:56:00Z	--- base_model: iestynmullinor/llama-2-13b-cf-ds language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/iestynmullinor/llama-2-13b-cf-ds <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q3_K_M.gguf) \| Q3_K_M \| 6.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 7.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q4_K_S.gguf) \| Q4_K_S \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q5_K_M.gguf) \| Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q8_0.gguf) \| Q8_0 \| 13.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF	mradermacher	2024-11-25T08:56:31Z	11	1	transformers	[ "transformers", "gguf", "ko", "en", "base_model:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T15:12:29Z	--- base_model: gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q2_K.gguf) \| i1-Q2_K \| 4.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 4.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 5.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 6.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 6.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0.gguf) \| i1-Q4_0 \| 6.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 6.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q6_K.gguf) \| i1-Q6_K \| 8.9 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF	mradermacher	2024-11-25T08:56:27Z	15	1	transformers	[ "transformers", "gguf", "ko", "base_model:juengsi/DT-EQ-SOLAR-10.7B-v0.1", "base_model:quantized:juengsi/DT-EQ-SOLAR-10.7B-v0.1", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-11-23T16:30:10Z	--- base_model: juengsi/DT-EQ-SOLAR-10.7B-v0.1 language: - ko library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/juengsi/DT-EQ-SOLAR-10.7B-v0.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q8_0.gguf) \| Q8_0 \| 11.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.f16.gguf) \| f16 \| 21.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/occiglot-10b-de-en-instruct-GGUF	mradermacher	2024-11-25T08:56:23Z	7	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mayflowergmbh/occiglot-10b-de-en-instruct", "base_model:quantized:mayflowergmbh/occiglot-10b-de-en-instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T16:57:11Z	--- base_model: mayflowergmbh/occiglot-10b-de-en-instruct language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mayflowergmbh/occiglot-10b-de-en-instruct <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q2_K.gguf) \| Q2_K \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q3_K_S.gguf) \| Q3_K_S \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q3_K_M.gguf) \| Q3_K_M \| 4.9 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q3_K_L.gguf) \| Q3_K_L \| 5.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.IQ4_XS.gguf) \| IQ4_XS \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 5.7 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q4_K_S.gguf) \| Q4_K_S \| 5.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q4_K_M.gguf) \| Q4_K_M \| 6.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q5_K_S.gguf) \| Q5_K_S \| 6.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q5_K_M.gguf) \| Q5_K_M \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q6_K.gguf) \| Q6_K \| 8.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q8_0.gguf) \| Q8_0 \| 10.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.f16.gguf) \| f16 \| 19.8 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF	mradermacher	2024-11-25T08:56:19Z	42	1	transformers	[ "transformers", "gguf", "llama-2", "code", "base_model:opencsg/opencsg-CodeLlama-34b-v0.1", "base_model:quantized:opencsg/opencsg-CodeLlama-34b-v0.1", "license:llama2", "endpoints_compatible", "region:us" ]	null	2024-11-23T17:27:45Z	--- base_model: opencsg/opencsg-CodeLlama-34b-v0.1 language: - code library_name: transformers license: llama2 quantized_by: mradermacher tags: - llama-2 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/opencsg/opencsg-CodeLlama-34b-v0.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q2_K.gguf) \| Q2_K \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 14.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 16.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 17.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 18.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 19.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 20.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 23.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q6_K.gguf) \| Q6_K \| 27.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q8_0.gguf) \| Q8_0 \| 36.0 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF	mradermacher	2024-11-25T08:56:15Z	55	1	transformers	[ "transformers", "gguf", "merge", "wizardlm", "mique", "en", "base_model:MaziyarPanahi/WizardLM-Math-70B-v0.1", "base_model:quantized:MaziyarPanahi/WizardLM-Math-70B-v0.1", "license:agpl-3.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-23T17:31:48Z	--- base_model: MaziyarPanahi/WizardLM-Math-70B-v0.1 language: - en library_name: transformers license: agpl-3.0 quantized_by: mradermacher tags: - merge - wizardlm - mique --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/MaziyarPanahi/WizardLM-Math-70B-v0.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 14.6 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 16.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 18.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 20.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q2_K.gguf) \| i1-Q2_K \| 25.6 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 26.7 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 28.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 30.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 30.0 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 33.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 36.2 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 36.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 39.1 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 39.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 41.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 47.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 48.9 \| \| \| [PART 1](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q6_K.gguf.part2of2) \| i1-Q6_K \| 56.7 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/free-solar-slerp-v0.3-i1-GGUF	mradermacher	2024-11-25T08:56:07Z	31	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:freewheelin/free-solar-slerp-v0.3", "base_model:quantized:freewheelin/free-solar-slerp-v0.3", "license:mit", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-23T17:55:36Z	--- base_model: freewheelin/free-solar-slerp-v0.3 language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/freewheelin/free-solar-slerp-v0.3 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/free-solar-slerp-v0.3-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q2_K.gguf) \| i1-Q2_K \| 4.2 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 4.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.9 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ3_M.gguf) \| i1-IQ3_M \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 5.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 6.3 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 6.3 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 6.3 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_0.gguf) \| i1-Q4_0 \| 6.3 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 6.3 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 6.7 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 7.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q6_K.gguf) \| i1-Q6_K \| 9.0 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF	mradermacher	2024-11-25T08:55:42Z	5	1	transformers	[ "transformers", "gguf", "ko", "base_model:juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1", "base_model:quantized:juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	null	2024-11-23T18:34:56Z	--- base_model: juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1 language: - ko library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q8_0.gguf) \| Q8_0 \| 11.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.f16.gguf) \| f16 \| 21.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF	mradermacher	2024-11-25T08:55:36Z	15	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:win10/EVA-Meissa-Coder-14B-Instruct", "base_model:quantized:win10/EVA-Meissa-Coder-14B-Instruct", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T19:00:49Z	--- base_model: win10/EVA-Meissa-Coder-14B-Instruct language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/win10/EVA-Meissa-Coder-14B-Instruct <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ1_S.gguf) \| i1-IQ1_S \| 3.7 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ1_M.gguf) \| i1-IQ1_M \| 4.0 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ2_S.gguf) \| i1-IQ2_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ2_M.gguf) \| i1-IQ2_M \| 5.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q2_K.gguf) \| i1-Q2_K \| 5.9 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 6.0 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 6.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 6.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ3_S.gguf) \| i1-IQ3_S \| 6.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ3_M.gguf) \| i1-IQ3_M \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 7.4 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 8.0 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 8.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 8.6 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 8.6 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 8.6 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_0.gguf) \| i1-Q4_0 \| 8.6 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 8.7 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 9.1 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 10.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q6_K.gguf) \| i1-Q6_K \| 12.2 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Fusion-7B-Quintessence-GGUF	mradermacher	2024-11-25T08:55:32Z	6	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ilevytate/Fusion-7B-Quintessence", "base_model:quantized:ilevytate/Fusion-7B-Quintessence", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T19:03:24Z	--- base_model: ilevytate/Fusion-7B-Quintessence language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/ilevytate/Fusion-7B-Quintessence <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Fusion-7B-Quintessence-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q2_K.gguf) \| Q2_K \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q3_K_S.gguf) \| Q3_K_S \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q3_K_M.gguf) \| Q3_K_M \| 10.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q3_K_L.gguf) \| Q3_K_L \| 11.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.IQ4_XS.gguf) \| IQ4_XS \| 11.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q4_K_S.gguf) \| Q4_K_S \| 12.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q4_K_M.gguf) \| Q4_K_M \| 12.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q5_K_S.gguf) \| Q5_K_S \| 14.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q5_K_M.gguf) \| Q5_K_M \| 15.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q6_K.gguf) \| Q6_K \| 17.5 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q8_0.gguf) \| Q8_0 \| 22.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/llm4decompile-6.7b-nsp-GGUF	mradermacher	2024-11-25T08:55:28Z	37	1	transformers	[ "transformers", "gguf", "decompile", "binary", "en", "base_model:arise-sustech/llm4decompile-6.7b-nsp", "base_model:quantized:arise-sustech/llm4decompile-6.7b-nsp", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-11-23T19:31:46Z	--- base_model: arise-sustech/llm4decompile-6.7b-nsp language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - decompile - binary --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/arise-sustech/llm4decompile-6.7b-nsp <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q3_K_S.gguf) \| Q3_K_S \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 3.9 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF	mradermacher	2024-11-25T08:55:04Z	340	1	transformers	[ "transformers", "gguf", "T3Q-ko-solar-sft-v3.0", "kyujinpy/KoCommercial-NoSSL", "en", "dataset:davidkim205/ko_common_gen", "base_model:chlee10/T3Q-ko-solar-sft-v3.0", "base_model:quantized:chlee10/T3Q-ko-solar-sft-v3.0", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]	null	2024-11-23T20:21:26Z	--- base_model: chlee10/T3Q-ko-solar-sft-v3.0 datasets: - davidkim205/ko_common_gen language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - T3Q-ko-solar-sft-v3.0 - kyujinpy/KoCommercial-NoSSL --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/chlee10/T3Q-ko-solar-sft-v3.0 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q2_K.gguf) \| i1-Q2_K \| 4.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 4.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 5.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 6.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 6.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_0.gguf) \| i1-Q4_0 \| 6.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 6.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q6_K.gguf) \| i1-Q6_K \| 8.9 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF	mradermacher	2024-11-25T08:55:00Z	34	1	transformers	[ "transformers", "gguf", "ko", "base_model:juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1", "base_model:quantized:juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1", "license:cc-by-4.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-23T20:36:50Z	--- base_model: juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1 language: - ko library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ1_S.gguf) \| i1-IQ1_S \| 2.5 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ1_M.gguf) \| i1-IQ1_M \| 2.7 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ2_S.gguf) \| i1-IQ2_S \| 3.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ2_M.gguf) \| i1-IQ2_M \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q2_K.gguf) \| i1-Q2_K \| 4.1 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 4.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 4.8 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ3_S.gguf) \| i1-IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ3_M.gguf) \| i1-IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 5.3 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 5.8 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_0_4_4.gguf) \| i1-Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_0_4_8.gguf) \| i1-Q4_0_4_8 \| 6.2 \| fast on arm+i8mm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_0_8_8.gguf) \| i1-Q4_0_8_8 \| 6.2 \| fast on arm+sve, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_0.gguf) \| i1-Q4_0 \| 6.2 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 6.2 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q6_K.gguf) \| i1-Q6_K \| 8.9 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/BioMistral-7B-finetuned-GGUF	mradermacher	2024-11-25T08:54:51Z	10	1	transformers	[ "transformers", "gguf", "en", "dataset:camel-ai/biology", "base_model:mridul3301/BioMistral-7B-finetuned", "base_model:quantized:mridul3301/BioMistral-7B-finetuned", "license:mit", "endpoints_compatible", "region:us" ]	null	2024-11-23T20:42:34Z	--- base_model: mridul3301/BioMistral-7B-finetuned datasets: - camel-ai/biology language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mridul3301/BioMistral-7B-finetuned <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/llama-2-13b-cf-of-GGUF	mradermacher	2024-11-25T08:54:31Z	5	1	transformers	[ "transformers", "gguf", "en", "base_model:iestynmullinor/llama-2-13b-cf-of", "base_model:quantized:iestynmullinor/llama-2-13b-cf-of", "endpoints_compatible", "region:us" ]	null	2024-11-23T21:21:58Z	--- base_model: iestynmullinor/llama-2-13b-cf-of language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/iestynmullinor/llama-2-13b-cf-of <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q2_K.gguf) \| Q2_K \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q3_K_S.gguf) \| Q3_K_S \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q3_K_M.gguf) \| Q3_K_M \| 6.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q3_K_L.gguf) \| Q3_K_L \| 7.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.IQ4_XS.gguf) \| IQ4_XS \| 7.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 7.5 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q4_K_S.gguf) \| Q4_K_S \| 7.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q4_K_M.gguf) \| Q4_K_M \| 8.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q5_K_S.gguf) \| Q5_K_S \| 9.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q5_K_M.gguf) \| Q5_K_M \| 9.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q6_K.gguf) \| Q6_K \| 10.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q8_0.gguf) \| Q8_0 \| 13.9 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/NEBULA-XB-v1.0-GGUF	mradermacher	2024-11-25T08:54:09Z	8	0	transformers	[ "transformers", "gguf", "en", "dataset:Open-Orca/SlimOrca", "base_model:TeeZee/NEBULA-XB-v1.0", "base_model:quantized:TeeZee/NEBULA-XB-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-25T04:52:10Z	--- base_model: TeeZee/NEBULA-XB-v1.0 datasets: - Open-Orca/SlimOrca language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/TeeZee/NEBULA-XB-v1.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/NEBULA-XB-v1.0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q2_K.gguf) \| Q2_K \| 8.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q3_K_S.gguf) \| Q3_K_S \| 10.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q3_K_M.gguf) \| Q3_K_M \| 11.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q3_K_L.gguf) \| Q3_K_L \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.IQ4_XS.gguf) \| IQ4_XS \| 13.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q4_K_S.gguf) \| Q4_K_S \| 13.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q4_K_M.gguf) \| Q4_K_M \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q5_K_S.gguf) \| Q5_K_S \| 16.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q5_K_M.gguf) \| Q5_K_M \| 16.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q6_K.gguf) \| Q6_K \| 19.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q8_0.gguf) \| Q8_0 \| 25.4 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF	mradermacher	2024-11-25T08:54:03Z	35	2	transformers	[ "transformers", "gguf", "en", "base_model:EmilMarian/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned", "base_model:quantized:EmilMarian/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T22:24:58Z	--- base_model: EmilMarian/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/EmilMarian/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 4.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF	mradermacher	2024-11-25T08:53:46Z	14	1	transformers	[ "transformers", "gguf", "ko", "en", "base_model:gwonny/nox-solar-10.7b-v4-kolon-all-10", "base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-all-10", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-23T23:34:17Z	--- base_model: gwonny/nox-solar-10.7b-v4-kolon-all-10 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> static quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-10 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 6.2 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q8_0.gguf) \| Q8_0 \| 11.5 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.f16.gguf) \| f16 \| 21.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
mradermacher/Hermes-2-Pro-11B-GGUF	mradermacher	2024-11-25T08:53:39Z	81	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "NousResearch/Hermes-2-Pro-Mistral-7B", "en", "base_model:mattshumer/Hermes-2-Pro-11B", "base_model:quantized:mattshumer/Hermes-2-Pro-11B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-24T01:01:56Z	--- base_model: mattshumer/Hermes-2-Pro-11B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - NousResearch/Hermes-2-Pro-Mistral-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/mattshumer/Hermes-2-Pro-11B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q2_K.gguf) \| Q2_K \| 4.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q3_K_S.gguf) \| Q3_K_S \| 5.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q3_K_M.gguf) \| Q3_K_M \| 5.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q3_K_L.gguf) \| Q3_K_L \| 6.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.IQ4_XS.gguf) \| IQ4_XS \| 6.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q4_0_4_4.gguf) \| Q4_0_4_4 \| 6.4 \| fast on arm, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q4_K_S.gguf) \| Q4_K_S \| 6.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q4_K_M.gguf) \| Q4_K_M \| 6.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q5_K_S.gguf) \| Q5_K_S \| 7.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q5_K_M.gguf) \| Q5_K_M \| 8.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q6_K.gguf) \| Q6_K \| 9.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q8_0.gguf) \| Q8_0 \| 12.0 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF	mradermacher	2024-11-25T08:48:40Z	9	1	transformers	[ "transformers", "gguf", "en", "base_model:migtissera/Tess-2.0-Mixtral-8x7B-v0.2", "base_model:quantized:migtissera/Tess-2.0-Mixtral-8x7B-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]	null	2024-11-24T19:49:30Z	--- base_model: migtissera/Tess-2.0-Mixtral-8x7B-v0.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/migtissera/Tess-2.0-Mixtral-8x7B-v0.2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ1_S.gguf) \| i1-IQ1_S \| 9.9 \| for the desperate \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ1_M.gguf) \| i1-IQ1_M \| 10.9 \| mostly desperate \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ2_XXS.gguf) \| i1-IQ2_XXS \| 12.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ2_XS.gguf) \| i1-IQ2_XS \| 14.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ2_S.gguf) \| i1-IQ2_S \| 14.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ2_M.gguf) \| i1-IQ2_M \| 15.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q2_K.gguf) \| i1-Q2_K \| 17.4 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ3_XXS.gguf) \| i1-IQ3_XXS \| 18.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ3_XS.gguf) \| i1-IQ3_XS \| 19.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ3_S.gguf) \| i1-IQ3_S \| 20.5 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q3_K_S.gguf) \| i1-Q3_K_S \| 20.5 \| IQ3_XS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ3_M.gguf) \| i1-IQ3_M \| 21.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q3_K_M.gguf) \| i1-Q3_K_M \| 22.6 \| IQ3_S probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q3_K_L.gguf) \| i1-Q3_K_L \| 24.3 \| IQ3_M probably better \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ4_XS.gguf) \| i1-IQ4_XS \| 25.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q4_0.gguf) \| i1-Q4_0 \| 26.7 \| fast, low quality \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 26.8 \| optimal size/speed/quality \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q4_K_M.gguf) \| i1-Q4_K_M \| 28.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q5_K_S.gguf) \| i1-Q5_K_S \| 32.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q5_K_M.gguf) \| i1-Q5_K_M \| 33.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q6_K.gguf) \| i1-Q6_K \| 38.5 \| practically like static Q6_K \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to. <!-- end -->
ibrahimchristopher/whisper-small-dv	ibrahimchristopher	2024-11-25T08:44:29Z	78	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ha", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-25T06:15:00Z	--- library_name: transformers language: - ha license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Ha - Ibrahim Ibrahim results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 metrics: - name: Wer type: wer value: 45.91914569031274 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Ha - Ibrahim Ibrahim This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.6920 - Wer Ortho: 48.8189 - Wer: 45.9191 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.0754 \| 3.1847 \| 500 \| 0.6920 \| 48.8189 \| 45.9191 \| ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Tokenizers 0.20.3
nonhmello/whisper_medium_nonhmello	nonhmello	2024-11-25T08:37:56Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "asr", "speech-recognition", "thai", "custom-model", "generated_from_trainer", "th", "base_model:biodatlab/whisper-th-medium-combined", "base_model:finetune:biodatlab/whisper-th-medium-combined", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-11-25T02:25:57Z	--- library_name: transformers language: - th license: apache-2.0 base_model: biodatlab/whisper-th-medium-combined tags: - asr - speech-recognition - thai - custom-model - generated_from_trainer metrics: - wer model-index: - name: Whisper Medium TH - Nonhmello results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Medium TH - Nonhmello This model is a fine-tuned version of [biodatlab/whisper-th-medium-combined](https://huggingface.co/biodatlab/whisper-th-medium-combined) on the Custom dataset on local machine dataset. It achieves the following results on the evaluation set: - Loss: 0.3068 - Wer: 77.4194 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:--------:\|:----:\|:---------------:\|:-------:\| \| 0.0092 \| 83.3333 \| 500 \| 0.2586 \| 90.3226 \| \| 0.0006 \| 166.6667 \| 1000 \| 0.3068 \| 77.4194 \| ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
Cylingo/Xinyuan-VL-2B	Cylingo	2024-11-25T08:24:55Z	134	7	transformers	[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "multimodal", "visual-question-answering", "en", "zh", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	visual-question-answering	2024-09-24T12:45:20Z	--- license: apache-2.0 language: - en - zh pipeline_tag: visual-question-answering tags: - multimodal library_name: transformers --- <div align=center><img src ="https://cdn-uploads.huggingface.co/production/uploads/6299c90ef1f2a097fcaa1293/XEfp5nnJOixkGAOyF8UtN.png"/></div> ## Introduction Xinyuan-VL-2B is a high-performance multimodal large model for the end-side from the Cylingo Group, which is fine-tuned with `Qwen/Qwen2-VL-2B-Instruct`, and uses more than 5M of multimodal data as well as a small amount of plain text data. It performs well on several authoritative Benchmarks. ## How to use In order to rely on the thriving ecology of the open source community, we chose to fine-tune [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) to form our `Cylingo/Xinyuan-VL- 2B`. Thus, using `Cylingo/Xinyuan-VL-2B` is consistent with using `Qwen/Qwen2-VL-2B-Instruct`: ```Python from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info # default: Load the model on the available device(s) model = Qwen2VLForConditionalGeneration.from_pretrained( "Cylingo/Xinyuan-VL-2B", torch_dtype="auto", device_map="auto" ) # default processer processor = AutoProcessor.from_pretrained("Cylingo/Xinyuan-VL-2B") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` ## Evaluation We evaluated [XinYuan-VL-2B](https://huggingface.co/thomas-yanxin/XinYuan-VL-2B) using the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) toolkit across the following benchmarks and found that XinYuan-VL-2B outperformed [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) released by Alibaba Cloud, as well as other models of comparable parameter scale that have significant influence in the open-source community. <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6299c90ef1f2a097fcaa1293/7ThTCYfd_lDzsvaFLlUv2.png"> </p> You can see the results in [opencompass/open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard): \| Benchamrk \| MiniCPM-2B \| InternVL-2B \| Qwen2-VL-2B \| XinYuan-VL-2B \| \| :---: \| :---: \| :---: \| :---: \| :---: \| \| MMB-CN-V11-Test \| 64.5 \| 68.9 \| 71.2 \| 74.3 \| \| MMB-EN-V11-Test \| 65.8 \| 70.2 \| 73.2 \| 76.5 \| \| MMB-EN \| 69.1 \| 74.4 \| 74.3 \| 78.9 \| \| MMB-CN \| 66.5 \| 71.2 \| 73.8 \| 76.12 \| \| CCBench \| 45.3 \| 74.7 \| 53.7 \| 55.5 \| \| MMT-Bench \| 53.5 \| 50.8 \| 54.5 \| 55.2 \| \| RealWorld \| 55.8 \| 57.3 \| 62.9 \| 63.9 \| \| SEEDBench\_IMG \| 67.1 \| 70.9 \| 72.86 \| 73.4 \| \| AI2D \| 56.3 \| 74.1 \| 74.7 \| 74.2 \| \| MMMU \| 38.2 \| 36.3 \| 41.1 \| 40.9 \| \| HallusionBench \| 36.2 \| 36.2 \| 42.4 \| 55.00 \| \| POPE \| 86.3 \| 86.3 \| 86.82 \| 89.42 \| \| MME \| 1808.6 \| 1876.8 \| 1872.0 \| 1854.9 \| \| MMStar \| 39.1 \| 49.8 \| 47.5 \| 51.87 \| \| SEEDBench2\_Plus \| 51.9 \| 59.9 \| 62.23 \| 62.98 \| \| BLINK \| 41.2 \| 42.8 \| 43.92 \| 42.98 \| \| OCRBench \| 605 \| 781 \| 794 \| 782 \| \| TextVQA \| 74.1 \| 73.4 \| 79.7** \| 77.6 \|
Miyoki/cve-mistral-7b-instruct-v0.3-bnb-4bit-02	Miyoki	2024-11-25T08:21:37Z	12	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-25T08:18:38Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: Miyoki - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tl81092/my-drug-model_2	tl81092	2024-11-25T08:14:46Z	105	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T08:14:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
JNolet/Qwen2.5-Coder-14B_v11.25.24.0_CodeInstruct	JNolet	2024-11-25T08:13:51Z	18	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Qwen/Qwen2.5-Coder-14B", "base_model:merge:Qwen/Qwen2.5-Coder-14B", "base_model:Qwen/Qwen2.5-Coder-14B-Instruct", "base_model:merge:Qwen/Qwen2.5-Coder-14B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T08:05:29Z	--- base_model: - Qwen/Qwen2.5-Coder-14B - Qwen/Qwen2.5-Coder-14B-Instruct library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) as a base. ### Models Merged The following models were included in the merge: * [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Qwen/Qwen2.5-Coder-14B-Instruct parameters: weight: 1 density: 1 merge_method: ties base_model: Qwen/Qwen2.5-Coder-14B parameters: weight: 1 density: 1 normalise: true int8_mask: true dtype: bfloat16 ```
Kasobi/distilbert-base-uncased-finetuned-emotion	Kasobi	2024-11-25T08:09:49Z	105	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-23T12:39:59Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2250 - Accuracy: 0.9245 - F1: 0.9245 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 250 \| 0.3164 \| 0.901 \| 0.8999 \| \| No log \| 2.0 \| 500 \| 0.2250 \| 0.9245 \| 0.9245 \| ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cpu - Datasets 3.1.0 - Tokenizers 0.20.3
Shinyaaa/Face-travel-05-v1	Shinyaaa	2024-11-25T08:05:00Z	103	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-11-25T08:04:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
msr2903/mrm8488-distilroberta-fine-tuned-financial-sentiment	msr2903	2024-11-25T07:58:40Z	134	0	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "dataset:NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed", "base_model:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "base_model:finetune:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T07:35:07Z	--- library_name: transformers datasets: - NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed base_model: - mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis --- ### Model Description <!-- Provide a longer summary of what this model is. --> This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis](https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis) on the [NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed](https://huggingface.co/datasets/NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed) dataset. It achieves the following results on the evaluation set: - Loss: 0.4090 - Accuracy: 0.9171 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Validation Loss \| \|:-------------:\|:-----:\|:---------------:\| \| 0.318500 \| 1.0 \| 0.294045 \| \| 0.281700 \| 2.0 \| 0.298364 \| \| 0.250100 \| 3.0 \| 0.302255 \| \| 0.186400 \| 4.0 \| 0.380530 \| \| 0.179100 \| 5.0 \| 0.409072 \|
SMARTICT/paraphrase-multilingual-MiniLM-L12-v2-ft-tr-rag-v1	SMARTICT	2024-11-25T07:48:31Z	44	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:8970", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-11-22T13:26:33Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:8970 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 widget: - source_sentence: 'Seri konum efekti tarafından oluşturulan şeklindeki seri konum eğrisini gösteren grafik. ''''''Seri konum etkisi'''''', bir kişinin, bir serideki ilk ve son ögeleri en iyi; ortanca ögeleri en kötü hatırlama eğilimidir. Bu terim, Hermann Ebbinghaus tarafından kendi üzerine yaptığı çalışmalar ile ve bu terim, hatırlama doğruluğunun, bir ögenin bir çalışma listesindeki konumunun bir fonksiyonu olarak değiştiği bulgusuna değinmektedir. Sırası fark etmeksizin (serbest hatırlama) listedeki ögelerin hatırlanması istenildiğinde, insanlar listenin sonundaki ögeleri hatırlamaya başlama eğilimindedir ve bu ögeleri en iyi şekilde hatırlarlar (''''''sonluk etkisi''''''). Daha önceki liste ögeleri arasında, ilk birkaç öge, orta ögelerden daha sık hatırlanır (''''''ilklik etkisi''''''). İlklik etkisi için önerilen bir neden, sunulan ilk ögelerin kendilerine ayrılmış daha fazla miktarda işlem nedeniyle en etkin şekilde hareketsiz bellekte depolanmasıdır. (İlk liste ögesi kendi başına prova edilebilir; ikincisi, birincisi ile birlikte prova edilmek zorundadır, üçüncü, birincisi ve ikincisi ile birlikte, ve böyle devam eder.) Ögeler hızlı bir şekilde sunulduğunda ilklik etkisi azalır ve yavaş sunulduğunda artar (her bir ögenin işlenmesini ve böylece kalıcı depolanmasını azaltan ve arttıran faktörler). Daha uzun sunum listelerinin ilklik etkisini azalttığı bulunmuştur. Sonluk etkisi için teorileşmiş bir neden, bu ögelerin geri hatırlanması talep edildiğinde hala aktif hafızada bulunmasıdır. Hiçbirinden yararlanmayan ögeler (ortanca ögeler) en kötü şekilde geri çağrılır. Sonluk etkisi için ek bir açıklama zamansal bağlamla ilgilidir: Mevcut zamansal bağlam, daha yeni ögelerin, farklı bir zamansal bağlamda (listenin başlarında) incelenen ögelere göre daha yüksek geri hatırlama olasılığına sahip olacağını haber veren bir geri hatırlama işareti olarak kullanılabilir. Araya giren bir görev verildiğinde sonluk etkisi azalır. Araya giren görevler, çalışan belleği kullanır, ve dikkat dağıtıcı aktivite süresi 15 ila 30 saniyeyi aşarsa, sonluk etkisini bozabilir. Ek olarak, geri hatırlama testten hemen sonra gelirse, sonluk etkisi çalışılan listenin uzunluğuna, veya sunum hızına bakılmaksızın istikrarlıdır. Kalıcı uzun süreli hafıza oluşturma kabiliyeti zayıf olan amnezyaklar ilklik etkisi göstermezler, ancak hatırlama çalışmadan hemen sonra gelirse bir sonluk etkisi gösterirler. Alzheimer hastalığı olan kişiler daha düşük bir ilklik etkisi sergiler, ancak hatırlamada bir sonluk etkisi göstermezler. İlklik etkisi İlklik etkisi, psikolojide ve sosyolojide, kişinin ilk verilen bilgiyi daha sonra verilen bilgiden daha iyi hatırlamasına neden olan bir bilişsel önyargıdır. Örneğin, yeterince uzun bir kelime listesini okuyan bir kişinin, listenin başındaki kelimeleri hatırlaması listenin ortasındakileri hatırlamasından daha yüksek ihtimallidir. Birçok araştırmacı bu olguyu serbest hatırlama null testler yoluyla açıklamaya çalışmıştır. Coluccia, Gamboz ve Brandimonte (2011), serbest hatırlamayı katılımcıların herhangi bir telkin olmaksızın bilgileri hatırlamaya çalışması olarak açıklamaktadır. 20. yüzyılın sonlarındaki bazı deneylerde, kendilerine sunulan bir listede test edileceklerini bilen katılımcıların ögeleri prova edeceği kaydedildi: Ögeler sunulduğunda katılımcılar bu ögeleri kendilerine tekrar edecek ve yeni ögeler sunuldukça katılımcılar daha yeni maddelerle birlikte önceki ögeleri prova etmeye devam edeceklerdi. İlklik etkisinin ögelerin sunumu arasında daha fazla zaman olduğunda hatırlama üzerinde daha büyük bir etkisi olduğu, böylece katılımcıların önceki (asal) ögeleri prova etme şansının daha yüksek olacağı gösterilmiştir. Açık prova katılımcıların prova örüntülerini test etmek için kullanılan bir teknikti. Bu tekniğin kullanıldığı bir deneyde, katılımcılardan akla gelen ögeleri yüksek sesle söylemeleri istendi. Bu şekilde deneyci, katılımcıların listenin başındaki ögeleri listenin ortasındaki ögelerden daha çok böylece onları daha sık prova yapacağını ve daha sonra listenin ortasındaki ögelerden daha iyi hatırlayacağını görebildi. Brodie ve Murdock tarafından yapılan başka bir deneyde, sonluk etkisinin ilklik etkisinden kısmen sorumlu olduğu bulunmuştur. Deneylerinde, aynı zamanda açık prova tekniğini kullandılar ve katılımcıların daha önceki ögeleri daha fazla prova yapmasının yanı sıra, listenin başındaki kelimeleri provada daha sonra söylediklerini keşfettiler. Bu şekilde, daha önceki ögeler prova yolu sayesinde test sonuna daha yakındı ve kısmen sonluk etkisi ile 2013 yılında yapılan bir araştırma, ilklik etkisinin, edimsel koşullama olarak da bilinen bir öğrenme süreci olan tekrarlanan seçim deneyime dayalı karar verme sürecinde de önemli olduğunu göstermiştir. Yazarlar, takip eden davranışın ilk ödülünün değerine verilen önemi göstermiş ve bu olguyu sonuç önceliği olarak ifade etmişlerdir. Başka bir çalışmada, katılımcılar iki cümleden birini aldı. Örneğin, cümlelerin biri "Steve akıllı, çalışkan, eleştirel, fevri ve kıskançtır."; diğeri ise "Steve kıskanç, fevri, eleştirel, çalışkan ve akıllıdır." olabilir. Bu iki cümle aynı bilgileri içerir. Birincisi başlangıçta pozitif özellikleri gösterirken, ikincisi olumsuz özelliklere sahiptir. Araştırmacılar, katılımcıların Steve''i ilk cümle verildiğinde ikincisine kıyasla daha olumlu buldular. Sonluk etkisi İki geleneksel teori sınıfı sonluk etkisini açıklar. Çift depo modelleri Bu modeller, en son listelenen çalışma ögelerinin oldukça erişilebilir kısa süreli ara bellekten, yani insan hafızasındaki kısa süreli depodan (KSD) alındığını varsayar. Bu, daha sonra incelenen ögelerin, daha önce incelenen ögelere göre bir avantaja sahip olmasını sağlar, çünkü daha önceki çalışma ögelerinin uzun süreli bellek deposundan (USD) geriye getirilmesi için daha fazla çaba harcanması gerekir. Bu tür modellerin önemli bir tahmini, alıkoyma döneminde (liste sunumu ile test arasındaki süre) 10-30 saniye aritmetik problemleri çözme gibi dikkat dağıtıcı bir sunumun yenilik etkisini azaltmasıdır. KSD sınırlı kapasiteye sahip olduğundan, dikkat dağınıklığı daha sonraki çalışma listesi ögelerini KSD''den değiştirir, böylece testte bu ögeler sadece USD''den alınabilir ve kısa süreli ara bellekten daha kolay alınabilme avantajlarını yitirebilir. Bu nedenle, çift depolu modeller, hem anlık hatırlama görevlerindeki sonluk etkisini hem de gecikmeli serbest geri hatırlama görevinde böyle bir etkinin zayıflamasını başarılı bir şekilde açıklar. Bununla birlikte, bu modelle ilgili büyük bir sorun, uyarıcılar arası zaman aralığı (aralıksız çeldirici görev) sırasında her çalışma maddesi arasında bir dikkat dağılması olduğunda, gecikmeli hatırlamada gözlemlenen uzun süreli etkisini tahmin edememesidir. Dikkatin dağılması, son çalışma maddesinden sonra hala mevcut olduğundan, çalışma maddesini KSD''den, sonluk etkisi azaltılacak şekilde Bu uzun vadeli sonluk etkisinin varlığı, anlık ve uzun süreli sonluk etkilerinin ortak bir mekanizmayı paylaşması olasılığını arttırmaktadır. Tek depo modelleri Tek depo teorilerine göre, dizisel konum etkilerinden tek bir mekanizma sorumludur. İlk model türü, her bir liste ögesinin incelenmesi ile test arasındaki sürenin, bir ögenin alınırken bellek izinin göreceli rekabetçiliğini belirlediği göreceli zamansal farklılığa dayanmaktadır. Bu modelde, liste sonu ögelerinin daha belirgin ve dolayısıyla daha kolay alınabileceği Başka bir model türü, ögelerin bellekten geri alınmasının yalnızca kişinin çalışma ögesinin kendisini değil, aynı zamanda çalışma bağlamını zihinsel temsiline bağlı olduğunu öne süren bağlamsal değişkenliğe dayanmaktadır. Bağlam zamanla değiştiğinden ve gittikçe değiştiğinden, bellek ögelerini geri almak için yarıştığında, anlık serbest hatırlama testinde, daha yakın zamanda incelenen ögelerin test bağlamıyla daha benzer kodlama bağlamları olacaktır ve geriye getirme olasılığı daha yüksektir. Anlık serbest hatırlama dışında, bu modeller gecikmeli serbest hatırlama ve sürekli çeldirici serbest hatırlama koşullarında sonluk etkisinin varlığını veya yokluğunu da tahmin edebilir. Gecikmeli hatırlama koşulları altında, test bağlamı artan tutma aralığıyla uzaklaşarak zayıflamış bir sonluk etkisi yaratır. Sürekli çeldirici hatırlama koşullarında, artan yorumlama aralıkları çalışma bağlamı ve test bağlamı arasındaki benzerlikleri azaltırken, maddeler arasındaki göreli benzerlikler değişmeden kalmaktadır. Hatırlama işlemi rekabetçi olduğu sürece, son ögeler kazanacaktır, bu nedenle bir sonluk etkisi gözlenir. Oran kuralı Genel olarak, sonluk etkisi ile ilgili önemli bir ampirik gözlem, mutlak tutma aralıkları (çalışma sonu ile test süresi arasındaki süre) veya sunumlar arası aralıklar (farklı çalışma ögeleri arasındaki süre) olmamasıdır. Bunun yerine, sonluk miktarı ile belirlenen oran; mutlak tutma aralıkları ve sunumlar arası aralıklar oranı (oran kuralı). Sonuç olarak, bu oran sabit kaldığı sürece, aralıkların mutlak değerlerinden bağımsız olarak yenilik gözlenecektir, böylece ''''''zaman ölçeği değişmezliği'''''' olarak bilinen bir fenomen olan tüm zaman ölçeklerinde yenilik gözlenebilir. Bu, yeniliğin KSD''nin büyüklüğüne ve KSD''deki ögelerin yer değiştirmesini yöneten kurala bağlı olduğunu varsayan çift depo modelleri ile çelişmektedir. Olası açıklamalar daha sonra tek, aynı bir mekanizma yoluyla ortaya çıkan sonluk etkisini açıklar ya da anlık ve uzun süreli sonluk etkileri için iki farklı mekanizmayı öngörebilen farklı bir modelle yeniden açıklar. Böyle bir açıklama Davelaar ve ark. (2005), tek bileşenli bir bellek modeli tarafından açıklanamayan anlık ve uzun süreli sonluk fenomenleri arasında ayrışmalar olduğunu, anlık ve sonluk açıklayan bir KSD''nin varlığını savunan ve bir saniye uzun süreli sonluğu açıklayan bağlamsal kaymaya dayanan mekanizmadır. İlgili etkiler 1977''de William Crano özellikle birbirinin zıttı olduğu söylenen ilklik ve sonluk etkileri başta olmak üzere sıra etkilerinin doğasını belirten bir çalışma hazırlamaya karar verdi. Crano tarafından test edilen özellikler: Anlam değişimi hipotezi Bir listenin başındaki ögeler, katılımcıların listenin geri kalanının da uymasını beklediği bir tema oluşturur. Katılımcı, listedeki bazı kelimelerin anlamlarını belirlediği beklentiye uyacak şekilde değiştirir. Watkins ve Peynircioğlu (1984), katılımcıların kelimelerin anlamlarını değiştirerek belirlenen temadan uzaklaşarak da olsa sunulan bilgideki sapmayı azalttığını açıklamıştır. Tutarsızlık durumda saymama Katılımcılar, kendilerine sunulan önceki maddelerle tutarlı olmayan bilgileri dikkate almazlar. Başka bir deyişle, tutarsızlık durumda saymama, sunulan diğer bilgilerle tutarsız olan bilgileri tutarlı olanlardan daha az önemli görmeyi içerir (Devine ve Ostrom, 1985). Dikkat azaltma hipotezi Önce sunulan bilgilerin katılımcılar üzerinde daha sonra sunulan bilgilerden daha fazla etkisi vardır ve bu bilgiler tutarlı olsa bile öncelikli bir etkinin ortaya çıkmasına neden olur. Steiner ve Rain (1989) insanların başlangıçta sunulan bilgilere daha fazla dikkat ettiklerini, ancak kendilerine sonradan sunulan bilgilere giderek daha az dikkat ettiklerini açıklamaktadır. İlklik etkisi, katılımcıların başlangıç bilgilerine dikkat etmeleri ve daha sonra sunulan bilgileri görmezden gelmeleri nedeniyle oluşur. Öte yandan, katılımcılar sürekli olarak bilgiye dikkat etmek zorunda oldukları bir durumdaysa, sonluk etkisi oluşabilir. ''''''Süreklilik etkisi'''''' veya gecikme etkisi, başarılı bir geri çağırma sonra, bir sonraki geri çağrılan ögenin, yakın bir seri konumdan ziyade, uzak bir seri konumdan gelme olasılığının düşük olduğunu tahmin eder (Kahana, Howard, Zaromb ve Wingfiend, 2002). İki ögenin seri konumu arasındaki fark seri konum gecikmesi olarak adlandırılır. Koşullu yanıt olasılığı olarak adlandırılan bir başka faktör, belirli bir seri konum gecikmesini hatırlama olasılığıdır. Ayrıca bakınız Anchoring Clive Wearing Serbest Hatırlama Henry Molaison İknada İlklik Yasası Öğrenme Eğrisi Hafıza Eğilimleri Listesi Bilişsel Eğilimler Listesi Sonucun İlkliği Öğrenme İlkeleri Tepe-Uç Kuralı Anımsama Yumrusu Kaynakça ;Atıflar ;Basılı eserler Konuyla ilgili yayınlar Liebermann, David A. L''''earning and memory: An integrative approach.'''' Belmont, CA: Thomson Wadsworth, 2004, Kategori:Bellek süreçleri eğilimler' sentences: - Sultan Bey'in hayatının ikinci kısmını oluşturan önemli olay nedir? - Aslanbaba hangi ilçeye bağlı bir mahalledir? - Seri konum eğrisinin şeklini hangi etmenlerin belirlediği anlatıyor musunuz? - source_sentence: (doğum adı '''David Gordon Kirkpatrick''' 13 Haziran 1927 19 Eylül 2003), Avustralyalı country müzik şarkıcısı ve söz yazarıydı. Avustralya için bir kültür ikonuydu ve ülkenin en çok ödül alan yıldızlarından biriydi. Haziran 1927'de Nulla Nulla Creek'te bir çiftçinin oğlu olarak doğan Dusty, ilk şarkısı "The Way the Cowboy Dies"ı 1937'de yazdı ve 1938'de 11 yaşındayken "Slim Dusty" sahne adını aldı. Yetmiş yıla yakın kariyerinde çok sayıda kayıt yaptı. Yüzden fazla albüm çıkardı, yedi milyondan fazla kayıt sattı ve 70'in üzerinde altın ve platin albüm sertifikası kazandı". Sidney 2000 Olimpiyat Oyunlarının kapanış töreninde Avustralya'da çok ünlü bir şarkı olan "Waltzing Matilda"yı seslendirdi. 1951'de Dusty, şarkıcı-söz yazarı Joy McKean ile evlendi ve onun desteğiyle Avustralya'da büyük başarılar elde etti. Çiftin, şarkıcı-söz yazarı olan Anne Kirkpatrick ve David Kirkpatrick adlı iki çocukları oldu. Akciğer ve böbrek kanseri ile uzun bir mücadelenin ardından 19 Eylül 2003'te 76 yaşında Yeni Güney Galler'deki evinde öldü. Kaynakça Hristiyanlar erkek şarkıcı-şarkı yazarları Şeref Nişanı sahipleri erkek gitaristler kanserinden ölenler Kategori:Böbrek kanserinden ölenler Kategori:Yeni Güney Galler'de kanserden ölenler asıllı Avustralyalılar gitaristler country şarkıcıları Kategori:ARIA Hall of Fame üyeleri Kategori:ARIA Ödülü sahipleri Kategori:APRA Ödülü sahipleri gitaristler Kategori:21. yüzyıl gitaristleri Kategori:20. yüzyıl gitaristleri Kategori:2003 yılında ölenler Kategori:1927 doğumlular sentences: - Bu Hollandalı aktrisin adı nedir? - Kimdi Slim Dusty? - Dusty Springfield'in müzik kariyeri ne kadar sürmüştür? - source_sentence: 14 Aralık 1929 tarihli Milliyet gazetesinde İstanbul'da Kır Koşusu Eski logosu '''Türkiye Atletizm Federasyonu''' ('''TAF'''), atletizm sporunun Türkiye'deki yönetim teşkilatı olan spor federasyonu. 1922'de Türkiye İdman Cemiyetleri İttifakı (TİCİ) bünyesinde kurulan Türkiye Atletizm Federasyonu, aynı yıl Uluslararası Atletizm Federasyonları Birliği (IAAF) üyeliğine kabul edildi. Görev yapmış başkanlar Türkiye Atletizm Federasyonu'nun kronolojik sırayla başkanları; Ali Seyfi Beyti Ahmet Fetgeri Burhan Felek Vildan Aşir Savaşır Saffet Gürol Adnan Hün İrfan Şahinbaş İsmail Hakkı Güngör Ali Naili Moran Refik Tagay Sadun Özdede Nejat Kök Behçet Beylem Erol Zorlu Kurthan Fişek Jerfi Fıratlı Nuri Turan Abdullah Kökpınar Cüneyt Koryürek Yılmaz Sazak İlker Çetin Hüseyin Manioğlu Ali Ergenç Muharrem Dalkılıç Aşkın Tuna Fikret Çetinkaya Semra Aksu Hüseyin Yıldırım Mehmet Yurdadön Mehmet Terzi Hüseyin Yıldırım Fatih Çintimar Kaynakça Dış bağlantılar Federasyonun resmi sitesi Atletizm Federasyon Kategori:Avrupa Atletizm Birliği üyesi federasyonlar Kategori:Ankara merkezli kuruluşlar Osmanlı kurulan oluşumlar kurulan spor kuruluşları sentences: - Leandro Pereira kimdir? - Türkiye Atletizm Federasyonu ne zaman kuruldu? - P.E.N. nedir? - source_sentence: '''''İlkbaharda Dağ Yolunda Yürümek'''' ''''''Ma Yuan'''''' (; 1160''lar-1225), Güney Song Hanedanı döneminde yaşamış Çinli bir ressamdı. Çalışmaları, Xia Gui''ninkiyle birlikte, sözde Ma-Xia resim okulunun temelini oluşturdu ve dönemin en iyileri arasında kabul edilmektedir. Eserleri hem Zhe okulunun Çinli sanatçılarına hem de ilk Japon ressamlar Shūbun ve Sesshū''ye ilham verdi. Kaynakça Dunlop, Ronald Ossory. 1954. ''''Landscape Painting: Ma Yüan to Picasso''''. London: Seeley, Service Co. Little, Stephen. '''' Taoism and the Arts of China,'''' p. 160. Chicago: Art Institute of Chicago. Dış bağlantılar Ma Yuan Painting Gallery at China Online Museum Sung and Yuan paintings an exhibition catalog from The Metropolitan Museum of Art Libraries (fully available online as PDF), which contains material on Ma Yuan (see list of paintings) doğanlar doğumlular Kategori:1225 yılında ölenler Kategori:Çinli ressamlar Kategori:Song Hanedanı kişileri Kategori:12. yüzyıl ressamları Kategori:13. yüzyıl ressamları' sentences: - Denon hangi sanatsal hareketle ilişkilendirilir? - Hammâd bin Süleyman'ın hocası kimdir? - Ma Yuan hangi okulun ressamıydı? - source_sentence: 'veya ''''''Afrika insansıları'''''', ilk kez John Edward Gray tarafından 1825 yılında tanımlanmış bir Hominidae alt familyasıdır. Açıklama (insansı) aile ağacı sol Mevcut (5 tür) ve soyu tükenmiş türleriyle birlikte iki oymak içerir: ''''''Hominini'''''' oymağı ve ''''''Gorillini'''''' oymağı. Kimi yazarlar ise, ''''Pan'''' cinsinin bazen kendi üçüncü oymağı Panini''ye ait olduğunu düşünür. Homininae, orangutanların (Ponginae alt familyası) hominid soyundan ayrılmasından (yaklaşık 16 myö) sonra ortaya çıkan, insanlarla orangutanlara göre daha yakın akraba olan tüm hominidleri içerir. Bu alt familyadaki canlılar, ''''hominine'''' veya ''''hominineler'''' olarak tanımlanır. Evrim Homininae alt familyasının yaşı son ortak atası) tahminlere göre 14 ila 12.5 milyon yıldır Gorillini ve Hominini oymaklarına ayrılmasının ("goril insan son ortak atası", GHLCA) geç Miyosen''de, nakayamai''''nin yaşadığı döneme yakın bir zamanda, ila 10 milyon yıl önce gerçekleştiği tahmin edilmiştir (TGHLCA). ''''Pan-Homo'''' bölünmesine kadar (5-7 myö) gorillerin ve ''''Pan-Homo'''' atalarının melezlendiğine dair kanıtlar vardır. Filogeni Parins-Fukuchi ''''ve 2019''daki çalışmasına göre oluşturulmuş, soyu tükenmiş homininleri içeren bir Homininae kladogramı: Ayrıca bakınız son ortak ata Ponginae Notlar Kaynakça Dış bağlantılar Kategori:John Edward Gray tarafından adlandırılmış taksonlar tanımlanan taksonlar' sentences: - Homininae alt familyası ilk kez ne zaman ve kim tarafından tanımlandı? - Amr Hassan Zaki hangi takımlarda forma giymiştir? - KKTC spor kulübü hangi şehirde kurulmuştur? pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: MiniLM-L12-TR results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.559679037111334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6720160481444333 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7141424272818455 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7542627883650953 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.559679037111334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22400534938147776 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1428284854563691 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07542627883650951 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.559679037111334 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6720160481444333 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7141424272818455 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7542627883650953 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6573432687197566 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6262999315406539 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6317830440458849 name: Cosine Map@100 --- # MiniLM-L12-TR This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the json dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - Model Type: Sentence Transformer - Base model: [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision 8d6b950845285729817bf8e1af1861502c2fed0c --> - Maximum Sequence Length: 128 tokens - Output Dimensionality: 384 dimensions - Similarity Function: Cosine Similarity - Training Dataset: - json - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("SMARTICT/paraphrase-multilingual-MiniLM-L12-v2-ft-tr-rag-v1") # Run inference sentences = [ 'veya \'\'\'Afrika insansıları\'\'\', ilk kez John Edward Gray tarafından 1825 yılında tanımlanmış bir Hominidae alt familyasıdır. Açıklama (insansı) aile ağacı sol Mevcut (5 tür) ve soyu tükenmiş türleriyle birlikte iki oymak içerir: \'\'\'Hominini\'\'\' oymağı ve \'\'\'Gorillini\'\'\' oymağı. Kimi yazarlar ise, \'\'Pan\'\' cinsinin bazen kendi üçüncü oymağı Panini\'ye ait olduğunu düşünür. Homininae, orangutanların (Ponginae alt familyası) hominid soyundan ayrılmasından (yaklaşık 16 myö) sonra ortaya çıkan, insanlarla orangutanlara göre daha yakın akraba olan tüm hominidleri içerir. Bu alt familyadaki canlılar, \'\'hominine\'\' veya \'\'hominineler\'\' olarak tanımlanır. Evrim Homininae alt familyasının yaşı son ortak atası) tahminlere göre 14 ila 12.5 milyon yıldır Gorillini ve Hominini oymaklarına ayrılmasının ("goril insan son ortak atası", GHLCA) geç Miyosen\'de, nakayamai\'\'nin yaşadığı döneme yakın bir zamanda, ila 10 milyon yıl önce gerçekleştiği tahmin edilmiştir (TGHLCA). \'\'Pan-Homo\'\' bölünmesine kadar (5-7 myö) gorillerin ve \'\'Pan-Homo\'\' atalarının melezlendiğine dair kanıtlar vardır. Filogeni Parins-Fukuchi \'\'ve 2019\'daki çalışmasına göre oluşturulmuş, soyu tükenmiş homininleri içeren bir Homininae kladogramı: Ayrıca bakınız son ortak ata Ponginae Notlar Kaynakça Dış bağlantılar Kategori:John Edward Gray tarafından adlandırılmış taksonlar tanımlanan taksonlar', 'Homininae alt familyası ilk kez ne zaman ve kim tarafından tanımlandı?', 'Amr Hassan Zaki hangi takımlarda forma giymiştir?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_384` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) \| Metric \| Value \| \|:--------------------\|:-----------\| \| cosine_accuracy@1 \| 0.5597 \| \| cosine_accuracy@3 \| 0.672 \| \| cosine_accuracy@5 \| 0.7141 \| \| cosine_accuracy@10 \| 0.7543 \| \| cosine_precision@1 \| 0.5597 \| \| cosine_precision@3 \| 0.224 \| \| cosine_precision@5 \| 0.1428 \| \| cosine_precision@10 \| 0.0754 \| \| cosine_recall@1 \| 0.5597 \| \| cosine_recall@3 \| 0.672 \| \| cosine_recall@5 \| 0.7141 \| \| cosine_recall@10 \| 0.7543 \| \| cosine_ndcg@10 \| 0.6573 \| \| cosine_mrr@10 \| 0.6263 \| \| cosine_map@100 \| 0.6318 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### json * Dataset: json * Size: 8,970 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: \| \| positive \| anchor \| \|:--------\|:-------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 68 tokens</li><li>mean: 124.21 tokens</li><li>max: 128 tokens</li></ul> \| <ul><li>min: 2 tokens</li><li>mean: 14.35 tokens</li><li>max: 35 tokens</li></ul> \| * Samples: \| positive \| anchor \| \|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------\| \| <code>Diyarbakır ilinin Bismil ilçesine bağlı bir mahalledir. Tarihçe Mahallenin adı, 1928 yılı kayıtlarında olarak geçmektedir. Coğrafya Diyarbakır il merkezine 57 km, Bismil ilçe merkezine 22 km uzaklıktadır. Nüfus Yıllara göre mahalle nüfus verileri 2007 2000 185 1997 165 Kaynakça Dış bağlantılar Yerelnet mahalleleri</code> \| <code>Mahallenin adı ne zaman kaydedilmiştir?</code> \| \| <code>'''karmaşık neden''', '''nedensel aşırı '''nedensel veya '''indirgeme safsatası''', bir sonucun birkaç nedenden kaynaklanması mümkünken; bir tek nedeni olduğu varsayıldığında ortaya çıkan kuşkulu neden safsatasıdır. Mantıksal olarak şu şekilde açıklanabilir: "X, Y'ye neden oldu; bu nedenle, X, Y'nin tek nedeniydi" Nedensel aşırı basitleştirme, birleşik olasılıkların göz ardı edildiği belirli bir tür yanlış ikilemdir. Diğer bir deyişle, "A ve ve C" veya "A ve ama değil" şeklindeki öncüller dikkate alınmadığında olası nedenlerin "A veya veya C" olduğu varsayılır. Kaynakça</code> \| <code>Karmaşık neden safsatası nedir ve nasıl oluşur?</code> \| \| <code>Akyazı Sakarya ili ilçesi Akyazı, Adıyaman Adıyaman ili merkez ilçesine bağlı köy Akyazı, Besni Adıyaman ili Besni ilçesine bağlı köy Akyazı, Amasya Amasya ili merkez ilçesine bağlı köy Akyazı, Adilcevaz Bitlis ili Adilcevaz ilçesine bağlı köy Akyazı, Düzce Düzce ili merkez ilçesine bağlı köy Akyazı, Çorum Çorum ili merkez ilçesine bağlı köy Akyazı, Aziziye Erzurum ili Aziziye ilçesine bağlı mahalle Akyazı, Kızıltepe Mardin ili Kızıltepe ilçesine bağlı mahalle Akyazı, Asarcık Samsun ili Asarcık ilçesine bağlı mahalle Akyazı, Ortahisar Trabzon ili Ortahisar ilçesine bağlı mahalle</code> \| <code>Akyazı adında kaç köy vardır?</code> \| * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `tf32`: False - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs \| Epoch \| Step \| Training Loss \| dim_384_cosine_ndcg@10 \| \|:----------:\|:------:\|:-------------:\|:----------------------:\| \| 0.5694 \| 10 \| 0.8456 \| - \| \| 0.9680 \| 17 \| - \| 0.5968 \| \| 1.1388 \| 20 \| 0.4964 \| - \| \| 1.7082 \| 30 \| 0.393 \| - \| \| 1.9929 \| 35 \| - \| 0.6429 \| \| 2.2776 \| 40 \| 0.3235 \| - \| \| 2.8470 \| 50 \| 0.2816 \| - \| \| 2.9609 \| 52 \| - \| 0.6532 \| \| 3.4164 \| 60 \| 0.2653 \| - \| \| 3.9858 \| 70 \| 0.2408 \| 0.6576 \| \| 4.5552 \| 80 \| 0.2379 \| - \| \| 4.8399 \| 85 \| - \| 0.6573 \| * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.7 - Sentence Transformers: 3.3.1 - Transformers: 4.41.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.1.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
MakiAi/Llama-3-2-3B-Instruct-bnb-4bit-OKU-v1-10epochs	MakiAi	2024-11-25T07:43:32Z	104	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T07:41:41Z	--- base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MakiAi - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-3B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MakiAi/Llama-3-2-3B-Instruct-bnb-4bit-OKU-v1-10epochs-adapter	MakiAi	2024-11-25T07:39:20Z	107	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T07:37:13Z	--- base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: MakiAi - License: apache-2.0 - Finetuned from model : unsloth/Llama-3.2-3B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MayBashendy/Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold1	MayBashendy	2024-11-25T07:35:14Z	162	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T07:19:13Z	--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5329 - Qwk: 0.5 - Mse: 0.5329 - Rmse: 0.7300 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 0.0073 \| 2 \| 4.3121 \| -0.0167 \| 4.3121 \| 2.0766 \| \| No log \| 0.0147 \| 4 \| 2.4805 \| -0.0610 \| 2.4805 \| 1.5749 \| \| No log \| 0.0220 \| 6 \| 2.3076 \| 0.1290 \| 2.3076 \| 1.5191 \| \| No log \| 0.0293 \| 8 \| 2.6382 \| 0.0656 \| 2.6382 \| 1.6242 \| \| No log \| 0.0366 \| 10 \| 2.2411 \| 0.1529 \| 2.2411 \| 1.4970 \| \| No log \| 0.0440 \| 12 \| 1.5364 \| 0.1322 \| 1.5364 \| 1.2395 \| \| No log \| 0.0513 \| 14 \| 1.5650 \| 0.0556 \| 1.5650 \| 1.2510 \| \| No log \| 0.0586 \| 16 \| 1.4212 \| 0.0556 \| 1.4212 \| 1.1921 \| \| No log \| 0.0659 \| 18 \| 1.5321 \| 0.0870 \| 1.5321 \| 1.2378 \| \| No log \| 0.0733 \| 20 \| 1.1517 \| 0.1266 \| 1.1517 \| 1.0732 \| \| No log \| 0.0806 \| 22 \| 0.7274 \| 0.1905 \| 0.7274 \| 0.8529 \| \| No log \| 0.0879 \| 24 \| 0.6328 \| 0.1639 \| 0.6328 \| 0.7955 \| \| No log \| 0.0952 \| 26 \| 0.7007 \| 0.1290 \| 0.7007 \| 0.8371 \| \| No log \| 0.1026 \| 28 \| 0.8374 \| 0.0 \| 0.8374 \| 0.9151 \| \| No log \| 0.1099 \| 30 \| 1.4702 \| 0.1395 \| 1.4702 \| 1.2125 \| \| No log \| 0.1172 \| 32 \| 1.7090 \| 0.1818 \| 1.7090 \| 1.3073 \| \| No log \| 0.1245 \| 34 \| 1.4305 \| 0.1111 \| 1.4305 \| 1.1960 \| \| No log \| 0.1319 \| 36 \| 1.0387 \| 0.0308 \| 1.0387 \| 1.0192 \| \| No log \| 0.1392 \| 38 \| 0.8915 \| 0.0308 \| 0.8915 \| 0.9442 \| \| No log \| 0.1465 \| 40 \| 0.7929 \| 0.1563 \| 0.7929 \| 0.8905 \| \| No log \| 0.1538 \| 42 \| 1.0850 \| 0.1266 \| 1.0850 \| 1.0416 \| \| No log \| 0.1612 \| 44 \| 1.2941 \| 0.1869 \| 1.2941 \| 1.1376 \| \| No log \| 0.1685 \| 46 \| 2.0278 \| 0.0559 \| 2.0278 \| 1.4240 \| \| No log \| 0.1758 \| 48 \| 1.8639 \| 0.0930 \| 1.8639 \| 1.3652 \| \| No log \| 0.1832 \| 50 \| 1.0180 \| 0.1127 \| 1.0180 \| 1.0090 \| \| No log \| 0.1905 \| 52 \| 0.5459 \| 0.4407 \| 0.5459 \| 0.7389 \| \| No log \| 0.1978 \| 54 \| 0.5375 \| 0.2105 \| 0.5375 \| 0.7331 \| \| No log \| 0.2051 \| 56 \| 0.6592 \| 0.2623 \| 0.6592 \| 0.8119 \| \| No log \| 0.2125 \| 58 \| 0.9970 \| 0.0 \| 0.9970 \| 0.9985 \| \| No log \| 0.2198 \| 60 \| 1.2622 \| 0.0 \| 1.2622 \| 1.1235 \| \| No log \| 0.2271 \| 62 \| 1.2415 \| 0.0 \| 1.2415 \| 1.1142 \| \| No log \| 0.2344 \| 64 \| 0.9522 \| 0.0 \| 0.9522 \| 0.9758 \| \| No log \| 0.2418 \| 66 \| 0.6901 \| 0.1905 \| 0.6901 \| 0.8307 \| \| No log \| 0.2491 \| 68 \| 0.5747 \| 0.0339 \| 0.5747 \| 0.7581 \| \| No log \| 0.2564 \| 70 \| 0.5642 \| 0.0339 \| 0.5642 \| 0.7512 \| \| No log \| 0.2637 \| 72 \| 0.6176 \| 0.2000 \| 0.6176 \| 0.7859 \| \| No log \| 0.2711 \| 74 \| 0.6859 \| 0.2258 \| 0.6859 \| 0.8282 \| \| No log \| 0.2784 \| 76 \| 0.8934 \| 0.1563 \| 0.8934 \| 0.9452 \| \| No log \| 0.2857 \| 78 \| 0.9462 \| 0.1563 \| 0.9462 \| 0.9727 \| \| No log \| 0.2930 \| 80 \| 0.8605 \| 0.1905 \| 0.8605 \| 0.9276 \| \| No log \| 0.3004 \| 82 \| 0.7688 \| 0.1905 \| 0.7688 \| 0.8768 \| \| No log \| 0.3077 \| 84 \| 0.6366 \| 0.0339 \| 0.6366 \| 0.7979 \| \| No log \| 0.3150 \| 86 \| 0.6321 \| 0.0339 \| 0.6321 \| 0.7951 \| \| No log \| 0.3223 \| 88 \| 0.7504 \| 0.2000 \| 0.7504 \| 0.8663 \| \| No log \| 0.3297 \| 90 \| 0.8476 \| 0.2258 \| 0.8476 \| 0.9206 \| \| No log \| 0.3370 \| 92 \| 0.7770 \| 0.2258 \| 0.7770 \| 0.8815 \| \| No log \| 0.3443 \| 94 \| 0.6783 \| 0.2000 \| 0.6783 \| 0.8236 \| \| No log \| 0.3516 \| 96 \| 0.5899 \| 0.2105 \| 0.5899 \| 0.7680 \| \| No log \| 0.3590 \| 98 \| 0.5677 \| 0.1429 \| 0.5677 \| 0.7535 \| \| No log \| 0.3663 \| 100 \| 0.5985 \| 0.2105 \| 0.5985 \| 0.7737 \| \| No log \| 0.3736 \| 102 \| 0.6789 \| 0.3390 \| 0.6789 \| 0.8239 \| \| No log \| 0.3810 \| 104 \| 0.7268 \| 0.2258 \| 0.7268 \| 0.8525 \| \| No log \| 0.3883 \| 106 \| 0.8349 \| 0.1563 \| 0.8349 \| 0.9137 \| \| No log \| 0.3956 \| 108 \| 0.7980 \| 0.1563 \| 0.7980 \| 0.8933 \| \| No log \| 0.4029 \| 110 \| 0.6952 \| 0.1563 \| 0.6952 \| 0.8338 \| \| No log \| 0.4103 \| 112 \| 0.6014 \| 0.2258 \| 0.6014 \| 0.7755 \| \| No log \| 0.4176 \| 114 \| 0.4932 \| 0.3793 \| 0.4932 \| 0.7023 \| \| No log \| 0.4249 \| 116 \| 0.4988 \| 0.3333 \| 0.4988 \| 0.7062 \| \| No log \| 0.4322 \| 118 \| 0.5628 \| 0.1818 \| 0.5628 \| 0.7502 \| \| No log \| 0.4396 \| 120 \| 0.6661 \| -0.0755 \| 0.6661 \| 0.8161 \| \| No log \| 0.4469 \| 122 \| 0.7210 \| -0.0408 \| 0.7210 \| 0.8491 \| \| No log \| 0.4542 \| 124 \| 0.7354 \| 0.0400 \| 0.7354 \| 0.8576 \| \| No log \| 0.4615 \| 126 \| 0.6108 \| 0.1818 \| 0.6108 \| 0.7816 \| \| No log \| 0.4689 \| 128 \| 0.6323 \| 0.2373 \| 0.6323 \| 0.7952 \| \| No log \| 0.4762 \| 130 \| 0.7069 \| 0.1724 \| 0.7069 \| 0.8408 \| \| No log \| 0.4835 \| 132 \| 0.7427 \| 0.2105 \| 0.7427 \| 0.8618 \| \| No log \| 0.4908 \| 134 \| 0.7664 \| -0.0755 \| 0.7664 \| 0.8754 \| \| No log \| 0.4982 \| 136 \| 0.7678 \| 0.0 \| 0.7678 \| 0.8762 \| \| No log \| 0.5055 \| 138 \| 0.7011 \| 0.1509 \| 0.7011 \| 0.8373 \| \| No log \| 0.5128 \| 140 \| 0.6116 \| 0.2105 \| 0.6116 \| 0.7820 \| \| No log \| 0.5201 \| 142 \| 0.6154 \| 0.2105 \| 0.6154 \| 0.7845 \| \| No log \| 0.5275 \| 144 \| 0.8091 \| 0.3200 \| 0.8091 \| 0.8995 \| \| No log \| 0.5348 \| 146 \| 0.9449 \| 0.2895 \| 0.9449 \| 0.9720 \| \| No log \| 0.5421 \| 148 \| 0.9432 \| 0.2895 \| 0.9432 \| 0.9712 \| \| No log \| 0.5495 \| 150 \| 0.7335 \| 0.2258 \| 0.7335 \| 0.8564 \| \| No log \| 0.5568 \| 152 \| 0.5818 \| 0.4407 \| 0.5818 \| 0.7627 \| \| No log \| 0.5641 \| 154 \| 0.5265 \| 0.3226 \| 0.5265 \| 0.7256 \| \| No log \| 0.5714 \| 156 \| 0.5171 \| 0.3284 \| 0.5171 \| 0.7191 \| \| No log \| 0.5788 \| 158 \| 0.5730 \| 0.3836 \| 0.5730 \| 0.7569 \| \| No log \| 0.5861 \| 160 \| 0.6338 \| 0.3836 \| 0.6338 \| 0.7961 \| \| No log \| 0.5934 \| 162 \| 0.7125 \| 0.3836 \| 0.7125 \| 0.8441 \| \| No log \| 0.6007 \| 164 \| 0.9304 \| 0.3762 \| 0.9304 \| 0.9646 \| \| No log \| 0.6081 \| 166 \| 0.8147 \| 0.2921 \| 0.8147 \| 0.9026 \| \| No log \| 0.6154 \| 168 \| 0.7102 \| 0.2258 \| 0.7102 \| 0.8428 \| \| No log \| 0.6227 \| 170 \| 0.7472 \| 0.3143 \| 0.7472 \| 0.8644 \| \| No log \| 0.6300 \| 172 \| 0.8547 \| 0.2857 \| 0.8547 \| 0.9245 \| \| No log \| 0.6374 \| 174 \| 0.9096 \| 0.2826 \| 0.9096 \| 0.9537 \| \| No log \| 0.6447 \| 176 \| 0.9356 \| 0.3077 \| 0.9356 \| 0.9673 \| \| No log \| 0.6520 \| 178 \| 0.8476 \| 0.1972 \| 0.8476 \| 0.9207 \| \| No log \| 0.6593 \| 180 \| 0.8232 \| 0.0727 \| 0.8232 \| 0.9073 \| \| No log \| 0.6667 \| 182 \| 0.8438 \| 0.0727 \| 0.8438 \| 0.9186 \| \| No log \| 0.6740 \| 184 \| 0.9355 \| 0.0282 \| 0.9355 \| 0.9672 \| \| No log \| 0.6813 \| 186 \| 0.9849 \| 0.2308 \| 0.9849 \| 0.9924 \| \| No log \| 0.6886 \| 188 \| 0.9396 \| 0.2308 \| 0.9396 \| 0.9693 \| \| No log \| 0.6960 \| 190 \| 0.8917 \| 0.1600 \| 0.8917 \| 0.9443 \| \| No log \| 0.7033 \| 192 \| 0.9917 \| -0.1000 \| 0.9917 \| 0.9958 \| \| No log \| 0.7106 \| 194 \| 0.9400 \| 0.0625 \| 0.9400 \| 0.9695 \| \| No log \| 0.7179 \| 196 \| 0.8076 \| 0.2192 \| 0.8076 \| 0.8986 \| \| No log \| 0.7253 \| 198 \| 0.8555 \| 0.3544 \| 0.8555 \| 0.9249 \| \| No log \| 0.7326 \| 200 \| 0.8250 \| 0.2059 \| 0.8250 \| 0.9083 \| \| No log \| 0.7399 \| 202 \| 0.8208 \| -0.1887 \| 0.8208 \| 0.9060 \| \| No log \| 0.7473 \| 204 \| 0.8430 \| -0.0800 \| 0.8430 \| 0.9181 \| \| No log \| 0.7546 \| 206 \| 0.7761 \| 0.0 \| 0.7761 \| 0.8809 \| \| No log \| 0.7619 \| 208 \| 0.7385 \| 0.0 \| 0.7385 \| 0.8594 \| \| No log \| 0.7692 \| 210 \| 0.7824 \| 0.2105 \| 0.7824 \| 0.8846 \| \| No log \| 0.7766 \| 212 \| 0.7792 \| 0.0 \| 0.7792 \| 0.8827 \| \| No log \| 0.7839 \| 214 \| 0.7983 \| 0.0 \| 0.7983 \| 0.8935 \| \| No log \| 0.7912 \| 216 \| 0.8124 \| 0.0 \| 0.8124 \| 0.9013 \| \| No log \| 0.7985 \| 218 \| 0.8442 \| 0.0690 \| 0.8442 \| 0.9188 \| \| No log \| 0.8059 \| 220 \| 0.8922 \| 0.0690 \| 0.8922 \| 0.9446 \| \| No log \| 0.8132 \| 222 \| 0.8998 \| 0.1231 \| 0.8998 \| 0.9486 \| \| No log \| 0.8205 \| 224 \| 0.8578 \| 0.0 \| 0.8578 \| 0.9262 \| \| No log \| 0.8278 \| 226 \| 0.8195 \| 0.1639 \| 0.8195 \| 0.9053 \| \| No log \| 0.8352 \| 228 \| 0.8399 \| 0.2623 \| 0.8399 \| 0.9165 \| \| No log \| 0.8425 \| 230 \| 0.9020 \| 0.2247 \| 0.9020 \| 0.9497 \| \| No log \| 0.8498 \| 232 \| 0.9211 \| 0.2069 \| 0.9211 \| 0.9598 \| \| No log \| 0.8571 \| 234 \| 0.8606 \| 0.4000 \| 0.8606 \| 0.9277 \| \| No log \| 0.8645 \| 236 \| 0.8381 \| 0.4396 \| 0.8381 \| 0.9155 \| \| No log \| 0.8718 \| 238 \| 0.7499 \| 0.2895 \| 0.7499 \| 0.8659 \| \| No log \| 0.8791 \| 240 \| 0.7132 \| 0.3284 \| 0.7132 \| 0.8445 \| \| No log \| 0.8864 \| 242 \| 0.7819 \| 0.3143 \| 0.7819 \| 0.8843 \| \| No log \| 0.8938 \| 244 \| 0.7760 \| 0.2154 \| 0.7760 \| 0.8809 \| \| No log \| 0.9011 \| 246 \| 0.7711 \| 0.2154 \| 0.7711 \| 0.8781 \| \| No log \| 0.9084 \| 248 \| 0.8401 \| 0.25 \| 0.8401 \| 0.9166 \| \| No log \| 0.9158 \| 250 \| 0.9757 \| 0.2041 \| 0.9757 \| 0.9878 \| \| No log \| 0.9231 \| 252 \| 0.9501 \| 0.1758 \| 0.9501 \| 0.9747 \| \| No log \| 0.9304 \| 254 \| 0.8671 \| 0.3415 \| 0.8671 \| 0.9312 \| \| No log \| 0.9377 \| 256 \| 0.8438 \| 0.3250 \| 0.8438 \| 0.9186 \| \| No log \| 0.9451 \| 258 \| 0.8097 \| 0.3377 \| 0.8097 \| 0.8998 \| \| No log \| 0.9524 \| 260 \| 0.7259 \| 0.4935 \| 0.7259 \| 0.8520 \| \| No log \| 0.9597 \| 262 \| 0.6525 \| 0.3014 \| 0.6525 \| 0.8078 \| \| No log \| 0.9670 \| 264 \| 0.6580 \| 0.4935 \| 0.6580 \| 0.8112 \| \| No log \| 0.9744 \| 266 \| 0.8068 \| 0.3544 \| 0.8068 \| 0.8982 \| \| No log \| 0.9817 \| 268 \| 0.8286 \| 0.24 \| 0.8286 \| 0.9103 \| \| No log \| 0.9890 \| 270 \| 0.7183 \| 0.3836 \| 0.7183 \| 0.8475 \| \| No log \| 0.9963 \| 272 \| 0.5954 \| 0.4706 \| 0.5954 \| 0.7716 \| \| No log \| 1.0037 \| 274 \| 0.5651 \| 0.2759 \| 0.5651 \| 0.7517 \| \| No log \| 1.0110 \| 276 \| 0.5733 \| 0.3390 \| 0.5733 \| 0.7572 \| \| No log \| 1.0183 \| 278 \| 0.6954 \| 0.3836 \| 0.6954 \| 0.8339 \| \| No log \| 1.0256 \| 280 \| 0.8501 \| 0.24 \| 0.8501 \| 0.9220 \| \| No log \| 1.0330 \| 282 \| 0.7854 \| 0.2703 \| 0.7854 \| 0.8862 \| \| No log \| 1.0403 \| 284 \| 0.6793 \| 0.3014 \| 0.6793 \| 0.8242 \| \| No log \| 1.0476 \| 286 \| 0.6577 \| 0.3077 \| 0.6577 \| 0.8110 \| \| No log \| 1.0549 \| 288 \| 0.6799 \| 0.1905 \| 0.6799 \| 0.8246 \| \| No log \| 1.0623 \| 290 \| 0.7062 \| 0.1356 \| 0.7062 \| 0.8403 \| \| No log \| 1.0696 \| 292 \| 0.8099 \| 0.0870 \| 0.8099 \| 0.8999 \| \| No log \| 1.0769 \| 294 \| 0.8894 \| 0.0571 \| 0.8894 \| 0.9431 \| \| No log \| 1.0842 \| 296 \| 0.9339 \| 0.2222 \| 0.9339 \| 0.9664 \| \| No log \| 1.0916 \| 298 \| 0.8429 \| 0.0 \| 0.8429 \| 0.9181 \| \| No log \| 1.0989 \| 300 \| 0.7654 \| 0.1290 \| 0.7654 \| 0.8749 \| \| No log \| 1.1062 \| 302 \| 0.7428 \| 0.1290 \| 0.7428 \| 0.8619 \| \| No log \| 1.1136 \| 304 \| 0.7310 \| 0.1290 \| 0.7310 \| 0.8550 \| \| No log \| 1.1209 \| 306 \| 0.7282 \| 0.2623 \| 0.7282 \| 0.8533 \| \| No log \| 1.1282 \| 308 \| 0.7923 \| 0.1818 \| 0.7923 \| 0.8901 \| \| No log \| 1.1355 \| 310 \| 0.7819 \| 0.1739 \| 0.7819 \| 0.8843 \| \| No log \| 1.1429 \| 312 \| 0.7345 \| 0.2059 \| 0.7345 \| 0.8570 \| \| No log \| 1.1502 \| 314 \| 0.7329 \| 0.2059 \| 0.7329 \| 0.8561 \| \| No log \| 1.1575 \| 316 \| 0.7583 \| 0.2059 \| 0.7583 \| 0.8708 \| \| No log \| 1.1648 \| 318 \| 0.7983 \| 0.0833 \| 0.7983 \| 0.8935 \| \| No log \| 1.1722 \| 320 \| 0.7779 \| 0.2192 \| 0.7779 \| 0.8820 \| \| No log \| 1.1795 \| 322 \| 0.6693 \| 0.2623 \| 0.6693 \| 0.8181 \| \| No log \| 1.1868 \| 324 \| 0.6334 \| 0.2000 \| 0.6334 \| 0.7958 \| \| No log \| 1.1941 \| 326 \| 0.6238 \| 0.2759 \| 0.6238 \| 0.7898 \| \| No log \| 1.2015 \| 328 \| 0.6233 \| 0.2353 \| 0.6233 \| 0.7895 \| \| No log \| 1.2088 \| 330 \| 0.6391 \| 0.4 \| 0.6391 \| 0.7994 \| \| No log \| 1.2161 \| 332 \| 0.6363 \| 0.4 \| 0.6363 \| 0.7977 \| \| No log \| 1.2234 \| 334 \| 0.6430 \| 0.3810 \| 0.6430 \| 0.8018 \| \| No log \| 1.2308 \| 336 \| 0.6525 \| 0.3077 \| 0.6525 \| 0.8078 \| \| No log \| 1.2381 \| 338 \| 0.6687 \| 0.3636 \| 0.6687 \| 0.8178 \| \| No log \| 1.2454 \| 340 \| 0.6741 \| 0.3636 \| 0.6741 \| 0.8210 \| \| No log \| 1.2527 \| 342 \| 0.6576 \| 0.2500 \| 0.6576 \| 0.8109 \| \| No log \| 1.2601 \| 344 \| 0.6509 \| 0.4 \| 0.6509 \| 0.8068 \| \| No log \| 1.2674 \| 346 \| 0.6404 \| 0.3571 \| 0.6404 \| 0.8002 \| \| No log \| 1.2747 \| 348 \| 0.6342 \| 0.2759 \| 0.6342 \| 0.7964 \| \| No log \| 1.2821 \| 350 \| 0.6277 \| 0.2222 \| 0.6277 \| 0.7922 \| \| No log \| 1.2894 \| 352 \| 0.6099 \| 0.3571 \| 0.6099 \| 0.7810 \| \| No log \| 1.2967 \| 354 \| 0.6032 \| 0.3390 \| 0.6032 \| 0.7767 \| \| No log \| 1.3040 \| 356 \| 0.6133 \| 0.3390 \| 0.6133 \| 0.7832 \| \| No log \| 1.3114 \| 358 \| 0.6409 \| 0.2941 \| 0.6409 \| 0.8005 \| \| No log \| 1.3187 \| 360 \| 0.6656 \| 0.1429 \| 0.6656 \| 0.8158 \| \| No log \| 1.3260 \| 362 \| 0.6355 \| 0.3636 \| 0.6355 \| 0.7972 \| \| No log \| 1.3333 \| 364 \| 0.6714 \| 0.3662 \| 0.6714 \| 0.8194 \| \| No log \| 1.3407 \| 366 \| 0.7443 \| 0.3478 \| 0.7443 \| 0.8627 \| \| No log \| 1.3480 \| 368 \| 0.6946 \| 0.3377 \| 0.6946 \| 0.8335 \| \| No log \| 1.3553 \| 370 \| 0.6518 \| 0.3284 \| 0.6518 \| 0.8074 \| \| No log \| 1.3626 \| 372 \| 0.6539 \| 0.3284 \| 0.6539 \| 0.8086 \| \| No log \| 1.3700 \| 374 \| 0.6596 \| 0.3284 \| 0.6596 \| 0.8122 \| \| No log \| 1.3773 \| 376 \| 0.6561 \| 0.2059 \| 0.6561 \| 0.8100 \| \| No log \| 1.3846 \| 378 \| 0.6469 \| 0.2059 \| 0.6469 \| 0.8043 \| \| No log \| 1.3919 \| 380 \| 0.6530 \| 0.3636 \| 0.6530 \| 0.8081 \| \| No log \| 1.3993 \| 382 \| 0.6661 \| 0.2609 \| 0.6661 \| 0.8162 \| \| No log \| 1.4066 \| 384 \| 0.6523 \| 0.3636 \| 0.6523 \| 0.8077 \| \| No log \| 1.4139 \| 386 \| 0.6462 \| 0.2941 \| 0.6462 \| 0.8039 \| \| No log \| 1.4212 \| 388 \| 0.6491 \| 0.3143 \| 0.6491 \| 0.8057 \| \| No log \| 1.4286 \| 390 \| 0.6460 \| 0.3143 \| 0.6460 \| 0.8037 \| \| No log \| 1.4359 \| 392 \| 0.6472 \| 0.3143 \| 0.6472 \| 0.8045 \| \| No log \| 1.4432 \| 394 \| 0.6201 \| 0.3284 \| 0.6201 \| 0.7875 \| \| No log \| 1.4505 \| 396 \| 0.6237 \| 0.5135 \| 0.6237 \| 0.7898 \| \| No log \| 1.4579 \| 398 \| 0.6301 \| 0.5135 \| 0.6301 \| 0.7938 \| \| No log \| 1.4652 \| 400 \| 0.6250 \| 0.3662 \| 0.6250 \| 0.7906 \| \| No log \| 1.4725 \| 402 \| 0.6350 \| 0.3824 \| 0.6350 \| 0.7968 \| \| No log \| 1.4799 \| 404 \| 0.6210 \| 0.2623 \| 0.6210 \| 0.7880 \| \| No log \| 1.4872 \| 406 \| 0.6001 \| 0.4407 \| 0.6001 \| 0.7746 \| \| No log \| 1.4945 \| 408 \| 0.5934 \| 0.4407 \| 0.5934 \| 0.7703 \| \| No log \| 1.5018 \| 410 \| 0.5976 \| 0.2623 \| 0.5976 \| 0.7730 \| \| No log \| 1.5092 \| 412 \| 0.6302 \| 0.2500 \| 0.6302 \| 0.7939 \| \| No log \| 1.5165 \| 414 \| 0.6600 \| 0.1231 \| 0.6600 \| 0.8124 \| \| No log \| 1.5238 \| 416 \| 0.6476 \| 0.2500 \| 0.6476 \| 0.8047 \| \| No log \| 1.5311 \| 418 \| 0.5943 \| 0.2258 \| 0.5943 \| 0.7709 \| \| No log \| 1.5385 \| 420 \| 0.5791 \| 0.3793 \| 0.5791 \| 0.7610 \| \| No log \| 1.5458 \| 422 \| 0.5757 \| 0.3793 \| 0.5757 \| 0.7587 \| \| No log \| 1.5531 \| 424 \| 0.5579 \| 0.2800 \| 0.5579 \| 0.7469 \| \| No log \| 1.5604 \| 426 \| 0.5364 \| 0.4231 \| 0.5364 \| 0.7324 \| \| No log \| 1.5678 \| 428 \| 0.5809 \| 0.2727 \| 0.5809 \| 0.7621 \| \| No log \| 1.5751 \| 430 \| 0.7448 \| 0.2941 \| 0.7448 \| 0.8630 \| \| No log \| 1.5824 \| 432 \| 0.7859 \| 0.24 \| 0.7859 \| 0.8865 \| \| No log \| 1.5897 \| 434 \| 0.6662 \| 0.2941 \| 0.6662 \| 0.8162 \| \| No log \| 1.5971 \| 436 \| 0.5368 \| 0.2500 \| 0.5368 \| 0.7327 \| \| No log \| 1.6044 \| 438 \| 0.5174 \| 0.4375 \| 0.5174 \| 0.7193 \| \| No log \| 1.6117 \| 440 \| 0.5398 \| 0.4194 \| 0.5398 \| 0.7347 \| \| No log \| 1.6190 \| 442 \| 0.5335 \| 0.4762 \| 0.5335 \| 0.7304 \| \| No log \| 1.6264 \| 444 \| 0.5624 \| 0.2941 \| 0.5624 \| 0.7499 \| \| No log \| 1.6337 \| 446 \| 0.5967 \| 0.4156 \| 0.5967 \| 0.7724 \| \| No log \| 1.6410 \| 448 \| 0.5637 \| 0.3284 \| 0.5637 \| 0.7508 \| \| No log \| 1.6484 \| 450 \| 0.5546 \| 0.4923 \| 0.5546 \| 0.7447 \| \| No log \| 1.6557 \| 452 \| 0.5538 \| 0.3810 \| 0.5538 \| 0.7442 \| \| No log \| 1.6630 \| 454 \| 0.5660 \| 0.3158 \| 0.5660 \| 0.7523 \| \| No log \| 1.6703 \| 456 \| 0.5565 \| 0.3390 \| 0.5565 \| 0.7460 \| \| No log \| 1.6777 \| 458 \| 0.5662 \| 0.2623 \| 0.5662 \| 0.7525 \| \| No log \| 1.6850 \| 460 \| 0.5690 \| 0.4 \| 0.5690 \| 0.7543 \| \| No log \| 1.6923 \| 462 \| 0.5852 \| 0.2623 \| 0.5852 \| 0.7650 \| \| No log \| 1.6996 \| 464 \| 0.6280 \| 0.2500 \| 0.6280 \| 0.7925 \| \| No log \| 1.7070 \| 466 \| 0.5984 \| 0.2258 \| 0.5984 \| 0.7736 \| \| No log \| 1.7143 \| 468 \| 0.5443 \| 0.2623 \| 0.5443 \| 0.7378 \| \| No log \| 1.7216 \| 470 \| 0.5330 \| 0.4375 \| 0.5330 \| 0.7301 \| \| No log \| 1.7289 \| 472 \| 0.5178 \| 0.4375 \| 0.5178 \| 0.7196 \| \| No log \| 1.7363 \| 474 \| 0.5149 \| 0.3077 \| 0.5149 \| 0.7176 \| \| No log \| 1.7436 \| 476 \| 0.5277 \| 0.3077 \| 0.5277 \| 0.7265 \| \| No log \| 1.7509 \| 478 \| 0.5569 \| 0.3662 \| 0.5569 \| 0.7462 \| \| No log \| 1.7582 \| 480 \| 0.6063 \| 0.3662 \| 0.6063 \| 0.7787 \| \| No log \| 1.7656 \| 482 \| 0.5898 \| 0.3662 \| 0.5898 \| 0.7680 \| \| No log \| 1.7729 \| 484 \| 0.5742 \| 0.3662 \| 0.5742 \| 0.7577 \| \| No log \| 1.7802 \| 486 \| 0.5415 \| 0.3662 \| 0.5415 \| 0.7358 \| \| No log \| 1.7875 \| 488 \| 0.5302 \| 0.3143 \| 0.5302 \| 0.7281 \| \| No log \| 1.7949 \| 490 \| 0.5378 \| 0.3143 \| 0.5378 \| 0.7333 \| \| No log \| 1.8022 \| 492 \| 0.5824 \| 0.3077 \| 0.5824 \| 0.7631 \| \| No log \| 1.8095 \| 494 \| 0.6343 \| 0.3684 \| 0.6343 \| 0.7965 \| \| No log \| 1.8168 \| 496 \| 0.6774 \| 0.3544 \| 0.6774 \| 0.8231 \| \| No log \| 1.8242 \| 498 \| 0.6618 \| 0.1972 \| 0.6618 \| 0.8135 \| \| 0.4394 \| 1.8315 \| 500 \| 0.6764 \| 0.3333 \| 0.6764 \| 0.8224 \| \| 0.4394 \| 1.8388 \| 502 \| 0.5942 \| 0.3077 \| 0.5942 \| 0.7708 \| \| 0.4394 \| 1.8462 \| 504 \| 0.5660 \| 0.4375 \| 0.5660 \| 0.7524 \| \| 0.4394 \| 1.8535 \| 506 \| 0.5729 \| 0.3810 \| 0.5729 \| 0.7569 \| \| 0.4394 \| 1.8608 \| 508 \| 0.5689 \| 0.4375 \| 0.5689 \| 0.7542 \| \| 0.4394 \| 1.8681 \| 510 \| 0.6016 \| 0.3077 \| 0.6016 \| 0.7756 \| \| 0.4394 \| 1.8755 \| 512 \| 0.6428 \| 0.1739 \| 0.6428 \| 0.8017 \| \| 0.4394 \| 1.8828 \| 514 \| 0.6807 \| 0.1429 \| 0.6807 \| 0.8251 \| \| 0.4394 \| 1.8901 \| 516 \| 0.7161 \| 0.1039 \| 0.7161 \| 0.8462 \| \| 0.4394 \| 1.8974 \| 518 \| 0.7035 \| 0.1039 \| 0.7035 \| 0.8387 \| \| 0.4394 \| 1.9048 \| 520 \| 0.6607 \| 0.2727 \| 0.6607 \| 0.8128 \| \| 0.4394 \| 1.9121 \| 522 \| 0.6605 \| 0.4348 \| 0.6605 \| 0.8127 \| \| 0.4394 \| 1.9194 \| 524 \| 0.6348 \| 0.4762 \| 0.6348 \| 0.7967 \| \| 0.4394 \| 1.9267 \| 526 \| 0.6357 \| 0.0870 \| 0.6357 \| 0.7973 \| \| 0.4394 \| 1.9341 \| 528 \| 0.6515 \| 0.1972 \| 0.6515 \| 0.8071 \| \| 0.4394 \| 1.9414 \| 530 \| 0.6058 \| 0.0909 \| 0.6058 \| 0.7783 \| \| 0.4394 \| 1.9487 \| 532 \| 0.5824 \| 0.0339 \| 0.5824 \| 0.7631 \| \| 0.4394 \| 1.9560 \| 534 \| 0.5448 \| 0.1000 \| 0.5448 \| 0.7381 \| \| 0.4394 \| 1.9634 \| 536 \| 0.5489 \| 0.0 \| 0.5489 \| 0.7409 \| \| 0.4394 \| 1.9707 \| 538 \| 0.5613 \| 0.2623 \| 0.5613 \| 0.7492 \| \| 0.4394 \| 1.9780 \| 540 \| 0.5872 \| 0.3478 \| 0.5872 \| 0.7663 \| \| 0.4394 \| 1.9853 \| 542 \| 0.6412 \| 0.0909 \| 0.6412 \| 0.8007 \| \| 0.4394 \| 1.9927 \| 544 \| 0.6194 \| 0.3478 \| 0.6194 \| 0.7870 \| \| 0.4394 \| 2.0 \| 546 \| 0.5844 \| 0.3478 \| 0.5844 \| 0.7644 \| \| 0.4394 \| 2.0073 \| 548 \| 0.5885 \| 0.3226 \| 0.5885 \| 0.7672 \| \| 0.4394 \| 2.0147 \| 550 \| 0.6054 \| 0.3662 \| 0.6054 \| 0.7780 \| \| 0.4394 \| 2.0220 \| 552 \| 0.5808 \| 0.3607 \| 0.5808 \| 0.7621 \| \| 0.4394 \| 2.0293 \| 554 \| 0.5551 \| 0.3284 \| 0.5551 \| 0.7450 \| \| 0.4394 \| 2.0366 \| 556 \| 0.5516 \| 0.2941 \| 0.5516 \| 0.7427 \| \| 0.4394 \| 2.0440 \| 558 \| 0.5470 \| 0.4407 \| 0.5470 \| 0.7396 \| \| 0.4394 \| 2.0513 \| 560 \| 0.5433 \| 0.4828 \| 0.5433 \| 0.7371 \| \| 0.4394 \| 2.0586 \| 562 \| 0.5563 \| 0.3284 \| 0.5563 \| 0.7458 \| \| 0.4394 \| 2.0659 \| 564 \| 0.5637 \| 0.3284 \| 0.5637 \| 0.7508 \| \| 0.4394 \| 2.0733 \| 566 \| 0.5684 \| 0.3284 \| 0.5684 \| 0.7539 \| \| 0.4394 \| 2.0806 \| 568 \| 0.5737 \| 0.4348 \| 0.5737 \| 0.7574 \| \| 0.4394 \| 2.0879 \| 570 \| 0.5828 \| 0.3836 \| 0.5828 \| 0.7634 \| \| 0.4394 \| 2.0952 \| 572 \| 0.5724 \| 0.4923 \| 0.5724 \| 0.7566 \| \| 0.4394 \| 2.1026 \| 574 \| 0.6167 \| 0.4 \| 0.6167 \| 0.7853 \| \| 0.4394 \| 2.1099 \| 576 \| 0.6451 \| 0.5 \| 0.6451 \| 0.8032 \| \| 0.4394 \| 2.1172 \| 578 \| 0.6168 \| 0.5 \| 0.6168 \| 0.7854 \| \| 0.4394 \| 2.1245 \| 580 \| 0.5485 \| 0.3284 \| 0.5485 \| 0.7406 \| \| 0.4394 \| 2.1319 \| 582 \| 0.5082 \| 0.4375 \| 0.5082 \| 0.7129 \| \| 0.4394 \| 2.1392 \| 584 \| 0.5101 \| 0.4375 \| 0.5101 \| 0.7142 \| \| 0.4394 \| 2.1465 \| 586 \| 0.5350 \| 0.3636 \| 0.5350 \| 0.7314 \| \| 0.4394 \| 2.1538 \| 588 \| 0.6042 \| 0.5352 \| 0.6042 \| 0.7773 \| \| 0.4394 \| 2.1612 \| 590 \| 0.6711 \| 0.4179 \| 0.6711 \| 0.8192 \| \| 0.4394 \| 2.1685 \| 592 \| 0.6731 \| 0.4179 \| 0.6731 \| 0.8204 \| \| 0.4394 \| 2.1758 \| 594 \| 0.5895 \| 0.5352 \| 0.5895 \| 0.7678 \| \| 0.4394 \| 2.1832 \| 596 \| 0.5354 \| 0.4348 \| 0.5354 \| 0.7317 \| \| 0.4394 \| 2.1905 \| 598 \| 0.5138 \| 0.3390 \| 0.5138 \| 0.7168 \| \| 0.4394 \| 2.1978 \| 600 \| 0.5322 \| 0.4 \| 0.5322 \| 0.7295 \| \| 0.4394 \| 2.2051 \| 602 \| 0.5366 \| 0.4 \| 0.5366 \| 0.7325 \| \| 0.4394 \| 2.2125 \| 604 \| 0.5474 \| 0.3636 \| 0.5474 \| 0.7398 \| \| 0.4394 \| 2.2198 \| 606 \| 0.5727 \| 0.3810 \| 0.5727 \| 0.7568 \| \| 0.4394 \| 2.2271 \| 608 \| 0.5590 \| 0.2623 \| 0.5590 \| 0.7476 \| \| 0.4394 \| 2.2344 \| 610 \| 0.5206 \| 0.3390 \| 0.5206 \| 0.7215 \| \| 0.4394 \| 2.2418 \| 612 \| 0.5047 \| 0.2041 \| 0.5047 \| 0.7104 \| \| 0.4394 \| 2.2491 \| 614 \| 0.5069 \| 0.2041 \| 0.5069 \| 0.7120 \| \| 0.4394 \| 2.2564 \| 616 \| 0.5034 \| 0.2041 \| 0.5034 \| 0.7095 \| \| 0.4394 \| 2.2637 \| 618 \| 0.5097 \| 0.3774 \| 0.5097 \| 0.7139 \| \| 0.4394 \| 2.2711 \| 620 \| 0.5589 \| 0.2857 \| 0.5589 \| 0.7476 \| \| 0.4394 \| 2.2784 \| 622 \| 0.5975 \| 0.3077 \| 0.5975 \| 0.7730 \| \| 0.4394 \| 2.2857 \| 624 \| 0.6019 \| 0.4179 \| 0.6019 \| 0.7759 \| \| 0.4394 \| 2.2930 \| 626 \| 0.5930 \| 0.4179 \| 0.5930 \| 0.7701 \| \| 0.4394 \| 2.3004 \| 628 \| 0.5941 \| 0.3824 \| 0.5941 \| 0.7708 \| \| 0.4394 \| 2.3077 \| 630 \| 0.5769 \| 0.3636 \| 0.5769 \| 0.7596 \| \| 0.4394 \| 2.3150 \| 632 \| 0.5728 \| 0.4857 \| 0.5728 \| 0.7568 \| \| 0.4394 \| 2.3223 \| 634 \| 0.5773 \| 0.4857 \| 0.5773 \| 0.7598 \| \| 0.4394 \| 2.3297 \| 636 \| 0.5714 \| 0.4375 \| 0.5714 \| 0.7559 \| \| 0.4394 \| 2.3370 \| 638 \| 0.5587 \| 0.3636 \| 0.5587 \| 0.7475 \| \| 0.4394 \| 2.3443 \| 640 \| 0.5817 \| 0.2857 \| 0.5817 \| 0.7627 \| \| 0.4394 \| 2.3516 \| 642 \| 0.6151 \| 0.3077 \| 0.6151 \| 0.7843 \| \| 0.4394 \| 2.3590 \| 644 \| 0.6204 \| 0.3077 \| 0.6204 \| 0.7876 \| \| 0.4394 \| 2.3663 \| 646 \| 0.5875 \| 0.4179 \| 0.5875 \| 0.7665 \| \| 0.4394 \| 2.3736 \| 648 \| 0.5832 \| 0.4179 \| 0.5832 \| 0.7637 \| \| 0.4394 \| 2.3810 \| 650 \| 0.5847 \| 0.4179 \| 0.5847 \| 0.7647 \| \| 0.4394 \| 2.3883 \| 652 \| 0.5908 \| 0.4179 \| 0.5908 \| 0.7686 \| \| 0.4394 \| 2.3956 \| 654 \| 0.6069 \| 0.4348 \| 0.6069 \| 0.7791 \| \| 0.4394 \| 2.4029 \| 656 \| 0.6180 \| 0.4 \| 0.6180 \| 0.7861 \| \| 0.4394 \| 2.4103 \| 658 \| 0.6022 \| 0.4 \| 0.6022 \| 0.7760 \| \| 0.4394 \| 2.4176 \| 660 \| 0.5792 \| 0.3824 \| 0.5792 \| 0.7610 \| \| 0.4394 \| 2.4249 \| 662 \| 0.5708 \| 0.4167 \| 0.5708 \| 0.7555 \| \| 0.4394 \| 2.4322 \| 664 \| 0.5678 \| 0.3662 \| 0.5678 \| 0.7535 \| \| 0.4394 \| 2.4396 \| 666 \| 0.5786 \| 0.2500 \| 0.5786 \| 0.7607 \| \| 0.4394 \| 2.4469 \| 668 \| 0.5853 \| 0.2500 \| 0.5853 \| 0.7650 \| \| 0.4394 \| 2.4542 \| 670 \| 0.5670 \| 0.2500 \| 0.5670 \| 0.7530 \| \| 0.4394 \| 2.4615 \| 672 \| 0.5476 \| 0.3226 \| 0.5476 \| 0.7400 \| \| 0.4394 \| 2.4689 \| 674 \| 0.5228 \| 0.3607 \| 0.5228 \| 0.7230 \| \| 0.4394 \| 2.4762 \| 676 \| 0.5152 \| 0.4762 \| 0.5152 \| 0.7178 \| \| 0.4394 \| 2.4835 \| 678 \| 0.5273 \| 0.4706 \| 0.5273 \| 0.7261 \| \| 0.4394 \| 2.4908 \| 680 \| 0.5398 \| 0.4167 \| 0.5398 \| 0.7347 \| \| 0.4394 \| 2.4982 \| 682 \| 0.5394 \| 0.3684 \| 0.5394 \| 0.7344 \| \| 0.4394 \| 2.5055 \| 684 \| 0.5716 \| 0.4304 \| 0.5716 \| 0.7560 \| \| 0.4394 \| 2.5128 \| 686 \| 0.5776 \| 0.3846 \| 0.5776 \| 0.7600 \| \| 0.4394 \| 2.5201 \| 688 \| 0.5735 \| 0.3704 \| 0.5735 \| 0.7573 \| \| 0.4394 \| 2.5275 \| 690 \| 0.5887 \| 0.4 \| 0.5887 \| 0.7673 \| \| 0.4394 \| 2.5348 \| 692 \| 0.5916 \| 0.4 \| 0.5916 \| 0.7691 \| \| 0.4394 \| 2.5421 \| 694 \| 0.5472 \| 0.4 \| 0.5472 \| 0.7397 \| \| 0.4394 \| 2.5495 \| 696 \| 0.5257 \| 0.2222 \| 0.5257 \| 0.7251 \| \| 0.4394 \| 2.5568 \| 698 \| 0.5270 \| 0.2222 \| 0.5270 \| 0.7259 \| \| 0.4394 \| 2.5641 \| 700 \| 0.5273 \| 0.2222 \| 0.5273 \| 0.7262 \| \| 0.4394 \| 2.5714 \| 702 \| 0.5283 \| 0.2222 \| 0.5283 \| 0.7268 \| \| 0.4394 \| 2.5788 \| 704 \| 0.5297 \| 0.3158 \| 0.5297 \| 0.7278 \| \| 0.4394 \| 2.5861 \| 706 \| 0.5331 \| 0.4194 \| 0.5331 \| 0.7301 \| \| 0.4394 \| 2.5934 \| 708 \| 0.5773 \| 0.5312 \| 0.5773 \| 0.7598 \| \| 0.4394 \| 2.6007 \| 710 \| 0.6296 \| 0.5 \| 0.6296 \| 0.7935 \| \| 0.4394 \| 2.6081 \| 712 \| 0.5837 \| 0.4 \| 0.5837 \| 0.7640 \| \| 0.4394 \| 2.6154 \| 714 \| 0.5268 \| 0.4590 \| 0.5268 \| 0.7258 \| \| 0.4394 \| 2.6227 \| 716 \| 0.5174 \| 0.3438 \| 0.5174 \| 0.7193 \| \| 0.4394 \| 2.6300 \| 718 \| 0.5159 \| 0.4590 \| 0.5159 \| 0.7183 \| \| 0.4394 \| 2.6374 \| 720 \| 0.5134 \| 0.4706 \| 0.5134 \| 0.7165 \| \| 0.4394 \| 2.6447 \| 722 \| 0.5149 \| 0.3636 \| 0.5149 \| 0.7175 \| \| 0.4394 \| 2.6520 \| 724 \| 0.5306 \| 0.4194 \| 0.5306 \| 0.7284 \| \| 0.4394 \| 2.6593 \| 726 \| 0.5574 \| 0.3836 \| 0.5574 \| 0.7466 \| \| 0.4394 \| 2.6667 \| 728 \| 0.5736 \| 0.3333 \| 0.5736 \| 0.7574 \| \| 0.4394 \| 2.6740 \| 730 \| 0.5960 \| 0.3333 \| 0.5960 \| 0.7720 \| \| 0.4394 \| 2.6813 \| 732 \| 0.5995 \| 0.3377 \| 0.5995 \| 0.7742 \| \| 0.4394 \| 2.6886 \| 734 \| 0.6189 \| 0.2941 \| 0.6189 \| 0.7867 \| \| 0.4394 \| 2.6960 \| 736 \| 0.5905 \| 0.3478 \| 0.5905 \| 0.7685 \| \| 0.4394 \| 2.7033 \| 738 \| 0.5434 \| 0.3077 \| 0.5434 \| 0.7372 \| \| 0.4394 \| 2.7106 \| 740 \| 0.5324 \| 0.4857 \| 0.5324 \| 0.7297 \| \| 0.4394 \| 2.7179 \| 742 \| 0.5279 \| 0.4857 \| 0.5279 \| 0.7265 \| \| 0.4394 \| 2.7253 \| 744 \| 0.5274 \| 0.4857 \| 0.5274 \| 0.7262 \| \| 0.4394 \| 2.7326 \| 746 \| 0.5309 \| 0.3824 \| 0.5309 \| 0.7286 \| \| 0.4394 \| 2.7399 \| 748 \| 0.5575 \| 0.3438 \| 0.5575 \| 0.7467 \| \| 0.4394 \| 2.7473 \| 750 \| 0.5721 \| 0.2941 \| 0.5721 \| 0.7564 \| \| 0.4394 \| 2.7546 \| 752 \| 0.5613 \| 0.2857 \| 0.5613 \| 0.7492 \| \| 0.4394 \| 2.7619 \| 754 \| 0.5312 \| 0.4 \| 0.5312 \| 0.7288 \| \| 0.4394 \| 2.7692 \| 756 \| 0.5035 \| 0.4590 \| 0.5035 \| 0.7096 \| \| 0.4394 \| 2.7766 \| 758 \| 0.5060 \| 0.3284 \| 0.5060 \| 0.7113 \| \| 0.4394 \| 2.7839 \| 760 \| 0.5022 \| 0.4348 \| 0.5022 \| 0.7087 \| \| 0.4394 \| 2.7912 \| 762 \| 0.4806 \| 0.3077 \| 0.4806 \| 0.6933 \| \| 0.4394 \| 2.7985 \| 764 \| 0.4960 \| 0.4545 \| 0.4960 \| 0.7043 \| \| 0.4394 \| 2.8059 \| 766 \| 0.5268 \| 0.4 \| 0.5268 \| 0.7258 \| \| 0.4394 \| 2.8132 \| 768 \| 0.5510 \| 0.3478 \| 0.5510 \| 0.7423 \| \| 0.4394 \| 2.8205 \| 770 \| 0.5621 \| 0.2941 \| 0.5621 \| 0.7497 \| \| 0.4394 \| 2.8278 \| 772 \| 0.5218 \| 0.4 \| 0.5218 \| 0.7223 \| \| 0.4394 \| 2.8352 \| 774 \| 0.5103 \| 0.3824 \| 0.5103 \| 0.7144 \| \| 0.4394 \| 2.8425 \| 776 \| 0.5223 \| 0.4179 \| 0.5223 \| 0.7227 \| \| 0.4394 \| 2.8498 \| 778 \| 0.5300 \| 0.4324 \| 0.5300 \| 0.7280 \| \| 0.4394 \| 2.8571 \| 780 \| 0.5428 \| 0.3636 \| 0.5428 \| 0.7368 \| \| 0.4394 \| 2.8645 \| 782 \| 0.5500 \| 0.3077 \| 0.5500 \| 0.7416 \| \| 0.4394 \| 2.8718 \| 784 \| 0.5484 \| 0.3077 \| 0.5484 \| 0.7406 \| \| 0.4394 \| 2.8791 \| 786 \| 0.5431 \| 0.3000 \| 0.5431 \| 0.7370 \| \| 0.4394 \| 2.8864 \| 788 \| 0.5381 \| 0.3000 \| 0.5381 \| 0.7336 \| \| 0.4394 \| 2.8938 \| 790 \| 0.5317 \| 0.3390 \| 0.5317 \| 0.7292 \| \| 0.4394 \| 2.9011 \| 792 \| 0.5342 \| 0.3390 \| 0.5342 \| 0.7309 \| \| 0.4394 \| 2.9084 \| 794 \| 0.5367 \| 0.3390 \| 0.5367 \| 0.7326 \| \| 0.4394 \| 2.9158 \| 796 \| 0.5334 \| 0.4 \| 0.5334 \| 0.7303 \| \| 0.4394 \| 2.9231 \| 798 \| 0.5481 \| 0.3077 \| 0.5481 \| 0.7403 \| \| 0.4394 \| 2.9304 \| 800 \| 0.5912 \| 0.3636 \| 0.5912 \| 0.7689 \| \| 0.4394 \| 2.9377 \| 802 \| 0.6102 \| 0.3514 \| 0.6102 \| 0.7812 \| \| 0.4394 \| 2.9451 \| 804 \| 0.5929 \| 0.3636 \| 0.5929 \| 0.7700 \| \| 0.4394 \| 2.9524 \| 806 \| 0.5741 \| 0.3143 \| 0.5741 \| 0.7577 \| \| 0.4394 \| 2.9597 \| 808 \| 0.5686 \| 0.3143 \| 0.5686 \| 0.7541 \| \| 0.4394 \| 2.9670 \| 810 \| 0.5581 \| 0.3077 \| 0.5581 \| 0.7471 \| \| 0.4394 \| 2.9744 \| 812 \| 0.5456 \| 0.3077 \| 0.5456 \| 0.7386 \| \| 0.4394 \| 2.9817 \| 814 \| 0.5521 \| 0.4348 \| 0.5521 \| 0.7430 \| \| 0.4394 \| 2.9890 \| 816 \| 0.5743 \| 0.4658 \| 0.5743 \| 0.7578 \| \| 0.4394 \| 2.9963 \| 818 \| 0.5881 \| 0.3200 \| 0.5881 \| 0.7669 \| \| 0.4394 \| 3.0037 \| 820 \| 0.6378 \| 0.3571 \| 0.6378 \| 0.7986 \| \| 0.4394 \| 3.0110 \| 822 \| 0.6884 \| 0.3571 \| 0.6884 \| 0.8297 \| \| 0.4394 \| 3.0183 \| 824 \| 0.7122 \| 0.3077 \| 0.7122 \| 0.8439 \| \| 0.4394 \| 3.0256 \| 826 \| 0.7344 \| 0.3077 \| 0.7344 \| 0.8570 \| \| 0.4394 \| 3.0330 \| 828 \| 0.7099 \| 0.3077 \| 0.7099 \| 0.8425 \| \| 0.4394 \| 3.0403 \| 830 \| 0.6956 \| 0.3077 \| 0.6956 \| 0.8340 \| \| 0.4394 \| 3.0476 \| 832 \| 0.6589 \| 0.2921 \| 0.6589 \| 0.8117 \| \| 0.4394 \| 3.0549 \| 834 \| 0.6135 \| 0.3377 \| 0.6135 \| 0.7833 \| \| 0.4394 \| 3.0623 \| 836 \| 0.5683 \| 0.3284 \| 0.5683 \| 0.7538 \| \| 0.4394 \| 3.0696 \| 838 \| 0.5688 \| 0.3077 \| 0.5688 \| 0.7542 \| \| 0.4394 \| 3.0769 \| 840 \| 0.6306 \| 0.4179 \| 0.6306 \| 0.7941 \| \| 0.4394 \| 3.0842 \| 842 \| 0.6851 \| 0.4179 \| 0.6851 \| 0.8277 \| \| 0.4394 \| 3.0916 \| 844 \| 0.8455 \| 0.2105 \| 0.8455 \| 0.9195 \| \| 0.4394 \| 3.0989 \| 846 \| 0.9481 \| 0.2105 \| 0.9481 \| 0.9737 \| \| 0.4394 \| 3.1062 \| 848 \| 0.9267 \| 0.2105 \| 0.9267 \| 0.9626 \| \| 0.4394 \| 3.1136 \| 850 \| 0.7810 \| 0.2105 \| 0.7810 \| 0.8837 \| \| 0.4394 \| 3.1209 \| 852 \| 0.6059 \| 0.3077 \| 0.6059 \| 0.7784 \| \| 0.4394 \| 3.1282 \| 854 \| 0.5549 \| 0.3077 \| 0.5549 \| 0.7449 \| \| 0.4394 \| 3.1355 \| 856 \| 0.5495 \| 0.2500 \| 0.5495 \| 0.7413 \| \| 0.4394 \| 3.1429 \| 858 \| 0.5506 \| 0.3636 \| 0.5506 \| 0.7420 \| \| 0.4394 \| 3.1502 \| 860 \| 0.5643 \| 0.3636 \| 0.5643 \| 0.7512 \| \| 0.4394 \| 3.1575 \| 862 \| 0.5817 \| 0.2623 \| 0.5817 \| 0.7627 \| \| 0.4394 \| 3.1648 \| 864 \| 0.5854 \| 0.2623 \| 0.5854 \| 0.7651 \| \| 0.4394 \| 3.1722 \| 866 \| 0.5982 \| 0.2623 \| 0.5982 \| 0.7734 \| \| 0.4394 \| 3.1795 \| 868 \| 0.6201 \| 0.3226 \| 0.6201 \| 0.7875 \| \| 0.4394 \| 3.1868 \| 870 \| 0.6246 \| 0.3226 \| 0.6246 \| 0.7903 \| \| 0.4394 \| 3.1941 \| 872 \| 0.6608 \| 0.1972 \| 0.6608 \| 0.8129 \| \| 0.4394 \| 3.2015 \| 874 \| 0.6514 \| 0.1972 \| 0.6514 \| 0.8071 \| \| 0.4394 \| 3.2088 \| 876 \| 0.6241 \| 0.3226 \| 0.6241 \| 0.7900 \| \| 0.4394 \| 3.2161 \| 878 \| 0.5893 \| 0.2623 \| 0.5893 \| 0.7677 \| \| 0.4394 \| 3.2234 \| 880 \| 0.5654 \| 0.4211 \| 0.5654 \| 0.7519 \| \| 0.4394 \| 3.2308 \| 882 \| 0.5684 \| 0.3571 \| 0.5684 \| 0.7540 \| \| 0.4394 \| 3.2381 \| 884 \| 0.5743 \| 0.3607 \| 0.5743 \| 0.7579 \| \| 0.4394 \| 3.2454 \| 886 \| 0.5726 \| 0.4211 \| 0.5726 \| 0.7567 \| \| 0.4394 \| 3.2527 \| 888 \| 0.5852 \| 0.3390 \| 0.5852 \| 0.7650 \| \| 0.4394 \| 3.2601 \| 890 \| 0.6132 \| 0.3607 \| 0.6132 \| 0.7831 \| \| 0.4394 \| 3.2674 \| 892 \| 0.6081 \| 0.3607 \| 0.6081 \| 0.7798 \| \| 0.4394 \| 3.2747 \| 894 \| 0.5994 \| 0.4179 \| 0.5994 \| 0.7742 \| \| 0.4394 \| 3.2821 \| 896 \| 0.5885 \| 0.4923 \| 0.5885 \| 0.7672 \| \| 0.4394 \| 3.2894 \| 898 \| 0.5921 \| 0.4 \| 0.5921 \| 0.7695 \| \| 0.4394 \| 3.2967 \| 900 \| 0.5924 \| 0.4 \| 0.5924 \| 0.7697 \| \| 0.4394 \| 3.3040 \| 902 \| 0.5828 \| 0.4 \| 0.5828 \| 0.7634 \| \| 0.4394 \| 3.3114 \| 904 \| 0.5967 \| 0.4 \| 0.5967 \| 0.7725 \| \| 0.4394 \| 3.3187 \| 906 \| 0.6072 \| 0.4 \| 0.6072 \| 0.7792 \| \| 0.4394 \| 3.3260 \| 908 \| 0.6175 \| 0.2759 \| 0.6175 \| 0.7858 \| \| 0.4394 \| 3.3333 \| 910 \| 0.6268 \| 0.3390 \| 0.6268 \| 0.7917 \| \| 0.4394 \| 3.3407 \| 912 \| 0.6329 \| 0.4 \| 0.6329 \| 0.7955 \| \| 0.4394 \| 3.3480 \| 914 \| 0.6434 \| 0.2623 \| 0.6434 \| 0.8021 \| \| 0.4394 \| 3.3553 \| 916 \| 0.6510 \| 0.2623 \| 0.6510 \| 0.8068 \| \| 0.4394 \| 3.3626 \| 918 \| 0.6418 \| 0.4 \| 0.6418 \| 0.8011 \| \| 0.4394 \| 3.3700 \| 920 \| 0.6444 \| 0.4 \| 0.6444 \| 0.8028 \| \| 0.4394 \| 3.3773 \| 922 \| 0.6565 \| 0.2258 \| 0.6565 \| 0.8103 \| \| 0.4394 \| 3.3846 \| 924 \| 0.6755 \| 0.1739 \| 0.6755 \| 0.8219 \| \| 0.4394 \| 3.3919 \| 926 \| 0.7127 \| 0.25 \| 0.7127 \| 0.8442 \| \| 0.4394 \| 3.3993 \| 928 \| 0.8145 \| 0.2192 \| 0.8145 \| 0.9025 \| \| 0.4394 \| 3.4066 \| 930 \| 0.8950 \| 0.2410 \| 0.8950 \| 0.9461 \| \| 0.4394 \| 3.4139 \| 932 \| 0.8864 \| 0.2410 \| 0.8864 \| 0.9415 \| \| 0.4394 \| 3.4212 \| 934 \| 0.7805 \| 0.3077 \| 0.7805 \| 0.8835 \| \| 0.4394 \| 3.4286 \| 936 \| 0.7266 \| 0.2597 \| 0.7266 \| 0.8524 \| \| 0.4394 \| 3.4359 \| 938 \| 0.6929 \| 0.3294 \| 0.6929 \| 0.8324 \| \| 0.4394 \| 3.4432 \| 940 \| 0.6470 \| 0.3704 \| 0.6470 \| 0.8044 \| \| 0.4394 \| 3.4505 \| 942 \| 0.6227 \| 0.475 \| 0.6227 \| 0.7891 \| \| 0.4394 \| 3.4579 \| 944 \| 0.6089 \| 0.475 \| 0.6089 \| 0.7803 \| \| 0.4394 \| 3.4652 \| 946 \| 0.5860 \| 0.4923 \| 0.5860 \| 0.7655 \| \| 0.4394 \| 3.4725 \| 948 \| 0.6036 \| 0.2857 \| 0.6036 \| 0.7769 \| \| 0.4394 \| 3.4799 \| 950 \| 0.6775 \| 0.2192 \| 0.6775 \| 0.8231 \| \| 0.4394 \| 3.4872 \| 952 \| 0.7340 \| 0.2192 \| 0.7340 \| 0.8567 \| \| 0.4394 \| 3.4945 \| 954 \| 0.7522 \| 0.2192 \| 0.7522 \| 0.8673 \| \| 0.4394 \| 3.5018 \| 956 \| 0.7190 \| 0.2192 \| 0.7190 \| 0.8480 \| \| 0.4394 \| 3.5092 \| 958 \| 0.6386 \| 0.2817 \| 0.6386 \| 0.7991 \| \| 0.4394 \| 3.5165 \| 960 \| 0.6002 \| 0.2857 \| 0.6002 \| 0.7747 \| \| 0.4394 \| 3.5238 \| 962 \| 0.6018 \| 0.2857 \| 0.6018 \| 0.7758 \| \| 0.4394 \| 3.5311 \| 964 \| 0.6261 \| 0.2857 \| 0.6261 \| 0.7913 \| \| 0.4394 \| 3.5385 \| 966 \| 0.6825 \| 0.2192 \| 0.6825 \| 0.8261 \| \| 0.4394 \| 3.5458 \| 968 \| 0.7351 \| 0.2192 \| 0.7351 \| 0.8574 \| \| 0.4394 \| 3.5531 \| 970 \| 0.7172 \| 0.2192 \| 0.7172 \| 0.8469 \| \| 0.4394 \| 3.5604 \| 972 \| 0.6545 \| 0.25 \| 0.6545 \| 0.8090 \| \| 0.4394 \| 3.5678 \| 974 \| 0.6189 \| 0.2857 \| 0.6189 \| 0.7867 \| \| 0.4394 \| 3.5751 \| 976 \| 0.6039 \| 0.3824 \| 0.6039 \| 0.7771 \| \| 0.4394 \| 3.5824 \| 978 \| 0.6051 \| 0.3836 \| 0.6051 \| 0.7779 \| \| 0.4394 \| 3.5897 \| 980 \| 0.6115 \| 0.3836 \| 0.6115 \| 0.7820 \| \| 0.4394 \| 3.5971 \| 982 \| 0.6087 \| 0.3824 \| 0.6087 \| 0.7802 \| \| 0.4394 \| 3.6044 \| 984 \| 0.5944 \| 0.4167 \| 0.5944 \| 0.7709 \| \| 0.4394 \| 3.6117 \| 986 \| 0.5826 \| 0.4857 \| 0.5826 \| 0.7633 \| \| 0.4394 \| 3.6190 \| 988 \| 0.5780 \| 0.475 \| 0.5780 \| 0.7602 \| \| 0.4394 \| 3.6264 \| 990 \| 0.5735 \| 0.4167 \| 0.5735 \| 0.7573 \| \| 0.4394 \| 3.6337 \| 992 \| 0.5578 \| 0.4167 \| 0.5578 \| 0.7469 \| \| 0.4394 \| 3.6410 \| 994 \| 0.5438 \| 0.4706 \| 0.5438 \| 0.7374 \| \| 0.4394 \| 3.6484 \| 996 \| 0.5378 \| 0.3438 \| 0.5378 \| 0.7333 \| \| 0.4394 \| 3.6557 \| 998 \| 0.5408 \| 0.4 \| 0.5408 \| 0.7354 \| \| 0.1143 \| 3.6630 \| 1000 \| 0.5414 \| 0.4000 \| 0.5414 \| 0.7358 \| \| 0.1143 \| 3.6703 \| 1002 \| 0.5372 \| 0.3438 \| 0.5372 \| 0.7330 \| \| 0.1143 \| 3.6777 \| 1004 \| 0.5406 \| 0.3478 \| 0.5406 \| 0.7353 \| \| 0.1143 \| 3.6850 \| 1006 \| 0.5488 \| 0.4179 \| 0.5488 \| 0.7408 \| \| 0.1143 \| 3.6923 \| 1008 \| 0.5570 \| 0.4545 \| 0.5570 \| 0.7463 \| \| 0.1143 \| 3.6996 \| 1010 \| 0.5454 \| 0.4545 \| 0.5454 \| 0.7385 \| \| 0.1143 \| 3.7070 \| 1012 \| 0.5398 \| 0.2857 \| 0.5398 \| 0.7347 \| \| 0.1143 \| 3.7143 \| 1014 \| 0.5517 \| 0.3662 \| 0.5517 \| 0.7427 \| \| 0.1143 \| 3.7216 \| 1016 \| 0.5589 \| 0.3662 \| 0.5589 \| 0.7476 \| \| 0.1143 \| 3.7289 \| 1018 \| 0.5665 \| 0.3836 \| 0.5665 \| 0.7526 \| \| 0.1143 \| 3.7363 \| 1020 \| 0.5563 \| 0.3333 \| 0.5563 \| 0.7458 \| \| 0.1143 \| 3.7436 \| 1022 \| 0.5465 \| 0.3478 \| 0.5465 \| 0.7393 \| \| 0.1143 \| 3.7509 \| 1024 \| 0.5525 \| 0.4 \| 0.5525 \| 0.7433 \| \| 0.1143 \| 3.7582 \| 1026 \| 0.5444 \| 0.4 \| 0.5444 \| 0.7378 \| \| 0.1143 \| 3.7656 \| 1028 \| 0.5281 \| 0.4194 \| 0.5281 \| 0.7267 \| \| 0.1143 \| 3.7729 \| 1030 \| 0.5320 \| 0.3662 \| 0.5320 \| 0.7294 \| \| 0.1143 \| 3.7802 \| 1032 \| 0.5687 \| 0.2857 \| 0.5687 \| 0.7541 \| \| 0.1143 \| 3.7875 \| 1034 \| 0.5829 \| 0.3438 \| 0.5829 \| 0.7635 \| \| 0.1143 \| 3.7949 \| 1036 \| 0.5588 \| 0.2857 \| 0.5588 \| 0.7475 \| \| 0.1143 \| 3.8022 \| 1038 \| 0.5383 \| 0.3662 \| 0.5383 \| 0.7337 \| \| 0.1143 \| 3.8095 \| 1040 \| 0.5492 \| 0.4324 \| 0.5492 \| 0.7411 \| \| 0.1143 \| 3.8168 \| 1042 \| 0.5737 \| 0.3143 \| 0.5737 \| 0.7575 \| \| 0.1143 \| 3.8242 \| 1044 \| 0.5792 \| 0.3846 \| 0.5792 \| 0.7611 \| \| 0.1143 \| 3.8315 \| 1046 \| 0.5800 \| 0.4304 \| 0.5800 \| 0.7616 \| \| 0.1143 \| 3.8388 \| 1048 \| 0.5746 \| 0.4304 \| 0.5746 \| 0.7580 \| \| 0.1143 \| 3.8462 \| 1050 \| 0.5622 \| 0.3684 \| 0.5622 \| 0.7498 \| \| 0.1143 \| 3.8535 \| 1052 \| 0.5480 \| 0.3684 \| 0.5480 \| 0.7402 \| \| 0.1143 \| 3.8608 \| 1054 \| 0.5426 \| 0.3333 \| 0.5426 \| 0.7366 \| \| 0.1143 \| 3.8681 \| 1056 \| 0.5311 \| 0.4762 \| 0.5311 \| 0.7288 \| \| 0.1143 \| 3.8755 \| 1058 \| 0.5236 \| 0.4407 \| 0.5236 \| 0.7236 \| \| 0.1143 \| 3.8828 \| 1060 \| 0.5318 \| 0.2500 \| 0.5318 \| 0.7292 \| \| 0.1143 \| 3.8901 \| 1062 \| 0.5379 \| 0.2500 \| 0.5379 \| 0.7334 \| \| 0.1143 \| 3.8974 \| 1064 \| 0.5222 \| 0.3607 \| 0.5222 \| 0.7226 \| \| 0.1143 \| 3.9048 \| 1066 \| 0.5056 \| 0.4407 \| 0.5056 \| 0.7110 \| \| 0.1143 \| 3.9121 \| 1068 \| 0.5038 \| 0.4658 \| 0.5038 \| 0.7098 \| \| 0.1143 \| 3.9194 \| 1070 \| 0.5007 \| 0.4658 \| 0.5007 \| 0.7076 \| \| 0.1143 \| 3.9267 \| 1072 \| 0.4909 \| 0.4857 \| 0.4909 \| 0.7006 \| \| 0.1143 \| 3.9341 \| 1074 \| 0.5105 \| 0.5714 \| 0.5105 \| 0.7145 \| \| 0.1143 \| 3.9414 \| 1076 \| 0.5745 \| 0.4167 \| 0.5745 \| 0.7579 \| \| 0.1143 \| 3.9487 \| 1078 \| 0.5981 \| 0.4167 \| 0.5981 \| 0.7734 \| \| 0.1143 \| 3.9560 \| 1080 \| 0.5752 \| 0.4167 \| 0.5752 \| 0.7584 \| \| 0.1143 \| 3.9634 \| 1082 \| 0.5215 \| 0.5312 \| 0.5215 \| 0.7221 \| \| 0.1143 \| 3.9707 \| 1084 \| 0.4851 \| 0.3607 \| 0.4851 \| 0.6965 \| \| 0.1143 \| 3.9780 \| 1086 \| 0.4790 \| 0.4706 \| 0.4790 \| 0.6921 \| \| 0.1143 \| 3.9853 \| 1088 \| 0.5138 \| 0.3662 \| 0.5138 \| 0.7168 \| \| 0.1143 \| 3.9927 \| 1090 \| 0.5353 \| 0.3662 \| 0.5353 \| 0.7316 \| \| 0.1143 \| 4.0 \| 1092 \| 0.5272 \| 0.3662 \| 0.5272 \| 0.7261 \| \| 0.1143 \| 4.0073 \| 1094 \| 0.5186 \| 0.4762 \| 0.5186 \| 0.7201 \| \| 0.1143 \| 4.0147 \| 1096 \| 0.5170 \| 0.3774 \| 0.5170 \| 0.7190 \| \| 0.1143 \| 4.0220 \| 1098 \| 0.5283 \| 0.3774 \| 0.5283 \| 0.7269 \| \| 0.1143 \| 4.0293 \| 1100 \| 0.5382 \| 0.3774 \| 0.5382 \| 0.7336 \| \| 0.1143 \| 4.0366 \| 1102 \| 0.5581 \| 0.4706 \| 0.5581 \| 0.7471 \| \| 0.1143 \| 4.0440 \| 1104 \| 0.5907 \| 0.3636 \| 0.5907 \| 0.7686 \| \| 0.1143 \| 4.0513 \| 1106 \| 0.6598 \| 0.2941 \| 0.6598 \| 0.8123 \| \| 0.1143 \| 4.0586 \| 1108 \| 0.7703 \| 0.2941 \| 0.7703 \| 0.8777 \| \| 0.1143 \| 4.0659 \| 1110 \| 0.8313 \| 0.3014 \| 0.8313 \| 0.9118 \| \| 0.1143 \| 4.0733 \| 1112 \| 0.8157 \| 0.3014 \| 0.8157 \| 0.9032 \| \| 0.1143 \| 4.0806 \| 1114 \| 0.7357 \| 0.2941 \| 0.7357 \| 0.8577 \| \| 0.1143 \| 4.0879 \| 1116 \| 0.6680 \| 0.3143 \| 0.6680 \| 0.8173 \| \| 0.1143 \| 4.0952 \| 1118 \| 0.6461 \| 0.3143 \| 0.6461 \| 0.8038 \| \| 0.1143 \| 4.1026 \| 1120 \| 0.6621 \| 0.3143 \| 0.6621 \| 0.8137 \| \| 0.1143 \| 4.1099 \| 1122 \| 0.6931 \| 0.2941 \| 0.6931 \| 0.8325 \| \| 0.1143 \| 4.1172 \| 1124 \| 0.7210 \| 0.2941 \| 0.7210 \| 0.8491 \| \| 0.1143 \| 4.1245 \| 1126 \| 0.7188 \| 0.2941 \| 0.7188 \| 0.8478 \| \| 0.1143 \| 4.1319 \| 1128 \| 0.7036 \| 0.1905 \| 0.7036 \| 0.8388 \| \| 0.1143 \| 4.1392 \| 1130 \| 0.6642 \| 0.1724 \| 0.6642 \| 0.8150 \| \| 0.1143 \| 4.1465 \| 1132 \| 0.6442 \| 0.2000 \| 0.6442 \| 0.8026 \| \| 0.1143 \| 4.1538 \| 1134 \| 0.6241 \| 0.2623 \| 0.6241 \| 0.7900 \| \| 0.1143 \| 4.1612 \| 1136 \| 0.6157 \| 0.2857 \| 0.6157 \| 0.7846 \| \| 0.1143 \| 4.1685 \| 1138 \| 0.5988 \| 0.3478 \| 0.5988 \| 0.7738 \| \| 0.1143 \| 4.1758 \| 1140 \| 0.5894 \| 0.2623 \| 0.5894 \| 0.7677 \| \| 0.1143 \| 4.1832 \| 1142 \| 0.5887 \| 0.3636 \| 0.5887 \| 0.7672 \| \| 0.1143 \| 4.1905 \| 1144 \| 0.5813 \| 0.3636 \| 0.5813 \| 0.7624 \| \| 0.1143 \| 4.1978 \| 1146 \| 0.5722 \| 0.4167 \| 0.5722 \| 0.7565 \| \| 0.1143 \| 4.2051 \| 1148 \| 0.5623 \| 0.3478 \| 0.5623 \| 0.7499 \| \| 0.1143 \| 4.2125 \| 1150 \| 0.5490 \| 0.3478 \| 0.5490 \| 0.7409 \| \| 0.1143 \| 4.2198 \| 1152 \| 0.5421 \| 0.3478 \| 0.5421 \| 0.7363 \| \| 0.1143 \| 4.2271 \| 1154 \| 0.5436 \| 0.4179 \| 0.5436 \| 0.7373 \| \| 0.1143 \| 4.2344 \| 1156 \| 0.5422 \| 0.4179 \| 0.5422 \| 0.7364 \| \| 0.1143 \| 4.2418 \| 1158 \| 0.5436 \| 0.4179 \| 0.5436 \| 0.7373 \| \| 0.1143 \| 4.2491 \| 1160 \| 0.5430 \| 0.4179 \| 0.5430 \| 0.7369 \| \| 0.1143 \| 4.2564 \| 1162 \| 0.5419 \| 0.4179 \| 0.5419 \| 0.7361 \| \| 0.1143 \| 4.2637 \| 1164 \| 0.5488 \| 0.4706 \| 0.5488 \| 0.7408 \| \| 0.1143 \| 4.2711 \| 1166 \| 0.5601 \| 0.4179 \| 0.5601 \| 0.7484 \| \| 0.1143 \| 4.2784 \| 1168 \| 0.5667 \| 0.4179 \| 0.5667 \| 0.7528 \| \| 0.1143 \| 4.2857 \| 1170 \| 0.5588 \| 0.4706 \| 0.5588 \| 0.7475 \| \| 0.1143 \| 4.2930 \| 1172 \| 0.5596 \| 0.3077 \| 0.5596 \| 0.7480 \| \| 0.1143 \| 4.3004 \| 1174 \| 0.5684 \| 0.3077 \| 0.5684 \| 0.7539 \| \| 0.1143 \| 4.3077 \| 1176 \| 0.5721 \| 0.3077 \| 0.5721 \| 0.7563 \| \| 0.1143 \| 4.3150 \| 1178 \| 0.5731 \| 0.4348 \| 0.5731 \| 0.7570 \| \| 0.1143 \| 4.3223 \| 1180 \| 0.5817 \| 0.4658 \| 0.5817 \| 0.7627 \| \| 0.1143 \| 4.3297 \| 1182 \| 0.5898 \| 0.4658 \| 0.5898 \| 0.7680 \| \| 0.1143 \| 4.3370 \| 1184 \| 0.5876 \| 0.4658 \| 0.5876 \| 0.7666 \| \| 0.1143 \| 4.3443 \| 1186 \| 0.5899 \| 0.4658 \| 0.5899 \| 0.7680 \| \| 0.1143 \| 4.3516 \| 1188 \| 0.5864 \| 0.4706 \| 0.5864 \| 0.7658 \| \| 0.1143 \| 4.3590 \| 1190 \| 0.5837 \| 0.4706 \| 0.5837 \| 0.7640 \| \| 0.1143 \| 4.3663 \| 1192 \| 0.5813 \| 0.4179 \| 0.5813 \| 0.7624 \| \| 0.1143 \| 4.3736 \| 1194 \| 0.5738 \| 0.4194 \| 0.5738 \| 0.7575 \| \| 0.1143 \| 4.3810 \| 1196 \| 0.5648 \| 0.4194 \| 0.5648 \| 0.7515 \| \| 0.1143 \| 4.3883 \| 1198 \| 0.5529 \| 0.4762 \| 0.5529 \| 0.7436 \| \| 0.1143 \| 4.3956 \| 1200 \| 0.5436 \| 0.4375 \| 0.5436 \| 0.7373 \| \| 0.1143 \| 4.4029 \| 1202 \| 0.5540 \| 0.4923 \| 0.5540 \| 0.7443 \| \| 0.1143 \| 4.4103 \| 1204 \| 0.5701 \| 0.3824 \| 0.5701 \| 0.7551 \| \| 0.1143 \| 4.4176 \| 1206 \| 0.5621 \| 0.3824 \| 0.5621 \| 0.7497 \| \| 0.1143 \| 4.4249 \| 1208 \| 0.5417 \| 0.4375 \| 0.5417 \| 0.7360 \| \| 0.1143 \| 4.4322 \| 1210 \| 0.5344 \| 0.4762 \| 0.5344 \| 0.7310 \| \| 0.1143 \| 4.4396 \| 1212 \| 0.5432 \| 0.4179 \| 0.5432 \| 0.7370 \| \| 0.1143 \| 4.4469 \| 1214 \| 0.5499 \| 0.4179 \| 0.5499 \| 0.7415 \| \| 0.1143 \| 4.4542 \| 1216 \| 0.5482 \| 0.4179 \| 0.5482 \| 0.7404 \| \| 0.1143 \| 4.4615 \| 1218 \| 0.5493 \| 0.4179 \| 0.5493 \| 0.7412 \| \| 0.1143 \| 4.4689 \| 1220 \| 0.5286 \| 0.4179 \| 0.5286 \| 0.7271 \| \| 0.1143 \| 4.4762 \| 1222 \| 0.5128 \| 0.4407 \| 0.5128 \| 0.7161 \| \| 0.1143 \| 4.4835 \| 1224 \| 0.5087 \| 0.4407 \| 0.5087 \| 0.7133 \| \| 0.1143 \| 4.4908 \| 1226 \| 0.5101 \| 0.4407 \| 0.5101 \| 0.7142 \| \| 0.1143 \| 4.4982 \| 1228 \| 0.5108 \| 0.4407 \| 0.5108 \| 0.7147 \| \| 0.1143 \| 4.5055 \| 1230 \| 0.5117 \| 0.3793 \| 0.5117 \| 0.7153 \| \| 0.1143 \| 4.5128 \| 1232 \| 0.5148 \| 0.4211 \| 0.5148 \| 0.7175 \| \| 0.1143 \| 4.5201 \| 1234 \| 0.5074 \| 0.3793 \| 0.5074 \| 0.7124 \| \| 0.1143 \| 4.5275 \| 1236 \| 0.5035 \| 0.4407 \| 0.5035 \| 0.7095 \| \| 0.1143 \| 4.5348 \| 1238 \| 0.5034 \| 0.4407 \| 0.5034 \| 0.7095 \| \| 0.1143 \| 4.5421 \| 1240 \| 0.5095 \| 0.1818 \| 0.5095 \| 0.7138 \| \| 0.1143 \| 4.5495 \| 1242 \| 0.5172 \| 0.2500 \| 0.5172 \| 0.7191 \| \| 0.1143 \| 4.5568 \| 1244 \| 0.5273 \| 0.4828 \| 0.5273 \| 0.7261 \| \| 0.1143 \| 4.5641 \| 1246 \| 0.5454 \| 0.4828 \| 0.5454 \| 0.7385 \| \| 0.1143 \| 4.5714 \| 1248 \| 0.5448 \| 0.4828 \| 0.5448 \| 0.7381 \| \| 0.1143 \| 4.5788 \| 1250 \| 0.5454 \| 0.4000 \| 0.5454 \| 0.7385 \| \| 0.1143 \| 4.5861 \| 1252 \| 0.5368 \| 0.2500 \| 0.5368 \| 0.7327 \| \| 0.1143 \| 4.5934 \| 1254 \| 0.5316 \| 0.1818 \| 0.5316 \| 0.7291 \| \| 0.1143 \| 4.6007 \| 1256 \| 0.5241 \| 0.3333 \| 0.5241 \| 0.7239 \| \| 0.1143 \| 4.6081 \| 1258 \| 0.5251 \| 0.3333 \| 0.5251 \| 0.7247 \| \| 0.1143 \| 4.6154 \| 1260 \| 0.5255 \| 0.3333 \| 0.5255 \| 0.7249 \| \| 0.1143 \| 4.6227 \| 1262 \| 0.5295 \| 0.4407 \| 0.5295 \| 0.7276 \| \| 0.1143 \| 4.6300 \| 1264 \| 0.5422 \| 0.4590 \| 0.5422 \| 0.7363 \| \| 0.1143 \| 4.6374 \| 1266 \| 0.5419 \| 0.4658 \| 0.5419 \| 0.7362 \| \| 0.1143 \| 4.6447 \| 1268 \| 0.5345 \| 0.4545 \| 0.5345 \| 0.7311 \| \| 0.1143 \| 4.6520 \| 1270 \| 0.5347 \| 0.3077 \| 0.5347 \| 0.7312 \| \| 0.1143 \| 4.6593 \| 1272 \| 0.5360 \| 0.4348 \| 0.5360 \| 0.7321 \| \| 0.1143 \| 4.6667 \| 1274 \| 0.5461 \| 0.3377 \| 0.5461 \| 0.7390 \| \| 0.1143 \| 4.6740 \| 1276 \| 0.5432 \| 0.4444 \| 0.5432 \| 0.7370 \| \| 0.1143 \| 4.6813 \| 1278 \| 0.5521 \| 0.4578 \| 0.5521 \| 0.7430 \| \| 0.1143 \| 4.6886 \| 1280 \| 0.5509 \| 0.4578 \| 0.5509 \| 0.7422 \| \| 0.1143 \| 4.6960 \| 1282 \| 0.5790 \| 0.4615 \| 0.5790 \| 0.7609 \| \| 0.1143 \| 4.7033 \| 1284 \| 0.5741 \| 0.4615 \| 0.5741 \| 0.7577 \| \| 0.1143 \| 4.7106 \| 1286 \| 0.5505 \| 0.4615 \| 0.5505 \| 0.7420 \| \| 0.1143 \| 4.7179 \| 1288 \| 0.5444 \| 0.4407 \| 0.5444 \| 0.7378 \| \| 0.1143 \| 4.7253 \| 1290 \| 0.5377 \| 0.4407 \| 0.5377 \| 0.7333 \| \| 0.1143 \| 4.7326 \| 1292 \| 0.5183 \| 0.3810 \| 0.5183 \| 0.7199 \| \| 0.1143 \| 4.7399 \| 1294 \| 0.5145 \| 0.3810 \| 0.5145 \| 0.7173 \| \| 0.1143 \| 4.7473 \| 1296 \| 0.5093 \| 0.3810 \| 0.5093 \| 0.7136 \| \| 0.1143 \| 4.7546 \| 1298 \| 0.5178 \| 0.5312 \| 0.5178 \| 0.7196 \| \| 0.1143 \| 4.7619 \| 1300 \| 0.5614 \| 0.4507 \| 0.5614 \| 0.7493 \| \| 0.1143 \| 4.7692 \| 1302 \| 0.6205 \| 0.4304 \| 0.6205 \| 0.7877 \| \| 0.1143 \| 4.7766 \| 1304 \| 0.6633 \| 0.4304 \| 0.6633 \| 0.8144 \| \| 0.1143 \| 4.7839 \| 1306 \| 0.6658 \| 0.4304 \| 0.6658 \| 0.8160 \| \| 0.1143 \| 4.7912 \| 1308 \| 0.6320 \| 0.4615 \| 0.6320 \| 0.7950 \| \| 0.1143 \| 4.7985 \| 1310 \| 0.5866 \| 0.4615 \| 0.5866 \| 0.7659 \| \| 0.1143 \| 4.8059 \| 1312 \| 0.5547 \| 0.4935 \| 0.5547 \| 0.7448 \| \| 0.1143 \| 4.8132 \| 1314 \| 0.5454 \| 0.3014 \| 0.5454 \| 0.7385 \| \| 0.1143 \| 4.8205 \| 1316 \| 0.5463 \| 0.3077 \| 0.5463 \| 0.7391 \| \| 0.1143 \| 4.8278 \| 1318 \| 0.5439 \| 0.3000 \| 0.5439 \| 0.7375 \| \| 0.1143 \| 4.8352 \| 1320 \| 0.5485 \| 0.3000 \| 0.5485 \| 0.7406 \| \| 0.1143 \| 4.8425 \| 1322 \| 0.5573 \| 0.4474 \| 0.5573 \| 0.7465 \| \| 0.1143 \| 4.8498 \| 1324 \| 0.5751 \| 0.4474 \| 0.5751 \| 0.7583 \| \| 0.1143 \| 4.8571 \| 1326 \| 0.5801 \| 0.4474 \| 0.5801 \| 0.7616 \| \| 0.1143 \| 4.8645 \| 1328 \| 0.5763 \| 0.2703 \| 0.5763 \| 0.7591 \| \| 0.1143 \| 4.8718 \| 1330 \| 0.5680 \| 0.2703 \| 0.5680 \| 0.7537 \| \| 0.1143 \| 4.8791 \| 1332 \| 0.5531 \| 0.2373 \| 0.5531 \| 0.7437 \| \| 0.1143 \| 4.8864 \| 1334 \| 0.5443 \| 0.2373 \| 0.5443 \| 0.7378 \| \| 0.1143 \| 4.8938 \| 1336 \| 0.5449 \| 0.2373 \| 0.5449 \| 0.7382 \| \| 0.1143 \| 4.9011 \| 1338 \| 0.5440 \| 0.3000 \| 0.5440 \| 0.7376 \| \| 0.1143 \| 4.9084 \| 1340 \| 0.5456 \| 0.3000 \| 0.5456 \| 0.7386 \| \| 0.1143 \| 4.9158 \| 1342 \| 0.5576 \| 0.1818 \| 0.5576 \| 0.7467 \| \| 0.1143 \| 4.9231 \| 1344 \| 0.5666 \| 0.2500 \| 0.5666 \| 0.7528 \| \| 0.1143 \| 4.9304 \| 1346 \| 0.5575 \| 0.1818 \| 0.5575 \| 0.7467 \| \| 0.1143 \| 4.9377 \| 1348 \| 0.5477 \| 0.2373 \| 0.5477 \| 0.7401 \| \| 0.1143 \| 4.9451 \| 1350 \| 0.5585 \| 0.2909 \| 0.5585 \| 0.7473 \| \| 0.1143 \| 4.9524 \| 1352 \| 0.5740 \| 0.2373 \| 0.5740 \| 0.7576 \| \| 0.1143 \| 4.9597 \| 1354 \| 0.5771 \| 0.3000 \| 0.5771 \| 0.7597 \| \| 0.1143 \| 4.9670 \| 1356 \| 0.5632 \| 0.3607 \| 0.5632 \| 0.7505 \| \| 0.1143 \| 4.9744 \| 1358 \| 0.5554 \| 0.2759 \| 0.5554 \| 0.7453 \| \| 0.1143 \| 4.9817 \| 1360 \| 0.5923 \| 0.3684 \| 0.5923 \| 0.7696 \| \| 0.1143 \| 4.9890 \| 1362 \| 0.6620 \| 0.3836 \| 0.6620 \| 0.8136 \| \| 0.1143 \| 4.9963 \| 1364 \| 0.6963 \| 0.3836 \| 0.6963 \| 0.8344 \| \| 0.1143 \| 5.0037 \| 1366 \| 0.6720 \| 0.3836 \| 0.6720 \| 0.8197 \| \| 0.1143 \| 5.0110 \| 1368 \| 0.6244 \| 0.3836 \| 0.6244 \| 0.7902 \| \| 0.1143 \| 5.0183 \| 1370 \| 0.5813 \| 0.3077 \| 0.5813 \| 0.7624 \| \| 0.1143 \| 5.0256 \| 1372 \| 0.5446 \| 0.2500 \| 0.5446 \| 0.7380 \| \| 0.1143 \| 5.0330 \| 1374 \| 0.5357 \| 0.4211 \| 0.5357 \| 0.7319 \| \| 0.1143 \| 5.0403 \| 1376 \| 0.5512 \| 0.3571 \| 0.5512 \| 0.7424 \| \| 0.1143 \| 5.0476 \| 1378 \| 0.5718 \| 0.3571 \| 0.5718 \| 0.7562 \| \| 0.1143 \| 5.0549 \| 1380 \| 0.5826 \| 0.3636 \| 0.5826 \| 0.7633 \| \| 0.1143 \| 5.0623 \| 1382 \| 0.5946 \| 0.3636 \| 0.5946 \| 0.7711 \| \| 0.1143 \| 5.0696 \| 1384 \| 0.6004 \| 0.3836 \| 0.6004 \| 0.7749 \| \| 0.1143 \| 5.0769 \| 1386 \| 0.6060 \| 0.3836 \| 0.6060 \| 0.7784 \| \| 0.1143 \| 5.0842 \| 1388 \| 0.6213 \| 0.4 \| 0.6213 \| 0.7882 \| \| 0.1143 \| 5.0916 \| 1390 \| 0.6363 \| 0.4507 \| 0.6363 \| 0.7977 \| \| 0.1143 \| 5.0989 \| 1392 \| 0.6506 \| 0.3333 \| 0.6506 \| 0.8066 \| \| 0.1143 \| 5.1062 \| 1394 \| 0.6585 \| 0.2817 \| 0.6585 \| 0.8115 \| \| 0.1143 \| 5.1136 \| 1396 \| 0.6567 \| 0.4 \| 0.6567 \| 0.8103 \| \| 0.1143 \| 5.1209 \| 1398 \| 0.6670 \| 0.3077 \| 0.6670 \| 0.8167 \| \| 0.1143 \| 5.1282 \| 1400 \| 0.6849 \| 0.3077 \| 0.6849 \| 0.8276 \| \| 0.1143 \| 5.1355 \| 1402 \| 0.6744 \| 0.3077 \| 0.6744 \| 0.8212 \| \| 0.1143 \| 5.1429 \| 1404 \| 0.6510 \| 0.3077 \| 0.6510 \| 0.8068 \| \| 0.1143 \| 5.1502 \| 1406 \| 0.6246 \| 0.4179 \| 0.6246 \| 0.7903 \| \| 0.1143 \| 5.1575 \| 1408 \| 0.5984 \| 0.3000 \| 0.5984 \| 0.7736 \| \| 0.1143 \| 5.1648 \| 1410 \| 0.5763 \| 0.3000 \| 0.5763 \| 0.7591 \| \| 0.1143 \| 5.1722 \| 1412 \| 0.5652 \| 0.3607 \| 0.5652 \| 0.7518 \| \| 0.1143 \| 5.1795 \| 1414 \| 0.5632 \| 0.4348 \| 0.5632 \| 0.7504 \| \| 0.1143 \| 5.1868 \| 1416 \| 0.5569 \| 0.4348 \| 0.5569 \| 0.7463 \| \| 0.1143 \| 5.1941 \| 1418 \| 0.5537 \| 0.5714 \| 0.5537 \| 0.7441 \| \| 0.1143 \| 5.2015 \| 1420 \| 0.5302 \| 0.5714 \| 0.5302 \| 0.7281 \| \| 0.1143 \| 5.2088 \| 1422 \| 0.5109 \| 0.5714 \| 0.5109 \| 0.7148 \| \| 0.1143 \| 5.2161 \| 1424 \| 0.4934 \| 0.5714 \| 0.4934 \| 0.7024 \| \| 0.1143 \| 5.2234 \| 1426 \| 0.4786 \| 0.5714 \| 0.4786 \| 0.6918 \| \| 0.1143 \| 5.2308 \| 1428 \| 0.4825 \| 0.5714 \| 0.4825 \| 0.6946 \| \| 0.1143 \| 5.2381 \| 1430 \| 0.4879 \| 0.5714 \| 0.4879 \| 0.6985 \| \| 0.1143 \| 5.2454 \| 1432 \| 0.5100 \| 0.5312 \| 0.5100 \| 0.7141 \| \| 0.1143 \| 5.2527 \| 1434 \| 0.5228 \| 0.5312 \| 0.5228 \| 0.7231 \| \| 0.1143 \| 5.2601 \| 1436 \| 0.5326 \| 0.4507 \| 0.5326 \| 0.7298 \| \| 0.1143 \| 5.2674 \| 1438 \| 0.5174 \| 0.5312 \| 0.5174 \| 0.7193 \| \| 0.1143 \| 5.2747 \| 1440 \| 0.5059 \| 0.5588 \| 0.5059 \| 0.7113 \| \| 0.1143 \| 5.2821 \| 1442 \| 0.4966 \| 0.4179 \| 0.4966 \| 0.7047 \| \| 0.1143 \| 5.2894 \| 1444 \| 0.4972 \| 0.3636 \| 0.4972 \| 0.7051 \| \| 0.1143 \| 5.2967 \| 1446 \| 0.5015 \| 0.4179 \| 0.5015 \| 0.7081 \| \| 0.1143 \| 5.3040 \| 1448 \| 0.5136 \| 0.4179 \| 0.5136 \| 0.7166 \| \| 0.1143 \| 5.3114 \| 1450 \| 0.5291 \| 0.5714 \| 0.5291 \| 0.7274 \| \| 0.1143 \| 5.3187 \| 1452 \| 0.5439 \| 0.5714 \| 0.5439 \| 0.7375 \| \| 0.1143 \| 5.3260 \| 1454 \| 0.5473 \| 0.5714 \| 0.5473 \| 0.7398 \| \| 0.1143 \| 5.3333 \| 1456 \| 0.5538 \| 0.4857 \| 0.5538 \| 0.7442 \| \| 0.1143 \| 5.3407 \| 1458 \| 0.5484 \| 0.4179 \| 0.5484 \| 0.7405 \| \| 0.1143 \| 5.3480 \| 1460 \| 0.5461 \| 0.3662 \| 0.5461 \| 0.7390 \| \| 0.1143 \| 5.3553 \| 1462 \| 0.5479 \| 0.3143 \| 0.5479 \| 0.7402 \| \| 0.1143 \| 5.3626 \| 1464 \| 0.5568 \| 0.3143 \| 0.5568 \| 0.7462 \| \| 0.1143 \| 5.3700 \| 1466 \| 0.5584 \| 0.3143 \| 0.5584 \| 0.7472 \| \| 0.1143 \| 5.3773 \| 1468 \| 0.5606 \| 0.3143 \| 0.5606 \| 0.7487 \| \| 0.1143 \| 5.3846 \| 1470 \| 0.5728 \| 0.3662 \| 0.5728 \| 0.7568 \| \| 0.1143 \| 5.3919 \| 1472 \| 0.5775 \| 0.4167 \| 0.5775 \| 0.7600 \| \| 0.1143 \| 5.3993 \| 1474 \| 0.5806 \| 0.4167 \| 0.5806 \| 0.7620 \| \| 0.1143 \| 5.4066 \| 1476 \| 0.5719 \| 0.4179 \| 0.5719 \| 0.7562 \| \| 0.1143 \| 5.4139 \| 1478 \| 0.5519 \| 0.3000 \| 0.5519 \| 0.7429 \| \| 0.1143 \| 5.4212 \| 1480 \| 0.5372 \| 0.4407 \| 0.5372 \| 0.7329 \| \| 0.1143 \| 5.4286 \| 1482 \| 0.5357 \| 0.4194 \| 0.5357 \| 0.7319 \| \| 0.1143 \| 5.4359 \| 1484 \| 0.5434 \| 0.4179 \| 0.5434 \| 0.7372 \| \| 0.1143 \| 5.4432 \| 1486 \| 0.5581 \| 0.4 \| 0.5581 \| 0.7471 \| \| 0.1143 \| 5.4505 \| 1488 \| 0.5640 \| 0.4 \| 0.5640 \| 0.7510 \| \| 0.1143 \| 5.4579 \| 1490 \| 0.5585 \| 0.3636 \| 0.5585 \| 0.7474 \| \| 0.1143 \| 5.4652 \| 1492 \| 0.5518 \| 0.4179 \| 0.5518 \| 0.7429 \| \| 0.1143 \| 5.4725 \| 1494 \| 0.5509 \| 0.4706 \| 0.5509 \| 0.7423 \| \| 0.1143 \| 5.4799 \| 1496 \| 0.5699 \| 0.3077 \| 0.5699 \| 0.7549 \| \| 0.1143 \| 5.4872 \| 1498 \| 0.6039 \| 0.4324 \| 0.6039 \| 0.7771 \| \| 0.0785 \| 5.4945 \| 1500 \| 0.6255 \| 0.4 \| 0.6255 \| 0.7909 \| \| 0.0785 \| 5.5018 \| 1502 \| 0.6286 \| 0.4 \| 0.6286 \| 0.7928 \| \| 0.0785 \| 5.5092 \| 1504 \| 0.6112 \| 0.4 \| 0.6112 \| 0.7818 \| \| 0.0785 \| 5.5165 \| 1506 \| 0.5890 \| 0.3836 \| 0.5890 \| 0.7675 \| \| 0.0785 \| 5.5238 \| 1508 \| 0.5915 \| 0.3636 \| 0.5915 \| 0.7691 \| \| 0.0785 \| 5.5311 \| 1510 \| 0.6011 \| 0.4 \| 0.6011 \| 0.7753 \| \| 0.0785 \| 5.5385 \| 1512 \| 0.6219 \| 0.3377 \| 0.6219 \| 0.7886 \| \| 0.0785 \| 5.5458 \| 1514 \| 0.6203 \| 0.3377 \| 0.6203 \| 0.7876 \| \| 0.0785 \| 5.5531 \| 1516 \| 0.5927 \| 0.3377 \| 0.5927 \| 0.7699 \| \| 0.0785 \| 5.5604 \| 1518 \| 0.5717 \| 0.3226 \| 0.5717 \| 0.7561 \| \| 0.0785 \| 5.5678 \| 1520 \| 0.5418 \| 0.3636 \| 0.5418 \| 0.7361 \| \| 0.0785 \| 5.5751 \| 1522 \| 0.5330 \| 0.4762 \| 0.5330 \| 0.7301 \| \| 0.0785 \| 5.5824 \| 1524 \| 0.5321 \| 0.4762 \| 0.5321 \| 0.7295 \| \| 0.0785 \| 5.5897 \| 1526 \| 0.5302 \| 0.4762 \| 0.5302 \| 0.7281 \| \| 0.0785 \| 5.5971 \| 1528 \| 0.5304 \| 0.4828 \| 0.5304 \| 0.7283 \| \| 0.0785 \| 5.6044 \| 1530 \| 0.5411 \| 0.3607 \| 0.5411 \| 0.7356 \| \| 0.0785 \| 5.6117 \| 1532 \| 0.5745 \| 0.3478 \| 0.5745 \| 0.7580 \| \| 0.0785 \| 5.6190 \| 1534 \| 0.6291 \| 0.3836 \| 0.6291 \| 0.7931 \| \| 0.0785 \| 5.6264 \| 1536 \| 0.6675 \| 0.3836 \| 0.6675 \| 0.8170 \| \| 0.0785 \| 5.6337 \| 1538 \| 0.6625 \| 0.3836 \| 0.6625 \| 0.8139 \| \| 0.0785 \| 5.6410 \| 1540 \| 0.6231 \| 0.4615 \| 0.6231 \| 0.7894 \| \| 0.0785 \| 5.6484 \| 1542 \| 0.5886 \| 0.4 \| 0.5886 \| 0.7672 \| \| 0.0785 \| 5.6557 \| 1544 \| 0.5588 \| 0.3607 \| 0.5588 \| 0.7475 \| \| 0.0785 \| 5.6630 \| 1546 \| 0.5505 \| 0.3636 \| 0.5505 \| 0.7419 \| \| 0.0785 \| 5.6703 \| 1548 \| 0.5476 \| 0.3662 \| 0.5476 \| 0.7400 \| \| 0.0785 \| 5.6777 \| 1550 \| 0.5485 \| 0.4 \| 0.5485 \| 0.7406 \| \| 0.0785 \| 5.6850 \| 1552 \| 0.5457 \| 0.3662 \| 0.5457 \| 0.7387 \| \| 0.0785 \| 5.6923 \| 1554 \| 0.5478 \| 0.3607 \| 0.5478 \| 0.7401 \| \| 0.0785 \| 5.6996 \| 1556 \| 0.5622 \| 0.3607 \| 0.5622 \| 0.7498 \| \| 0.0785 \| 5.7070 \| 1558 \| 0.5911 \| 0.25 \| 0.5911 \| 0.7688 \| \| 0.0785 \| 5.7143 \| 1560 \| 0.6215 \| 0.3836 \| 0.6215 \| 0.7883 \| \| 0.0785 \| 5.7216 \| 1562 \| 0.6482 \| 0.3836 \| 0.6482 \| 0.8051 \| \| 0.0785 \| 5.7289 \| 1564 \| 0.6569 \| 0.3836 \| 0.6569 \| 0.8105 \| \| 0.0785 \| 5.7363 \| 1566 \| 0.6452 \| 0.3836 \| 0.6452 \| 0.8032 \| \| 0.0785 \| 5.7436 \| 1568 \| 0.6124 \| 0.3836 \| 0.6124 \| 0.7825 \| \| 0.0785 \| 5.7509 \| 1570 \| 0.5850 \| 0.25 \| 0.5850 \| 0.7649 \| \| 0.0785 \| 5.7582 \| 1572 \| 0.5645 \| 0.2154 \| 0.5645 \| 0.7514 \| \| 0.0785 \| 5.7656 \| 1574 \| 0.5427 \| 0.2500 \| 0.5427 \| 0.7367 \| \| 0.0785 \| 5.7729 \| 1576 \| 0.5345 \| 0.2500 \| 0.5345 \| 0.7311 \| \| 0.0785 \| 5.7802 \| 1578 \| 0.5340 \| 0.2500 \| 0.5340 \| 0.7308 \| \| 0.0785 \| 5.7875 \| 1580 \| 0.5423 \| 0.2759 \| 0.5423 \| 0.7364 \| \| 0.0785 \| 5.7949 \| 1582 \| 0.5488 \| 0.2759 \| 0.5488 \| 0.7408 \| \| 0.0785 \| 5.8022 \| 1584 \| 0.5647 \| 0.2154 \| 0.5647 \| 0.7515 \| \| 0.0785 \| 5.8095 \| 1586 \| 0.5873 \| 0.2154 \| 0.5873 \| 0.7663 \| \| 0.0785 \| 5.8168 \| 1588 \| 0.5979 \| 0.2154 \| 0.5979 \| 0.7732 \| \| 0.0785 \| 5.8242 \| 1590 \| 0.5858 \| 0.2154 \| 0.5858 \| 0.7653 \| \| 0.0785 \| 5.8315 \| 1592 \| 0.5605 \| 0.2154 \| 0.5605 \| 0.7487 \| \| 0.0785 \| 5.8388 \| 1594 \| 0.5355 \| 0.2500 \| 0.5355 \| 0.7318 \| \| 0.0785 \| 5.8462 \| 1596 \| 0.5271 \| 0.3607 \| 0.5271 \| 0.7260 \| \| 0.0785 \| 5.8535 \| 1598 \| 0.5243 \| 0.3607 \| 0.5243 \| 0.7241 \| \| 0.0785 \| 5.8608 \| 1600 \| 0.5297 \| 0.3607 \| 0.5297 \| 0.7278 \| \| 0.0785 \| 5.8681 \| 1602 \| 0.5378 \| 0.3226 \| 0.5378 \| 0.7333 \| \| 0.0785 \| 5.8755 \| 1604 \| 0.5375 \| 0.3226 \| 0.5375 \| 0.7332 \| \| 0.0785 \| 5.8828 \| 1606 \| 0.5447 \| 0.3810 \| 0.5447 \| 0.7380 \| \| 0.0785 \| 5.8901 \| 1608 \| 0.5549 \| 0.3810 \| 0.5549 \| 0.7449 \| \| 0.0785 \| 5.8974 \| 1610 \| 0.5440 \| 0.3810 \| 0.5440 \| 0.7375 \| \| 0.0785 \| 5.9048 \| 1612 \| 0.5320 \| 0.3607 \| 0.5320 \| 0.7294 \| \| 0.0785 \| 5.9121 \| 1614 \| 0.5329 \| 0.3607 \| 0.5329 \| 0.7300 \| \| 0.0785 \| 5.9194 \| 1616 \| 0.5427 \| 0.3607 \| 0.5427 \| 0.7367 \| \| 0.0785 \| 5.9267 \| 1618 \| 0.5505 \| 0.3000 \| 0.5505 \| 0.7420 \| \| 0.0785 \| 5.9341 \| 1620 \| 0.5538 \| 0.4407 \| 0.5538 \| 0.7441 \| \| 0.0785 \| 5.9414 \| 1622 \| 0.5582 \| 0.4706 \| 0.5582 \| 0.7471 \| \| 0.0785 \| 5.9487 \| 1624 \| 0.5661 \| 0.4179 \| 0.5661 \| 0.7524 \| \| 0.0785 \| 5.9560 \| 1626 \| 0.5699 \| 0.3636 \| 0.5699 \| 0.7549 \| \| 0.0785 \| 5.9634 \| 1628 \| 0.5730 \| 0.3636 \| 0.5730 \| 0.7570 \| \| 0.0785 \| 5.9707 \| 1630 \| 0.5679 \| 0.3636 \| 0.5679 \| 0.7536 \| \| 0.0785 \| 5.9780 \| 1632 \| 0.5599 \| 0.4211 \| 0.5599 \| 0.7482 \| \| 0.0785 \| 5.9853 \| 1634 \| 0.5522 \| 0.4211 \| 0.5522 \| 0.7431 \| \| 0.0785 \| 5.9927 \| 1636 \| 0.5549 \| 0.3333 \| 0.5549 \| 0.7449 \| \| 0.0785 \| 6.0 \| 1638 \| 0.5564 \| 0.3333 \| 0.5564 \| 0.7459 \| \| 0.0785 \| 6.0073 \| 1640 \| 0.5510 \| 0.3333 \| 0.5510 \| 0.7423 \| \| 0.0785 \| 6.0147 \| 1642 \| 0.5467 \| 0.3333 \| 0.5467 \| 0.7394 \| \| 0.0785 \| 6.0220 \| 1644 \| 0.5392 \| 0.4407 \| 0.5392 \| 0.7343 \| \| 0.0785 \| 6.0293 \| 1646 \| 0.5342 \| 0.4828 \| 0.5342 \| 0.7309 \| \| 0.0785 \| 6.0366 \| 1648 \| 0.5362 \| 0.4211 \| 0.5362 \| 0.7323 \| \| 0.0785 \| 6.0440 \| 1650 \| 0.5381 \| 0.4211 \| 0.5381 \| 0.7336 \| \| 0.0785 \| 6.0513 \| 1652 \| 0.5411 \| 0.4828 \| 0.5411 \| 0.7356 \| \| 0.0785 \| 6.0586 \| 1654 \| 0.5436 \| 0.4828 \| 0.5436 \| 0.7373 \| \| 0.0785 \| 6.0659 \| 1656 \| 0.5494 \| 0.4407 \| 0.5494 \| 0.7412 \| \| 0.0785 \| 6.0733 \| 1658 \| 0.5486 \| 0.4407 \| 0.5486 \| 0.7407 \| \| 0.0785 \| 6.0806 \| 1660 \| 0.5475 \| 0.4375 \| 0.5475 \| 0.7399 \| \| 0.0785 \| 6.0879 \| 1662 \| 0.5544 \| 0.4407 \| 0.5544 \| 0.7446 \| \| 0.0785 \| 6.0952 \| 1664 \| 0.5639 \| 0.4407 \| 0.5639 \| 0.7509 \| \| 0.0785 \| 6.1026 \| 1666 \| 0.5616 \| 0.4375 \| 0.5616 \| 0.7494 \| \| 0.0785 \| 6.1099 \| 1668 \| 0.5488 \| 0.4375 \| 0.5488 \| 0.7408 \| \| 0.0785 \| 6.1172 \| 1670 \| 0.5373 \| 0.4375 \| 0.5373 \| 0.7330 \| \| 0.0785 \| 6.1245 \| 1672 \| 0.5328 \| 0.4375 \| 0.5328 \| 0.7300 \| \| 0.0785 \| 6.1319 \| 1674 \| 0.5309 \| 0.4762 \| 0.5309 \| 0.7286 \| \| 0.0785 \| 6.1392 \| 1676 \| 0.5343 \| 0.4194 \| 0.5343 \| 0.7310 \| \| 0.0785 \| 6.1465 \| 1678 \| 0.5386 \| 0.4194 \| 0.5386 \| 0.7339 \| \| 0.0785 \| 6.1538 \| 1680 \| 0.5375 \| 0.4194 \| 0.5375 \| 0.7332 \| \| 0.0785 \| 6.1612 \| 1682 \| 0.5348 \| 0.4828 \| 0.5348 \| 0.7313 \| \| 0.0785 \| 6.1685 \| 1684 \| 0.5329 \| 0.4407 \| 0.5329 \| 0.7300 \| \| 0.0785 \| 6.1758 \| 1686 \| 0.5310 \| 0.4828 \| 0.5310 \| 0.7287 \| \| 0.0785 \| 6.1832 \| 1688 \| 0.5338 \| 0.4828 \| 0.5338 \| 0.7306 \| \| 0.0785 \| 6.1905 \| 1690 \| 0.5376 \| 0.4828 \| 0.5376 \| 0.7332 \| \| 0.0785 \| 6.1978 \| 1692 \| 0.5447 \| 0.4762 \| 0.5447 \| 0.7380 \| \| 0.0785 \| 6.2051 \| 1694 \| 0.5505 \| 0.4762 \| 0.5505 \| 0.7420 \| \| 0.0785 \| 6.2125 \| 1696 \| 0.5531 \| 0.4762 \| 0.5531 \| 0.7437 \| \| 0.0785 \| 6.2198 \| 1698 \| 0.5579 \| 0.4375 \| 0.5579 \| 0.7470 \| \| 0.0785 \| 6.2271 \| 1700 \| 0.5778 \| 0.3000 \| 0.5778 \| 0.7601 \| \| 0.0785 \| 6.2344 \| 1702 \| 0.6168 \| 0.3810 \| 0.6168 \| 0.7854 \| \| 0.0785 \| 6.2418 \| 1704 \| 0.6542 \| 0.4 \| 0.6542 \| 0.8088 \| \| 0.0785 \| 6.2491 \| 1706 \| 0.6772 \| 0.4 \| 0.6772 \| 0.8229 \| \| 0.0785 \| 6.2564 \| 1708 \| 0.6775 \| 0.3810 \| 0.6775 \| 0.8231 \| \| 0.0785 \| 6.2637 \| 1710 \| 0.6582 \| 0.3810 \| 0.6582 \| 0.8113 \| \| 0.0785 \| 6.2711 \| 1712 \| 0.6226 \| 0.3824 \| 0.6226 \| 0.7890 \| \| 0.0785 \| 6.2784 \| 1714 \| 0.6071 \| 0.4324 \| 0.6071 \| 0.7792 \| \| 0.0785 \| 6.2857 \| 1716 \| 0.6268 \| 0.4304 \| 0.6268 \| 0.7917 \| \| 0.0785 \| 6.2930 \| 1718 \| 0.6376 \| 0.4304 \| 0.6376 \| 0.7985 \| \| 0.0785 \| 6.3004 \| 1720 \| 0.6409 \| 0.4304 \| 0.6409 \| 0.8006 \| \| 0.0785 \| 6.3077 \| 1722 \| 0.6393 \| 0.4304 \| 0.6393 \| 0.7996 \| \| 0.0785 \| 6.3150 \| 1724 \| 0.6354 \| 0.4304 \| 0.6354 \| 0.7971 \| \| 0.0785 \| 6.3223 \| 1726 \| 0.6314 \| 0.3333 \| 0.6314 \| 0.7946 \| \| 0.0785 \| 6.3297 \| 1728 \| 0.6413 \| 0.3544 \| 0.6413 \| 0.8008 \| \| 0.0785 \| 6.3370 \| 1730 \| 0.6527 \| 0.4 \| 0.6527 \| 0.8079 \| \| 0.0785 \| 6.3443 \| 1732 \| 0.6517 \| 0.4 \| 0.6517 \| 0.8073 \| \| 0.0785 \| 6.3516 \| 1734 \| 0.6385 \| 0.4 \| 0.6385 \| 0.7991 \| \| 0.0785 \| 6.3590 \| 1736 \| 0.6126 \| 0.2759 \| 0.6126 \| 0.7827 \| \| 0.0785 \| 6.3663 \| 1738 \| 0.5898 \| 0.3571 \| 0.5898 \| 0.7680 \| \| 0.0785 \| 6.3736 \| 1740 \| 0.5878 \| 0.3571 \| 0.5878 \| 0.7667 \| \| 0.0785 \| 6.3810 \| 1742 \| 0.5816 \| 0.3571 \| 0.5816 \| 0.7626 \| \| 0.0785 \| 6.3883 \| 1744 \| 0.5882 \| 0.3571 \| 0.5882 \| 0.7670 \| \| 0.0785 \| 6.3956 \| 1746 \| 0.6022 \| 0.4375 \| 0.6022 \| 0.7760 \| \| 0.0785 \| 6.4029 \| 1748 \| 0.6130 \| 0.4375 \| 0.6130 \| 0.7829 \| \| 0.0785 \| 6.4103 \| 1750 \| 0.6340 \| 0.3077 \| 0.6340 \| 0.7963 \| \| 0.0785 \| 6.4176 \| 1752 \| 0.6440 \| 0.3077 \| 0.6440 \| 0.8025 \| \| 0.0785 \| 6.4249 \| 1754 \| 0.6436 \| 0.3077 \| 0.6436 \| 0.8022 \| \| 0.0785 \| 6.4322 \| 1756 \| 0.6396 \| 0.3077 \| 0.6396 \| 0.7997 \| \| 0.0785 \| 6.4396 \| 1758 \| 0.6209 \| 0.4 \| 0.6209 \| 0.7879 \| \| 0.0785 \| 6.4469 \| 1760 \| 0.6296 \| 0.4 \| 0.6296 \| 0.7935 \| \| 0.0785 \| 6.4542 \| 1762 \| 0.6375 \| 0.4 \| 0.6375 \| 0.7984 \| \| 0.0785 \| 6.4615 \| 1764 \| 0.6117 \| 0.3514 \| 0.6117 \| 0.7821 \| \| 0.0785 \| 6.4689 \| 1766 \| 0.5947 \| 0.4658 \| 0.5947 \| 0.7712 \| \| 0.0785 \| 6.4762 \| 1768 \| 0.5827 \| 0.4545 \| 0.5827 \| 0.7633 \| \| 0.0785 \| 6.4835 \| 1770 \| 0.5708 \| 0.4545 \| 0.5708 \| 0.7555 \| \| 0.0785 \| 6.4908 \| 1772 \| 0.5596 \| 0.4545 \| 0.5596 \| 0.7481 \| \| 0.0785 \| 6.4982 \| 1774 \| 0.5544 \| 0.4590 \| 0.5544 \| 0.7446 \| \| 0.0785 \| 6.5055 \| 1776 \| 0.5494 \| 0.4590 \| 0.5494 \| 0.7412 \| \| 0.0785 \| 6.5128 \| 1778 \| 0.5529 \| 0.4590 \| 0.5529 \| 0.7436 \| \| 0.0785 \| 6.5201 \| 1780 \| 0.5577 \| 0.4590 \| 0.5577 \| 0.7468 \| \| 0.0785 \| 6.5275 \| 1782 \| 0.5497 \| 0.3571 \| 0.5497 \| 0.7415 \| \| 0.0785 \| 6.5348 \| 1784 \| 0.5359 \| 0.4407 \| 0.5359 \| 0.7321 \| \| 0.0785 \| 6.5421 \| 1786 \| 0.5334 \| 0.4211 \| 0.5334 \| 0.7303 \| \| 0.0785 \| 6.5495 \| 1788 \| 0.5440 \| 0.3333 \| 0.5440 \| 0.7376 \| \| 0.0785 \| 6.5568 \| 1790 \| 0.5522 \| 0.3333 \| 0.5522 \| 0.7431 \| \| 0.0785 \| 6.5641 \| 1792 \| 0.5509 \| 0.3333 \| 0.5509 \| 0.7422 \| \| 0.0785 \| 6.5714 \| 1794 \| 0.5431 \| 0.3571 \| 0.5431 \| 0.7369 \| \| 0.0785 \| 6.5788 \| 1796 \| 0.5425 \| 0.4407 \| 0.5425 \| 0.7366 \| \| 0.0785 \| 6.5861 \| 1798 \| 0.5610 \| 0.4 \| 0.5610 \| 0.7490 \| \| 0.0785 \| 6.5934 \| 1800 \| 0.5857 \| 0.2105 \| 0.5857 \| 0.7653 \| \| 0.0785 \| 6.6007 \| 1802 \| 0.6020 \| 0.2500 \| 0.6020 \| 0.7759 \| \| 0.0785 \| 6.6081 \| 1804 \| 0.5989 \| 0.3478 \| 0.5989 \| 0.7739 \| \| 0.0785 \| 6.6154 \| 1806 \| 0.5875 \| 0.4706 \| 0.5875 \| 0.7665 \| \| 0.0785 \| 6.6227 \| 1808 \| 0.5905 \| 0.4706 \| 0.5905 \| 0.7684 \| \| 0.0785 \| 6.6300 \| 1810 \| 0.5894 \| 0.4706 \| 0.5894 \| 0.7677 \| \| 0.0785 \| 6.6374 \| 1812 \| 0.5904 \| 0.4706 \| 0.5904 \| 0.7684 \| \| 0.0785 \| 6.6447 \| 1814 \| 0.5963 \| 0.4706 \| 0.5963 \| 0.7722 \| \| 0.0785 \| 6.6520 \| 1816 \| 0.5869 \| 0.4545 \| 0.5869 \| 0.7661 \| \| 0.0785 \| 6.6593 \| 1818 \| 0.5739 \| 0.4545 \| 0.5739 \| 0.7576 \| \| 0.0785 \| 6.6667 \| 1820 \| 0.5618 \| 0.4407 \| 0.5618 \| 0.7496 \| \| 0.0785 \| 6.6740 \| 1822 \| 0.5597 \| 0.4828 \| 0.5597 \| 0.7481 \| \| 0.0785 \| 6.6813 \| 1824 \| 0.5619 \| 0.4828 \| 0.5619 \| 0.7496 \| \| 0.0785 \| 6.6886 \| 1826 \| 0.5629 \| 0.4407 \| 0.5629 \| 0.7503 \| \| 0.0785 \| 6.6960 \| 1828 \| 0.5663 \| 0.4407 \| 0.5663 \| 0.7525 \| \| 0.0785 \| 6.7033 \| 1830 \| 0.5694 \| 0.4407 \| 0.5694 \| 0.7546 \| \| 0.0785 \| 6.7106 \| 1832 \| 0.5728 \| 0.3607 \| 0.5728 \| 0.7568 \| \| 0.0785 \| 6.7179 \| 1834 \| 0.5708 \| 0.3607 \| 0.5708 \| 0.7555 \| \| 0.0785 \| 6.7253 \| 1836 \| 0.5598 \| 0.3607 \| 0.5598 \| 0.7482 \| \| 0.0785 \| 6.7326 \| 1838 \| 0.5501 \| 0.4407 \| 0.5501 \| 0.7417 \| \| 0.0785 \| 6.7399 \| 1840 \| 0.5444 \| 0.4407 \| 0.5444 \| 0.7378 \| \| 0.0785 \| 6.7473 \| 1842 \| 0.5413 \| 0.4407 \| 0.5413 \| 0.7357 \| \| 0.0785 \| 6.7546 \| 1844 \| 0.5408 \| 0.4828 \| 0.5408 \| 0.7354 \| \| 0.0785 \| 6.7619 \| 1846 \| 0.5459 \| 0.4211 \| 0.5459 \| 0.7388 \| \| 0.0785 \| 6.7692 \| 1848 \| 0.5445 \| 0.4211 \| 0.5445 \| 0.7379 \| \| 0.0785 \| 6.7766 \| 1850 \| 0.5371 \| 0.4211 \| 0.5371 \| 0.7329 \| \| 0.0785 \| 6.7839 \| 1852 \| 0.5333 \| 0.4211 \| 0.5333 \| 0.7303 \| \| 0.0785 \| 6.7912 \| 1854 \| 0.5341 \| 0.4407 \| 0.5341 \| 0.7308 \| \| 0.0785 \| 6.7985 \| 1856 \| 0.5402 \| 0.4407 \| 0.5402 \| 0.7350 \| \| 0.0785 \| 6.8059 \| 1858 \| 0.5510 \| 0.4 \| 0.5510 \| 0.7423 \| \| 0.0785 \| 6.8132 \| 1860 \| 0.5658 \| 0.3571 \| 0.5658 \| 0.7522 \| \| 0.0785 \| 6.8205 \| 1862 \| 0.5707 \| 0.4706 \| 0.5707 \| 0.7555 \| \| 0.0785 \| 6.8278 \| 1864 \| 0.5679 \| 0.4179 \| 0.5679 \| 0.7536 \| \| 0.0785 \| 6.8352 \| 1866 \| 0.5624 \| 0.4545 \| 0.5624 \| 0.7499 \| \| 0.0785 \| 6.8425 \| 1868 \| 0.5571 \| 0.4828 \| 0.5571 \| 0.7464 \| \| 0.0785 \| 6.8498 \| 1870 \| 0.5582 \| 0.4211 \| 0.5582 \| 0.7472 \| \| 0.0785 \| 6.8571 \| 1872 \| 0.5629 \| 0.4211 \| 0.5629 \| 0.7503 \| \| 0.0785 \| 6.8645 \| 1874 \| 0.5692 \| 0.3571 \| 0.5692 \| 0.7545 \| \| 0.0785 \| 6.8718 \| 1876 \| 0.5711 \| 0.3571 \| 0.5711 \| 0.7557 \| \| 0.0785 \| 6.8791 \| 1878 \| 0.5676 \| 0.4211 \| 0.5676 \| 0.7534 \| \| 0.0785 \| 6.8864 \| 1880 \| 0.5624 \| 0.4211 \| 0.5624 \| 0.7500 \| \| 0.0785 \| 6.8938 \| 1882 \| 0.5502 \| 0.4828 \| 0.5502 \| 0.7418 \| \| 0.0785 \| 6.9011 \| 1884 \| 0.5424 \| 0.4828 \| 0.5424 \| 0.7365 \| \| 0.0785 \| 6.9084 \| 1886 \| 0.5386 \| 0.4407 \| 0.5386 \| 0.7339 \| \| 0.0785 \| 6.9158 \| 1888 \| 0.5338 \| 0.4407 \| 0.5338 \| 0.7306 \| \| 0.0785 \| 6.9231 \| 1890 \| 0.5356 \| 0.4 \| 0.5356 \| 0.7319 \| \| 0.0785 \| 6.9304 \| 1892 \| 0.5434 \| 0.4590 \| 0.5434 \| 0.7371 \| \| 0.0785 \| 6.9377 \| 1894 \| 0.5461 \| 0.4590 \| 0.5461 \| 0.7390 \| \| 0.0785 \| 6.9451 \| 1896 \| 0.5444 \| 0.4590 \| 0.5444 \| 0.7379 \| \| 0.0785 \| 6.9524 \| 1898 \| 0.5455 \| 0.4 \| 0.5455 \| 0.7386 \| \| 0.0785 \| 6.9597 \| 1900 \| 0.5497 \| 0.4179 \| 0.5497 \| 0.7414 \| \| 0.0785 \| 6.9670 \| 1902 \| 0.5565 \| 0.4179 \| 0.5565 \| 0.7460 \| \| 0.0785 \| 6.9744 \| 1904 \| 0.5577 \| 0.3478 \| 0.5577 \| 0.7468 \| \| 0.0785 \| 6.9817 \| 1906 \| 0.5550 \| 0.3478 \| 0.5550 \| 0.7450 \| \| 0.0785 \| 6.9890 \| 1908 \| 0.5493 \| 0.3226 \| 0.5493 \| 0.7412 \| \| 0.0785 \| 6.9963 \| 1910 \| 0.5453 \| 0.4590 \| 0.5453 \| 0.7384 \| \| 0.0785 \| 7.0037 \| 1912 \| 0.5432 \| 0.4590 \| 0.5432 \| 0.7370 \| \| 0.0785 \| 7.0110 \| 1914 \| 0.5445 \| 0.3226 \| 0.5445 \| 0.7379 \| \| 0.0785 \| 7.0183 \| 1916 \| 0.5450 \| 0.3226 \| 0.5450 \| 0.7383 \| \| 0.0785 \| 7.0256 \| 1918 \| 0.5438 \| 0.3226 \| 0.5438 \| 0.7374 \| \| 0.0785 \| 7.0330 \| 1920 \| 0.5457 \| 0.3226 \| 0.5457 \| 0.7387 \| \| 0.0785 \| 7.0403 \| 1922 \| 0.5393 \| 0.3226 \| 0.5393 \| 0.7344 \| \| 0.0785 \| 7.0476 \| 1924 \| 0.5270 \| 0.4590 \| 0.5270 \| 0.7259 \| \| 0.0785 \| 7.0549 \| 1926 \| 0.5209 \| 0.4407 \| 0.5209 \| 0.7218 \| \| 0.0785 \| 7.0623 \| 1928 \| 0.5182 \| 0.4407 \| 0.5182 \| 0.7199 \| \| 0.0785 \| 7.0696 \| 1930 \| 0.5177 \| 0.4407 \| 0.5177 \| 0.7195 \| \| 0.0785 \| 7.0769 \| 1932 \| 0.5186 \| 0.4407 \| 0.5186 \| 0.7201 \| \| 0.0785 \| 7.0842 \| 1934 \| 0.5213 \| 0.4590 \| 0.5213 \| 0.7220 \| \| 0.0785 \| 7.0916 \| 1936 \| 0.5270 \| 0.3226 \| 0.5270 \| 0.7260 \| \| 0.0785 \| 7.0989 \| 1938 \| 0.5305 \| 0.3226 \| 0.5305 \| 0.7284 \| \| 0.0785 \| 7.1062 \| 1940 \| 0.5364 \| 0.3226 \| 0.5364 \| 0.7324 \| \| 0.0785 \| 7.1136 \| 1942 \| 0.5438 \| 0.3226 \| 0.5438 \| 0.7374 \| \| 0.0785 \| 7.1209 \| 1944 \| 0.5539 \| 0.3478 \| 0.5539 \| 0.7442 \| \| 0.0785 \| 7.1282 \| 1946 \| 0.5597 \| 0.3478 \| 0.5597 \| 0.7482 \| \| 0.0785 \| 7.1355 \| 1948 \| 0.5635 \| 0.3478 \| 0.5635 \| 0.7507 \| \| 0.0785 \| 7.1429 \| 1950 \| 0.5595 \| 0.3824 \| 0.5595 \| 0.7480 \| \| 0.0785 \| 7.1502 \| 1952 \| 0.5540 \| 0.4407 \| 0.5540 \| 0.7443 \| \| 0.0785 \| 7.1575 \| 1954 \| 0.5507 \| 0.4375 \| 0.5507 \| 0.7421 \| \| 0.0785 \| 7.1648 \| 1956 \| 0.5478 \| 0.4348 \| 0.5478 \| 0.7401 \| \| 0.0785 \| 7.1722 \| 1958 \| 0.5478 \| 0.3824 \| 0.5478 \| 0.7401 \| \| 0.0785 \| 7.1795 \| 1960 \| 0.5443 \| 0.3824 \| 0.5443 \| 0.7378 \| \| 0.0785 \| 7.1868 \| 1962 \| 0.5416 \| 0.3793 \| 0.5416 \| 0.7359 \| \| 0.0785 \| 7.1941 \| 1964 \| 0.5451 \| 0.4407 \| 0.5451 \| 0.7383 \| \| 0.0785 \| 7.2015 \| 1966 \| 0.5532 \| 0.5 \| 0.5532 \| 0.7438 \| \| 0.0785 \| 7.2088 \| 1968 \| 0.5682 \| 0.3478 \| 0.5682 \| 0.7538 \| \| 0.0785 \| 7.2161 \| 1970 \| 0.5856 \| 0.3478 \| 0.5856 \| 0.7652 \| \| 0.0785 \| 7.2234 \| 1972 \| 0.5935 \| 0.3478 \| 0.5935 \| 0.7704 \| \| 0.0785 \| 7.2308 \| 1974 \| 0.5892 \| 0.3478 \| 0.5892 \| 0.7676 \| \| 0.0785 \| 7.2381 \| 1976 \| 0.5806 \| 0.3478 \| 0.5806 \| 0.7620 \| \| 0.0785 \| 7.2454 \| 1978 \| 0.5707 \| 0.3478 \| 0.5707 \| 0.7555 \| \| 0.0785 \| 7.2527 \| 1980 \| 0.5673 \| 0.3478 \| 0.5673 \| 0.7532 \| \| 0.0785 \| 7.2601 \| 1982 \| 0.5657 \| 0.3478 \| 0.5657 \| 0.7521 \| \| 0.0785 \| 7.2674 \| 1984 \| 0.5613 \| 0.3478 \| 0.5613 \| 0.7492 \| \| 0.0785 \| 7.2747 \| 1986 \| 0.5587 \| 0.3478 \| 0.5587 \| 0.7475 \| \| 0.0785 \| 7.2821 \| 1988 \| 0.5552 \| 0.3478 \| 0.5552 \| 0.7451 \| \| 0.0785 \| 7.2894 \| 1990 \| 0.5536 \| 0.3478 \| 0.5536 \| 0.7440 \| \| 0.0785 \| 7.2967 \| 1992 \| 0.5563 \| 0.3478 \| 0.5563 \| 0.7458 \| \| 0.0785 \| 7.3040 \| 1994 \| 0.5599 \| 0.3478 \| 0.5599 \| 0.7483 \| \| 0.0785 \| 7.3114 \| 1996 \| 0.5662 \| 0.3478 \| 0.5662 \| 0.7524 \| \| 0.0785 \| 7.3187 \| 1998 \| 0.5687 \| 0.3478 \| 0.5687 \| 0.7541 \| \| 0.0555 \| 7.3260 \| 2000 \| 0.5800 \| 0.3478 \| 0.5800 \| 0.7616 \| \| 0.0555 \| 7.3333 \| 2002 \| 0.5922 \| 0.3478 \| 0.5922 \| 0.7696 \| \| 0.0555 \| 7.3407 \| 2004 \| 0.6046 \| 0.3478 \| 0.6046 \| 0.7776 \| \| 0.0555 \| 7.3480 \| 2006 \| 0.6190 \| 0.4 \| 0.6190 \| 0.7868 \| \| 0.0555 \| 7.3553 \| 2008 \| 0.6313 \| 0.3077 \| 0.6313 \| 0.7945 \| \| 0.0555 \| 7.3626 \| 2010 \| 0.6427 \| 0.25 \| 0.6427 \| 0.8017 \| \| 0.0555 \| 7.3700 \| 2012 \| 0.6403 \| 0.25 \| 0.6403 \| 0.8002 \| \| 0.0555 \| 7.3773 \| 2014 \| 0.6257 \| 0.3077 \| 0.6257 \| 0.7910 \| \| 0.0555 \| 7.3846 \| 2016 \| 0.6071 \| 0.4 \| 0.6071 \| 0.7792 \| \| 0.0555 \| 7.3919 \| 2018 \| 0.5883 \| 0.3478 \| 0.5883 \| 0.7670 \| \| 0.0555 \| 7.3993 \| 2020 \| 0.5755 \| 0.3478 \| 0.5755 \| 0.7586 \| \| 0.0555 \| 7.4066 \| 2022 \| 0.5697 \| 0.3824 \| 0.5697 \| 0.7548 \| \| 0.0555 \| 7.4139 \| 2024 \| 0.5716 \| 0.3824 \| 0.5716 \| 0.7560 \| \| 0.0555 \| 7.4212 \| 2026 \| 0.5698 \| 0.3824 \| 0.5698 \| 0.7549 \| \| 0.0555 \| 7.4286 \| 2028 \| 0.5649 \| 0.4545 \| 0.5649 \| 0.7516 \| \| 0.0555 \| 7.4359 \| 2030 \| 0.5636 \| 0.4545 \| 0.5636 \| 0.7508 \| \| 0.0555 \| 7.4432 \| 2032 \| 0.5669 \| 0.4545 \| 0.5669 \| 0.7529 \| \| 0.0555 \| 7.4505 \| 2034 \| 0.5736 \| 0.4545 \| 0.5736 \| 0.7574 \| \| 0.0555 \| 7.4579 \| 2036 \| 0.5813 \| 0.4545 \| 0.5813 \| 0.7624 \| \| 0.0555 \| 7.4652 \| 2038 \| 0.5913 \| 0.3824 \| 0.5913 \| 0.7690 \| \| 0.0555 \| 7.4725 \| 2040 \| 0.5968 \| 0.3824 \| 0.5968 \| 0.7725 \| \| 0.0555 \| 7.4799 \| 2042 \| 0.6034 \| 0.3824 \| 0.6034 \| 0.7768 \| \| 0.0555 \| 7.4872 \| 2044 \| 0.6114 \| 0.3478 \| 0.6114 \| 0.7819 \| \| 0.0555 \| 7.4945 \| 2046 \| 0.6161 \| 0.3478 \| 0.6161 \| 0.7849 \| \| 0.0555 \| 7.5018 \| 2048 \| 0.6128 \| 0.3478 \| 0.6128 \| 0.7828 \| \| 0.0555 \| 7.5092 \| 2050 \| 0.6013 \| 0.3478 \| 0.6013 \| 0.7755 \| \| 0.0555 \| 7.5165 \| 2052 \| 0.5807 \| 0.3478 \| 0.5807 \| 0.7620 \| \| 0.0555 \| 7.5238 \| 2054 \| 0.5669 \| 0.3824 \| 0.5669 \| 0.7529 \| \| 0.0555 \| 7.5311 \| 2056 \| 0.5605 \| 0.3824 \| 0.5605 \| 0.7486 \| \| 0.0555 \| 7.5385 \| 2058 \| 0.5567 \| 0.3478 \| 0.5567 \| 0.7461 \| \| 0.0555 \| 7.5458 \| 2060 \| 0.5571 \| 0.3478 \| 0.5571 \| 0.7464 \| \| 0.0555 \| 7.5531 \| 2062 \| 0.5519 \| 0.3478 \| 0.5519 \| 0.7429 \| \| 0.0555 \| 7.5604 \| 2064 \| 0.5492 \| 0.2500 \| 0.5492 \| 0.7411 \| \| 0.0555 \| 7.5678 \| 2066 \| 0.5532 \| 0.2500 \| 0.5532 \| 0.7438 \| \| 0.0555 \| 7.5751 \| 2068 \| 0.5684 \| 0.3077 \| 0.5684 \| 0.7539 \| \| 0.0555 \| 7.5824 \| 2070 \| 0.5736 \| 0.3077 \| 0.5736 \| 0.7574 \| \| 0.0555 \| 7.5897 \| 2072 \| 0.5827 \| 0.3077 \| 0.5827 \| 0.7633 \| \| 0.0555 \| 7.5971 \| 2074 \| 0.5899 \| 0.3077 \| 0.5899 \| 0.7680 \| \| 0.0555 \| 7.6044 \| 2076 \| 0.5898 \| 0.3077 \| 0.5898 \| 0.7680 \| \| 0.0555 \| 7.6117 \| 2078 \| 0.5746 \| 0.3077 \| 0.5746 \| 0.7580 \| \| 0.0555 \| 7.6190 \| 2080 \| 0.5513 \| 0.3478 \| 0.5513 \| 0.7425 \| \| 0.0555 \| 7.6264 \| 2082 \| 0.5376 \| 0.4706 \| 0.5376 \| 0.7332 \| \| 0.0555 \| 7.6337 \| 2084 \| 0.5317 \| 0.4545 \| 0.5317 \| 0.7292 \| \| 0.0555 \| 7.6410 \| 2086 \| 0.5350 \| 0.4923 \| 0.5350 \| 0.7314 \| \| 0.0555 \| 7.6484 \| 2088 \| 0.5422 \| 0.48 \| 0.5422 \| 0.7363 \| \| 0.0555 \| 7.6557 \| 2090 \| 0.5505 \| 0.48 \| 0.5505 \| 0.7420 \| \| 0.0555 \| 7.6630 \| 2092 \| 0.5577 \| 0.4507 \| 0.5577 \| 0.7468 \| \| 0.0555 \| 7.6703 \| 2094 \| 0.5662 \| 0.5075 \| 0.5662 \| 0.7524 \| \| 0.0555 \| 7.6777 \| 2096 \| 0.5695 \| 0.5075 \| 0.5695 \| 0.7547 \| \| 0.0555 \| 7.6850 \| 2098 \| 0.5703 \| 0.5075 \| 0.5703 \| 0.7552 \| \| 0.0555 \| 7.6923 \| 2100 \| 0.5664 \| 0.5075 \| 0.5664 \| 0.7526 \| \| 0.0555 \| 7.6996 \| 2102 \| 0.5653 \| 0.5075 \| 0.5653 \| 0.7518 \| \| 0.0555 \| 7.7070 \| 2104 \| 0.5644 \| 0.4545 \| 0.5644 \| 0.7513 \| \| 0.0555 \| 7.7143 \| 2106 \| 0.5670 \| 0.4545 \| 0.5670 \| 0.7530 \| \| 0.0555 \| 7.7216 \| 2108 \| 0.5719 \| 0.5075 \| 0.5719 \| 0.7562 \| \| 0.0555 \| 7.7289 \| 2110 \| 0.5746 \| 0.5075 \| 0.5746 \| 0.7580 \| \| 0.0555 \| 7.7363 \| 2112 \| 0.5802 \| 0.3478 \| 0.5802 \| 0.7617 \| \| 0.0555 \| 7.7436 \| 2114 \| 0.5827 \| 0.3478 \| 0.5827 \| 0.7633 \| \| 0.0555 \| 7.7509 \| 2116 \| 0.5757 \| 0.3478 \| 0.5757 \| 0.7588 \| \| 0.0555 \| 7.7582 \| 2118 \| 0.5691 \| 0.3478 \| 0.5691 \| 0.7544 \| \| 0.0555 \| 7.7656 \| 2120 \| 0.5626 \| 0.3478 \| 0.5626 \| 0.7501 \| \| 0.0555 \| 7.7729 \| 2122 \| 0.5588 \| 0.3824 \| 0.5588 \| 0.7475 \| \| 0.0555 \| 7.7802 \| 2124 \| 0.5517 \| 0.5075 \| 0.5517 \| 0.7428 \| \| 0.0555 \| 7.7875 \| 2126 \| 0.5434 \| 0.5075 \| 0.5434 \| 0.7372 \| \| 0.0555 \| 7.7949 \| 2128 \| 0.5372 \| 0.4375 \| 0.5372 \| 0.7330 \| \| 0.0555 \| 7.8022 \| 2130 \| 0.5356 \| 0.4375 \| 0.5356 \| 0.7319 \| \| 0.0555 \| 7.8095 \| 2132 \| 0.5345 \| 0.4375 \| 0.5345 \| 0.7311 \| \| 0.0555 \| 7.8168 \| 2134 \| 0.5349 \| 0.4407 \| 0.5349 \| 0.7313 \| \| 0.0555 \| 7.8242 \| 2136 \| 0.5366 \| 0.4407 \| 0.5366 \| 0.7325 \| \| 0.0555 \| 7.8315 \| 2138 \| 0.5416 \| 0.4545 \| 0.5416 \| 0.7359 \| \| 0.0555 \| 7.8388 \| 2140 \| 0.5494 \| 0.5075 \| 0.5494 \| 0.7412 \| \| 0.0555 \| 7.8462 \| 2142 \| 0.5597 \| 0.3478 \| 0.5597 \| 0.7481 \| \| 0.0555 \| 7.8535 \| 2144 \| 0.5673 \| 0.3478 \| 0.5673 \| 0.7532 \| \| 0.0555 \| 7.8608 \| 2146 \| 0.5786 \| 0.3478 \| 0.5786 \| 0.7606 \| \| 0.0555 \| 7.8681 \| 2148 \| 0.5814 \| 0.3478 \| 0.5814 \| 0.7625 \| \| 0.0555 \| 7.8755 \| 2150 \| 0.5745 \| 0.3478 \| 0.5745 \| 0.7579 \| \| 0.0555 \| 7.8828 \| 2152 \| 0.5703 \| 0.3478 \| 0.5703 \| 0.7552 \| \| 0.0555 \| 7.8901 \| 2154 \| 0.5699 \| 0.3478 \| 0.5699 \| 0.7549 \| \| 0.0555 \| 7.8974 \| 2156 \| 0.5678 \| 0.3478 \| 0.5678 \| 0.7536 \| \| 0.0555 \| 7.9048 \| 2158 \| 0.5636 \| 0.5075 \| 0.5636 \| 0.7507 \| \| 0.0555 \| 7.9121 \| 2160 \| 0.5581 \| 0.5075 \| 0.5581 \| 0.7470 \| \| 0.0555 \| 7.9194 \| 2162 \| 0.5490 \| 0.4545 \| 0.5490 \| 0.7409 \| \| 0.0555 \| 7.9267 \| 2164 \| 0.5494 \| 0.4545 \| 0.5494 \| 0.7412 \| \| 0.0555 \| 7.9341 \| 2166 \| 0.5558 \| 0.5075 \| 0.5558 \| 0.7455 \| \| 0.0555 \| 7.9414 \| 2168 \| 0.5637 \| 0.5075 \| 0.5637 \| 0.7508 \| \| 0.0555 \| 7.9487 \| 2170 \| 0.5699 \| 0.3478 \| 0.5699 \| 0.7549 \| \| 0.0555 \| 7.9560 \| 2172 \| 0.5751 \| 0.3478 \| 0.5751 \| 0.7584 \| \| 0.0555 \| 7.9634 \| 2174 \| 0.5744 \| 0.3478 \| 0.5744 \| 0.7579 \| \| 0.0555 \| 7.9707 \| 2176 \| 0.5774 \| 0.3478 \| 0.5774 \| 0.7599 \| \| 0.0555 \| 7.9780 \| 2178 \| 0.5806 \| 0.3478 \| 0.5806 \| 0.7620 \| \| 0.0555 \| 7.9853 \| 2180 \| 0.5831 \| 0.3478 \| 0.5831 \| 0.7636 \| \| 0.0555 \| 7.9927 \| 2182 \| 0.5831 \| 0.3478 \| 0.5831 \| 0.7636 \| \| 0.0555 \| 8.0 \| 2184 \| 0.5836 \| 0.3478 \| 0.5836 \| 0.7640 \| \| 0.0555 \| 8.0073 \| 2186 \| 0.5885 \| 0.3478 \| 0.5885 \| 0.7671 \| \| 0.0555 \| 8.0147 \| 2188 \| 0.5972 \| 0.3478 \| 0.5972 \| 0.7728 \| \| 0.0555 \| 8.0220 \| 2190 \| 0.6014 \| 0.3478 \| 0.6014 \| 0.7755 \| \| 0.0555 \| 8.0293 \| 2192 \| 0.6106 \| 0.3478 \| 0.6106 \| 0.7814 \| \| 0.0555 \| 8.0366 \| 2194 \| 0.6189 \| 0.3478 \| 0.6189 \| 0.7867 \| \| 0.0555 \| 8.0440 \| 2196 \| 0.6244 \| 0.3478 \| 0.6244 \| 0.7902 \| \| 0.0555 \| 8.0513 \| 2198 \| 0.6226 \| 0.3478 \| 0.6226 \| 0.7891 \| \| 0.0555 \| 8.0586 \| 2200 \| 0.6172 \| 0.3478 \| 0.6172 \| 0.7856 \| \| 0.0555 \| 8.0659 \| 2202 \| 0.6123 \| 0.3514 \| 0.6123 \| 0.7825 \| \| 0.0555 \| 8.0733 \| 2204 \| 0.6068 \| 0.4507 \| 0.6068 \| 0.7790 \| \| 0.0555 \| 8.0806 \| 2206 \| 0.6067 \| 0.4474 \| 0.6067 \| 0.7789 \| \| 0.0555 \| 8.0879 \| 2208 \| 0.6093 \| 0.4474 \| 0.6093 \| 0.7806 \| \| 0.0555 \| 8.0952 \| 2210 \| 0.6127 \| 0.4474 \| 0.6127 \| 0.7828 \| \| 0.0555 \| 8.1026 \| 2212 \| 0.6125 \| 0.4474 \| 0.6125 \| 0.7826 \| \| 0.0555 \| 8.1099 \| 2214 \| 0.6125 \| 0.4474 \| 0.6125 \| 0.7826 \| \| 0.0555 \| 8.1172 \| 2216 \| 0.6110 \| 0.4474 \| 0.6110 \| 0.7817 \| \| 0.0555 \| 8.1245 \| 2218 \| 0.6084 \| 0.4474 \| 0.6084 \| 0.7800 \| \| 0.0555 \| 8.1319 \| 2220 \| 0.6068 \| 0.4474 \| 0.6068 \| 0.7790 \| \| 0.0555 \| 8.1392 \| 2222 \| 0.6060 \| 0.4507 \| 0.6060 \| 0.7785 \| \| 0.0555 \| 8.1465 \| 2224 \| 0.6050 \| 0.4507 \| 0.6050 \| 0.7778 \| \| 0.0555 \| 8.1538 \| 2226 \| 0.6020 \| 0.5 \| 0.6020 \| 0.7759 \| \| 0.0555 \| 8.1612 \| 2228 \| 0.5998 \| 0.5 \| 0.5998 \| 0.7745 \| \| 0.0555 \| 8.1685 \| 2230 \| 0.5957 \| 0.5 \| 0.5957 \| 0.7718 \| \| 0.0555 \| 8.1758 \| 2232 \| 0.5928 \| 0.5 \| 0.5928 \| 0.7699 \| \| 0.0555 \| 8.1832 \| 2234 \| 0.5901 \| 0.5075 \| 0.5901 \| 0.7682 \| \| 0.0555 \| 8.1905 \| 2236 \| 0.5867 \| 0.3824 \| 0.5867 \| 0.7660 \| \| 0.0555 \| 8.1978 \| 2238 \| 0.5851 \| 0.3478 \| 0.5851 \| 0.7649 \| \| 0.0555 \| 8.2051 \| 2240 \| 0.5822 \| 0.3478 \| 0.5822 \| 0.7630 \| \| 0.0555 \| 8.2125 \| 2242 \| 0.5833 \| 0.3478 \| 0.5833 \| 0.7637 \| \| 0.0555 \| 8.2198 \| 2244 \| 0.5853 \| 0.3478 \| 0.5853 \| 0.7651 \| \| 0.0555 \| 8.2271 \| 2246 \| 0.5863 \| 0.3478 \| 0.5863 \| 0.7657 \| \| 0.0555 \| 8.2344 \| 2248 \| 0.5829 \| 0.3824 \| 0.5829 \| 0.7635 \| \| 0.0555 \| 8.2418 \| 2250 \| 0.5773 \| 0.5075 \| 0.5773 \| 0.7598 \| \| 0.0555 \| 8.2491 \| 2252 \| 0.5775 \| 0.5 \| 0.5775 \| 0.7599 \| \| 0.0555 \| 8.2564 \| 2254 \| 0.5807 \| 0.5 \| 0.5807 \| 0.7620 \| \| 0.0555 \| 8.2637 \| 2256 \| 0.5820 \| 0.5 \| 0.5820 \| 0.7629 \| \| 0.0555 \| 8.2711 \| 2258 \| 0.5824 \| 0.3836 \| 0.5824 \| 0.7631 \| \| 0.0555 \| 8.2784 \| 2260 \| 0.5776 \| 0.5 \| 0.5776 \| 0.7600 \| \| 0.0555 \| 8.2857 \| 2262 \| 0.5725 \| 0.5 \| 0.5725 \| 0.7567 \| \| 0.0555 \| 8.2930 \| 2264 \| 0.5685 \| 0.5 \| 0.5685 \| 0.7540 \| \| 0.0555 \| 8.3004 \| 2266 \| 0.5670 \| 0.5 \| 0.5670 \| 0.7530 \| \| 0.0555 \| 8.3077 \| 2268 \| 0.5669 \| 0.3824 \| 0.5669 \| 0.7529 \| \| 0.0555 \| 8.3150 \| 2270 \| 0.5702 \| 0.3824 \| 0.5702 \| 0.7551 \| \| 0.0555 \| 8.3223 \| 2272 \| 0.5737 \| 0.3824 \| 0.5737 \| 0.7574 \| \| 0.0555 \| 8.3297 \| 2274 \| 0.5774 \| 0.3478 \| 0.5774 \| 0.7599 \| \| 0.0555 \| 8.3370 \| 2276 \| 0.5804 \| 0.3478 \| 0.5804 \| 0.7618 \| \| 0.0555 \| 8.3443 \| 2278 \| 0.5754 \| 0.3478 \| 0.5754 \| 0.7585 \| \| 0.0555 \| 8.3516 \| 2280 \| 0.5720 \| 0.3478 \| 0.5720 \| 0.7563 \| \| 0.0555 \| 8.3590 \| 2282 \| 0.5672 \| 0.3478 \| 0.5672 \| 0.7531 \| \| 0.0555 \| 8.3663 \| 2284 \| 0.5628 \| 0.3478 \| 0.5628 \| 0.7502 \| \| 0.0555 \| 8.3736 \| 2286 \| 0.5574 \| 0.3478 \| 0.5574 \| 0.7466 \| \| 0.0555 \| 8.3810 \| 2288 \| 0.5503 \| 0.3824 \| 0.5503 \| 0.7418 \| \| 0.0555 \| 8.3883 \| 2290 \| 0.5463 \| 0.3607 \| 0.5463 \| 0.7391 \| \| 0.0555 \| 8.3956 \| 2292 \| 0.5475 \| 0.3607 \| 0.5475 \| 0.7399 \| \| 0.0555 \| 8.4029 \| 2294 \| 0.5492 \| 0.3478 \| 0.5492 \| 0.7411 \| \| 0.0555 \| 8.4103 \| 2296 \| 0.5484 \| 0.3478 \| 0.5484 \| 0.7406 \| \| 0.0555 \| 8.4176 \| 2298 \| 0.5486 \| 0.3478 \| 0.5486 \| 0.7407 \| \| 0.0555 \| 8.4249 \| 2300 \| 0.5507 \| 0.3478 \| 0.5507 \| 0.7421 \| \| 0.0555 \| 8.4322 \| 2302 \| 0.5524 \| 0.3478 \| 0.5524 \| 0.7432 \| \| 0.0555 \| 8.4396 \| 2304 \| 0.5508 \| 0.3478 \| 0.5508 \| 0.7421 \| \| 0.0555 \| 8.4469 \| 2306 \| 0.5476 \| 0.3824 \| 0.5476 \| 0.7400 \| \| 0.0555 \| 8.4542 \| 2308 \| 0.5475 \| 0.3824 \| 0.5475 \| 0.7400 \| \| 0.0555 \| 8.4615 \| 2310 \| 0.5535 \| 0.3824 \| 0.5535 \| 0.7440 \| \| 0.0555 \| 8.4689 \| 2312 \| 0.5645 \| 0.3478 \| 0.5645 \| 0.7513 \| \| 0.0555 \| 8.4762 \| 2314 \| 0.5741 \| 0.3478 \| 0.5741 \| 0.7577 \| \| 0.0555 \| 8.4835 \| 2316 \| 0.5803 \| 0.3478 \| 0.5803 \| 0.7617 \| \| 0.0555 \| 8.4908 \| 2318 \| 0.5842 \| 0.3478 \| 0.5842 \| 0.7644 \| \| 0.0555 \| 8.4982 \| 2320 \| 0.5849 \| 0.3478 \| 0.5849 \| 0.7648 \| \| 0.0555 \| 8.5055 \| 2322 \| 0.5799 \| 0.3478 \| 0.5799 \| 0.7615 \| \| 0.0555 \| 8.5128 \| 2324 \| 0.5778 \| 0.3478 \| 0.5778 \| 0.7601 \| \| 0.0555 \| 8.5201 \| 2326 \| 0.5814 \| 0.3478 \| 0.5814 \| 0.7625 \| \| 0.0555 \| 8.5275 \| 2328 \| 0.5837 \| 0.3478 \| 0.5837 \| 0.7640 \| \| 0.0555 \| 8.5348 \| 2330 \| 0.5807 \| 0.3478 \| 0.5807 \| 0.7620 \| \| 0.0555 \| 8.5421 \| 2332 \| 0.5739 \| 0.3478 \| 0.5739 \| 0.7576 \| \| 0.0555 \| 8.5495 \| 2334 \| 0.5650 \| 0.3824 \| 0.5650 \| 0.7517 \| \| 0.0555 \| 8.5568 \| 2336 \| 0.5598 \| 0.3824 \| 0.5598 \| 0.7482 \| \| 0.0555 \| 8.5641 \| 2338 \| 0.5569 \| 0.3836 \| 0.5569 \| 0.7462 \| \| 0.0555 \| 8.5714 \| 2340 \| 0.5582 \| 0.3836 \| 0.5582 \| 0.7471 \| \| 0.0555 \| 8.5788 \| 2342 \| 0.5617 \| 0.3824 \| 0.5617 \| 0.7494 \| \| 0.0555 \| 8.5861 \| 2344 \| 0.5664 \| 0.3824 \| 0.5664 \| 0.7526 \| \| 0.0555 \| 8.5934 \| 2346 \| 0.5708 \| 0.3478 \| 0.5708 \| 0.7555 \| \| 0.0555 \| 8.6007 \| 2348 \| 0.5735 \| 0.3478 \| 0.5735 \| 0.7573 \| \| 0.0555 \| 8.6081 \| 2350 \| 0.5779 \| 0.3478 \| 0.5779 \| 0.7602 \| \| 0.0555 \| 8.6154 \| 2352 \| 0.5804 \| 0.3478 \| 0.5804 \| 0.7619 \| \| 0.0555 \| 8.6227 \| 2354 \| 0.5842 \| 0.3478 \| 0.5842 \| 0.7644 \| \| 0.0555 \| 8.6300 \| 2356 \| 0.5875 \| 0.3478 \| 0.5875 \| 0.7665 \| \| 0.0555 \| 8.6374 \| 2358 \| 0.5864 \| 0.3478 \| 0.5864 \| 0.7658 \| \| 0.0555 \| 8.6447 \| 2360 \| 0.5830 \| 0.3478 \| 0.5830 \| 0.7636 \| \| 0.0555 \| 8.6520 \| 2362 \| 0.5796 \| 0.3478 \| 0.5796 \| 0.7613 \| \| 0.0555 \| 8.6593 \| 2364 \| 0.5794 \| 0.3478 \| 0.5794 \| 0.7612 \| \| 0.0555 \| 8.6667 \| 2366 \| 0.5771 \| 0.3478 \| 0.5771 \| 0.7597 \| \| 0.0555 \| 8.6740 \| 2368 \| 0.5672 \| 0.3478 \| 0.5672 \| 0.7532 \| \| 0.0555 \| 8.6813 \| 2370 \| 0.5574 \| 0.3478 \| 0.5574 \| 0.7466 \| \| 0.0555 \| 8.6886 \| 2372 \| 0.5500 \| 0.3824 \| 0.5500 \| 0.7416 \| \| 0.0555 \| 8.6960 \| 2374 \| 0.5448 \| 0.3824 \| 0.5448 \| 0.7381 \| \| 0.0555 \| 8.7033 \| 2376 \| 0.5383 \| 0.5075 \| 0.5383 \| 0.7337 \| \| 0.0555 \| 8.7106 \| 2378 \| 0.5318 \| 0.5 \| 0.5318 \| 0.7292 \| \| 0.0555 \| 8.7179 \| 2380 \| 0.5312 \| 0.5 \| 0.5312 \| 0.7288 \| \| 0.0555 \| 8.7253 \| 2382 \| 0.5346 \| 0.5 \| 0.5346 \| 0.7312 \| \| 0.0555 \| 8.7326 \| 2384 \| 0.5389 \| 0.5075 \| 0.5389 \| 0.7341 \| \| 0.0555 \| 8.7399 \| 2386 \| 0.5445 \| 0.5075 \| 0.5445 \| 0.7379 \| \| 0.0555 \| 8.7473 \| 2388 \| 0.5493 \| 0.3824 \| 0.5493 \| 0.7411 \| \| 0.0555 \| 8.7546 \| 2390 \| 0.5550 \| 0.3824 \| 0.5550 \| 0.7450 \| \| 0.0555 \| 8.7619 \| 2392 \| 0.5604 \| 0.3824 \| 0.5604 \| 0.7486 \| \| 0.0555 \| 8.7692 \| 2394 \| 0.5638 \| 0.3824 \| 0.5638 \| 0.7509 \| \| 0.0555 \| 8.7766 \| 2396 \| 0.5686 \| 0.3824 \| 0.5686 \| 0.7541 \| \| 0.0555 \| 8.7839 \| 2398 \| 0.5788 \| 0.3478 \| 0.5788 \| 0.7608 \| \| 0.0555 \| 8.7912 \| 2400 \| 0.5859 \| 0.3478 \| 0.5859 \| 0.7654 \| \| 0.0555 \| 8.7985 \| 2402 \| 0.5961 \| 0.3478 \| 0.5961 \| 0.7721 \| \| 0.0555 \| 8.8059 \| 2404 \| 0.6048 \| 0.3478 \| 0.6048 \| 0.7777 \| \| 0.0555 \| 8.8132 \| 2406 \| 0.6118 \| 0.4 \| 0.6118 \| 0.7821 \| \| 0.0555 \| 8.8205 \| 2408 \| 0.6112 \| 0.4 \| 0.6112 \| 0.7818 \| \| 0.0555 \| 8.8278 \| 2410 \| 0.6079 \| 0.3478 \| 0.6079 \| 0.7797 \| \| 0.0555 \| 8.8352 \| 2412 \| 0.6000 \| 0.3478 \| 0.6000 \| 0.7746 \| \| 0.0555 \| 8.8425 \| 2414 \| 0.5932 \| 0.3478 \| 0.5932 \| 0.7702 \| \| 0.0555 \| 8.8498 \| 2416 \| 0.5907 \| 0.3478 \| 0.5907 \| 0.7686 \| \| 0.0555 \| 8.8571 \| 2418 \| 0.5910 \| 0.3478 \| 0.5910 \| 0.7688 \| \| 0.0555 \| 8.8645 \| 2420 \| 0.5863 \| 0.3478 \| 0.5863 \| 0.7657 \| \| 0.0555 \| 8.8718 \| 2422 \| 0.5829 \| 0.3824 \| 0.5829 \| 0.7634 \| \| 0.0555 \| 8.8791 \| 2424 \| 0.5810 \| 0.3824 \| 0.5810 \| 0.7622 \| \| 0.0555 \| 8.8864 \| 2426 \| 0.5768 \| 0.5075 \| 0.5768 \| 0.7595 \| \| 0.0555 \| 8.8938 \| 2428 \| 0.5718 \| 0.5075 \| 0.5718 \| 0.7562 \| \| 0.0555 \| 8.9011 \| 2430 \| 0.5658 \| 0.4507 \| 0.5658 \| 0.7522 \| \| 0.0555 \| 8.9084 \| 2432 \| 0.5589 \| 0.4507 \| 0.5589 \| 0.7476 \| \| 0.0555 \| 8.9158 \| 2434 \| 0.5549 \| 0.4507 \| 0.5549 \| 0.7449 \| \| 0.0555 \| 8.9231 \| 2436 \| 0.5510 \| 0.4507 \| 0.5510 \| 0.7423 \| \| 0.0555 \| 8.9304 \| 2438 \| 0.5464 \| 0.4507 \| 0.5464 \| 0.7392 \| \| 0.0555 \| 8.9377 \| 2440 \| 0.5425 \| 0.4375 \| 0.5425 \| 0.7365 \| \| 0.0555 \| 8.9451 \| 2442 \| 0.5389 \| 0.4407 \| 0.5389 \| 0.7341 \| \| 0.0555 \| 8.9524 \| 2444 \| 0.5371 \| 0.4407 \| 0.5371 \| 0.7328 \| \| 0.0555 \| 8.9597 \| 2446 \| 0.5380 \| 0.4407 \| 0.5380 \| 0.7335 \| \| 0.0555 \| 8.9670 \| 2448 \| 0.5404 \| 0.4407 \| 0.5404 \| 0.7351 \| \| 0.0555 \| 8.9744 \| 2450 \| 0.5450 \| 0.5075 \| 0.5450 \| 0.7383 \| \| 0.0555 \| 8.9817 \| 2452 \| 0.5494 \| 0.5075 \| 0.5494 \| 0.7412 \| \| 0.0555 \| 8.9890 \| 2454 \| 0.5516 \| 0.3824 \| 0.5516 \| 0.7427 \| \| 0.0555 \| 8.9963 \| 2456 \| 0.5544 \| 0.3824 \| 0.5544 \| 0.7446 \| \| 0.0555 \| 9.0037 \| 2458 \| 0.5561 \| 0.3824 \| 0.5561 \| 0.7457 \| \| 0.0555 \| 9.0110 \| 2460 \| 0.5573 \| 0.3824 \| 0.5573 \| 0.7466 \| \| 0.0555 \| 9.0183 \| 2462 \| 0.5567 \| 0.3824 \| 0.5567 \| 0.7461 \| \| 0.0555 \| 9.0256 \| 2464 \| 0.5550 \| 0.3824 \| 0.5550 \| 0.7450 \| \| 0.0555 \| 9.0330 \| 2466 \| 0.5523 \| 0.3824 \| 0.5523 \| 0.7432 \| \| 0.0555 \| 9.0403 \| 2468 \| 0.5508 \| 0.3824 \| 0.5508 \| 0.7422 \| \| 0.0555 \| 9.0476 \| 2470 \| 0.5516 \| 0.3824 \| 0.5516 \| 0.7427 \| \| 0.0555 \| 9.0549 \| 2472 \| 0.5523 \| 0.3824 \| 0.5523 \| 0.7432 \| \| 0.0555 \| 9.0623 \| 2474 \| 0.5501 \| 0.3824 \| 0.5501 \| 0.7417 \| \| 0.0555 \| 9.0696 \| 2476 \| 0.5470 \| 0.3824 \| 0.5470 \| 0.7396 \| \| 0.0555 \| 9.0769 \| 2478 \| 0.5457 \| 0.5075 \| 0.5457 \| 0.7387 \| \| 0.0555 \| 9.0842 \| 2480 \| 0.5438 \| 0.5 \| 0.5438 \| 0.7374 \| \| 0.0555 \| 9.0916 \| 2482 \| 0.5429 \| 0.5 \| 0.5429 \| 0.7368 \| \| 0.0555 \| 9.0989 \| 2484 \| 0.5433 \| 0.5 \| 0.5433 \| 0.7371 \| \| 0.0555 \| 9.1062 \| 2486 \| 0.5455 \| 0.5 \| 0.5455 \| 0.7386 \| \| 0.0555 \| 9.1136 \| 2488 \| 0.5484 \| 0.3824 \| 0.5484 \| 0.7405 \| \| 0.0555 \| 9.1209 \| 2490 \| 0.5499 \| 0.3824 \| 0.5499 \| 0.7416 \| \| 0.0555 \| 9.1282 \| 2492 \| 0.5478 \| 0.3824 \| 0.5478 \| 0.7402 \| \| 0.0555 \| 9.1355 \| 2494 \| 0.5446 \| 0.5 \| 0.5446 \| 0.7379 \| \| 0.0555 \| 9.1429 \| 2496 \| 0.5405 \| 0.5 \| 0.5405 \| 0.7352 \| \| 0.0555 \| 9.1502 \| 2498 \| 0.5382 \| 0.3607 \| 0.5382 \| 0.7336 \| \| 0.0473 \| 9.1575 \| 2500 \| 0.5385 \| 0.3607 \| 0.5385 \| 0.7338 \| \| 0.0473 \| 9.1648 \| 2502 \| 0.5384 \| 0.3607 \| 0.5384 \| 0.7338 \| \| 0.0473 \| 9.1722 \| 2504 \| 0.5363 \| 0.3607 \| 0.5363 \| 0.7323 \| \| 0.0473 \| 9.1795 \| 2506 \| 0.5358 \| 0.3607 \| 0.5358 \| 0.7320 \| \| 0.0473 \| 9.1868 \| 2508 \| 0.5379 \| 0.3607 \| 0.5379 \| 0.7334 \| \| 0.0473 \| 9.1941 \| 2510 \| 0.5395 \| 0.3607 \| 0.5395 \| 0.7345 \| \| 0.0473 \| 9.2015 \| 2512 \| 0.5413 \| 0.3607 \| 0.5413 \| 0.7357 \| \| 0.0473 \| 9.2088 \| 2514 \| 0.5407 \| 0.3607 \| 0.5407 \| 0.7353 \| \| 0.0473 \| 9.2161 \| 2516 \| 0.5389 \| 0.3607 \| 0.5389 \| 0.7341 \| \| 0.0473 \| 9.2234 \| 2518 \| 0.5366 \| 0.3607 \| 0.5366 \| 0.7325 \| \| 0.0473 \| 9.2308 \| 2520 \| 0.5352 \| 0.3607 \| 0.5352 \| 0.7316 \| \| 0.0473 \| 9.2381 \| 2522 \| 0.5352 \| 0.3607 \| 0.5352 \| 0.7316 \| \| 0.0473 \| 9.2454 \| 2524 \| 0.5366 \| 0.3607 \| 0.5366 \| 0.7325 \| \| 0.0473 \| 9.2527 \| 2526 \| 0.5385 \| 0.3607 \| 0.5385 \| 0.7338 \| \| 0.0473 \| 9.2601 \| 2528 \| 0.5397 \| 0.3607 \| 0.5397 \| 0.7347 \| \| 0.0473 \| 9.2674 \| 2530 \| 0.5410 \| 0.3607 \| 0.5410 \| 0.7355 \| \| 0.0473 \| 9.2747 \| 2532 \| 0.5408 \| 0.3607 \| 0.5408 \| 0.7354 \| \| 0.0473 \| 9.2821 \| 2534 \| 0.5413 \| 0.3607 \| 0.5413 \| 0.7357 \| \| 0.0473 \| 9.2894 \| 2536 \| 0.5418 \| 0.3607 \| 0.5418 \| 0.7361 \| \| 0.0473 \| 9.2967 \| 2538 \| 0.5420 \| 0.3607 \| 0.5420 \| 0.7362 \| \| 0.0473 \| 9.3040 \| 2540 \| 0.5434 \| 0.3636 \| 0.5434 \| 0.7372 \| \| 0.0473 \| 9.3114 \| 2542 \| 0.5449 \| 0.3607 \| 0.5449 \| 0.7382 \| \| 0.0473 \| 9.3187 \| 2544 \| 0.5487 \| 0.3607 \| 0.5487 \| 0.7407 \| \| 0.0473 \| 9.3260 \| 2546 \| 0.5532 \| 0.3824 \| 0.5532 \| 0.7437 \| \| 0.0473 \| 9.3333 \| 2548 \| 0.5542 \| 0.3824 \| 0.5542 \| 0.7444 \| \| 0.0473 \| 9.3407 \| 2550 \| 0.5540 \| 0.3607 \| 0.5540 \| 0.7443 \| \| 0.0473 \| 9.3480 \| 2552 \| 0.5528 \| 0.3607 \| 0.5528 \| 0.7435 \| \| 0.0473 \| 9.3553 \| 2554 \| 0.5523 \| 0.3607 \| 0.5523 \| 0.7431 \| \| 0.0473 \| 9.3626 \| 2556 \| 0.5526 \| 0.3607 \| 0.5526 \| 0.7434 \| \| 0.0473 \| 9.3700 \| 2558 \| 0.5538 \| 0.3607 \| 0.5538 \| 0.7442 \| \| 0.0473 \| 9.3773 \| 2560 \| 0.5546 \| 0.3607 \| 0.5546 \| 0.7447 \| \| 0.0473 \| 9.3846 \| 2562 \| 0.5537 \| 0.3607 \| 0.5537 \| 0.7441 \| \| 0.0473 \| 9.3919 \| 2564 \| 0.5520 \| 0.3607 \| 0.5520 \| 0.7430 \| \| 0.0473 \| 9.3993 \| 2566 \| 0.5510 \| 0.3607 \| 0.5510 \| 0.7423 \| \| 0.0473 \| 9.4066 \| 2568 \| 0.5495 \| 0.3607 \| 0.5495 \| 0.7413 \| \| 0.0473 \| 9.4139 \| 2570 \| 0.5473 \| 0.3607 \| 0.5473 \| 0.7398 \| \| 0.0473 \| 9.4212 \| 2572 \| 0.5458 \| 0.3607 \| 0.5458 \| 0.7388 \| \| 0.0473 \| 9.4286 \| 2574 \| 0.5447 \| 0.3607 \| 0.5447 \| 0.7380 \| \| 0.0473 \| 9.4359 \| 2576 \| 0.5444 \| 0.3607 \| 0.5444 \| 0.7379 \| \| 0.0473 \| 9.4432 \| 2578 \| 0.5450 \| 0.3607 \| 0.5450 \| 0.7383 \| \| 0.0473 \| 9.4505 \| 2580 \| 0.5457 \| 0.3607 \| 0.5457 \| 0.7387 \| \| 0.0473 \| 9.4579 \| 2582 \| 0.5457 \| 0.3607 \| 0.5457 \| 0.7387 \| \| 0.0473 \| 9.4652 \| 2584 \| 0.5455 \| 0.3607 \| 0.5455 \| 0.7386 \| \| 0.0473 \| 9.4725 \| 2586 \| 0.5447 \| 0.3607 \| 0.5447 \| 0.7380 \| \| 0.0473 \| 9.4799 \| 2588 \| 0.5451 \| 0.3607 \| 0.5451 \| 0.7383 \| \| 0.0473 \| 9.4872 \| 2590 \| 0.5463 \| 0.3607 \| 0.5463 \| 0.7391 \| \| 0.0473 \| 9.4945 \| 2592 \| 0.5489 \| 0.3226 \| 0.5489 \| 0.7409 \| \| 0.0473 \| 9.5018 \| 2594 \| 0.5505 \| 0.3226 \| 0.5505 \| 0.7420 \| \| 0.0473 \| 9.5092 \| 2596 \| 0.5524 \| 0.3226 \| 0.5524 \| 0.7432 \| \| 0.0473 \| 9.5165 \| 2598 \| 0.5526 \| 0.3226 \| 0.5526 \| 0.7434 \| \| 0.0473 \| 9.5238 \| 2600 \| 0.5506 \| 0.3226 \| 0.5506 \| 0.7420 \| \| 0.0473 \| 9.5311 \| 2602 \| 0.5478 \| 0.3226 \| 0.5478 \| 0.7402 \| \| 0.0473 \| 9.5385 \| 2604 \| 0.5454 \| 0.3226 \| 0.5454 \| 0.7385 \| \| 0.0473 \| 9.5458 \| 2606 \| 0.5432 \| 0.3226 \| 0.5432 \| 0.7370 \| \| 0.0473 \| 9.5531 \| 2608 \| 0.5422 \| 0.3226 \| 0.5422 \| 0.7364 \| \| 0.0473 \| 9.5604 \| 2610 \| 0.5400 \| 0.3607 \| 0.5400 \| 0.7348 \| \| 0.0473 \| 9.5678 \| 2612 \| 0.5376 \| 0.3607 \| 0.5376 \| 0.7332 \| \| 0.0473 \| 9.5751 \| 2614 \| 0.5358 \| 0.3607 \| 0.5358 \| 0.7320 \| \| 0.0473 \| 9.5824 \| 2616 \| 0.5338 \| 0.3607 \| 0.5338 \| 0.7306 \| \| 0.0473 \| 9.5897 \| 2618 \| 0.5327 \| 0.3607 \| 0.5327 \| 0.7298 \| \| 0.0473 \| 9.5971 \| 2620 \| 0.5313 \| 0.3607 \| 0.5313 \| 0.7289 \| \| 0.0473 \| 9.6044 \| 2622 \| 0.5309 \| 0.3607 \| 0.5309 \| 0.7286 \| \| 0.0473 \| 9.6117 \| 2624 \| 0.5304 \| 0.3607 \| 0.5304 \| 0.7283 \| \| 0.0473 \| 9.6190 \| 2626 \| 0.5296 \| 0.3607 \| 0.5296 \| 0.7278 \| \| 0.0473 \| 9.6264 \| 2628 \| 0.5293 \| 0.3607 \| 0.5293 \| 0.7275 \| \| 0.0473 \| 9.6337 \| 2630 \| 0.5291 \| 0.3607 \| 0.5291 \| 0.7274 \| \| 0.0473 \| 9.6410 \| 2632 \| 0.5293 \| 0.3607 \| 0.5293 \| 0.7275 \| \| 0.0473 \| 9.6484 \| 2634 \| 0.5293 \| 0.3607 \| 0.5293 \| 0.7276 \| \| 0.0473 \| 9.6557 \| 2636 \| 0.5295 \| 0.3607 \| 0.5295 \| 0.7276 \| \| 0.0473 \| 9.6630 \| 2638 \| 0.5301 \| 0.3607 \| 0.5301 \| 0.7281 \| \| 0.0473 \| 9.6703 \| 2640 \| 0.5303 \| 0.3607 \| 0.5303 \| 0.7282 \| \| 0.0473 \| 9.6777 \| 2642 \| 0.5308 \| 0.3607 \| 0.5308 \| 0.7286 \| \| 0.0473 \| 9.6850 \| 2644 \| 0.5316 \| 0.3607 \| 0.5316 \| 0.7291 \| \| 0.0473 \| 9.6923 \| 2646 \| 0.5322 \| 0.3607 \| 0.5322 \| 0.7295 \| \| 0.0473 \| 9.6996 \| 2648 \| 0.5325 \| 0.3607 \| 0.5325 \| 0.7297 \| \| 0.0473 \| 9.7070 \| 2650 \| 0.5333 \| 0.3607 \| 0.5333 \| 0.7303 \| \| 0.0473 \| 9.7143 \| 2652 \| 0.5337 \| 0.3607 \| 0.5337 \| 0.7306 \| \| 0.0473 \| 9.7216 \| 2654 \| 0.5338 \| 0.3607 \| 0.5338 \| 0.7306 \| \| 0.0473 \| 9.7289 \| 2656 \| 0.5336 \| 0.3607 \| 0.5336 \| 0.7305 \| \| 0.0473 \| 9.7363 \| 2658 \| 0.5331 \| 0.3607 \| 0.5331 \| 0.7301 \| \| 0.0473 \| 9.7436 \| 2660 \| 0.5324 \| 0.3607 \| 0.5324 \| 0.7296 \| \| 0.0473 \| 9.7509 \| 2662 \| 0.5320 \| 0.3607 \| 0.5320 \| 0.7294 \| \| 0.0473 \| 9.7582 \| 2664 \| 0.5319 \| 0.3607 \| 0.5319 \| 0.7293 \| \| 0.0473 \| 9.7656 \| 2666 \| 0.5318 \| 0.5 \| 0.5318 \| 0.7293 \| \| 0.0473 \| 9.7729 \| 2668 \| 0.5320 \| 0.5 \| 0.5320 \| 0.7294 \| \| 0.0473 \| 9.7802 \| 2670 \| 0.5320 \| 0.5 \| 0.5320 \| 0.7294 \| \| 0.0473 \| 9.7875 \| 2672 \| 0.5321 \| 0.5 \| 0.5321 \| 0.7295 \| \| 0.0473 \| 9.7949 \| 2674 \| 0.5321 \| 0.5 \| 0.5321 \| 0.7295 \| \| 0.0473 \| 9.8022 \| 2676 \| 0.5324 \| 0.5 \| 0.5324 \| 0.7297 \| \| 0.0473 \| 9.8095 \| 2678 \| 0.5327 \| 0.5 \| 0.5327 \| 0.7298 \| \| 0.0473 \| 9.8168 \| 2680 \| 0.5327 \| 0.5 \| 0.5327 \| 0.7299 \| \| 0.0473 \| 9.8242 \| 2682 \| 0.5324 \| 0.5 \| 0.5324 \| 0.7296 \| \| 0.0473 \| 9.8315 \| 2684 \| 0.5321 \| 0.5 \| 0.5321 \| 0.7295 \| \| 0.0473 \| 9.8388 \| 2686 \| 0.5321 \| 0.5 \| 0.5321 \| 0.7295 \| \| 0.0473 \| 9.8462 \| 2688 \| 0.5320 \| 0.5 \| 0.5320 \| 0.7294 \| \| 0.0473 \| 9.8535 \| 2690 \| 0.5320 \| 0.5 \| 0.5320 \| 0.7294 \| \| 0.0473 \| 9.8608 \| 2692 \| 0.5321 \| 0.5 \| 0.5321 \| 0.7295 \| \| 0.0473 \| 9.8681 \| 2694 \| 0.5322 \| 0.5 \| 0.5322 \| 0.7295 \| \| 0.0473 \| 9.8755 \| 2696 \| 0.5321 \| 0.5 \| 0.5321 \| 0.7295 \| \| 0.0473 \| 9.8828 \| 2698 \| 0.5321 \| 0.5 \| 0.5321 \| 0.7295 \| \| 0.0473 \| 9.8901 \| 2700 \| 0.5322 \| 0.5 \| 0.5322 \| 0.7295 \| \| 0.0473 \| 9.8974 \| 2702 \| 0.5322 \| 0.5 \| 0.5322 \| 0.7295 \| \| 0.0473 \| 9.9048 \| 2704 \| 0.5322 \| 0.5 \| 0.5322 \| 0.7295 \| \| 0.0473 \| 9.9121 \| 2706 \| 0.5322 \| 0.5 \| 0.5322 \| 0.7295 \| \| 0.0473 \| 9.9194 \| 2708 \| 0.5323 \| 0.5 \| 0.5323 \| 0.7296 \| \| 0.0473 \| 9.9267 \| 2710 \| 0.5325 \| 0.5 \| 0.5325 \| 0.7297 \| \| 0.0473 \| 9.9341 \| 2712 \| 0.5326 \| 0.5 \| 0.5326 \| 0.7298 \| \| 0.0473 \| 9.9414 \| 2714 \| 0.5328 \| 0.5 \| 0.5328 \| 0.7299 \| \| 0.0473 \| 9.9487 \| 2716 \| 0.5329 \| 0.5 \| 0.5329 \| 0.7300 \| \| 0.0473 \| 9.9560 \| 2718 \| 0.5330 \| 0.5 \| 0.5330 \| 0.7301 \| \| 0.0473 \| 9.9634 \| 2720 \| 0.5330 \| 0.5 \| 0.5330 \| 0.7301 \| \| 0.0473 \| 9.9707 \| 2722 \| 0.5330 \| 0.5 \| 0.5330 \| 0.7301 \| \| 0.0473 \| 9.9780 \| 2724 \| 0.5330 \| 0.5 \| 0.5330 \| 0.7301 \| \| 0.0473 \| 9.9853 \| 2726 \| 0.5330 \| 0.5 \| 0.5330 \| 0.7300 \| \| 0.0473 \| 9.9927 \| 2728 \| 0.5329 \| 0.5 \| 0.5329 \| 0.7300 \| \| 0.0473 \| 10.0 \| 2730 \| 0.5329 \| 0.5 \| 0.5329 \| 0.7300 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
eric10y/finetuning-sentiment-model-3000-samples	eric10y	2024-11-25T07:30:21Z	105	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T07:21:09Z	--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: finetuning-sentiment-model-3000-samples results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3
vitumafeni/tiny-crypto-sentiment-analysis	vitumafeni	2024-11-25T07:28:20Z	130	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T07:26:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
second-state/FLUX.1-Canny-dev-GGUF	second-state	2024-11-25T07:25:31Z	445	6	null	[ "gguf", "text-to-image", "image-generation", "flux", "en", "base_model:black-forest-labs/FLUX.1-Canny-dev", "base_model:quantized:black-forest-labs/FLUX.1-Canny-dev", "license:other", "region:us" ]	text-to-image	2024-11-25T02:50:33Z	--- base_model: black-forest-labs/FLUX.1-Canny-dev license: other license_name: flux-1-dev-non-commercial-license license_link: LICENSE.md model_creator: black-forest-labs model_name: FLUX.1-Canny-dev quantized_by: Second State Inc. language: - en tags: - text-to-image - image-generation - flux --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> > [!CAUTION] > T5 and Clip are still not provided in the original model # FLUX.1-Canny-dev-GGUF ## Original Model [black-forest-labs/FLUX.1-Canny-dev](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev) ## Run with LlamaEdge-StableDiffusion - Version: coming soon <!-- - Version: [0.1.4](https://github.com/LlamaEdge/sd-api-server/releases/tag/0.1.4) - Run as LlamaEdge service ```bash wasmedge --dir .:. sd-api-server.wasm \ --model-name flux1-canny-dev \ --diffusion-model flux1-canny-dev-Q4_0.gguf \ --vae ae.safetensors \ --clip-l clip_l.safetensors \ --t5xxl t5xxl-Q8_0.gguf ``` - Run with LoRA Assume that the LoRA model is located in the `lora-models` directory ```bash wasmedge --dir .:. \ --dir lora-models:lora-models \ sd-api-server.wasm \ --model-name flux1-canny-dev \ --diffusion-model flux1-canny-dev-Q4_0.gguf \ --vae ae.safetensors \ --clip-l clip_l.safetensors \ --t5xxl t5xxl-Q8_0.gguf \ --lora-model-dir lora-models ``` For details, see https://github.com/LlamaEdge/sd-api-server/blob/main/examples/flux_with_lora.md --> ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [ae.safetensors](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/ae.safetensors) \| f32 \| 32 \| 335 MB \| \| \| [flux1-canny-dev-Q2_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q2_K.gguf) \| Q2_K \| 2 \| 4.15 GB \| \| \| [flux1-canny-dev-Q3_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q3_K.gguf) \| Q3_K \| 3 \| 5.35 GB \| \| \| [flux1-canny-dev-Q4_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q4_0.gguf) \| Q4_0 \| 4 \| 6.93 GB \| \| \| [flux1-canny-dev-Q4_1.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q4_1.gguf) \| Q4_1 \| 4 \| 7.67 GB \| \| \| [flux1-canny-dev-Q4_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q4_K.gguf) \| Q4_K \| 4 \| 6.93 GB \| \| \| [flux1-canny-dev-Q5_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q5_0.gguf) \| Q5_0 \| 5 \| 8.40 GB \| \| \| [flux1-canny-dev-Q5_1.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q5_1.gguf) \| Q5_1 \| 5 \| 9.14 GB \| \| \| [flux1-canny-dev-Q8_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q8_0.gguf) \| Q8_0 \| 8 \| 12.6 GB \| \| \| [flux1-canny-dev.safetensors](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev.safetensors) \| f16 \| 16 \| 23.8 GB \| \| <!-- \| [clip_l-Q8_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/clip_l-Q8_0.gguf) \| Q8_0 \| 8 \| 131 MB \| \| \| [clip_l.safetensors](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/clip_l.safetensors) \| f16 \| 16 \| 246 MB \| \| \| [t5xxl-Q2_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl-Q2_K.gguf) \| Q2_K \| 2 \| 1.61 GB \| \| \| [t5xxl-Q3_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl-Q3_K.gguf) \| Q3_K \| 3 \| 2.10 GB \| \| \| [t5xxl-Q4_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl-Q4_0.gguf) \| Q4_0 \| 4 \| 2.75 GB \| \| \| [t5xxl-Q4_1.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl-Q4_1.gguf) \| Q4_0 \| 4 \| 3.06 GB \| \| \| [t5xxl-Q4_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl-Q4_K.gguf) \| Q4_K \| 4 \| 2.75 GB \| \| \| [t5xxl-Q5_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl-Q5_0.gguf) \| Q5_0 \| 5 \| 3.36 GB \| \| \| [t5xxl-Q5_1.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl-Q5_1.gguf) \| Q5_1 \| 5 \| 3.67 GB \| \| \| [t5xxl-Q8_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl-Q8_0.gguf) \| Q8_0 \| 8 \| 5.20 GB \| \| \| [t5xxl_fp16.safetensors](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/t5xxl_fp16.safetensors) \| f16 \| 16 \| 9.79 GB \| \| --> Quantized with stable-diffusion.cpp `master-c3eeb669`.
ankit5319/chronos-t5-small-fine-tuned	ankit5319	2024-11-25T07:22:04Z	174	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-11-25T07:21:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Shinyaaa/Travel-20-v1-on-RPC-10-v1	Shinyaaa	2024-11-25T07:20:42Z	102	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	feature-extraction	2024-11-25T07:20:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mlx-community/Llama-3.1-Tulu-3-8B-4bit	mlx-community	2024-11-25T07:19:51Z	81	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "en", "dataset:allenai/RLVR-GSM-MATH-IF-Mixed-Constraints", "base_model:allenai/Llama-3.1-Tulu-3-8B", "base_model:quantized:allenai/Llama-3.1-Tulu-3-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]	text-generation	2024-11-25T07:08:02Z	--- license: llama3.1 language: - en pipeline_tag: text-generation datasets: - allenai/RLVR-GSM-MATH-IF-Mixed-Constraints base_model: allenai/Llama-3.1-Tulu-3-8B library_name: transformers tags: - mlx --- # mlx-community/Llama-3.1-Tulu-3-8B-4bit The Model [mlx-community/Llama-3.1-Tulu-3-8B-4bit](https://huggingface.co/mlx-community/Llama-3.1-Tulu-3-8B-4bit) was converted to MLX format from [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) using mlx-lm version 0.20.0. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3.1-Tulu-3-8B-4bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```
MayBashendy/Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold0	MayBashendy	2024-11-25T07:19:11Z	161	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T07:06:32Z	--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold0 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold0 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6437 - Qwk: 0.3771 - Mse: 0.6437 - Rmse: 0.8023 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Qwk \| Mse \| Rmse \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:------:\|:------:\| \| No log \| 0.0082 \| 2 \| 3.8449 \| 0.0 \| 3.8449 \| 1.9608 \| \| No log \| 0.0165 \| 4 \| 2.5465 \| 0.0172 \| 2.5465 \| 1.5958 \| \| No log \| 0.0247 \| 6 \| 1.3961 \| 0.0491 \| 1.3961 \| 1.1816 \| \| No log \| 0.0329 \| 8 \| 1.3041 \| 0.0 \| 1.3041 \| 1.1420 \| \| No log \| 0.0412 \| 10 \| 1.1781 \| 0.0 \| 1.1781 \| 1.0854 \| \| No log \| 0.0494 \| 12 \| 1.2873 \| 0.0 \| 1.2873 \| 1.1346 \| \| No log \| 0.0576 \| 14 \| 1.6711 \| -0.0302 \| 1.6711 \| 1.2927 \| \| No log \| 0.0658 \| 16 \| 1.5391 \| 0.0 \| 1.5391 \| 1.2406 \| \| No log \| 0.0741 \| 18 \| 1.4694 \| 0.0491 \| 1.4694 \| 1.2122 \| \| No log \| 0.0823 \| 20 \| 1.5935 \| 0.0491 \| 1.5935 \| 1.2623 \| \| No log \| 0.0905 \| 22 \| 1.7652 \| -0.0096 \| 1.7652 \| 1.3286 \| \| No log \| 0.0988 \| 24 \| 1.9005 \| 0.0525 \| 1.9004 \| 1.3786 \| \| No log \| 0.1070 \| 26 \| 1.7812 \| -0.0328 \| 1.7812 \| 1.3346 \| \| No log \| 0.1152 \| 28 \| 1.6600 \| 0.0173 \| 1.6600 \| 1.2884 \| \| No log \| 0.1235 \| 30 \| 1.5788 \| 0.0491 \| 1.5788 \| 1.2565 \| \| No log \| 0.1317 \| 32 \| 1.4703 \| 0.0 \| 1.4703 \| 1.2126 \| \| No log \| 0.1399 \| 34 \| 1.2667 \| 0.0 \| 1.2667 \| 1.1255 \| \| No log \| 0.1481 \| 36 \| 1.0444 \| 0.0 \| 1.0444 \| 1.0220 \| \| No log \| 0.1564 \| 38 \| 0.8703 \| 0.0 \| 0.8703 \| 0.9329 \| \| No log \| 0.1646 \| 40 \| 0.7873 \| 0.0335 \| 0.7873 \| 0.8873 \| \| No log \| 0.1728 \| 42 \| 0.8284 \| 0.0335 \| 0.8284 \| 0.9102 \| \| No log \| 0.1811 \| 44 \| 0.9977 \| 0.0 \| 0.9977 \| 0.9988 \| \| No log \| 0.1893 \| 46 \| 1.3060 \| 0.0 \| 1.3060 \| 1.1428 \| \| No log \| 0.1975 \| 48 \| 1.4340 \| 0.0 \| 1.4340 \| 1.1975 \| \| No log \| 0.2058 \| 50 \| 1.3664 \| 0.0 \| 1.3664 \| 1.1689 \| \| No log \| 0.2140 \| 52 \| 1.2462 \| 0.0 \| 1.2462 \| 1.1163 \| \| No log \| 0.2222 \| 54 \| 1.1136 \| 0.0 \| 1.1136 \| 1.0553 \| \| No log \| 0.2305 \| 56 \| 1.2068 \| 0.0 \| 1.2068 \| 1.0986 \| \| No log \| 0.2387 \| 58 \| 1.1604 \| 0.0 \| 1.1604 \| 1.0772 \| \| No log \| 0.2469 \| 60 \| 0.9721 \| 0.0 \| 0.9721 \| 0.9860 \| \| No log \| 0.2551 \| 62 \| 0.8585 \| 0.0 \| 0.8585 \| 0.9265 \| \| No log \| 0.2634 \| 64 \| 0.8457 \| 0.0 \| 0.8457 \| 0.9196 \| \| No log \| 0.2716 \| 66 \| 0.8333 \| 0.0 \| 0.8333 \| 0.9129 \| \| No log \| 0.2798 \| 68 \| 0.9003 \| 0.0 \| 0.9003 \| 0.9489 \| \| No log \| 0.2881 \| 70 \| 1.0546 \| 0.0 \| 1.0546 \| 1.0269 \| \| No log \| 0.2963 \| 72 \| 1.1747 \| 0.0 \| 1.1747 \| 1.0838 \| \| No log \| 0.3045 \| 74 \| 1.2196 \| 0.0 \| 1.2196 \| 1.1044 \| \| No log \| 0.3128 \| 76 \| 1.1503 \| 0.0 \| 1.1503 \| 1.0725 \| \| No log \| 0.3210 \| 78 \| 1.0305 \| 0.0 \| 1.0305 \| 1.0151 \| \| No log \| 0.3292 \| 80 \| 0.9394 \| 0.0 \| 0.9394 \| 0.9692 \| \| No log \| 0.3374 \| 82 \| 0.9147 \| 0.0 \| 0.9147 \| 0.9564 \| \| No log \| 0.3457 \| 84 \| 0.9039 \| 0.0 \| 0.9039 \| 0.9507 \| \| No log \| 0.3539 \| 86 \| 0.9438 \| 0.0 \| 0.9438 \| 0.9715 \| \| No log \| 0.3621 \| 88 \| 1.1728 \| 0.0 \| 1.1728 \| 1.0830 \| \| No log \| 0.3704 \| 90 \| 1.5843 \| 0.125 \| 1.5843 \| 1.2587 \| \| No log \| 0.3786 \| 92 \| 1.5654 \| 0.125 \| 1.5654 \| 1.2512 \| \| No log \| 0.3868 \| 94 \| 1.2734 \| 0.0 \| 1.2734 \| 1.1284 \| \| No log \| 0.3951 \| 96 \| 0.9856 \| 0.0 \| 0.9856 \| 0.9928 \| \| No log \| 0.4033 \| 98 \| 0.7949 \| 0.1564 \| 0.7949 \| 0.8916 \| \| No log \| 0.4115 \| 100 \| 0.7168 \| 0.1192 \| 0.7168 \| 0.8466 \| \| No log \| 0.4198 \| 102 \| 0.7223 \| 0.2150 \| 0.7223 \| 0.8499 \| \| No log \| 0.4280 \| 104 \| 0.8128 \| 0.1747 \| 0.8128 \| 0.9015 \| \| No log \| 0.4362 \| 106 \| 0.7921 \| 0.1747 \| 0.7921 \| 0.8900 \| \| No log \| 0.4444 \| 108 \| 0.7928 \| 0.3046 \| 0.7928 \| 0.8904 \| \| No log \| 0.4527 \| 110 \| 0.7358 \| 0.3046 \| 0.7358 \| 0.8578 \| \| No log \| 0.4609 \| 112 \| 0.6887 \| 0.3695 \| 0.6887 \| 0.8299 \| \| No log \| 0.4691 \| 114 \| 0.7364 \| 0.1747 \| 0.7364 \| 0.8581 \| \| No log \| 0.4774 \| 116 \| 0.7567 \| 0.3197 \| 0.7567 \| 0.8699 \| \| No log \| 0.4856 \| 118 \| 0.8435 \| 0.1564 \| 0.8435 \| 0.9184 \| \| No log \| 0.4938 \| 120 \| 0.9194 \| 0.0 \| 0.9194 \| 0.9589 \| \| No log \| 0.5021 \| 122 \| 0.9321 \| 0.0 \| 0.9321 \| 0.9654 \| \| No log \| 0.5103 \| 124 \| 0.8488 \| 0.2821 \| 0.8488 \| 0.9213 \| \| No log \| 0.5185 \| 126 \| 0.7542 \| 0.2150 \| 0.7542 \| 0.8684 \| \| No log \| 0.5267 \| 128 \| 0.7265 \| 0.0339 \| 0.7265 \| 0.8524 \| \| No log \| 0.5350 \| 130 \| 0.7171 \| 0.1141 \| 0.7171 \| 0.8468 \| \| No log \| 0.5432 \| 132 \| 0.7488 \| 0.0503 \| 0.7488 \| 0.8654 \| \| No log \| 0.5514 \| 134 \| 0.7621 \| 0.1141 \| 0.7621 \| 0.8730 \| \| No log \| 0.5597 \| 136 \| 0.8116 \| -0.0426 \| 0.8116 \| 0.9009 \| \| No log \| 0.5679 \| 138 \| 0.8358 \| -0.2466 \| 0.8358 \| 0.9142 \| \| No log \| 0.5761 \| 140 \| 0.8469 \| -0.1099 \| 0.8469 \| 0.9203 \| \| No log \| 0.5844 \| 142 \| 0.8581 \| -0.0769 \| 0.8581 \| 0.9263 \| \| No log \| 0.5926 \| 144 \| 0.8125 \| -0.1004 \| 0.8125 \| 0.9014 \| \| No log \| 0.6008 \| 146 \| 0.7305 \| -0.0048 \| 0.7305 \| 0.8547 \| \| No log \| 0.6091 \| 148 \| 0.6800 \| 0.1340 \| 0.6800 \| 0.8246 \| \| No log \| 0.6173 \| 150 \| 0.6528 \| 0.1558 \| 0.6528 \| 0.8079 \| \| No log \| 0.6255 \| 152 \| 0.6511 \| 0.0339 \| 0.6511 \| 0.8069 \| \| No log \| 0.6337 \| 154 \| 0.6960 \| -0.0048 \| 0.6960 \| 0.8342 \| \| No log \| 0.6420 \| 156 \| 0.6974 \| 0.0735 \| 0.6974 \| 0.8351 \| \| No log \| 0.6502 \| 158 \| 0.7287 \| 0.0916 \| 0.7287 \| 0.8536 \| \| No log \| 0.6584 \| 160 \| 0.7260 \| 0.0916 \| 0.7260 \| 0.8521 \| \| No log \| 0.6667 \| 162 \| 0.7071 \| 0.0916 \| 0.7071 \| 0.8409 \| \| No log \| 0.6749 \| 164 \| 0.6744 \| 0.0916 \| 0.6744 \| 0.8212 \| \| No log \| 0.6831 \| 166 \| 0.6773 \| -0.0678 \| 0.6773 \| 0.8230 \| \| No log \| 0.6914 \| 168 \| 0.7038 \| 0.0567 \| 0.7038 \| 0.8389 \| \| No log \| 0.6996 \| 170 \| 0.7098 \| -0.1053 \| 0.7098 \| 0.8425 \| \| No log \| 0.7078 \| 172 \| 0.7049 \| 0.0099 \| 0.7049 \| 0.8396 \| \| No log \| 0.7160 \| 174 \| 0.7244 \| 0.0916 \| 0.7244 \| 0.8511 \| \| No log \| 0.7243 \| 176 \| 0.6981 \| 0.2759 \| 0.6981 \| 0.8355 \| \| No log \| 0.7325 \| 178 \| 0.6879 \| 0.1962 \| 0.6879 \| 0.8294 \| \| No log \| 0.7407 \| 180 \| 0.6943 \| 0.1560 \| 0.6943 \| 0.8333 \| \| No log \| 0.7490 \| 182 \| 0.6909 \| 0.2373 \| 0.6909 \| 0.8312 \| \| No log \| 0.7572 \| 184 \| 0.6780 \| 0.0916 \| 0.6780 \| 0.8234 \| \| No log \| 0.7654 \| 186 \| 0.6530 \| 0.0503 \| 0.6530 \| 0.8081 \| \| No log \| 0.7737 \| 188 \| 0.6458 \| 0.1560 \| 0.6458 \| 0.8036 \| \| No log \| 0.7819 \| 190 \| 0.7552 \| 0.3046 \| 0.7552 \| 0.8690 \| \| No log \| 0.7901 \| 192 \| 0.8491 \| 0.1026 \| 0.8491 \| 0.9215 \| \| No log \| 0.7984 \| 194 \| 0.7692 \| 0.2150 \| 0.7692 \| 0.8770 \| \| No log \| 0.8066 \| 196 \| 0.7988 \| 0.0503 \| 0.7988 \| 0.8937 \| \| No log \| 0.8148 \| 198 \| 0.9089 \| 0.2186 \| 0.9089 \| 0.9534 \| \| No log \| 0.8230 \| 200 \| 0.9674 \| 0.0909 \| 0.9674 \| 0.9836 \| \| No log \| 0.8313 \| 202 \| 0.8486 \| 0.1600 \| 0.8486 \| 0.9212 \| \| No log \| 0.8395 \| 204 \| 0.7004 \| 0.0735 \| 0.7004 \| 0.8369 \| \| No log \| 0.8477 \| 206 \| 0.7193 \| 0.1755 \| 0.7193 \| 0.8481 \| \| No log \| 0.8560 \| 208 \| 0.7631 \| 0.2889 \| 0.7631 \| 0.8735 \| \| No log \| 0.8642 \| 210 \| 0.7775 \| 0.1563 \| 0.7775 \| 0.8818 \| \| No log \| 0.8724 \| 212 \| 0.6998 \| 0.2553 \| 0.6998 \| 0.8366 \| \| No log \| 0.8807 \| 214 \| 0.6873 \| 0.2373 \| 0.6873 \| 0.8290 \| \| No log \| 0.8889 \| 216 \| 0.7307 \| 0.0916 \| 0.7307 \| 0.8548 \| \| No log \| 0.8971 \| 218 \| 0.7571 \| 0.0503 \| 0.7571 \| 0.8701 \| \| No log \| 0.9053 \| 220 \| 0.7653 \| 0.1356 \| 0.7653 \| 0.8748 \| \| No log \| 0.9136 \| 222 \| 0.8030 \| 0.1356 \| 0.8030 \| 0.8961 \| \| No log \| 0.9218 \| 224 \| 0.8217 \| 0.1765 \| 0.8217 \| 0.9065 \| \| No log \| 0.9300 \| 226 \| 0.8519 \| 0.0099 \| 0.8519 \| 0.9230 \| \| No log \| 0.9383 \| 228 \| 0.8988 \| 0.1793 \| 0.8988 \| 0.9480 \| \| No log \| 0.9465 \| 230 \| 0.8815 \| 0.1793 \| 0.8815 \| 0.9389 \| \| No log \| 0.9547 \| 232 \| 0.9057 \| 0.1793 \| 0.9057 \| 0.9517 \| \| No log \| 0.9630 \| 234 \| 0.9843 \| 0.0557 \| 0.9843 \| 0.9921 \| \| No log \| 0.9712 \| 236 \| 1.0257 \| 0.0833 \| 1.0257 \| 1.0128 \| \| No log \| 0.9794 \| 238 \| 0.9546 \| 0.2186 \| 0.9546 \| 0.9770 \| \| No log \| 0.9877 \| 240 \| 0.8870 \| 0.0503 \| 0.8870 \| 0.9418 \| \| No log \| 0.9959 \| 242 \| 0.8678 \| 0.0916 \| 0.8678 \| 0.9315 \| \| No log \| 1.0041 \| 244 \| 0.8923 \| 0.0916 \| 0.8923 \| 0.9446 \| \| No log \| 1.0123 \| 246 \| 0.8906 \| 0.0916 \| 0.8906 \| 0.9437 \| \| No log \| 1.0206 \| 248 \| 0.8417 \| 0.0916 \| 0.8417 \| 0.9175 \| \| No log \| 1.0288 \| 250 \| 0.8226 \| 0.0099 \| 0.8226 \| 0.9070 \| \| No log \| 1.0370 \| 252 \| 0.8301 \| 0.0099 \| 0.8301 \| 0.9111 \| \| No log \| 1.0453 \| 254 \| 0.8631 \| 0.0916 \| 0.8631 \| 0.9290 \| \| No log \| 1.0535 \| 256 \| 0.9591 \| 0.0258 \| 0.9591 \| 0.9793 \| \| No log \| 1.0617 \| 258 \| 1.0055 \| 0.0 \| 1.0055 \| 1.0028 \| \| No log \| 1.0700 \| 260 \| 0.9585 \| 0.0258 \| 0.9585 \| 0.9790 \| \| No log \| 1.0782 \| 262 \| 0.9307 \| 0.0916 \| 0.9307 \| 0.9647 \| \| No log \| 1.0864 \| 264 \| 0.9216 \| 0.2186 \| 0.9216 \| 0.9600 \| \| No log \| 1.0947 \| 266 \| 0.8643 \| 0.1340 \| 0.8643 \| 0.9297 \| \| No log \| 1.1029 \| 268 \| 0.7913 \| 0.0916 \| 0.7913 \| 0.8895 \| \| No log \| 1.1111 \| 270 \| 0.7503 \| 0.0916 \| 0.7503 \| 0.8662 \| \| No log \| 1.1193 \| 272 \| 0.7356 \| 0.0916 \| 0.7356 \| 0.8577 \| \| No log \| 1.1276 \| 274 \| 0.7775 \| -0.0294 \| 0.7775 \| 0.8818 \| \| No log \| 1.1358 \| 276 \| 0.8031 \| 0.0099 \| 0.8031 \| 0.8961 \| \| No log \| 1.1440 \| 278 \| 0.8682 \| 0.1793 \| 0.8682 \| 0.9318 \| \| No log \| 1.1523 \| 280 \| 0.9049 \| 0.1793 \| 0.9049 \| 0.9513 \| \| No log \| 1.1605 \| 282 \| 0.9445 \| 0.25 \| 0.9445 \| 0.9718 \| \| No log \| 1.1687 \| 284 \| 1.0603 \| 0.1463 \| 1.0603 \| 1.0297 \| \| No log \| 1.1770 \| 286 \| 1.0686 \| 0.1738 \| 1.0686 \| 1.0337 \| \| No log \| 1.1852 \| 288 \| 0.9538 \| 0.1072 \| 0.9538 \| 0.9766 \| \| No log \| 1.1934 \| 290 \| 0.8921 \| 0.1793 \| 0.8921 \| 0.9445 \| \| No log \| 1.2016 \| 292 \| 0.8276 \| 0.1793 \| 0.8276 \| 0.9097 \| \| No log \| 1.2099 \| 294 \| 0.7594 \| 0.2533 \| 0.7594 \| 0.8715 \| \| No log \| 1.2181 \| 296 \| 0.7813 \| 0.0785 \| 0.7813 \| 0.8839 \| \| No log \| 1.2263 \| 298 \| 0.8477 \| 0.1370 \| 0.8477 \| 0.9207 \| \| No log \| 1.2346 \| 300 \| 0.8624 \| 0.1773 \| 0.8624 \| 0.9286 \| \| No log \| 1.2428 \| 302 \| 0.8222 \| 0.1168 \| 0.8222 \| 0.9067 \| \| No log \| 1.2510 \| 304 \| 0.7986 \| 0.3318 \| 0.7986 \| 0.8937 \| \| No log \| 1.2593 \| 306 \| 0.9415 \| 0.0557 \| 0.9415 \| 0.9703 \| \| No log \| 1.2675 \| 308 \| 1.1720 \| 0.1649 \| 1.1720 \| 1.0826 \| \| No log \| 1.2757 \| 310 \| 1.2318 \| 0.1649 \| 1.2318 \| 1.1099 \| \| No log \| 1.2840 \| 312 \| 1.2591 \| 0.1276 \| 1.2591 \| 1.1221 \| \| No log \| 1.2922 \| 314 \| 1.1760 \| 0.0440 \| 1.1760 \| 1.0844 \| \| No log \| 1.3004 \| 316 \| 1.0025 \| 0.0 \| 1.0025 \| 1.0013 \| \| No log \| 1.3086 \| 318 \| 0.8959 \| 0.1793 \| 0.8959 \| 0.9465 \| \| No log \| 1.3169 \| 320 \| 0.9102 \| 0.0099 \| 0.9102 \| 0.9540 \| \| No log \| 1.3251 \| 322 \| 0.8541 \| 0.0503 \| 0.8541 \| 0.9242 \| \| No log \| 1.3333 \| 324 \| 0.7833 \| 0.0503 \| 0.7833 \| 0.8850 \| \| No log \| 1.3416 \| 326 \| 0.7566 \| 0.0916 \| 0.7566 \| 0.8698 \| \| No log \| 1.3498 \| 328 \| 0.7682 \| 0.0258 \| 0.7682 \| 0.8765 \| \| No log \| 1.3580 \| 330 \| 0.7707 \| 0.0099 \| 0.7707 \| 0.8779 \| \| No log \| 1.3663 \| 332 \| 0.7559 \| -0.0678 \| 0.7559 \| 0.8694 \| \| No log \| 1.3745 \| 334 \| 0.7305 \| -0.0048 \| 0.7305 \| 0.8547 \| \| No log \| 1.3827 \| 336 \| 0.6950 \| -0.0294 \| 0.6950 \| 0.8336 \| \| No log \| 1.3909 \| 338 \| 0.7029 \| 0.0503 \| 0.7029 \| 0.8384 \| \| No log \| 1.3992 \| 340 \| 0.7535 \| 0.2186 \| 0.7535 \| 0.8680 \| \| No log \| 1.4074 \| 342 \| 0.7803 \| 0.2186 \| 0.7803 \| 0.8834 \| \| No log \| 1.4156 \| 344 \| 0.7621 \| 0.1793 \| 0.7621 \| 0.8730 \| \| No log \| 1.4239 \| 346 \| 0.7755 \| 0.1034 \| 0.7755 \| 0.8806 \| \| No log \| 1.4321 \| 348 \| 0.7882 \| 0.2696 \| 0.7882 \| 0.8878 \| \| No log \| 1.4403 \| 350 \| 0.7956 \| 0.2533 \| 0.7956 \| 0.8919 \| \| No log \| 1.4486 \| 352 \| 0.7774 \| 0.2154 \| 0.7774 \| 0.8817 \| \| No log \| 1.4568 \| 354 \| 0.7527 \| 0.2533 \| 0.7527 \| 0.8676 \| \| No log \| 1.4650 \| 356 \| 0.7524 \| 0.1409 \| 0.7524 \| 0.8674 \| \| No log \| 1.4733 \| 358 \| 0.7902 \| 0.2186 \| 0.7902 \| 0.8889 \| \| No log \| 1.4815 \| 360 \| 0.8423 \| 0.1398 \| 0.8423 \| 0.9177 \| \| No log \| 1.4897 \| 362 \| 0.8313 \| 0.1398 \| 0.8313 \| 0.9118 \| \| No log \| 1.4979 \| 364 \| 0.8102 \| 0.2186 \| 0.8102 \| 0.9001 \| \| No log \| 1.5062 \| 366 \| 0.8309 \| 0.1000 \| 0.8309 \| 0.9116 \| \| No log \| 1.5144 \| 368 \| 0.8751 \| 0.1398 \| 0.8751 \| 0.9355 \| \| No log \| 1.5226 \| 370 \| 0.8400 \| 0.2186 \| 0.8400 \| 0.9165 \| \| No log \| 1.5309 \| 372 \| 0.7666 \| 0.1034 \| 0.7666 \| 0.8755 \| \| No log \| 1.5391 \| 374 \| 0.7353 \| 0.2533 \| 0.7353 \| 0.8575 \| \| No log \| 1.5473 \| 376 \| 0.7245 \| 0.2533 \| 0.7245 \| 0.8512 \| \| No log \| 1.5556 \| 378 \| 0.7324 \| 0.1034 \| 0.7324 \| 0.8558 \| \| No log \| 1.5638 \| 380 \| 0.7641 \| 0.1409 \| 0.7641 \| 0.8741 \| \| No log \| 1.5720 \| 382 \| 0.8041 \| 0.1000 \| 0.8041 \| 0.8967 \| \| No log \| 1.5802 \| 384 \| 0.8160 \| 0.1398 \| 0.8160 \| 0.9033 \| \| No log \| 1.5885 \| 386 \| 0.8120 \| 0.1398 \| 0.8120 \| 0.9011 \| \| No log \| 1.5967 \| 388 \| 0.8348 \| 0.1398 \| 0.8348 \| 0.9137 \| \| No log \| 1.6049 \| 390 \| 0.8076 \| 0.1398 \| 0.8076 \| 0.8987 \| \| No log \| 1.6132 \| 392 \| 0.7741 \| 0.1398 \| 0.7741 \| 0.8798 \| \| No log \| 1.6214 \| 394 \| 0.7262 \| -0.0418 \| 0.7262 \| 0.8522 \| \| No log \| 1.6296 \| 396 \| 0.6940 \| -0.0153 \| 0.6940 \| 0.8330 \| \| No log \| 1.6379 \| 398 \| 0.6703 \| 0.0099 \| 0.6703 \| 0.8187 \| \| No log \| 1.6461 \| 400 \| 0.6676 \| 0.0099 \| 0.6676 \| 0.8170 \| \| No log \| 1.6543 \| 402 \| 0.6581 \| 0.0099 \| 0.6581 \| 0.8112 \| \| No log \| 1.6626 \| 404 \| 0.6687 \| -0.0294 \| 0.6687 \| 0.8177 \| \| No log \| 1.6708 \| 406 \| 0.7005 \| -0.0294 \| 0.7005 \| 0.8369 \| \| No log \| 1.6790 \| 408 \| 0.7436 \| 0.0828 \| 0.7436 \| 0.8623 \| \| No log \| 1.6872 \| 410 \| 0.7897 \| 0.1000 \| 0.7897 \| 0.8886 \| \| No log \| 1.6955 \| 412 \| 0.8040 \| 0.0612 \| 0.8040 \| 0.8967 \| \| No log \| 1.7037 \| 414 \| 0.8163 \| 0.0612 \| 0.8163 \| 0.9035 \| \| No log \| 1.7119 \| 416 \| 0.8136 \| 0.0233 \| 0.8136 \| 0.9020 \| \| No log \| 1.7202 \| 418 \| 0.8150 \| 0.0233 \| 0.8150 \| 0.9028 \| \| No log \| 1.7284 \| 420 \| 0.8136 \| 0.0233 \| 0.8136 \| 0.9020 \| \| No log \| 1.7366 \| 422 \| 0.7821 \| 0.0233 \| 0.7821 \| 0.8844 \| \| No log \| 1.7449 \| 424 \| 0.7412 \| -0.0553 \| 0.7412 \| 0.8609 \| \| No log \| 1.7531 \| 426 \| 0.7101 \| -0.0294 \| 0.7101 \| 0.8427 \| \| No log \| 1.7613 \| 428 \| 0.6934 \| -0.0294 \| 0.6934 \| 0.8327 \| \| No log \| 1.7695 \| 430 \| 0.7020 \| -0.0294 \| 0.7020 \| 0.8378 \| \| No log \| 1.7778 \| 432 \| 0.7327 \| 0.0828 \| 0.7327 \| 0.8560 \| \| No log \| 1.7860 \| 434 \| 0.7482 \| 0.0828 \| 0.7482 \| 0.8650 \| \| No log \| 1.7942 \| 436 \| 0.7700 \| 0.1209 \| 0.7700 \| 0.8775 \| \| No log \| 1.8025 \| 438 \| 0.7710 \| 0.0828 \| 0.7710 \| 0.8780 \| \| No log \| 1.8107 \| 440 \| 0.7464 \| -0.0553 \| 0.7464 \| 0.8639 \| \| No log \| 1.8189 \| 442 \| 0.7121 \| -0.0943 \| 0.7121 \| 0.8438 \| \| No log \| 1.8272 \| 444 \| 0.6957 \| -0.0294 \| 0.6957 \| 0.8341 \| \| No log \| 1.8354 \| 446 \| 0.6885 \| 0.0339 \| 0.6885 \| 0.8298 \| \| No log \| 1.8436 \| 448 \| 0.7024 \| -0.0153 \| 0.7024 \| 0.8381 \| \| No log \| 1.8519 \| 450 \| 0.7452 \| 0.1600 \| 0.7452 \| 0.8632 \| \| No log \| 1.8601 \| 452 \| 0.7658 \| 0.1398 \| 0.7658 \| 0.8751 \| \| No log \| 1.8683 \| 454 \| 0.7684 \| 0.1398 \| 0.7684 \| 0.8766 \| \| No log \| 1.8765 \| 456 \| 0.7694 \| 0.1398 \| 0.7694 \| 0.8772 \| \| No log \| 1.8848 \| 458 \| 0.7904 \| 0.1398 \| 0.7904 \| 0.8891 \| \| No log \| 1.8930 \| 460 \| 0.7767 \| 0.1600 \| 0.7767 \| 0.8813 \| \| No log \| 1.9012 \| 462 \| 0.7151 \| 0.2921 \| 0.7151 \| 0.8457 \| \| No log \| 1.9095 \| 464 \| 0.7042 \| 0.1783 \| 0.7042 \| 0.8391 \| \| No log \| 1.9177 \| 466 \| 0.7133 \| 0.1783 \| 0.7133 \| 0.8446 \| \| No log \| 1.9259 \| 468 \| 0.7119 \| 0.1783 \| 0.7119 \| 0.8437 \| \| No log \| 1.9342 \| 470 \| 0.6935 \| 0.2154 \| 0.6935 \| 0.8328 \| \| No log \| 1.9424 \| 472 \| 0.7027 \| 0.2921 \| 0.7027 \| 0.8383 \| \| No log \| 1.9506 \| 474 \| 0.7269 \| 0.3724 \| 0.7269 \| 0.8526 \| \| No log \| 1.9588 \| 476 \| 0.7440 \| 0.2186 \| 0.7440 \| 0.8626 \| \| No log \| 1.9671 \| 478 \| 0.7271 \| 0.2588 \| 0.7271 \| 0.8527 \| \| No log \| 1.9753 \| 480 \| 0.6672 \| 0.2186 \| 0.6672 \| 0.8169 \| \| No log \| 1.9835 \| 482 \| 0.5915 \| 0.3467 \| 0.5915 \| 0.7691 \| \| No log \| 1.9918 \| 484 \| 0.5644 \| 0.1560 \| 0.5644 \| 0.7512 \| \| No log \| 2.0 \| 486 \| 0.5817 \| 0.2329 \| 0.5817 \| 0.7627 \| \| No log \| 2.0082 \| 488 \| 0.6373 \| 0.2500 \| 0.6373 \| 0.7983 \| \| No log \| 2.0165 \| 490 \| 0.6611 \| 0.2500 \| 0.6611 \| 0.8131 \| \| No log \| 2.0247 \| 492 \| 0.6433 \| 0.2500 \| 0.6433 \| 0.8021 \| \| No log \| 2.0329 \| 494 \| 0.6117 \| 0.2889 \| 0.6117 \| 0.7821 \| \| No log \| 2.0412 \| 496 \| 0.5556 \| 0.2725 \| 0.5556 \| 0.7454 \| \| No log \| 2.0494 \| 498 \| 0.5430 \| 0.2794 \| 0.5430 \| 0.7369 \| \| 0.4608 \| 2.0576 \| 500 \| 0.5797 \| 0.1985 \| 0.5797 \| 0.7614 \| \| 0.4608 \| 2.0658 \| 502 \| 0.5947 \| 0.1340 \| 0.5947 \| 0.7712 \| \| 0.4608 \| 2.0741 \| 504 \| 0.5880 \| 0.1558 \| 0.5880 \| 0.7668 \| \| 0.4608 \| 2.0823 \| 506 \| 0.5929 \| 0.2794 \| 0.5929 \| 0.7700 \| \| 0.4608 \| 2.0905 \| 508 \| 0.5988 \| 0.2794 \| 0.5988 \| 0.7738 \| \| 0.4608 \| 2.0988 \| 510 \| 0.6130 \| 0.1962 \| 0.6130 \| 0.7829 \| \| 0.4608 \| 2.1070 \| 512 \| 0.6189 \| 0.2373 \| 0.6189 \| 0.7867 \| \| 0.4608 \| 2.1152 \| 514 \| 0.6166 \| 0.2794 \| 0.6166 \| 0.7852 \| \| 0.4608 \| 2.1235 \| 516 \| 0.6265 \| 0.3318 \| 0.6265 \| 0.7915 \| \| 0.4608 \| 2.1317 \| 518 \| 0.6417 \| 0.3333 \| 0.6417 \| 0.8011 \| \| 0.4608 \| 2.1399 \| 520 \| 0.6221 \| 0.3811 \| 0.6221 \| 0.7887 \| \| 0.4608 \| 2.1481 \| 522 \| 0.6069 \| 0.4740 \| 0.6069 \| 0.7791 \| \| 0.4608 \| 2.1564 \| 524 \| 0.5840 \| 0.4740 \| 0.5840 \| 0.7642 \| \| 0.4608 \| 2.1646 \| 526 \| 0.5537 \| 0.4394 \| 0.5537 \| 0.7441 \| \| 0.4608 \| 2.1728 \| 528 \| 0.5402 \| 0.4394 \| 0.5402 \| 0.7350 \| \| 0.4608 \| 2.1811 \| 530 \| 0.5344 \| 0.4394 \| 0.5344 \| 0.7311 \| \| 0.4608 \| 2.1893 \| 532 \| 0.5366 \| 0.4394 \| 0.5366 \| 0.7325 \| \| 0.4608 \| 2.1975 \| 534 \| 0.5417 \| 0.3467 \| 0.5417 \| 0.7360 \| \| 0.4608 \| 2.2058 \| 536 \| 0.5570 \| 0.3077 \| 0.5570 \| 0.7463 \| \| 0.4608 \| 2.2140 \| 538 \| 0.5678 \| 0.3077 \| 0.5678 \| 0.7535 \| \| 0.4608 \| 2.2222 \| 540 \| 0.5726 \| 0.4394 \| 0.5726 \| 0.7567 \| \| 0.4608 \| 2.2305 \| 542 \| 0.5863 \| 0.4394 \| 0.5863 \| 0.7657 \| \| 0.4608 \| 2.2387 \| 544 \| 0.5828 \| 0.4394 \| 0.5828 \| 0.7634 \| \| 0.4608 \| 2.2469 \| 546 \| 0.5640 \| 0.4394 \| 0.5640 \| 0.7510 \| \| 0.4608 \| 2.2551 \| 548 \| 0.5501 \| 0.3077 \| 0.5501 \| 0.7417 \| \| 0.4608 \| 2.2634 \| 550 \| 0.5549 \| 0.3077 \| 0.5549 \| 0.7449 \| \| 0.4608 \| 2.2716 \| 552 \| 0.5648 \| 0.3077 \| 0.5648 \| 0.7515 \| \| 0.4608 \| 2.2798 \| 554 \| 0.5753 \| 0.3077 \| 0.5753 \| 0.7585 \| \| 0.4608 \| 2.2881 \| 556 \| 0.5908 \| 0.3318 \| 0.5908 \| 0.7686 \| \| 0.4608 \| 2.2963 \| 558 \| 0.6116 \| 0.3724 \| 0.6116 \| 0.7821 \| \| 0.4608 \| 2.3045 \| 560 \| 0.6260 \| 0.3724 \| 0.6260 \| 0.7912 \| \| 0.4608 \| 2.3128 \| 562 \| 0.6291 \| 0.3724 \| 0.6291 \| 0.7931 \| \| 0.4608 \| 2.3210 \| 564 \| 0.6709 \| 0.3724 \| 0.6709 \| 0.8191 \| \| 0.4608 \| 2.3292 \| 566 \| 0.6848 \| 0.3724 \| 0.6848 \| 0.8275 \| \| 0.4608 \| 2.3374 \| 568 \| 0.6502 \| 0.3724 \| 0.6502 \| 0.8063 \| \| 0.4608 \| 2.3457 \| 570 \| 0.6072 \| 0.3724 \| 0.6072 \| 0.7792 \| \| 0.4608 \| 2.3539 \| 572 \| 0.5894 \| 0.1765 \| 0.5894 \| 0.7678 \| \| 0.4608 \| 2.3621 \| 574 \| 0.5911 \| 0.1560 \| 0.5911 \| 0.7688 \| \| 0.4608 \| 2.3704 \| 576 \| 0.5899 \| 0.1962 \| 0.5899 \| 0.7680 \| \| 0.4608 \| 2.3786 \| 578 \| 0.5935 \| 0.1765 \| 0.5935 \| 0.7704 \| \| 0.4608 \| 2.3868 \| 580 \| 0.6156 \| 0.2613 \| 0.6156 \| 0.7846 \| \| 0.4608 \| 2.3951 \| 582 \| 0.6616 \| 0.3724 \| 0.6616 \| 0.8134 \| \| 0.4608 \| 2.4033 \| 584 \| 0.6960 \| 0.2186 \| 0.6960 \| 0.8343 \| \| 0.4608 \| 2.4115 \| 586 \| 0.6885 \| 0.2186 \| 0.6885 \| 0.8297 \| \| 0.4608 \| 2.4198 \| 588 \| 0.6483 \| 0.3724 \| 0.6483 \| 0.8052 \| \| 0.4608 \| 2.4280 \| 590 \| 0.6295 \| 0.2613 \| 0.6295 \| 0.7934 \| \| 0.4608 \| 2.4362 \| 592 \| 0.6179 \| 0.2794 \| 0.6179 \| 0.7861 \| \| 0.4608 \| 2.4444 \| 594 \| 0.6188 \| 0.1962 \| 0.6188 \| 0.7866 \| \| 0.4608 \| 2.4527 \| 596 \| 0.6167 \| 0.1962 \| 0.6167 \| 0.7853 \| \| 0.4608 \| 2.4609 \| 598 \| 0.6165 \| 0.2373 \| 0.6165 \| 0.7851 \| \| 0.4608 \| 2.4691 \| 600 \| 0.6327 \| 0.1558 \| 0.6327 \| 0.7954 \| \| 0.4608 \| 2.4774 \| 602 \| 0.6478 \| 0.2186 \| 0.6478 \| 0.8049 \| \| 0.4608 \| 2.4856 \| 604 \| 0.6660 \| 0.2186 \| 0.6660 \| 0.8161 \| \| 0.4608 \| 2.4938 \| 606 \| 0.6790 \| 0.2186 \| 0.6790 \| 0.8240 \| \| 0.4608 \| 2.5021 \| 608 \| 0.6569 \| 0.2759 \| 0.6569 \| 0.8105 \| \| 0.4608 \| 2.5103 \| 610 \| 0.6135 \| 0.3467 \| 0.6135 \| 0.7833 \| \| 0.4608 \| 2.5185 \| 612 \| 0.6081 \| 0.3467 \| 0.6081 \| 0.7798 \| \| 0.4608 \| 2.5267 \| 614 \| 0.6175 \| 0.3467 \| 0.6175 \| 0.7858 \| \| 0.4608 \| 2.5350 \| 616 \| 0.6364 \| 0.3467 \| 0.6364 \| 0.7977 \| \| 0.4608 \| 2.5432 \| 618 \| 0.6775 \| 0.2364 \| 0.6775 \| 0.8231 \| \| 0.4608 \| 2.5514 \| 620 \| 0.7083 \| 0.3396 \| 0.7083 \| 0.8416 \| \| 0.4608 \| 2.5597 \| 622 \| 0.7059 \| 0.2759 \| 0.7059 \| 0.8402 \| \| 0.4608 \| 2.5679 \| 624 \| 0.7097 \| 0.2186 \| 0.7097 \| 0.8425 \| \| 0.4608 \| 2.5761 \| 626 \| 0.7355 \| 0.2000 \| 0.7355 \| 0.8576 \| \| 0.4608 \| 2.5844 \| 628 \| 0.7654 \| 0.2000 \| 0.7654 \| 0.8749 \| \| 0.4608 \| 2.5926 \| 630 \| 0.7487 \| 0.2000 \| 0.7487 \| 0.8653 \| \| 0.4608 \| 2.6008 \| 632 \| 0.7130 \| 0.2000 \| 0.7130 \| 0.8444 \| \| 0.4608 \| 2.6091 \| 634 \| 0.6732 \| 0.1600 \| 0.6732 \| 0.8205 \| \| 0.4608 \| 2.6173 \| 636 \| 0.6652 \| 0.1600 \| 0.6652 \| 0.8156 \| \| 0.4608 \| 2.6255 \| 638 \| 0.6602 \| 0.1600 \| 0.6602 \| 0.8125 \| \| 0.4608 \| 2.6337 \| 640 \| 0.6635 \| 0.2000 \| 0.6635 \| 0.8146 \| \| 0.4608 \| 2.6420 \| 642 \| 0.6839 \| 0.2000 \| 0.6839 \| 0.8270 \| \| 0.4608 \| 2.6502 \| 644 \| 0.6558 \| 0.2000 \| 0.6558 \| 0.8098 \| \| 0.4608 \| 2.6584 \| 646 \| 0.6363 \| 0.1600 \| 0.6363 \| 0.7977 \| \| 0.4608 \| 2.6667 \| 648 \| 0.6270 \| 0.1600 \| 0.6270 \| 0.7918 \| \| 0.4608 \| 2.6749 \| 650 \| 0.6337 \| 0.1600 \| 0.6337 \| 0.7960 \| \| 0.4608 \| 2.6831 \| 652 \| 0.6327 \| 0.1600 \| 0.6327 \| 0.7954 \| \| 0.4608 \| 2.6914 \| 654 \| 0.6530 \| 0.1600 \| 0.6530 \| 0.8081 \| \| 0.4608 \| 2.6996 \| 656 \| 0.6802 \| 0.2000 \| 0.6802 \| 0.8248 \| \| 0.4608 \| 2.7078 \| 658 \| 0.7190 \| 0.2000 \| 0.7190 \| 0.8479 \| \| 0.4608 \| 2.7160 \| 660 \| 0.7140 \| 0.2000 \| 0.7140 \| 0.8450 \| \| 0.4608 \| 2.7243 \| 662 \| 0.6844 \| 0.1600 \| 0.6844 \| 0.8273 \| \| 0.4608 \| 2.7325 \| 664 \| 0.6335 \| 0.3724 \| 0.6335 \| 0.7959 \| \| 0.4608 \| 2.7407 \| 666 \| 0.6120 \| 0.1962 \| 0.6120 \| 0.7823 \| \| 0.4608 \| 2.7490 \| 668 \| 0.6157 \| 0.1560 \| 0.6157 \| 0.7846 \| \| 0.4608 \| 2.7572 \| 670 \| 0.6152 \| 0.1560 \| 0.6152 \| 0.7844 \| \| 0.4608 \| 2.7654 \| 672 \| 0.6157 \| 0.1560 \| 0.6157 \| 0.7847 \| \| 0.4608 \| 2.7737 \| 674 \| 0.6330 \| 0.3318 \| 0.6330 \| 0.7956 \| \| 0.4608 \| 2.7819 \| 676 \| 0.6575 \| 0.3318 \| 0.6575 \| 0.8108 \| \| 0.4608 \| 2.7901 \| 678 \| 0.6984 \| 0.1600 \| 0.6984 \| 0.8357 \| \| 0.4608 \| 2.7984 \| 680 \| 0.7294 \| 0.2000 \| 0.7294 \| 0.8541 \| \| 0.4608 \| 2.8066 \| 682 \| 0.7152 \| 0.2000 \| 0.7152 \| 0.8457 \| \| 0.4608 \| 2.8148 \| 684 \| 0.7148 \| 0.2000 \| 0.7148 \| 0.8455 \| \| 0.4608 \| 2.8230 \| 686 \| 0.6888 \| 0.2000 \| 0.6888 \| 0.8299 \| \| 0.4608 \| 2.8313 \| 688 \| 0.6352 \| 0.2186 \| 0.6352 \| 0.7970 \| \| 0.4608 \| 2.8395 \| 690 \| 0.6025 \| 0.3077 \| 0.6025 \| 0.7762 \| \| 0.4608 \| 2.8477 \| 692 \| 0.6244 \| 0.2150 \| 0.6244 \| 0.7902 \| \| 0.4608 \| 2.8560 \| 694 \| 0.6440 \| 0.1755 \| 0.6440 \| 0.8025 \| \| 0.4608 \| 2.8642 \| 696 \| 0.6430 \| 0.1755 \| 0.6430 \| 0.8019 \| \| 0.4608 \| 2.8724 \| 698 \| 0.6276 \| 0.1560 \| 0.6276 \| 0.7922 \| \| 0.4608 \| 2.8807 \| 700 \| 0.6191 \| 0.2921 \| 0.6191 \| 0.7869 \| \| 0.4608 \| 2.8889 \| 702 \| 0.6311 \| 0.3724 \| 0.6311 \| 0.7944 \| \| 0.4608 \| 2.8971 \| 704 \| 0.6476 \| 0.2000 \| 0.6476 \| 0.8047 \| \| 0.4608 \| 2.9053 \| 706 \| 0.6636 \| 0.2000 \| 0.6636 \| 0.8146 \| \| 0.4608 \| 2.9136 \| 708 \| 0.6691 \| 0.2000 \| 0.6691 \| 0.8180 \| \| 0.4608 \| 2.9218 \| 710 \| 0.6595 \| 0.2000 \| 0.6595 \| 0.8121 \| \| 0.4608 \| 2.9300 \| 712 \| 0.6259 \| 0.1600 \| 0.6259 \| 0.7911 \| \| 0.4608 \| 2.9383 \| 714 \| 0.6047 \| 0.3163 \| 0.6047 \| 0.7776 \| \| 0.4608 \| 2.9465 \| 716 \| 0.5931 \| 0.3724 \| 0.5931 \| 0.7701 \| \| 0.4608 \| 2.9547 \| 718 \| 0.5937 \| 0.3724 \| 0.5937 \| 0.7705 \| \| 0.4608 \| 2.9630 \| 720 \| 0.5991 \| 0.3724 \| 0.5991 \| 0.7740 \| \| 0.4608 \| 2.9712 \| 722 \| 0.5993 \| 0.3724 \| 0.5993 \| 0.7742 \| \| 0.4608 \| 2.9794 \| 724 \| 0.6208 \| 0.3724 \| 0.6208 \| 0.7879 \| \| 0.4608 \| 2.9877 \| 726 \| 0.6729 \| 0.2000 \| 0.6729 \| 0.8203 \| \| 0.4608 \| 2.9959 \| 728 \| 0.7148 \| 0.2000 \| 0.7148 \| 0.8455 \| \| 0.4608 \| 3.0041 \| 730 \| 0.7300 \| 0.2000 \| 0.7300 \| 0.8544 \| \| 0.4608 \| 3.0123 \| 732 \| 0.7470 \| 0.2000 \| 0.7470 \| 0.8643 \| \| 0.4608 \| 3.0206 \| 734 \| 0.7594 \| 0.2000 \| 0.7594 \| 0.8714 \| \| 0.4608 \| 3.0288 \| 736 \| 0.7383 \| 0.2000 \| 0.7383 \| 0.8593 \| \| 0.4608 \| 3.0370 \| 738 \| 0.7335 \| 0.2000 \| 0.7335 \| 0.8565 \| \| 0.4608 \| 3.0453 \| 740 \| 0.7054 \| 0.2186 \| 0.7054 \| 0.8399 \| \| 0.4608 \| 3.0535 \| 742 \| 0.6693 \| 0.2186 \| 0.6693 \| 0.8181 \| \| 0.4608 \| 3.0617 \| 744 \| 0.6355 \| 0.2921 \| 0.6355 \| 0.7972 \| \| 0.4608 \| 3.0700 \| 746 \| 0.6327 \| 0.3318 \| 0.6327 \| 0.7954 \| \| 0.4608 \| 3.0782 \| 748 \| 0.6501 \| 0.1600 \| 0.6501 \| 0.8063 \| \| 0.4608 \| 3.0864 \| 750 \| 0.6755 \| 0.2000 \| 0.6755 \| 0.8219 \| \| 0.4608 \| 3.0947 \| 752 \| 0.6930 \| 0.2000 \| 0.6930 \| 0.8325 \| \| 0.4608 \| 3.1029 \| 754 \| 0.7248 \| 0.2000 \| 0.7248 \| 0.8514 \| \| 0.4608 \| 3.1111 \| 756 \| 0.7025 \| 0.2000 \| 0.7025 \| 0.8382 \| \| 0.4608 \| 3.1193 \| 758 \| 0.6839 \| 0.1600 \| 0.6839 \| 0.8270 \| \| 0.4608 \| 3.1276 \| 760 \| 0.6746 \| 0.1600 \| 0.6746 \| 0.8213 \| \| 0.4608 \| 3.1358 \| 762 \| 0.6942 \| 0.2000 \| 0.6942 \| 0.8332 \| \| 0.4608 \| 3.1440 \| 764 \| 0.7235 \| 0.1398 \| 0.7235 \| 0.8506 \| \| 0.4608 \| 3.1523 \| 766 \| 0.7442 \| 0.1398 \| 0.7442 \| 0.8627 \| \| 0.4608 \| 3.1605 \| 768 \| 0.7152 \| 0.1398 \| 0.7152 \| 0.8457 \| \| 0.4608 \| 3.1687 \| 770 \| 0.6785 \| 0.1600 \| 0.6785 \| 0.8237 \| \| 0.4608 \| 3.1770 \| 772 \| 0.6604 \| 0.1600 \| 0.6604 \| 0.8126 \| \| 0.4608 \| 3.1852 \| 774 \| 0.6512 \| 0.2533 \| 0.6512 \| 0.8070 \| \| 0.4608 \| 3.1934 \| 776 \| 0.6512 \| 0.2533 \| 0.6512 \| 0.8070 \| \| 0.4608 \| 3.2016 \| 778 \| 0.6528 \| 0.0455 \| 0.6528 \| 0.8080 \| \| 0.4608 \| 3.2099 \| 780 \| 0.6800 \| 0.1600 \| 0.6800 \| 0.8246 \| \| 0.4608 \| 3.2181 \| 782 \| 0.7145 \| 0.1000 \| 0.7145 \| 0.8453 \| \| 0.4608 \| 3.2263 \| 784 \| 0.7507 \| 0.1398 \| 0.7507 \| 0.8664 \| \| 0.4608 \| 3.2346 \| 786 \| 0.7818 \| 0.1398 \| 0.7818 \| 0.8842 \| \| 0.4608 \| 3.2428 \| 788 \| 0.7708 \| 0.1000 \| 0.7708 \| 0.8780 \| \| 0.4608 \| 3.2510 \| 790 \| 0.7246 \| 0.1600 \| 0.7246 \| 0.8512 \| \| 0.4608 \| 3.2593 \| 792 \| 0.6926 \| 0.1600 \| 0.6926 \| 0.8322 \| \| 0.4608 \| 3.2675 \| 794 \| 0.6878 \| 0.2154 \| 0.6878 \| 0.8293 \| \| 0.4608 \| 3.2757 \| 796 \| 0.6934 \| 0.2154 \| 0.6934 \| 0.8327 \| \| 0.4608 \| 3.2840 \| 798 \| 0.6951 \| 0.0957 \| 0.6951 \| 0.8337 \| \| 0.4608 \| 3.2922 \| 800 \| 0.6838 \| 0.2154 \| 0.6838 \| 0.8269 \| \| 0.4608 \| 3.3004 \| 802 \| 0.6843 \| 0.2533 \| 0.6843 \| 0.8272 \| \| 0.4608 \| 3.3086 \| 804 \| 0.7108 \| 0.1600 \| 0.7108 \| 0.8431 \| \| 0.4608 \| 3.3169 \| 806 \| 0.7749 \| 0.1398 \| 0.7749 \| 0.8803 \| \| 0.4608 \| 3.3251 \| 808 \| 0.8174 \| 0.1398 \| 0.8174 \| 0.9041 \| \| 0.4608 \| 3.3333 \| 810 \| 0.8305 \| 0.1398 \| 0.8305 \| 0.9113 \| \| 0.4608 \| 3.3416 \| 812 \| 0.8008 \| 0.1398 \| 0.8008 \| 0.8949 \| \| 0.4608 \| 3.3498 \| 814 \| 0.7626 \| 0.1398 \| 0.7626 \| 0.8733 \| \| 0.4608 \| 3.3580 \| 816 \| 0.7119 \| 0.2000 \| 0.7119 \| 0.8438 \| \| 0.4608 \| 3.3663 \| 818 \| 0.6969 \| 0.1600 \| 0.6969 \| 0.8348 \| \| 0.4608 \| 3.3745 \| 820 \| 0.7092 \| 0.1600 \| 0.7092 \| 0.8421 \| \| 0.4608 \| 3.3827 \| 822 \| 0.7331 \| 0.2000 \| 0.7331 \| 0.8562 \| \| 0.4608 \| 3.3909 \| 824 \| 0.7654 \| 0.1398 \| 0.7654 \| 0.8749 \| \| 0.4608 \| 3.3992 \| 826 \| 0.7778 \| 0.1398 \| 0.7778 \| 0.8819 \| \| 0.4608 \| 3.4074 \| 828 \| 0.7790 \| 0.1398 \| 0.7790 \| 0.8826 \| \| 0.4608 \| 3.4156 \| 830 \| 0.7525 \| 0.1398 \| 0.7525 \| 0.8675 \| \| 0.4608 \| 3.4239 \| 832 \| 0.7099 \| 0.2000 \| 0.7099 \| 0.8425 \| \| 0.4608 \| 3.4321 \| 834 \| 0.6679 \| 0.2186 \| 0.6679 \| 0.8172 \| \| 0.4608 \| 3.4403 \| 836 \| 0.6515 \| 0.3318 \| 0.6515 \| 0.8071 \| \| 0.4608 \| 3.4486 \| 838 \| 0.6545 \| 0.3318 \| 0.6545 \| 0.8090 \| \| 0.4608 \| 3.4568 \| 840 \| 0.6770 \| 0.2000 \| 0.6770 \| 0.8228 \| \| 0.4608 \| 3.4650 \| 842 \| 0.7027 \| 0.2000 \| 0.7027 \| 0.8383 \| \| 0.4608 \| 3.4733 \| 844 \| 0.7563 \| 0.2000 \| 0.7563 \| 0.8697 \| \| 0.4608 \| 3.4815 \| 846 \| 0.7959 \| 0.1398 \| 0.7959 \| 0.8921 \| \| 0.4608 \| 3.4897 \| 848 \| 0.7839 \| 0.1398 \| 0.7839 \| 0.8854 \| \| 0.4608 \| 3.4979 \| 850 \| 0.7421 \| 0.2000 \| 0.7421 \| 0.8615 \| \| 0.4608 \| 3.5062 \| 852 \| 0.7016 \| 0.2000 \| 0.7016 \| 0.8376 \| \| 0.4608 \| 3.5144 \| 854 \| 0.6734 \| 0.2000 \| 0.6734 \| 0.8206 \| \| 0.4608 \| 3.5226 \| 856 \| 0.6503 \| 0.2000 \| 0.6503 \| 0.8064 \| \| 0.4608 \| 3.5309 \| 858 \| 0.6411 \| 0.2000 \| 0.6411 \| 0.8007 \| \| 0.4608 \| 3.5391 \| 860 \| 0.6339 \| 0.2000 \| 0.6339 \| 0.7962 \| \| 0.4608 \| 3.5473 \| 862 \| 0.6339 \| 0.2000 \| 0.6339 \| 0.7962 \| \| 0.4608 \| 3.5556 \| 864 \| 0.6477 \| 0.2000 \| 0.6477 \| 0.8048 \| \| 0.4608 \| 3.5638 \| 866 \| 0.6577 \| 0.2000 \| 0.6577 \| 0.8110 \| \| 0.4608 \| 3.5720 \| 868 \| 0.6815 \| 0.2188 \| 0.6815 \| 0.8255 \| \| 0.4608 \| 3.5802 \| 870 \| 0.7363 \| 0.3198 \| 0.7363 \| 0.8581 \| \| 0.4608 \| 3.5885 \| 872 \| 0.7513 \| 0.3198 \| 0.7513 \| 0.8668 \| \| 0.4608 \| 3.5967 \| 874 \| 0.7470 \| 0.3198 \| 0.7470 \| 0.8643 \| \| 0.4608 \| 3.6049 \| 876 \| 0.7090 \| 0.2188 \| 0.7090 \| 0.8420 \| \| 0.4608 \| 3.6132 \| 878 \| 0.6699 \| 0.2000 \| 0.6699 \| 0.8185 \| \| 0.4608 \| 3.6214 \| 880 \| 0.6369 \| 0.2000 \| 0.6369 \| 0.7981 \| \| 0.4608 \| 3.6296 \| 882 \| 0.6287 \| 0.2000 \| 0.6287 \| 0.7929 \| \| 0.4608 \| 3.6379 \| 884 \| 0.6310 \| 0.2000 \| 0.6310 \| 0.7944 \| \| 0.4608 \| 3.6461 \| 886 \| 0.6229 \| 0.1600 \| 0.6229 \| 0.7893 \| \| 0.4608 \| 3.6543 \| 888 \| 0.6169 \| 0.1600 \| 0.6169 \| 0.7855 \| \| 0.4608 \| 3.6626 \| 890 \| 0.6151 \| 0.3724 \| 0.6151 \| 0.7843 \| \| 0.4608 \| 3.6708 \| 892 \| 0.6142 \| 0.3318 \| 0.6142 \| 0.7837 \| \| 0.4608 \| 3.6790 \| 894 \| 0.6169 \| 0.2921 \| 0.6169 \| 0.7854 \| \| 0.4608 \| 3.6872 \| 896 \| 0.6177 \| 0.2921 \| 0.6177 \| 0.7859 \| \| 0.4608 \| 3.6955 \| 898 \| 0.6257 \| 0.3318 \| 0.6257 \| 0.7910 \| \| 0.4608 \| 3.7037 \| 900 \| 0.6257 \| 0.3318 \| 0.6257 \| 0.7910 \| \| 0.4608 \| 3.7119 \| 902 \| 0.6227 \| 0.3318 \| 0.6227 \| 0.7891 \| \| 0.4608 \| 3.7202 \| 904 \| 0.6164 \| 0.3318 \| 0.6164 \| 0.7851 \| \| 0.4608 \| 3.7284 \| 906 \| 0.6184 \| 0.3318 \| 0.6184 \| 0.7864 \| \| 0.4608 \| 3.7366 \| 908 \| 0.6787 \| 0.3107 \| 0.6787 \| 0.8238 \| \| 0.4608 \| 3.7449 \| 910 \| 0.7562 \| 0.3277 \| 0.7562 \| 0.8696 \| \| 0.4608 \| 3.7531 \| 912 \| 0.8121 \| 0.3277 \| 0.8121 \| 0.9011 \| \| 0.4608 \| 3.7613 \| 914 \| 0.8140 \| 0.3107 \| 0.8140 \| 0.9022 \| \| 0.4608 \| 3.7695 \| 916 \| 0.8356 \| 0.2566 \| 0.8356 \| 0.9141 \| \| 0.4608 \| 3.7778 \| 918 \| 0.7919 \| 0.3107 \| 0.7919 \| 0.8899 \| \| 0.4608 \| 3.7860 \| 920 \| 0.7292 \| 0.2000 \| 0.7292 \| 0.8540 \| \| 0.4608 \| 3.7942 \| 922 \| 0.6849 \| 0.2000 \| 0.6849 \| 0.8276 \| \| 0.4608 \| 3.8025 \| 924 \| 0.6415 \| 0.2588 \| 0.6415 \| 0.8010 \| \| 0.4608 \| 3.8107 \| 926 \| 0.6140 \| 0.3724 \| 0.6140 \| 0.7836 \| \| 0.4608 \| 3.8189 \| 928 \| 0.6121 \| 0.3318 \| 0.6121 \| 0.7823 \| \| 0.4608 \| 3.8272 \| 930 \| 0.6138 \| 0.3318 \| 0.6138 \| 0.7834 \| \| 0.4608 \| 3.8354 \| 932 \| 0.6229 \| 0.2186 \| 0.6229 \| 0.7892 \| \| 0.4608 \| 3.8436 \| 934 \| 0.6353 \| 0.2588 \| 0.6353 \| 0.7971 \| \| 0.4608 \| 3.8519 \| 936 \| 0.6542 \| 0.2000 \| 0.6542 \| 0.8088 \| \| 0.4608 \| 3.8601 \| 938 \| 0.6833 \| 0.2000 \| 0.6833 \| 0.8266 \| \| 0.4608 \| 3.8683 \| 940 \| 0.7102 \| 0.2000 \| 0.7102 \| 0.8427 \| \| 0.4608 \| 3.8765 \| 942 \| 0.6991 \| 0.2000 \| 0.6991 \| 0.8361 \| \| 0.4608 \| 3.8848 \| 944 \| 0.6823 \| 0.1600 \| 0.6823 \| 0.8260 \| \| 0.4608 \| 3.8930 \| 946 \| 0.6662 \| 0.2186 \| 0.6662 \| 0.8162 \| \| 0.4608 \| 3.9012 \| 948 \| 0.6658 \| 0.2186 \| 0.6658 \| 0.8160 \| \| 0.4608 \| 3.9095 \| 950 \| 0.6742 \| 0.2186 \| 0.6742 \| 0.8211 \| \| 0.4608 \| 3.9177 \| 952 \| 0.6798 \| 0.2186 \| 0.6798 \| 0.8245 \| \| 0.4608 \| 3.9259 \| 954 \| 0.7083 \| 0.1600 \| 0.7083 \| 0.8416 \| \| 0.4608 \| 3.9342 \| 956 \| 0.7263 \| 0.2000 \| 0.7263 \| 0.8523 \| \| 0.4608 \| 3.9424 \| 958 \| 0.7201 \| 0.2000 \| 0.7201 \| 0.8486 \| \| 0.4608 \| 3.9506 \| 960 \| 0.7136 \| 0.2000 \| 0.7136 \| 0.8448 \| \| 0.4608 \| 3.9588 \| 962 \| 0.6800 \| 0.2186 \| 0.6800 \| 0.8246 \| \| 0.4608 \| 3.9671 \| 964 \| 0.6480 \| 0.1793 \| 0.6480 \| 0.8050 \| \| 0.4608 \| 3.9753 \| 966 \| 0.6376 \| 0.1793 \| 0.6376 \| 0.7985 \| \| 0.4608 \| 3.9835 \| 968 \| 0.6514 \| 0.2186 \| 0.6514 \| 0.8071 \| \| 0.4608 \| 3.9918 \| 970 \| 0.6779 \| 0.2000 \| 0.6779 \| 0.8233 \| \| 0.4608 \| 4.0 \| 972 \| 0.7279 \| 0.3198 \| 0.7279 \| 0.8532 \| \| 0.4608 \| 4.0082 \| 974 \| 0.7666 \| 0.3198 \| 0.7666 \| 0.8755 \| \| 0.4608 \| 4.0165 \| 976 \| 0.7730 \| 0.3198 \| 0.7730 \| 0.8792 \| \| 0.4608 \| 4.0247 \| 978 \| 0.7249 \| 0.3198 \| 0.7249 \| 0.8514 \| \| 0.4608 \| 4.0329 \| 980 \| 0.6436 \| 0.3107 \| 0.6436 \| 0.8022 \| \| 0.4608 \| 4.0412 \| 982 \| 0.5838 \| 0.2364 \| 0.5838 \| 0.7641 \| \| 0.4608 \| 4.0494 \| 984 \| 0.5645 \| 0.3865 \| 0.5645 \| 0.7514 \| \| 0.4608 \| 4.0576 \| 986 \| 0.5648 \| 0.2696 \| 0.5648 \| 0.7515 \| \| 0.4608 \| 4.0658 \| 988 \| 0.5706 \| 0.2696 \| 0.5706 \| 0.7554 \| \| 0.4608 \| 4.0741 \| 990 \| 0.5778 \| 0.2696 \| 0.5778 \| 0.7601 \| \| 0.4608 \| 4.0823 \| 992 \| 0.5984 \| 0.3724 \| 0.5984 \| 0.7736 \| \| 0.4608 \| 4.0905 \| 994 \| 0.6383 \| 0.1600 \| 0.6383 \| 0.7989 \| \| 0.4608 \| 4.0988 \| 996 \| 0.6917 \| 0.2000 \| 0.6917 \| 0.8317 \| \| 0.4608 \| 4.1070 \| 998 \| 0.7845 \| 0.3107 \| 0.7845 \| 0.8857 \| \| 0.1067 \| 4.1152 \| 1000 \| 0.8507 \| 0.3277 \| 0.8507 \| 0.9223 \| \| 0.1067 \| 4.1235 \| 1002 \| 0.8551 \| 0.3277 \| 0.8551 \| 0.9247 \| \| 0.1067 \| 4.1317 \| 1004 \| 0.8002 \| 0.3277 \| 0.8002 \| 0.8946 \| \| 0.1067 \| 4.1399 \| 1006 \| 0.7005 \| 0.3107 \| 0.7005 \| 0.8370 \| \| 0.1067 \| 4.1481 \| 1008 \| 0.6174 \| 0.3163 \| 0.6174 \| 0.7858 \| \| 0.1067 \| 4.1564 \| 1010 \| 0.5818 \| 0.3724 \| 0.5818 \| 0.7627 \| \| 0.1067 \| 4.1646 \| 1012 \| 0.5679 \| 0.3724 \| 0.5679 \| 0.7536 \| \| 0.1067 \| 4.1728 \| 1014 \| 0.5659 \| 0.3318 \| 0.5659 \| 0.7523 \| \| 0.1067 \| 4.1811 \| 1016 \| 0.5647 \| 0.3724 \| 0.5647 \| 0.7515 \| \| 0.1067 \| 4.1893 \| 1018 \| 0.5690 \| 0.3724 \| 0.5690 \| 0.7543 \| \| 0.1067 \| 4.1975 \| 1020 \| 0.5874 \| 0.3163 \| 0.5874 \| 0.7664 \| \| 0.1067 \| 4.2058 \| 1022 \| 0.6182 \| 0.2000 \| 0.6182 \| 0.7863 \| \| 0.1067 \| 4.2140 \| 1024 \| 0.6451 \| 0.2000 \| 0.6451 \| 0.8032 \| \| 0.1067 \| 4.2222 \| 1026 \| 0.6470 \| 0.2000 \| 0.6470 \| 0.8043 \| \| 0.1067 \| 4.2305 \| 1028 \| 0.6411 \| 0.3107 \| 0.6411 \| 0.8007 \| \| 0.1067 \| 4.2387 \| 1030 \| 0.6326 \| 0.3107 \| 0.6326 \| 0.7954 \| \| 0.1067 \| 4.2469 \| 1032 \| 0.6159 \| 0.3107 \| 0.6159 \| 0.7848 \| \| 0.1067 \| 4.2551 \| 1034 \| 0.5866 \| 0.4154 \| 0.5866 \| 0.7659 \| \| 0.1067 \| 4.2634 \| 1036 \| 0.5535 \| 0.3163 \| 0.5535 \| 0.7440 \| \| 0.1067 \| 4.2716 \| 1038 \| 0.5404 \| 0.3724 \| 0.5404 \| 0.7351 \| \| 0.1067 \| 4.2798 \| 1040 \| 0.5345 \| 0.3318 \| 0.5345 \| 0.7311 \| \| 0.1067 \| 4.2881 \| 1042 \| 0.5368 \| 0.3724 \| 0.5368 \| 0.7326 \| \| 0.1067 \| 4.2963 \| 1044 \| 0.5432 \| 0.3724 \| 0.5432 \| 0.7370 \| \| 0.1067 \| 4.3045 \| 1046 \| 0.5531 \| 0.3163 \| 0.5531 \| 0.7437 \| \| 0.1067 \| 4.3128 \| 1048 \| 0.5643 \| 0.3163 \| 0.5643 \| 0.7512 \| \| 0.1067 \| 4.3210 \| 1050 \| 0.5742 \| 0.3163 \| 0.5742 \| 0.7578 \| \| 0.1067 \| 4.3292 \| 1052 \| 0.5848 \| 0.3163 \| 0.5848 \| 0.7647 \| \| 0.1067 \| 4.3374 \| 1054 \| 0.5892 \| 0.3163 \| 0.5892 \| 0.7676 \| \| 0.1067 \| 4.3457 \| 1056 \| 0.5874 \| 0.3724 \| 0.5874 \| 0.7664 \| \| 0.1067 \| 4.3539 \| 1058 \| 0.6102 \| 0.4154 \| 0.6102 \| 0.7811 \| \| 0.1067 \| 4.3621 \| 1060 \| 0.6437 \| 0.2727 \| 0.6437 \| 0.8023 \| \| 0.1067 \| 4.3704 \| 1062 \| 0.6690 \| 0.2727 \| 0.6690 \| 0.8179 \| \| 0.1067 \| 4.3786 \| 1064 \| 0.7169 \| 0.2727 \| 0.7169 \| 0.8467 \| \| 0.1067 \| 4.3868 \| 1066 \| 0.7977 \| 0.3277 \| 0.7977 \| 0.8932 \| \| 0.1067 \| 4.3951 \| 1068 \| 0.9043 \| 0.1421 \| 0.9043 \| 0.9509 \| \| 0.1067 \| 4.4033 \| 1070 \| 0.9346 \| 0.1421 \| 0.9346 \| 0.9668 \| \| 0.1067 \| 4.4115 \| 1072 \| 0.8760 \| 0.1421 \| 0.8760 \| 0.9360 \| \| 0.1067 \| 4.4198 \| 1074 \| 0.7776 \| 0.3107 \| 0.7776 \| 0.8818 \| \| 0.1067 \| 4.4280 \| 1076 \| 0.6973 \| 0.2727 \| 0.6973 \| 0.8350 \| \| 0.1067 \| 4.4362 \| 1078 \| 0.6421 \| 0.2186 \| 0.6421 \| 0.8013 \| \| 0.1067 \| 4.4444 \| 1080 \| 0.6260 \| 0.3724 \| 0.6260 \| 0.7912 \| \| 0.1067 \| 4.4527 \| 1082 \| 0.6141 \| 0.3724 \| 0.6141 \| 0.7837 \| \| 0.1067 \| 4.4609 \| 1084 \| 0.5965 \| 0.3724 \| 0.5965 \| 0.7724 \| \| 0.1067 \| 4.4691 \| 1086 \| 0.5922 \| 0.3724 \| 0.5922 \| 0.7696 \| \| 0.1067 \| 4.4774 \| 1088 \| 0.5860 \| 0.3724 \| 0.5860 \| 0.7655 \| \| 0.1067 \| 4.4856 \| 1090 \| 0.5774 \| 0.3724 \| 0.5774 \| 0.7599 \| \| 0.1067 \| 4.4938 \| 1092 \| 0.5751 \| 0.3724 \| 0.5751 \| 0.7584 \| \| 0.1067 \| 4.5021 \| 1094 \| 0.5759 \| 0.3724 \| 0.5759 \| 0.7589 \| \| 0.1067 \| 4.5103 \| 1096 \| 0.5726 \| 0.3724 \| 0.5726 \| 0.7567 \| \| 0.1067 \| 4.5185 \| 1098 \| 0.5822 \| 0.3724 \| 0.5822 \| 0.7630 \| \| 0.1067 \| 4.5267 \| 1100 \| 0.5873 \| 0.3724 \| 0.5873 \| 0.7664 \| \| 0.1067 \| 4.5350 \| 1102 \| 0.5880 \| 0.3724 \| 0.5880 \| 0.7668 \| \| 0.1067 \| 4.5432 \| 1104 \| 0.5947 \| 0.3724 \| 0.5947 \| 0.7712 \| \| 0.1067 \| 4.5514 \| 1106 \| 0.6063 \| 0.3724 \| 0.6063 \| 0.7787 \| \| 0.1067 \| 4.5597 \| 1108 \| 0.6089 \| 0.3724 \| 0.6089 \| 0.7804 \| \| 0.1067 \| 4.5679 \| 1110 \| 0.6153 \| 0.3724 \| 0.6153 \| 0.7844 \| \| 0.1067 \| 4.5761 \| 1112 \| 0.6160 \| 0.3724 \| 0.6160 \| 0.7849 \| \| 0.1067 \| 4.5844 \| 1114 \| 0.6238 \| 0.3163 \| 0.6238 \| 0.7898 \| \| 0.1067 \| 4.5926 \| 1116 \| 0.6326 \| 0.3163 \| 0.6326 \| 0.7954 \| \| 0.1067 \| 4.6008 \| 1118 \| 0.6173 \| 0.3163 \| 0.6173 \| 0.7857 \| \| 0.1067 \| 4.6091 \| 1120 \| 0.6053 \| 0.3163 \| 0.6053 \| 0.7780 \| \| 0.1067 \| 4.6173 \| 1122 \| 0.6107 \| 0.3163 \| 0.6107 \| 0.7815 \| \| 0.1067 \| 4.6255 \| 1124 \| 0.6035 \| 0.3163 \| 0.6035 \| 0.7769 \| \| 0.1067 \| 4.6337 \| 1126 \| 0.6111 \| 0.3163 \| 0.6111 \| 0.7817 \| \| 0.1067 \| 4.6420 \| 1128 \| 0.6276 \| 0.3163 \| 0.6276 \| 0.7922 \| \| 0.1067 \| 4.6502 \| 1130 \| 0.6515 \| 0.3107 \| 0.6515 \| 0.8072 \| \| 0.1067 \| 4.6584 \| 1132 \| 0.6731 \| 0.3107 \| 0.6731 \| 0.8204 \| \| 0.1067 \| 4.6667 \| 1134 \| 0.6614 \| 0.3107 \| 0.6614 \| 0.8133 \| \| 0.1067 \| 4.6749 \| 1136 \| 0.6400 \| 0.3107 \| 0.6400 \| 0.8000 \| \| 0.1067 \| 4.6831 \| 1138 \| 0.6019 \| 0.3163 \| 0.6019 \| 0.7758 \| \| 0.1067 \| 4.6914 \| 1140 \| 0.5688 \| 0.3724 \| 0.5688 \| 0.7542 \| \| 0.1067 \| 4.6996 \| 1142 \| 0.5518 \| 0.3724 \| 0.5518 \| 0.7428 \| \| 0.1067 \| 4.7078 \| 1144 \| 0.5512 \| 0.3609 \| 0.5512 \| 0.7424 \| \| 0.1067 \| 4.7160 \| 1146 \| 0.5574 \| 0.3226 \| 0.5574 \| 0.7466 \| \| 0.1067 \| 4.7243 \| 1148 \| 0.5579 \| 0.3609 \| 0.5579 \| 0.7469 \| \| 0.1067 \| 4.7325 \| 1150 \| 0.5574 \| 0.3865 \| 0.5574 \| 0.7466 \| \| 0.1067 \| 4.7407 \| 1152 \| 0.5764 \| 0.3724 \| 0.5764 \| 0.7592 \| \| 0.1067 \| 4.7490 \| 1154 \| 0.6318 \| 0.4154 \| 0.6318 \| 0.7948 \| \| 0.1067 \| 4.7572 \| 1156 \| 0.6887 \| 0.3198 \| 0.6887 \| 0.8299 \| \| 0.1067 \| 4.7654 \| 1158 \| 0.7032 \| 0.3198 \| 0.7032 \| 0.8386 \| \| 0.1067 \| 4.7737 \| 1160 \| 0.6678 \| 0.4529 \| 0.6678 \| 0.8172 \| \| 0.1067 \| 4.7819 \| 1162 \| 0.6315 \| 0.4154 \| 0.6315 \| 0.7947 \| \| 0.1067 \| 4.7901 \| 1164 \| 0.6128 \| 0.4167 \| 0.6128 \| 0.7828 \| \| 0.1067 \| 4.7984 \| 1166 \| 0.5894 \| 0.4154 \| 0.5894 \| 0.7677 \| \| 0.1067 \| 4.8066 \| 1168 \| 0.5796 \| 0.3163 \| 0.5796 \| 0.7613 \| \| 0.1067 \| 4.8148 \| 1170 \| 0.5729 \| 0.3163 \| 0.5729 \| 0.7569 \| \| 0.1067 \| 4.8230 \| 1172 \| 0.5792 \| 0.3163 \| 0.5792 \| 0.7611 \| \| 0.1067 \| 4.8313 \| 1174 \| 0.5800 \| 0.3163 \| 0.5800 \| 0.7616 \| \| 0.1067 \| 4.8395 \| 1176 \| 0.5854 \| 0.3163 \| 0.5854 \| 0.7651 \| \| 0.1067 \| 4.8477 \| 1178 \| 0.5778 \| 0.3163 \| 0.5778 \| 0.7601 \| \| 0.1067 \| 4.8560 \| 1180 \| 0.5690 \| 0.3163 \| 0.5690 \| 0.7543 \| \| 0.1067 \| 4.8642 \| 1182 \| 0.5619 \| 0.3163 \| 0.5619 \| 0.7496 \| \| 0.1067 \| 4.8724 \| 1184 \| 0.5602 \| 0.3163 \| 0.5602 \| 0.7484 \| \| 0.1067 \| 4.8807 \| 1186 \| 0.5590 \| 0.3163 \| 0.5591 \| 0.7477 \| \| 0.1067 \| 4.8889 \| 1188 \| 0.5542 \| 0.3163 \| 0.5542 \| 0.7444 \| \| 0.1067 \| 4.8971 \| 1190 \| 0.5554 \| 0.3163 \| 0.5554 \| 0.7452 \| \| 0.1067 \| 4.9053 \| 1192 \| 0.5586 \| 0.3163 \| 0.5586 \| 0.7474 \| \| 0.1067 \| 4.9136 \| 1194 \| 0.5645 \| 0.3163 \| 0.5645 \| 0.7513 \| \| 0.1067 \| 4.9218 \| 1196 \| 0.5658 \| 0.3163 \| 0.5658 \| 0.7522 \| \| 0.1067 \| 4.9300 \| 1198 \| 0.5701 \| 0.3163 \| 0.5701 \| 0.7551 \| \| 0.1067 \| 4.9383 \| 1200 \| 0.5671 \| 0.3163 \| 0.5671 \| 0.7531 \| \| 0.1067 \| 4.9465 \| 1202 \| 0.5657 \| 0.3163 \| 0.5657 \| 0.7521 \| \| 0.1067 \| 4.9547 \| 1204 \| 0.5573 \| 0.3163 \| 0.5573 \| 0.7465 \| \| 0.1067 \| 4.9630 \| 1206 \| 0.5621 \| 0.3163 \| 0.5621 \| 0.7497 \| \| 0.1067 \| 4.9712 \| 1208 \| 0.5581 \| 0.3163 \| 0.5581 \| 0.7471 \| \| 0.1067 \| 4.9794 \| 1210 \| 0.5770 \| 0.3255 \| 0.5770 \| 0.7596 \| \| 0.1067 \| 4.9877 \| 1212 \| 0.6160 \| 0.4529 \| 0.6160 \| 0.7848 \| \| 0.1067 \| 4.9959 \| 1214 \| 0.6855 \| 0.3198 \| 0.6855 \| 0.8280 \| \| 0.1067 \| 5.0041 \| 1216 \| 0.7575 \| 0.3198 \| 0.7575 \| 0.8703 \| \| 0.1067 \| 5.0123 \| 1218 \| 0.7589 \| 0.3198 \| 0.7589 \| 0.8712 \| \| 0.1067 \| 5.0206 \| 1220 \| 0.7158 \| 0.3198 \| 0.7158 \| 0.8460 \| \| 0.1067 \| 5.0288 \| 1222 \| 0.6280 \| 0.4529 \| 0.6280 \| 0.7925 \| \| 0.1067 \| 5.0370 \| 1224 \| 0.5834 \| 0.4167 \| 0.5834 \| 0.7638 \| \| 0.1067 \| 5.0453 \| 1226 \| 0.5561 \| 0.4637 \| 0.5561 \| 0.7457 \| \| 0.1067 \| 5.0535 \| 1228 \| 0.5489 \| 0.3771 \| 0.5489 \| 0.7409 \| \| 0.1067 \| 5.0617 \| 1230 \| 0.5482 \| 0.3771 \| 0.5482 \| 0.7404 \| \| 0.1067 \| 5.0700 \| 1232 \| 0.5362 \| 0.3724 \| 0.5362 \| 0.7323 \| \| 0.1067 \| 5.0782 \| 1234 \| 0.5372 \| 0.3724 \| 0.5372 \| 0.7329 \| \| 0.1067 \| 5.0864 \| 1236 \| 0.5379 \| 0.3724 \| 0.5379 \| 0.7334 \| \| 0.1067 \| 5.0947 \| 1238 \| 0.5419 \| 0.3318 \| 0.5419 \| 0.7361 \| \| 0.1067 \| 5.1029 \| 1240 \| 0.5436 \| 0.3318 \| 0.5436 \| 0.7373 \| \| 0.1067 \| 5.1111 \| 1242 \| 0.5462 \| 0.3724 \| 0.5462 \| 0.7391 \| \| 0.1067 \| 5.1193 \| 1244 \| 0.5527 \| 0.3724 \| 0.5527 \| 0.7434 \| \| 0.1067 \| 5.1276 \| 1246 \| 0.5586 \| 0.3724 \| 0.5586 \| 0.7474 \| \| 0.1067 \| 5.1358 \| 1248 \| 0.5621 \| 0.3724 \| 0.5621 \| 0.7497 \| \| 0.1067 \| 5.1440 \| 1250 \| 0.5642 \| 0.2921 \| 0.5642 \| 0.7511 \| \| 0.1067 \| 5.1523 \| 1252 \| 0.5681 \| 0.2921 \| 0.5681 \| 0.7537 \| \| 0.1067 \| 5.1605 \| 1254 \| 0.5697 \| 0.2921 \| 0.5697 \| 0.7548 \| \| 0.1067 \| 5.1687 \| 1256 \| 0.5701 \| 0.2921 \| 0.5701 \| 0.7550 \| \| 0.1067 \| 5.1770 \| 1258 \| 0.5750 \| 0.3724 \| 0.5750 \| 0.7583 \| \| 0.1067 \| 5.1852 \| 1260 \| 0.5924 \| 0.3724 \| 0.5924 \| 0.7697 \| \| 0.1067 \| 5.1934 \| 1262 \| 0.6060 \| 0.4140 \| 0.6060 \| 0.7785 \| \| 0.1067 \| 5.2016 \| 1264 \| 0.6008 \| 0.3724 \| 0.6008 \| 0.7751 \| \| 0.1067 \| 5.2099 \| 1266 \| 0.5907 \| 0.3724 \| 0.5907 \| 0.7686 \| \| 0.1067 \| 5.2181 \| 1268 \| 0.5801 \| 0.3724 \| 0.5801 \| 0.7616 \| \| 0.1067 \| 5.2263 \| 1270 \| 0.5799 \| 0.2921 \| 0.5799 \| 0.7615 \| \| 0.1067 \| 5.2346 \| 1272 \| 0.5806 \| 0.3467 \| 0.5806 \| 0.7620 \| \| 0.1067 \| 5.2428 \| 1274 \| 0.5769 \| 0.3467 \| 0.5769 \| 0.7596 \| \| 0.1067 \| 5.2510 \| 1276 \| 0.5722 \| 0.3865 \| 0.5722 \| 0.7564 \| \| 0.1067 \| 5.2593 \| 1278 \| 0.5707 \| 0.3724 \| 0.5707 \| 0.7554 \| \| 0.1067 \| 5.2675 \| 1280 \| 0.5736 \| 0.3724 \| 0.5736 \| 0.7574 \| \| 0.1067 \| 5.2757 \| 1282 \| 0.5781 \| 0.3724 \| 0.5781 \| 0.7603 \| \| 0.1067 \| 5.2840 \| 1284 \| 0.5893 \| 0.3724 \| 0.5893 \| 0.7677 \| \| 0.1067 \| 5.2922 \| 1286 \| 0.6013 \| 0.3724 \| 0.6013 \| 0.7754 \| \| 0.1067 \| 5.3004 \| 1288 \| 0.6058 \| 0.3724 \| 0.6058 \| 0.7783 \| \| 0.1067 \| 5.3086 \| 1290 \| 0.6026 \| 0.3724 \| 0.6026 \| 0.7763 \| \| 0.1067 \| 5.3169 \| 1292 \| 0.6021 \| 0.3724 \| 0.6021 \| 0.7760 \| \| 0.1067 \| 5.3251 \| 1294 \| 0.6085 \| 0.3724 \| 0.6085 \| 0.7801 \| \| 0.1067 \| 5.3333 \| 1296 \| 0.6104 \| 0.3724 \| 0.6104 \| 0.7813 \| \| 0.1067 \| 5.3416 \| 1298 \| 0.6099 \| 0.3724 \| 0.6099 \| 0.7810 \| \| 0.1067 \| 5.3498 \| 1300 \| 0.6138 \| 0.4273 \| 0.6138 \| 0.7834 \| \| 0.1067 \| 5.3580 \| 1302 \| 0.6232 \| 0.3467 \| 0.6232 \| 0.7894 \| \| 0.1067 \| 5.3663 \| 1304 \| 0.6391 \| 0.2696 \| 0.6391 \| 0.7995 \| \| 0.1067 \| 5.3745 \| 1306 \| 0.6443 \| 0.3077 \| 0.6443 \| 0.8027 \| \| 0.1067 \| 5.3827 \| 1308 \| 0.6407 \| 0.2921 \| 0.6407 \| 0.8004 \| \| 0.1067 \| 5.3909 \| 1310 \| 0.6412 \| 0.3724 \| 0.6412 \| 0.8008 \| \| 0.1067 \| 5.3992 \| 1312 \| 0.6475 \| 0.3724 \| 0.6475 \| 0.8046 \| \| 0.1067 \| 5.4074 \| 1314 \| 0.6641 \| 0.3724 \| 0.6641 \| 0.8149 \| \| 0.1067 \| 5.4156 \| 1316 \| 0.6809 \| 0.1600 \| 0.6809 \| 0.8252 \| \| 0.1067 \| 5.4239 \| 1318 \| 0.6994 \| 0.1818 \| 0.6994 \| 0.8363 \| \| 0.1067 \| 5.4321 \| 1320 \| 0.6984 \| 0.2846 \| 0.6984 \| 0.8357 \| \| 0.1067 \| 5.4403 \| 1322 \| 0.6712 \| 0.1818 \| 0.6712 \| 0.8193 \| \| 0.1067 \| 5.4486 \| 1324 \| 0.6343 \| 0.2186 \| 0.6343 \| 0.7964 \| \| 0.1067 \| 5.4568 \| 1326 \| 0.6057 \| 0.3724 \| 0.6057 \| 0.7782 \| \| 0.1067 \| 5.4650 \| 1328 \| 0.5926 \| 0.3724 \| 0.5926 \| 0.7698 \| \| 0.1067 \| 5.4733 \| 1330 \| 0.5906 \| 0.4273 \| 0.5906 \| 0.7685 \| \| 0.1067 \| 5.4815 \| 1332 \| 0.5892 \| 0.3865 \| 0.5892 \| 0.7676 \| \| 0.1067 \| 5.4897 \| 1334 \| 0.5892 \| 0.3077 \| 0.5892 \| 0.7676 \| \| 0.1067 \| 5.4979 \| 1336 \| 0.5866 \| 0.3467 \| 0.5866 \| 0.7659 \| \| 0.1067 \| 5.5062 \| 1338 \| 0.5847 \| 0.4273 \| 0.5847 \| 0.7647 \| \| 0.1067 \| 5.5144 \| 1340 \| 0.5820 \| 0.4273 \| 0.5820 \| 0.7629 \| \| 0.1067 \| 5.5226 \| 1342 \| 0.5855 \| 0.4273 \| 0.5855 \| 0.7652 \| \| 0.1067 \| 5.5309 \| 1344 \| 0.5909 \| 0.4277 \| 0.5909 \| 0.7687 \| \| 0.1067 \| 5.5391 \| 1346 \| 0.5976 \| 0.3811 \| 0.5976 \| 0.7730 \| \| 0.1067 \| 5.5473 \| 1348 \| 0.6043 \| 0.3811 \| 0.6043 \| 0.7774 \| \| 0.1067 \| 5.5556 \| 1350 \| 0.5986 \| 0.3811 \| 0.5986 \| 0.7737 \| \| 0.1067 \| 5.5638 \| 1352 \| 0.5869 \| 0.2759 \| 0.5869 \| 0.7661 \| \| 0.1067 \| 5.5720 \| 1354 \| 0.5819 \| 0.3865 \| 0.5819 \| 0.7628 \| \| 0.1067 \| 5.5802 \| 1356 \| 0.5902 \| 0.2759 \| 0.5902 \| 0.7682 \| \| 0.1067 \| 5.5885 \| 1358 \| 0.5856 \| 0.3865 \| 0.5856 \| 0.7652 \| \| 0.1067 \| 5.5967 \| 1360 \| 0.5777 \| 0.3865 \| 0.5777 \| 0.7601 \| \| 0.1067 \| 5.6049 \| 1362 \| 0.5730 \| 0.3865 \| 0.5730 \| 0.7570 \| \| 0.1067 \| 5.6132 \| 1364 \| 0.5739 \| 0.3865 \| 0.5739 \| 0.7576 \| \| 0.1067 \| 5.6214 \| 1366 \| 0.5833 \| 0.3865 \| 0.5833 \| 0.7638 \| \| 0.1067 \| 5.6296 \| 1368 \| 0.5973 \| 0.4772 \| 0.5973 \| 0.7729 \| \| 0.1067 \| 5.6379 \| 1370 \| 0.6009 \| 0.4772 \| 0.6009 \| 0.7752 \| \| 0.1067 \| 5.6461 \| 1372 \| 0.6135 \| 0.3463 \| 0.6135 \| 0.7833 \| \| 0.1067 \| 5.6543 \| 1374 \| 0.6249 \| 0.3463 \| 0.6249 \| 0.7905 \| \| 0.1067 \| 5.6626 \| 1376 \| 0.6465 \| 0.3811 \| 0.6465 \| 0.8041 \| \| 0.1067 \| 5.6708 \| 1378 \| 0.6563 \| 0.3333 \| 0.6563 \| 0.8101 \| \| 0.1067 \| 5.6790 \| 1380 \| 0.6677 \| 0.2846 \| 0.6677 \| 0.8171 \| \| 0.1067 \| 5.6872 \| 1382 \| 0.6573 \| 0.2846 \| 0.6573 \| 0.8107 \| \| 0.1067 \| 5.6955 \| 1384 \| 0.6353 \| 0.3255 \| 0.6353 \| 0.7971 \| \| 0.1067 \| 5.7037 \| 1386 \| 0.6239 \| 0.3255 \| 0.6239 \| 0.7899 \| \| 0.1067 \| 5.7119 \| 1388 \| 0.6212 \| 0.2186 \| 0.6212 \| 0.7882 \| \| 0.1067 \| 5.7202 \| 1390 \| 0.6240 \| 0.2186 \| 0.6240 \| 0.7899 \| \| 0.1067 \| 5.7284 \| 1392 \| 0.6335 \| 0.2186 \| 0.6335 \| 0.7959 \| \| 0.1067 \| 5.7366 \| 1394 \| 0.6243 \| 0.3724 \| 0.6243 \| 0.7902 \| \| 0.1067 \| 5.7449 \| 1396 \| 0.6067 \| 0.3724 \| 0.6067 \| 0.7789 \| \| 0.1067 \| 5.7531 \| 1398 \| 0.6060 \| 0.3724 \| 0.6060 \| 0.7785 \| \| 0.1067 \| 5.7613 \| 1400 \| 0.6136 \| 0.3724 \| 0.6136 \| 0.7833 \| \| 0.1067 \| 5.7695 \| 1402 \| 0.6249 \| 0.3724 \| 0.6249 \| 0.7905 \| \| 0.1067 \| 5.7778 \| 1404 \| 0.6320 \| 0.3724 \| 0.6320 \| 0.7950 \| \| 0.1067 \| 5.7860 \| 1406 \| 0.6302 \| 0.3724 \| 0.6302 \| 0.7938 \| \| 0.1067 \| 5.7942 \| 1408 \| 0.6485 \| 0.3724 \| 0.6485 \| 0.8053 \| \| 0.1067 \| 5.8025 \| 1410 \| 0.6884 \| 0.4615 \| 0.6884 \| 0.8297 \| \| 0.1067 \| 5.8107 \| 1412 \| 0.7049 \| 0.4615 \| 0.7049 \| 0.8396 \| \| 0.1067 \| 5.8189 \| 1414 \| 0.7113 \| 0.2948 \| 0.7113 \| 0.8434 \| \| 0.1067 \| 5.8272 \| 1416 \| 0.6979 \| 0.2948 \| 0.6979 \| 0.8354 \| \| 0.1067 \| 5.8354 \| 1418 \| 0.6645 \| 0.3333 \| 0.6645 \| 0.8152 \| \| 0.1067 \| 5.8436 \| 1420 \| 0.6171 \| 0.2186 \| 0.6171 \| 0.7855 \| \| 0.1067 \| 5.8519 \| 1422 \| 0.5984 \| 0.2186 \| 0.5984 \| 0.7736 \| \| 0.1067 \| 5.8601 \| 1424 \| 0.5909 \| 0.3724 \| 0.5909 \| 0.7687 \| \| 0.1067 \| 5.8683 \| 1426 \| 0.5855 \| 0.3724 \| 0.5855 \| 0.7652 \| \| 0.1067 \| 5.8765 \| 1428 \| 0.5823 \| 0.3724 \| 0.5823 \| 0.7631 \| \| 0.1067 \| 5.8848 \| 1430 \| 0.5827 \| 0.3724 \| 0.5827 \| 0.7633 \| \| 0.1067 \| 5.8930 \| 1432 \| 0.5836 \| 0.3724 \| 0.5836 \| 0.7640 \| \| 0.1067 \| 5.9012 \| 1434 \| 0.5890 \| 0.2186 \| 0.5890 \| 0.7675 \| \| 0.1067 \| 5.9095 \| 1436 \| 0.5924 \| 0.2186 \| 0.5924 \| 0.7697 \| \| 0.1067 \| 5.9177 \| 1438 \| 0.5934 \| 0.2186 \| 0.5934 \| 0.7703 \| \| 0.1067 \| 5.9259 \| 1440 \| 0.5975 \| 0.2186 \| 0.5975 \| 0.7730 \| \| 0.1067 \| 5.9342 \| 1442 \| 0.5973 \| 0.2186 \| 0.5973 \| 0.7729 \| \| 0.1067 \| 5.9424 \| 1444 \| 0.6052 \| 0.2186 \| 0.6052 \| 0.7779 \| \| 0.1067 \| 5.9506 \| 1446 \| 0.6085 \| 0.2355 \| 0.6085 \| 0.7800 \| \| 0.1067 \| 5.9588 \| 1448 \| 0.6136 \| 0.2355 \| 0.6136 \| 0.7833 \| \| 0.1067 \| 5.9671 \| 1450 \| 0.6272 \| 0.3333 \| 0.6272 \| 0.7920 \| \| 0.1067 \| 5.9753 \| 1452 \| 0.6377 \| 0.3333 \| 0.6377 \| 0.7986 \| \| 0.1067 \| 5.9835 \| 1454 \| 0.6601 \| 0.3333 \| 0.6601 \| 0.8124 \| \| 0.1067 \| 5.9918 \| 1456 \| 0.6702 \| 0.3333 \| 0.6702 \| 0.8186 \| \| 0.1067 \| 6.0 \| 1458 \| 0.6696 \| 0.3333 \| 0.6696 \| 0.8183 \| \| 0.1067 \| 6.0082 \| 1460 \| 0.7106 \| 0.3401 \| 0.7106 \| 0.8430 \| \| 0.1067 \| 6.0165 \| 1462 \| 0.7502 \| 0.3277 \| 0.7502 \| 0.8662 \| \| 0.1067 \| 6.0247 \| 1464 \| 0.7732 \| 0.3277 \| 0.7732 \| 0.8793 \| \| 0.1067 \| 6.0329 \| 1466 \| 0.7905 \| 0.3277 \| 0.7905 \| 0.8891 \| \| 0.1067 \| 6.0412 \| 1468 \| 0.7390 \| 0.3277 \| 0.7390 \| 0.8596 \| \| 0.1067 \| 6.0494 \| 1470 \| 0.6955 \| 0.3401 \| 0.6955 \| 0.8339 \| \| 0.1067 \| 6.0576 \| 1472 \| 0.6746 \| 0.3401 \| 0.6746 \| 0.8214 \| \| 0.1067 \| 6.0658 \| 1474 \| 0.6916 \| 0.3401 \| 0.6916 \| 0.8316 \| \| 0.1067 \| 6.0741 \| 1476 \| 0.7171 \| 0.3401 \| 0.7171 \| 0.8468 \| \| 0.1067 \| 6.0823 \| 1478 \| 0.7460 \| 0.3401 \| 0.7460 \| 0.8637 \| \| 0.1067 \| 6.0905 \| 1480 \| 0.7604 \| 0.4187 \| 0.7604 \| 0.8720 \| \| 0.1067 \| 6.0988 \| 1482 \| 0.7358 \| 0.3401 \| 0.7358 \| 0.8578 \| \| 0.1067 \| 6.1070 \| 1484 \| 0.6898 \| 0.3401 \| 0.6898 \| 0.8305 \| \| 0.1067 \| 6.1152 \| 1486 \| 0.6483 \| 0.4740 \| 0.6483 \| 0.8052 \| \| 0.1067 \| 6.1235 \| 1488 \| 0.6281 \| 0.4389 \| 0.6281 \| 0.7925 \| \| 0.1067 \| 6.1317 \| 1490 \| 0.6149 \| 0.4389 \| 0.6149 \| 0.7842 \| \| 0.1067 \| 6.1399 \| 1492 \| 0.6114 \| 0.4389 \| 0.6114 \| 0.7819 \| \| 0.1067 \| 6.1481 \| 1494 \| 0.6122 \| 0.4389 \| 0.6122 \| 0.7825 \| \| 0.1067 \| 6.1564 \| 1496 \| 0.6148 \| 0.4389 \| 0.6148 \| 0.7841 \| \| 0.1067 \| 6.1646 \| 1498 \| 0.6254 \| 0.3463 \| 0.6254 \| 0.7909 \| \| 0.0692 \| 6.1728 \| 1500 \| 0.6406 \| 0.3333 \| 0.6406 \| 0.8004 \| \| 0.0692 \| 6.1811 \| 1502 \| 0.6690 \| 0.3333 \| 0.6690 \| 0.8179 \| \| 0.0692 \| 6.1893 \| 1504 \| 0.6879 \| 0.3333 \| 0.6879 \| 0.8294 \| \| 0.0692 \| 6.1975 \| 1506 \| 0.6737 \| 0.3333 \| 0.6737 \| 0.8208 \| \| 0.0692 \| 6.2058 \| 1508 \| 0.6619 \| 0.3333 \| 0.6619 \| 0.8136 \| \| 0.0692 \| 6.2140 \| 1510 \| 0.6404 \| 0.3333 \| 0.6404 \| 0.8002 \| \| 0.0692 \| 6.2222 \| 1512 \| 0.6208 \| 0.2186 \| 0.6208 \| 0.7879 \| \| 0.0692 \| 6.2305 \| 1514 \| 0.6093 \| 0.2364 \| 0.6093 \| 0.7806 \| \| 0.0692 \| 6.2387 \| 1516 \| 0.6077 \| 0.3865 \| 0.6077 \| 0.7795 \| \| 0.0692 \| 6.2469 \| 1518 \| 0.6043 \| 0.3865 \| 0.6043 \| 0.7774 \| \| 0.0692 \| 6.2551 \| 1520 \| 0.6001 \| 0.3865 \| 0.6001 \| 0.7747 \| \| 0.0692 \| 6.2634 \| 1522 \| 0.5978 \| 0.3865 \| 0.5978 \| 0.7732 \| \| 0.0692 \| 6.2716 \| 1524 \| 0.5987 \| 0.2759 \| 0.5987 \| 0.7738 \| \| 0.0692 \| 6.2798 \| 1526 \| 0.6101 \| 0.2355 \| 0.6101 \| 0.7811 \| \| 0.0692 \| 6.2881 \| 1528 \| 0.6292 \| 0.3333 \| 0.6292 \| 0.7932 \| \| 0.0692 \| 6.2963 \| 1530 \| 0.6504 \| 0.3333 \| 0.6504 \| 0.8065 \| \| 0.0692 \| 6.3045 \| 1532 \| 0.6731 \| 0.2846 \| 0.6731 \| 0.8204 \| \| 0.0692 \| 6.3128 \| 1534 \| 0.6776 \| 0.2846 \| 0.6776 \| 0.8231 \| \| 0.0692 \| 6.3210 \| 1536 \| 0.6742 \| 0.2846 \| 0.6742 \| 0.8211 \| \| 0.0692 \| 6.3292 \| 1538 \| 0.6550 \| 0.3811 \| 0.6550 \| 0.8093 \| \| 0.0692 \| 6.3374 \| 1540 \| 0.6390 \| 0.3811 \| 0.6390 \| 0.7993 \| \| 0.0692 \| 6.3457 \| 1542 \| 0.6197 \| 0.3811 \| 0.6197 \| 0.7872 \| \| 0.0692 \| 6.3539 \| 1544 \| 0.6014 \| 0.4740 \| 0.6014 \| 0.7755 \| \| 0.0692 \| 6.3621 \| 1546 \| 0.5895 \| 0.4740 \| 0.5895 \| 0.7678 \| \| 0.0692 \| 6.3704 \| 1548 \| 0.5917 \| 0.4740 \| 0.5917 \| 0.7692 \| \| 0.0692 \| 6.3786 \| 1550 \| 0.5937 \| 0.3463 \| 0.5937 \| 0.7705 \| \| 0.0692 \| 6.3868 \| 1552 \| 0.5984 \| 0.3811 \| 0.5984 \| 0.7736 \| \| 0.0692 \| 6.3951 \| 1554 \| 0.6104 \| 0.3333 \| 0.6104 \| 0.7813 \| \| 0.0692 \| 6.4033 \| 1556 \| 0.6300 \| 0.3333 \| 0.6300 \| 0.7937 \| \| 0.0692 \| 6.4115 \| 1558 \| 0.6186 \| 0.3333 \| 0.6186 \| 0.7865 \| \| 0.0692 \| 6.4198 \| 1560 \| 0.6094 \| 0.3811 \| 0.6094 \| 0.7806 \| \| 0.0692 \| 6.4280 \| 1562 \| 0.6105 \| 0.3811 \| 0.6105 \| 0.7814 \| \| 0.0692 \| 6.4362 \| 1564 \| 0.6063 \| 0.2881 \| 0.6063 \| 0.7787 \| \| 0.0692 \| 6.4444 \| 1566 \| 0.6054 \| 0.2881 \| 0.6054 \| 0.7781 \| \| 0.0692 \| 6.4527 \| 1568 \| 0.6045 \| 0.2881 \| 0.6045 \| 0.7775 \| \| 0.0692 \| 6.4609 \| 1570 \| 0.6082 \| 0.2759 \| 0.6082 \| 0.7798 \| \| 0.0692 \| 6.4691 \| 1572 \| 0.6141 \| 0.2186 \| 0.6141 \| 0.7836 \| \| 0.0692 \| 6.4774 \| 1574 \| 0.6320 \| 0.2355 \| 0.6320 \| 0.7950 \| \| 0.0692 \| 6.4856 \| 1576 \| 0.6476 \| 0.2355 \| 0.6476 \| 0.8047 \| \| 0.0692 \| 6.4938 \| 1578 \| 0.6600 \| 0.1818 \| 0.6600 \| 0.8124 \| \| 0.0692 \| 6.5021 \| 1580 \| 0.6625 \| 0.2188 \| 0.6625 \| 0.8139 \| \| 0.0692 \| 6.5103 \| 1582 \| 0.6635 \| 0.2188 \| 0.6635 \| 0.8145 \| \| 0.0692 \| 6.5185 \| 1584 \| 0.6506 \| 0.2188 \| 0.6506 \| 0.8066 \| \| 0.0692 \| 6.5267 \| 1586 \| 0.6407 \| 0.1818 \| 0.6407 \| 0.8004 \| \| 0.0692 \| 6.5350 \| 1588 \| 0.6252 \| 0.2355 \| 0.6252 \| 0.7907 \| \| 0.0692 \| 6.5432 \| 1590 \| 0.6079 \| 0.2186 \| 0.6079 \| 0.7797 \| \| 0.0692 \| 6.5514 \| 1592 \| 0.5948 \| 0.2186 \| 0.5948 \| 0.7712 \| \| 0.0692 \| 6.5597 \| 1594 \| 0.5883 \| 0.4273 \| 0.5883 \| 0.7670 \| \| 0.0692 \| 6.5679 \| 1596 \| 0.5897 \| 0.3077 \| 0.5897 \| 0.7679 \| \| 0.0692 \| 6.5761 \| 1598 \| 0.5946 \| 0.3226 \| 0.5946 \| 0.7711 \| \| 0.0692 \| 6.5844 \| 1600 \| 0.5944 \| 0.3226 \| 0.5944 \| 0.7710 \| \| 0.0692 \| 6.5926 \| 1602 \| 0.5929 \| 0.3467 \| 0.5929 \| 0.7700 \| \| 0.0692 \| 6.6008 \| 1604 \| 0.5930 \| 0.3724 \| 0.5930 \| 0.7701 \| \| 0.0692 \| 6.6091 \| 1606 \| 0.5982 \| 0.3724 \| 0.5982 \| 0.7734 \| \| 0.0692 \| 6.6173 \| 1608 \| 0.6042 \| 0.3724 \| 0.6042 \| 0.7773 \| \| 0.0692 \| 6.6255 \| 1610 \| 0.6068 \| 0.3724 \| 0.6068 \| 0.7790 \| \| 0.0692 \| 6.6337 \| 1612 \| 0.6094 \| 0.3724 \| 0.6094 \| 0.7806 \| \| 0.0692 \| 6.6420 \| 1614 \| 0.6169 \| 0.2186 \| 0.6169 \| 0.7854 \| \| 0.0692 \| 6.6502 \| 1616 \| 0.6346 \| 0.2186 \| 0.6346 \| 0.7966 \| \| 0.0692 \| 6.6584 \| 1618 \| 0.6467 \| 0.2186 \| 0.6467 \| 0.8042 \| \| 0.0692 \| 6.6667 \| 1620 \| 0.6422 \| 0.2186 \| 0.6422 \| 0.8014 \| \| 0.0692 \| 6.6749 \| 1622 \| 0.6255 \| 0.2186 \| 0.6255 \| 0.7909 \| \| 0.0692 \| 6.6831 \| 1624 \| 0.6098 \| 0.3724 \| 0.6098 \| 0.7809 \| \| 0.0692 \| 6.6914 \| 1626 \| 0.5949 \| 0.3724 \| 0.5949 \| 0.7713 \| \| 0.0692 \| 6.6996 \| 1628 \| 0.5834 \| 0.3724 \| 0.5834 \| 0.7638 \| \| 0.0692 \| 6.7078 \| 1630 \| 0.5793 \| 0.3724 \| 0.5793 \| 0.7611 \| \| 0.0692 \| 6.7160 \| 1632 \| 0.5767 \| 0.3318 \| 0.5767 \| 0.7594 \| \| 0.0692 \| 6.7243 \| 1634 \| 0.5749 \| 0.2921 \| 0.5749 \| 0.7582 \| \| 0.0692 \| 6.7325 \| 1636 \| 0.5739 \| 0.2921 \| 0.5739 \| 0.7576 \| \| 0.0692 \| 6.7407 \| 1638 \| 0.5732 \| 0.2921 \| 0.5732 \| 0.7571 \| \| 0.0692 \| 6.7490 \| 1640 \| 0.5720 \| 0.3318 \| 0.5720 \| 0.7563 \| \| 0.0692 \| 6.7572 \| 1642 \| 0.5721 \| 0.3724 \| 0.5721 \| 0.7564 \| \| 0.0692 \| 6.7654 \| 1644 \| 0.5810 \| 0.3724 \| 0.5810 \| 0.7623 \| \| 0.0692 \| 6.7737 \| 1646 \| 0.5880 \| 0.2186 \| 0.5880 \| 0.7668 \| \| 0.0692 \| 6.7819 \| 1648 \| 0.5938 \| 0.2186 \| 0.5938 \| 0.7706 \| \| 0.0692 \| 6.7901 \| 1650 \| 0.5990 \| 0.2186 \| 0.5990 \| 0.7740 \| \| 0.0692 \| 6.7984 \| 1652 \| 0.5994 \| 0.3724 \| 0.5994 \| 0.7742 \| \| 0.0692 \| 6.8066 \| 1654 \| 0.6008 \| 0.3724 \| 0.6008 \| 0.7751 \| \| 0.0692 \| 6.8148 \| 1656 \| 0.6042 \| 0.3724 \| 0.6042 \| 0.7773 \| \| 0.0692 \| 6.8230 \| 1658 \| 0.6091 \| 0.2186 \| 0.6091 \| 0.7805 \| \| 0.0692 \| 6.8313 \| 1660 \| 0.6180 \| 0.2186 \| 0.6180 \| 0.7861 \| \| 0.0692 \| 6.8395 \| 1662 \| 0.6167 \| 0.2186 \| 0.6167 \| 0.7853 \| \| 0.0692 \| 6.8477 \| 1664 \| 0.6168 \| 0.2186 \| 0.6168 \| 0.7854 \| \| 0.0692 \| 6.8560 \| 1666 \| 0.6187 \| 0.2186 \| 0.6187 \| 0.7866 \| \| 0.0692 \| 6.8642 \| 1668 \| 0.6188 \| 0.2186 \| 0.6188 \| 0.7867 \| \| 0.0692 \| 6.8724 \| 1670 \| 0.6151 \| 0.2186 \| 0.6151 \| 0.7843 \| \| 0.0692 \| 6.8807 \| 1672 \| 0.6070 \| 0.2186 \| 0.6070 \| 0.7791 \| \| 0.0692 \| 6.8889 \| 1674 \| 0.6057 \| 0.2186 \| 0.6057 \| 0.7783 \| \| 0.0692 \| 6.8971 \| 1676 \| 0.6044 \| 0.1793 \| 0.6044 \| 0.7774 \| \| 0.0692 \| 6.9053 \| 1678 \| 0.6025 \| 0.1793 \| 0.6025 \| 0.7762 \| \| 0.0692 \| 6.9136 \| 1680 \| 0.5999 \| 0.1793 \| 0.5999 \| 0.7745 \| \| 0.0692 \| 6.9218 \| 1682 \| 0.5964 \| 0.3318 \| 0.5964 \| 0.7723 \| \| 0.0692 \| 6.9300 \| 1684 \| 0.5980 \| 0.3318 \| 0.5980 \| 0.7733 \| \| 0.0692 \| 6.9383 \| 1686 \| 0.6003 \| 0.1793 \| 0.6003 \| 0.7748 \| \| 0.0692 \| 6.9465 \| 1688 \| 0.6075 \| 0.1992 \| 0.6075 \| 0.7794 \| \| 0.0692 \| 6.9547 \| 1690 \| 0.6167 \| 0.1992 \| 0.6167 \| 0.7853 \| \| 0.0692 \| 6.9630 \| 1692 \| 0.6238 \| 0.1992 \| 0.6238 \| 0.7898 \| \| 0.0692 \| 6.9712 \| 1694 \| 0.6318 \| 0.1992 \| 0.6318 \| 0.7949 \| \| 0.0692 \| 6.9794 \| 1696 \| 0.6347 \| 0.1793 \| 0.6347 \| 0.7967 \| \| 0.0692 \| 6.9877 \| 1698 \| 0.6336 \| 0.1793 \| 0.6336 \| 0.7960 \| \| 0.0692 \| 6.9959 \| 1700 \| 0.6399 \| 0.2186 \| 0.6399 \| 0.7999 \| \| 0.0692 \| 7.0041 \| 1702 \| 0.6433 \| 0.1600 \| 0.6433 \| 0.8020 \| \| 0.0692 \| 7.0123 \| 1704 \| 0.6346 \| 0.2186 \| 0.6346 \| 0.7966 \| \| 0.0692 \| 7.0206 \| 1706 \| 0.6267 \| 0.2186 \| 0.6267 \| 0.7916 \| \| 0.0692 \| 7.0288 \| 1708 \| 0.6184 \| 0.2186 \| 0.6184 \| 0.7864 \| \| 0.0692 \| 7.0370 \| 1710 \| 0.6157 \| 0.2186 \| 0.6157 \| 0.7846 \| \| 0.0692 \| 7.0453 \| 1712 \| 0.6129 \| 0.1793 \| 0.6129 \| 0.7829 \| \| 0.0692 \| 7.0535 \| 1714 \| 0.6149 \| 0.2186 \| 0.6149 \| 0.7842 \| \| 0.0692 \| 7.0617 \| 1716 \| 0.6210 \| 0.1600 \| 0.6210 \| 0.7880 \| \| 0.0692 \| 7.0700 \| 1718 \| 0.6209 \| 0.2186 \| 0.6209 \| 0.7880 \| \| 0.0692 \| 7.0782 \| 1720 \| 0.6126 \| 0.1793 \| 0.6126 \| 0.7827 \| \| 0.0692 \| 7.0864 \| 1722 \| 0.6086 \| 0.1793 \| 0.6086 \| 0.7801 \| \| 0.0692 \| 7.0947 \| 1724 \| 0.6144 \| 0.1793 \| 0.6144 \| 0.7839 \| \| 0.0692 \| 7.1029 \| 1726 \| 0.6169 \| 0.1992 \| 0.6169 \| 0.7854 \| \| 0.0692 \| 7.1111 \| 1728 \| 0.6278 \| 0.1992 \| 0.6278 \| 0.7923 \| \| 0.0692 \| 7.1193 \| 1730 \| 0.6458 \| 0.2355 \| 0.6458 \| 0.8036 \| \| 0.0692 \| 7.1276 \| 1732 \| 0.6782 \| 0.2355 \| 0.6782 \| 0.8235 \| \| 0.0692 \| 7.1358 \| 1734 \| 0.6941 \| 0.1818 \| 0.6941 \| 0.8331 \| \| 0.0692 \| 7.1440 \| 1736 \| 0.6847 \| 0.2355 \| 0.6847 \| 0.8275 \| \| 0.0692 \| 7.1523 \| 1738 \| 0.6607 \| 0.2355 \| 0.6607 \| 0.8129 \| \| 0.0692 \| 7.1605 \| 1740 \| 0.6388 \| 0.2355 \| 0.6388 \| 0.7993 \| \| 0.0692 \| 7.1687 \| 1742 \| 0.6237 \| 0.1992 \| 0.6237 \| 0.7898 \| \| 0.0692 \| 7.1770 \| 1744 \| 0.6138 \| 0.3396 \| 0.6138 \| 0.7835 \| \| 0.0692 \| 7.1852 \| 1746 \| 0.6140 \| 0.3396 \| 0.6140 \| 0.7836 \| \| 0.0692 \| 7.1934 \| 1748 \| 0.6138 \| 0.3771 \| 0.6138 \| 0.7835 \| \| 0.0692 \| 7.2016 \| 1750 \| 0.6196 \| 0.2355 \| 0.6196 \| 0.7871 \| \| 0.0692 \| 7.2099 \| 1752 \| 0.6268 \| 0.2355 \| 0.6268 \| 0.7917 \| \| 0.0692 \| 7.2181 \| 1754 \| 0.6321 \| 0.2355 \| 0.6321 \| 0.7950 \| \| 0.0692 \| 7.2263 \| 1756 \| 0.6372 \| 0.2355 \| 0.6372 \| 0.7982 \| \| 0.0692 \| 7.2346 \| 1758 \| 0.6410 \| 0.2355 \| 0.6410 \| 0.8007 \| \| 0.0692 \| 7.2428 \| 1760 \| 0.6502 \| 0.2355 \| 0.6502 \| 0.8064 \| \| 0.0692 \| 7.2510 \| 1762 \| 0.6506 \| 0.2355 \| 0.6506 \| 0.8066 \| \| 0.0692 \| 7.2593 \| 1764 \| 0.6459 \| 0.2355 \| 0.6459 \| 0.8037 \| \| 0.0692 \| 7.2675 \| 1766 \| 0.6486 \| 0.2355 \| 0.6486 \| 0.8053 \| \| 0.0692 \| 7.2757 \| 1768 \| 0.6505 \| 0.2355 \| 0.6505 \| 0.8065 \| \| 0.0692 \| 7.2840 \| 1770 \| 0.6471 \| 0.3396 \| 0.6471 \| 0.8044 \| \| 0.0692 \| 7.2922 \| 1772 \| 0.6450 \| 0.3396 \| 0.6450 \| 0.8031 \| \| 0.0692 \| 7.3004 \| 1774 \| 0.6465 \| 0.3396 \| 0.6465 \| 0.8041 \| \| 0.0692 \| 7.3086 \| 1776 \| 0.6521 \| 0.3396 \| 0.6521 \| 0.8076 \| \| 0.0692 \| 7.3169 \| 1778 \| 0.6630 \| 0.1992 \| 0.6630 \| 0.8143 \| \| 0.0692 \| 7.3251 \| 1780 \| 0.6659 \| 0.1992 \| 0.6659 \| 0.8160 \| \| 0.0692 \| 7.3333 \| 1782 \| 0.6630 \| 0.3396 \| 0.6630 \| 0.8143 \| \| 0.0692 \| 7.3416 \| 1784 \| 0.6656 \| 0.1992 \| 0.6656 \| 0.8158 \| \| 0.0692 \| 7.3498 \| 1786 \| 0.6680 \| 0.2355 \| 0.6680 \| 0.8173 \| \| 0.0692 \| 7.3580 \| 1788 \| 0.6584 \| 0.3396 \| 0.6584 \| 0.8114 \| \| 0.0692 \| 7.3663 \| 1790 \| 0.6572 \| 0.3396 \| 0.6572 \| 0.8107 \| \| 0.0692 \| 7.3745 \| 1792 \| 0.6563 \| 0.3771 \| 0.6563 \| 0.8101 \| \| 0.0692 \| 7.3827 \| 1794 \| 0.6627 \| 0.3771 \| 0.6627 \| 0.8141 \| \| 0.0692 \| 7.3909 \| 1796 \| 0.6650 \| 0.2355 \| 0.6650 \| 0.8155 \| \| 0.0692 \| 7.3992 \| 1798 \| 0.6579 \| 0.2355 \| 0.6579 \| 0.8111 \| \| 0.0692 \| 7.4074 \| 1800 \| 0.6494 \| 0.3771 \| 0.6494 \| 0.8059 \| \| 0.0692 \| 7.4156 \| 1802 \| 0.6377 \| 0.3724 \| 0.6377 \| 0.7985 \| \| 0.0692 \| 7.4239 \| 1804 \| 0.6364 \| 0.3724 \| 0.6364 \| 0.7978 \| \| 0.0692 \| 7.4321 \| 1806 \| 0.6459 \| 0.3724 \| 0.6459 \| 0.8037 \| \| 0.0692 \| 7.4403 \| 1808 \| 0.6677 \| 0.2355 \| 0.6677 \| 0.8171 \| \| 0.0692 \| 7.4486 \| 1810 \| 0.6834 \| 0.2355 \| 0.6834 \| 0.8267 \| \| 0.0692 \| 7.4568 \| 1812 \| 0.7022 \| 0.2355 \| 0.7022 \| 0.8380 \| \| 0.0692 \| 7.4650 \| 1814 \| 0.7093 \| 0.2355 \| 0.7093 \| 0.8422 \| \| 0.0692 \| 7.4733 \| 1816 \| 0.7180 \| 0.2355 \| 0.7180 \| 0.8474 \| \| 0.0692 \| 7.4815 \| 1818 \| 0.7246 \| 0.3333 \| 0.7246 \| 0.8513 \| \| 0.0692 \| 7.4897 \| 1820 \| 0.7286 \| 0.3333 \| 0.7286 \| 0.8536 \| \| 0.0692 \| 7.4979 \| 1822 \| 0.7242 \| 0.3333 \| 0.7242 \| 0.8510 \| \| 0.0692 \| 7.5062 \| 1824 \| 0.7173 \| 0.3333 \| 0.7173 \| 0.8470 \| \| 0.0692 \| 7.5144 \| 1826 \| 0.7155 \| 0.3333 \| 0.7155 \| 0.8459 \| \| 0.0692 \| 7.5226 \| 1828 \| 0.6998 \| 0.2355 \| 0.6998 \| 0.8365 \| \| 0.0692 \| 7.5309 \| 1830 \| 0.6859 \| 0.2355 \| 0.6859 \| 0.8282 \| \| 0.0692 \| 7.5391 \| 1832 \| 0.6722 \| 0.2355 \| 0.6722 \| 0.8199 \| \| 0.0692 \| 7.5473 \| 1834 \| 0.6616 \| 0.3771 \| 0.6616 \| 0.8134 \| \| 0.0692 \| 7.5556 \| 1836 \| 0.6468 \| 0.3771 \| 0.6468 \| 0.8043 \| \| 0.0692 \| 7.5638 \| 1838 \| 0.6428 \| 0.3771 \| 0.6428 \| 0.8018 \| \| 0.0692 \| 7.5720 \| 1840 \| 0.6488 \| 0.3771 \| 0.6488 \| 0.8055 \| \| 0.0692 \| 7.5802 \| 1842 \| 0.6507 \| 0.3771 \| 0.6507 \| 0.8067 \| \| 0.0692 \| 7.5885 \| 1844 \| 0.6520 \| 0.3771 \| 0.6520 \| 0.8075 \| \| 0.0692 \| 7.5967 \| 1846 \| 0.6585 \| 0.3771 \| 0.6585 \| 0.8115 \| \| 0.0692 \| 7.6049 \| 1848 \| 0.6589 \| 0.3771 \| 0.6589 \| 0.8117 \| \| 0.0692 \| 7.6132 \| 1850 \| 0.6589 \| 0.3771 \| 0.6589 \| 0.8117 \| \| 0.0692 \| 7.6214 \| 1852 \| 0.6684 \| 0.2355 \| 0.6684 \| 0.8175 \| \| 0.0692 \| 7.6296 \| 1854 \| 0.6832 \| 0.2355 \| 0.6832 \| 0.8265 \| \| 0.0692 \| 7.6379 \| 1856 \| 0.7081 \| 0.3333 \| 0.7081 \| 0.8415 \| \| 0.0692 \| 7.6461 \| 1858 \| 0.7205 \| 0.2846 \| 0.7205 \| 0.8488 \| \| 0.0692 \| 7.6543 \| 1860 \| 0.7250 \| 0.2846 \| 0.7250 \| 0.8515 \| \| 0.0692 \| 7.6626 \| 1862 \| 0.7194 \| 0.2846 \| 0.7194 \| 0.8482 \| \| 0.0692 \| 7.6708 \| 1864 \| 0.7073 \| 0.3333 \| 0.7073 \| 0.8410 \| \| 0.0692 \| 7.6790 \| 1866 \| 0.6927 \| 0.2355 \| 0.6927 \| 0.8323 \| \| 0.0692 \| 7.6872 \| 1868 \| 0.6672 \| 0.2355 \| 0.6672 \| 0.8168 \| \| 0.0692 \| 7.6955 \| 1870 \| 0.6434 \| 0.3724 \| 0.6434 \| 0.8022 \| \| 0.0692 \| 7.7037 \| 1872 \| 0.6320 \| 0.3724 \| 0.6320 \| 0.7950 \| \| 0.0692 \| 7.7119 \| 1874 \| 0.6248 \| 0.3318 \| 0.6248 \| 0.7905 \| \| 0.0692 \| 7.7202 \| 1876 \| 0.6213 \| 0.3318 \| 0.6213 \| 0.7882 \| \| 0.0692 \| 7.7284 \| 1878 \| 0.6196 \| 0.3318 \| 0.6196 \| 0.7871 \| \| 0.0692 \| 7.7366 \| 1880 \| 0.6225 \| 0.3318 \| 0.6225 \| 0.7890 \| \| 0.0692 \| 7.7449 \| 1882 \| 0.6360 \| 0.3724 \| 0.6360 \| 0.7975 \| \| 0.0692 \| 7.7531 \| 1884 \| 0.6573 \| 0.3724 \| 0.6573 \| 0.8108 \| \| 0.0692 \| 7.7613 \| 1886 \| 0.6798 \| 0.3724 \| 0.6798 \| 0.8245 \| \| 0.0692 \| 7.7695 \| 1888 \| 0.6981 \| 0.3771 \| 0.6981 \| 0.8355 \| \| 0.0692 \| 7.7778 \| 1890 \| 0.7134 \| 0.2355 \| 0.7134 \| 0.8446 \| \| 0.0692 \| 7.7860 \| 1892 \| 0.7096 \| 0.2355 \| 0.7096 \| 0.8424 \| \| 0.0692 \| 7.7942 \| 1894 \| 0.6947 \| 0.2355 \| 0.6947 \| 0.8335 \| \| 0.0692 \| 7.8025 \| 1896 \| 0.6689 \| 0.2186 \| 0.6689 \| 0.8179 \| \| 0.0692 \| 7.8107 \| 1898 \| 0.6435 \| 0.3724 \| 0.6435 \| 0.8022 \| \| 0.0692 \| 7.8189 \| 1900 \| 0.6295 \| 0.3724 \| 0.6295 \| 0.7934 \| \| 0.0692 \| 7.8272 \| 1902 \| 0.6151 \| 0.3724 \| 0.6151 \| 0.7843 \| \| 0.0692 \| 7.8354 \| 1904 \| 0.6045 \| 0.3724 \| 0.6045 \| 0.7775 \| \| 0.0692 \| 7.8436 \| 1906 \| 0.5975 \| 0.3724 \| 0.5975 \| 0.7730 \| \| 0.0692 \| 7.8519 \| 1908 \| 0.5937 \| 0.3724 \| 0.5937 \| 0.7705 \| \| 0.0692 \| 7.8601 \| 1910 \| 0.5954 \| 0.3724 \| 0.5954 \| 0.7716 \| \| 0.0692 \| 7.8683 \| 1912 \| 0.5982 \| 0.3724 \| 0.5982 \| 0.7735 \| \| 0.0692 \| 7.8765 \| 1914 \| 0.6021 \| 0.3724 \| 0.6021 \| 0.7759 \| \| 0.0692 \| 7.8848 \| 1916 \| 0.6086 \| 0.3724 \| 0.6086 \| 0.7802 \| \| 0.0692 \| 7.8930 \| 1918 \| 0.6219 \| 0.3724 \| 0.6219 \| 0.7886 \| \| 0.0692 \| 7.9012 \| 1920 \| 0.6473 \| 0.3771 \| 0.6473 \| 0.8046 \| \| 0.0692 \| 7.9095 \| 1922 \| 0.6686 \| 0.3771 \| 0.6686 \| 0.8177 \| \| 0.0692 \| 7.9177 \| 1924 \| 0.6928 \| 0.3811 \| 0.6928 \| 0.8324 \| \| 0.0692 \| 7.9259 \| 1926 \| 0.7079 \| 0.4615 \| 0.7079 \| 0.8413 \| \| 0.0692 \| 7.9342 \| 1928 \| 0.7041 \| 0.3811 \| 0.7041 \| 0.8391 \| \| 0.0692 \| 7.9424 \| 1930 \| 0.6896 \| 0.3811 \| 0.6896 \| 0.8304 \| \| 0.0692 \| 7.9506 \| 1932 \| 0.6779 \| 0.3771 \| 0.6779 \| 0.8234 \| \| 0.0692 \| 7.9588 \| 1934 \| 0.6610 \| 0.3771 \| 0.6610 \| 0.8130 \| \| 0.0692 \| 7.9671 \| 1936 \| 0.6406 \| 0.3724 \| 0.6406 \| 0.8004 \| \| 0.0692 \| 7.9753 \| 1938 \| 0.6288 \| 0.3318 \| 0.6288 \| 0.7930 \| \| 0.0692 \| 7.9835 \| 1940 \| 0.6253 \| 0.3318 \| 0.6253 \| 0.7907 \| \| 0.0692 \| 7.9918 \| 1942 \| 0.6240 \| 0.3318 \| 0.6240 \| 0.7899 \| \| 0.0692 \| 8.0 \| 1944 \| 0.6227 \| 0.3318 \| 0.6227 \| 0.7891 \| \| 0.0692 \| 8.0082 \| 1946 \| 0.6165 \| 0.3318 \| 0.6165 \| 0.7852 \| \| 0.0692 \| 8.0165 \| 1948 \| 0.6127 \| 0.3724 \| 0.6127 \| 0.7827 \| \| 0.0692 \| 8.0247 \| 1950 \| 0.6124 \| 0.3724 \| 0.6124 \| 0.7826 \| \| 0.0692 \| 8.0329 \| 1952 \| 0.6120 \| 0.3724 \| 0.6120 \| 0.7823 \| \| 0.0692 \| 8.0412 \| 1954 \| 0.6186 \| 0.3724 \| 0.6186 \| 0.7865 \| \| 0.0692 \| 8.0494 \| 1956 \| 0.6321 \| 0.3724 \| 0.6321 \| 0.7950 \| \| 0.0692 \| 8.0576 \| 1958 \| 0.6434 \| 0.3724 \| 0.6434 \| 0.8021 \| \| 0.0692 \| 8.0658 \| 1960 \| 0.6600 \| 0.3771 \| 0.6600 \| 0.8124 \| \| 0.0692 \| 8.0741 \| 1962 \| 0.6698 \| 0.2355 \| 0.6698 \| 0.8184 \| \| 0.0692 \| 8.0823 \| 1964 \| 0.6623 \| 0.3771 \| 0.6623 \| 0.8138 \| \| 0.0692 \| 8.0905 \| 1966 \| 0.6544 \| 0.3724 \| 0.6544 \| 0.8089 \| \| 0.0692 \| 8.0988 \| 1968 \| 0.6476 \| 0.3724 \| 0.6476 \| 0.8047 \| \| 0.0692 \| 8.1070 \| 1970 \| 0.6421 \| 0.3724 \| 0.6421 \| 0.8013 \| \| 0.0692 \| 8.1152 \| 1972 \| 0.6377 \| 0.3724 \| 0.6377 \| 0.7985 \| \| 0.0692 \| 8.1235 \| 1974 \| 0.6314 \| 0.3318 \| 0.6314 \| 0.7946 \| \| 0.0692 \| 8.1317 \| 1976 \| 0.6277 \| 0.3318 \| 0.6277 \| 0.7923 \| \| 0.0692 \| 8.1399 \| 1978 \| 0.6263 \| 0.3318 \| 0.6263 \| 0.7914 \| \| 0.0692 \| 8.1481 \| 1980 \| 0.6240 \| 0.3318 \| 0.6240 \| 0.7900 \| \| 0.0692 \| 8.1564 \| 1982 \| 0.6229 \| 0.3318 \| 0.6229 \| 0.7892 \| \| 0.0692 \| 8.1646 \| 1984 \| 0.6233 \| 0.3318 \| 0.6233 \| 0.7895 \| \| 0.0692 \| 8.1728 \| 1986 \| 0.6248 \| 0.3318 \| 0.6248 \| 0.7905 \| \| 0.0692 \| 8.1811 \| 1988 \| 0.6294 \| 0.3724 \| 0.6294 \| 0.7934 \| \| 0.0692 \| 8.1893 \| 1990 \| 0.6369 \| 0.3724 \| 0.6369 \| 0.7981 \| \| 0.0692 \| 8.1975 \| 1992 \| 0.6457 \| 0.3724 \| 0.6457 \| 0.8035 \| \| 0.0692 \| 8.2058 \| 1994 \| 0.6554 \| 0.2355 \| 0.6554 \| 0.8096 \| \| 0.0692 \| 8.2140 \| 1996 \| 0.6601 \| 0.2355 \| 0.6601 \| 0.8125 \| \| 0.0692 \| 8.2222 \| 1998 \| 0.6605 \| 0.2355 \| 0.6605 \| 0.8127 \| \| 0.049 \| 8.2305 \| 2000 \| 0.6565 \| 0.2355 \| 0.6565 \| 0.8102 \| \| 0.049 \| 8.2387 \| 2002 \| 0.6439 \| 0.2355 \| 0.6439 \| 0.8024 \| \| 0.049 \| 8.2469 \| 2004 \| 0.6288 \| 0.3724 \| 0.6288 \| 0.7930 \| \| 0.049 \| 8.2551 \| 2006 \| 0.6161 \| 0.3724 \| 0.6161 \| 0.7849 \| \| 0.049 \| 8.2634 \| 2008 \| 0.6110 \| 0.3724 \| 0.6110 \| 0.7816 \| \| 0.049 \| 8.2716 \| 2010 \| 0.6071 \| 0.3318 \| 0.6071 \| 0.7792 \| \| 0.049 \| 8.2798 \| 2012 \| 0.6065 \| 0.3318 \| 0.6065 \| 0.7788 \| \| 0.049 \| 8.2881 \| 2014 \| 0.6065 \| 0.3318 \| 0.6065 \| 0.7788 \| \| 0.049 \| 8.2963 \| 2016 \| 0.6070 \| 0.3318 \| 0.6070 \| 0.7791 \| \| 0.049 \| 8.3045 \| 2018 \| 0.6090 \| 0.3318 \| 0.6090 \| 0.7804 \| \| 0.049 \| 8.3128 \| 2020 \| 0.6126 \| 0.3724 \| 0.6126 \| 0.7827 \| \| 0.049 \| 8.3210 \| 2022 \| 0.6177 \| 0.3724 \| 0.6177 \| 0.7859 \| \| 0.049 \| 8.3292 \| 2024 \| 0.6258 \| 0.3724 \| 0.6258 \| 0.7911 \| \| 0.049 \| 8.3374 \| 2026 \| 0.6331 \| 0.3724 \| 0.6331 \| 0.7957 \| \| 0.049 \| 8.3457 \| 2028 \| 0.6384 \| 0.3724 \| 0.6384 \| 0.7990 \| \| 0.049 \| 8.3539 \| 2030 \| 0.6449 \| 0.3724 \| 0.6449 \| 0.8031 \| \| 0.049 \| 8.3621 \| 2032 \| 0.6463 \| 0.3724 \| 0.6463 \| 0.8039 \| \| 0.049 \| 8.3704 \| 2034 \| 0.6405 \| 0.3724 \| 0.6405 \| 0.8003 \| \| 0.049 \| 8.3786 \| 2036 \| 0.6380 \| 0.3724 \| 0.6380 \| 0.7987 \| \| 0.049 \| 8.3868 \| 2038 \| 0.6386 \| 0.3724 \| 0.6386 \| 0.7992 \| \| 0.049 \| 8.3951 \| 2040 \| 0.6387 \| 0.3724 \| 0.6387 \| 0.7992 \| \| 0.049 \| 8.4033 \| 2042 \| 0.6349 \| 0.3724 \| 0.6349 \| 0.7968 \| \| 0.049 \| 8.4115 \| 2044 \| 0.6330 \| 0.3724 \| 0.6330 \| 0.7956 \| \| 0.049 \| 8.4198 \| 2046 \| 0.6300 \| 0.3724 \| 0.6300 \| 0.7937 \| \| 0.049 \| 8.4280 \| 2048 \| 0.6275 \| 0.3724 \| 0.6275 \| 0.7922 \| \| 0.049 \| 8.4362 \| 2050 \| 0.6270 \| 0.3724 \| 0.6270 \| 0.7918 \| \| 0.049 \| 8.4444 \| 2052 \| 0.6259 \| 0.3724 \| 0.6259 \| 0.7911 \| \| 0.049 \| 8.4527 \| 2054 \| 0.6310 \| 0.3724 \| 0.6310 \| 0.7944 \| \| 0.049 \| 8.4609 \| 2056 \| 0.6363 \| 0.3771 \| 0.6363 \| 0.7977 \| \| 0.049 \| 8.4691 \| 2058 \| 0.6430 \| 0.3771 \| 0.6430 \| 0.8019 \| \| 0.049 \| 8.4774 \| 2060 \| 0.6499 \| 0.3771 \| 0.6499 \| 0.8062 \| \| 0.049 \| 8.4856 \| 2062 \| 0.6491 \| 0.3771 \| 0.6491 \| 0.8056 \| \| 0.049 \| 8.4938 \| 2064 \| 0.6505 \| 0.3771 \| 0.6505 \| 0.8065 \| \| 0.049 \| 8.5021 \| 2066 \| 0.6514 \| 0.3771 \| 0.6514 \| 0.8071 \| \| 0.049 \| 8.5103 \| 2068 \| 0.6522 \| 0.3771 \| 0.6522 \| 0.8076 \| \| 0.049 \| 8.5185 \| 2070 \| 0.6528 \| 0.3771 \| 0.6528 \| 0.8079 \| \| 0.049 \| 8.5267 \| 2072 \| 0.6535 \| 0.3771 \| 0.6535 \| 0.8084 \| \| 0.049 \| 8.5350 \| 2074 \| 0.6525 \| 0.3771 \| 0.6525 \| 0.8078 \| \| 0.049 \| 8.5432 \| 2076 \| 0.6489 \| 0.3771 \| 0.6489 \| 0.8056 \| \| 0.049 \| 8.5514 \| 2078 \| 0.6478 \| 0.3771 \| 0.6478 \| 0.8048 \| \| 0.049 \| 8.5597 \| 2080 \| 0.6491 \| 0.3771 \| 0.6491 \| 0.8057 \| \| 0.049 \| 8.5679 \| 2082 \| 0.6485 \| 0.3771 \| 0.6485 \| 0.8053 \| \| 0.049 \| 8.5761 \| 2084 \| 0.6435 \| 0.3771 \| 0.6435 \| 0.8022 \| \| 0.049 \| 8.5844 \| 2086 \| 0.6410 \| 0.3771 \| 0.6410 \| 0.8006 \| \| 0.049 \| 8.5926 \| 2088 \| 0.6415 \| 0.3771 \| 0.6415 \| 0.8009 \| \| 0.049 \| 8.6008 \| 2090 \| 0.6361 \| 0.3771 \| 0.6361 \| 0.7976 \| \| 0.049 \| 8.6091 \| 2092 \| 0.6311 \| 0.3724 \| 0.6311 \| 0.7944 \| \| 0.049 \| 8.6173 \| 2094 \| 0.6297 \| 0.3724 \| 0.6297 \| 0.7935 \| \| 0.049 \| 8.6255 \| 2096 \| 0.6302 \| 0.3724 \| 0.6302 \| 0.7938 \| \| 0.049 \| 8.6337 \| 2098 \| 0.6291 \| 0.3724 \| 0.6291 \| 0.7931 \| \| 0.049 \| 8.6420 \| 2100 \| 0.6286 \| 0.3771 \| 0.6286 \| 0.7928 \| \| 0.049 \| 8.6502 \| 2102 \| 0.6291 \| 0.3771 \| 0.6291 \| 0.7932 \| \| 0.049 \| 8.6584 \| 2104 \| 0.6278 \| 0.3771 \| 0.6278 \| 0.7923 \| \| 0.049 \| 8.6667 \| 2106 \| 0.6239 \| 0.3771 \| 0.6239 \| 0.7899 \| \| 0.049 \| 8.6749 \| 2108 \| 0.6212 \| 0.3724 \| 0.6212 \| 0.7882 \| \| 0.049 \| 8.6831 \| 2110 \| 0.6200 \| 0.3724 \| 0.6200 \| 0.7874 \| \| 0.049 \| 8.6914 \| 2112 \| 0.6194 \| 0.3724 \| 0.6194 \| 0.7870 \| \| 0.049 \| 8.6996 \| 2114 \| 0.6191 \| 0.3724 \| 0.6191 \| 0.7868 \| \| 0.049 \| 8.7078 \| 2116 \| 0.6220 \| 0.3724 \| 0.6220 \| 0.7886 \| \| 0.049 \| 8.7160 \| 2118 \| 0.6242 \| 0.3724 \| 0.6242 \| 0.7901 \| \| 0.049 \| 8.7243 \| 2120 \| 0.6277 \| 0.3771 \| 0.6277 \| 0.7923 \| \| 0.049 \| 8.7325 \| 2122 \| 0.6324 \| 0.3771 \| 0.6324 \| 0.7952 \| \| 0.049 \| 8.7407 \| 2124 \| 0.6402 \| 0.3771 \| 0.6402 \| 0.8001 \| \| 0.049 \| 8.7490 \| 2126 \| 0.6490 \| 0.3771 \| 0.6490 \| 0.8056 \| \| 0.049 \| 8.7572 \| 2128 \| 0.6551 \| 0.3771 \| 0.6551 \| 0.8094 \| \| 0.049 \| 8.7654 \| 2130 \| 0.6629 \| 0.3811 \| 0.6629 \| 0.8142 \| \| 0.049 \| 8.7737 \| 2132 \| 0.6697 \| 0.25 \| 0.6697 \| 0.8184 \| \| 0.049 \| 8.7819 \| 2134 \| 0.6754 \| 0.25 \| 0.6754 \| 0.8218 \| \| 0.049 \| 8.7901 \| 2136 \| 0.6786 \| 0.25 \| 0.6786 \| 0.8237 \| \| 0.049 \| 8.7984 \| 2138 \| 0.6735 \| 0.25 \| 0.6735 \| 0.8207 \| \| 0.049 \| 8.8066 \| 2140 \| 0.6687 \| 0.3811 \| 0.6687 \| 0.8178 \| \| 0.049 \| 8.8148 \| 2142 \| 0.6623 \| 0.3811 \| 0.6623 \| 0.8138 \| \| 0.049 \| 8.8230 \| 2144 \| 0.6538 \| 0.3771 \| 0.6538 \| 0.8086 \| \| 0.049 \| 8.8313 \| 2146 \| 0.6508 \| 0.3771 \| 0.6508 \| 0.8067 \| \| 0.049 \| 8.8395 \| 2148 \| 0.6494 \| 0.3771 \| 0.6494 \| 0.8059 \| \| 0.049 \| 8.8477 \| 2150 \| 0.6483 \| 0.3771 \| 0.6483 \| 0.8052 \| \| 0.049 \| 8.8560 \| 2152 \| 0.6490 \| 0.3771 \| 0.6490 \| 0.8056 \| \| 0.049 \| 8.8642 \| 2154 \| 0.6531 \| 0.3771 \| 0.6531 \| 0.8082 \| \| 0.049 \| 8.8724 \| 2156 \| 0.6534 \| 0.3771 \| 0.6534 \| 0.8084 \| \| 0.049 \| 8.8807 \| 2158 \| 0.6536 \| 0.3771 \| 0.6536 \| 0.8085 \| \| 0.049 \| 8.8889 \| 2160 \| 0.6551 \| 0.3771 \| 0.6551 \| 0.8094 \| \| 0.049 \| 8.8971 \| 2162 \| 0.6585 \| 0.3771 \| 0.6585 \| 0.8115 \| \| 0.049 \| 8.9053 \| 2164 \| 0.6636 \| 0.3771 \| 0.6636 \| 0.8146 \| \| 0.049 \| 8.9136 \| 2166 \| 0.6703 \| 0.3811 \| 0.6703 \| 0.8187 \| \| 0.049 \| 8.9218 \| 2168 \| 0.6764 \| 0.25 \| 0.6764 \| 0.8224 \| \| 0.049 \| 8.9300 \| 2170 \| 0.6813 \| 0.25 \| 0.6813 \| 0.8254 \| \| 0.049 \| 8.9383 \| 2172 \| 0.6770 \| 0.25 \| 0.6770 \| 0.8228 \| \| 0.049 \| 8.9465 \| 2174 \| 0.6734 \| 0.25 \| 0.6734 \| 0.8206 \| \| 0.049 \| 8.9547 \| 2176 \| 0.6623 \| 0.3771 \| 0.6623 \| 0.8138 \| \| 0.049 \| 8.9630 \| 2178 \| 0.6515 \| 0.3771 \| 0.6515 \| 0.8071 \| \| 0.049 \| 8.9712 \| 2180 \| 0.6446 \| 0.3771 \| 0.6446 \| 0.8029 \| \| 0.049 \| 8.9794 \| 2182 \| 0.6384 \| 0.3771 \| 0.6384 \| 0.7990 \| \| 0.049 \| 8.9877 \| 2184 \| 0.6327 \| 0.3771 \| 0.6327 \| 0.7954 \| \| 0.049 \| 8.9959 \| 2186 \| 0.6299 \| 0.3771 \| 0.6299 \| 0.7937 \| \| 0.049 \| 9.0041 \| 2188 \| 0.6287 \| 0.3771 \| 0.6287 \| 0.7929 \| \| 0.049 \| 9.0123 \| 2190 \| 0.6276 \| 0.3771 \| 0.6276 \| 0.7922 \| \| 0.049 \| 9.0206 \| 2192 \| 0.6292 \| 0.3771 \| 0.6292 \| 0.7932 \| \| 0.049 \| 9.0288 \| 2194 \| 0.6322 \| 0.3771 \| 0.6322 \| 0.7951 \| \| 0.049 \| 9.0370 \| 2196 \| 0.6348 \| 0.3771 \| 0.6348 \| 0.7967 \| \| 0.049 \| 9.0453 \| 2198 \| 0.6341 \| 0.3771 \| 0.6341 \| 0.7963 \| \| 0.049 \| 9.0535 \| 2200 \| 0.6337 \| 0.3771 \| 0.6337 \| 0.7960 \| \| 0.049 \| 9.0617 \| 2202 \| 0.6330 \| 0.3771 \| 0.6330 \| 0.7956 \| \| 0.049 \| 9.0700 \| 2204 \| 0.6337 \| 0.3771 \| 0.6337 \| 0.7960 \| \| 0.049 \| 9.0782 \| 2206 \| 0.6353 \| 0.3771 \| 0.6353 \| 0.7970 \| \| 0.049 \| 9.0864 \| 2208 \| 0.6354 \| 0.3771 \| 0.6354 \| 0.7971 \| \| 0.049 \| 9.0947 \| 2210 \| 0.6340 \| 0.3771 \| 0.6340 \| 0.7962 \| \| 0.049 \| 9.1029 \| 2212 \| 0.6323 \| 0.3771 \| 0.6323 \| 0.7952 \| \| 0.049 \| 9.1111 \| 2214 \| 0.6307 \| 0.3771 \| 0.6307 \| 0.7942 \| \| 0.049 \| 9.1193 \| 2216 \| 0.6310 \| 0.3771 \| 0.6310 \| 0.7944 \| \| 0.049 \| 9.1276 \| 2218 \| 0.6330 \| 0.3771 \| 0.6330 \| 0.7956 \| \| 0.049 \| 9.1358 \| 2220 \| 0.6362 \| 0.3771 \| 0.6362 \| 0.7976 \| \| 0.049 \| 9.1440 \| 2222 \| 0.6371 \| 0.3771 \| 0.6371 \| 0.7982 \| \| 0.049 \| 9.1523 \| 2224 \| 0.6375 \| 0.2355 \| 0.6375 \| 0.7984 \| \| 0.049 \| 9.1605 \| 2226 \| 0.6362 \| 0.2355 \| 0.6362 \| 0.7976 \| \| 0.049 \| 9.1687 \| 2228 \| 0.6317 \| 0.3771 \| 0.6317 \| 0.7948 \| \| 0.049 \| 9.1770 \| 2230 \| 0.6271 \| 0.3771 \| 0.6271 \| 0.7919 \| \| 0.049 \| 9.1852 \| 2232 \| 0.6244 \| 0.3771 \| 0.6244 \| 0.7902 \| \| 0.049 \| 9.1934 \| 2234 \| 0.6226 \| 0.3771 \| 0.6226 \| 0.7891 \| \| 0.049 \| 9.2016 \| 2236 \| 0.6229 \| 0.3771 \| 0.6229 \| 0.7893 \| \| 0.049 \| 9.2099 \| 2238 \| 0.6242 \| 0.3771 \| 0.6242 \| 0.7901 \| \| 0.049 \| 9.2181 \| 2240 \| 0.6268 \| 0.3771 \| 0.6268 \| 0.7917 \| \| 0.049 \| 9.2263 \| 2242 \| 0.6308 \| 0.2355 \| 0.6308 \| 0.7942 \| \| 0.049 \| 9.2346 \| 2244 \| 0.6343 \| 0.2355 \| 0.6343 \| 0.7964 \| \| 0.049 \| 9.2428 \| 2246 \| 0.6362 \| 0.2355 \| 0.6362 \| 0.7977 \| \| 0.049 \| 9.2510 \| 2248 \| 0.6356 \| 0.3771 \| 0.6356 \| 0.7973 \| \| 0.049 \| 9.2593 \| 2250 \| 0.6348 \| 0.3771 \| 0.6348 \| 0.7967 \| \| 0.049 \| 9.2675 \| 2252 \| 0.6353 \| 0.3771 \| 0.6353 \| 0.7971 \| \| 0.049 \| 9.2757 \| 2254 \| 0.6365 \| 0.3771 \| 0.6365 \| 0.7978 \| \| 0.049 \| 9.2840 \| 2256 \| 0.6386 \| 0.3771 \| 0.6386 \| 0.7991 \| \| 0.049 \| 9.2922 \| 2258 \| 0.6411 \| 0.3771 \| 0.6411 \| 0.8007 \| \| 0.049 \| 9.3004 \| 2260 \| 0.6432 \| 0.3771 \| 0.6432 \| 0.8020 \| \| 0.049 \| 9.3086 \| 2262 \| 0.6466 \| 0.3771 \| 0.6466 \| 0.8041 \| \| 0.049 \| 9.3169 \| 2264 \| 0.6496 \| 0.2355 \| 0.6496 \| 0.8060 \| \| 0.049 \| 9.3251 \| 2266 \| 0.6533 \| 0.2355 \| 0.6533 \| 0.8083 \| \| 0.049 \| 9.3333 \| 2268 \| 0.6567 \| 0.2355 \| 0.6567 \| 0.8104 \| \| 0.049 \| 9.3416 \| 2270 \| 0.6590 \| 0.2355 \| 0.6590 \| 0.8118 \| \| 0.049 \| 9.3498 \| 2272 \| 0.6598 \| 0.2355 \| 0.6598 \| 0.8123 \| \| 0.049 \| 9.3580 \| 2274 \| 0.6620 \| 0.2355 \| 0.6620 \| 0.8136 \| \| 0.049 \| 9.3663 \| 2276 \| 0.6626 \| 0.2355 \| 0.6626 \| 0.8140 \| \| 0.049 \| 9.3745 \| 2278 \| 0.6644 \| 0.2355 \| 0.6644 \| 0.8151 \| \| 0.049 \| 9.3827 \| 2280 \| 0.6670 \| 0.2355 \| 0.6670 \| 0.8167 \| \| 0.049 \| 9.3909 \| 2282 \| 0.6688 \| 0.2355 \| 0.6688 \| 0.8178 \| \| 0.049 \| 9.3992 \| 2284 \| 0.6715 \| 0.2355 \| 0.6715 \| 0.8195 \| \| 0.049 \| 9.4074 \| 2286 \| 0.6731 \| 0.2355 \| 0.6731 \| 0.8204 \| \| 0.049 \| 9.4156 \| 2288 \| 0.6725 \| 0.2355 \| 0.6725 \| 0.8200 \| \| 0.049 \| 9.4239 \| 2290 \| 0.6693 \| 0.2355 \| 0.6693 \| 0.8181 \| \| 0.049 \| 9.4321 \| 2292 \| 0.6665 \| 0.2355 \| 0.6665 \| 0.8164 \| \| 0.049 \| 9.4403 \| 2294 \| 0.6625 \| 0.2355 \| 0.6625 \| 0.8140 \| \| 0.049 \| 9.4486 \| 2296 \| 0.6584 \| 0.2355 \| 0.6584 \| 0.8114 \| \| 0.049 \| 9.4568 \| 2298 \| 0.6572 \| 0.2355 \| 0.6572 \| 0.8107 \| \| 0.049 \| 9.4650 \| 2300 \| 0.6573 \| 0.2355 \| 0.6573 \| 0.8108 \| \| 0.049 \| 9.4733 \| 2302 \| 0.6573 \| 0.2355 \| 0.6573 \| 0.8107 \| \| 0.049 \| 9.4815 \| 2304 \| 0.6582 \| 0.2355 \| 0.6582 \| 0.8113 \| \| 0.049 \| 9.4897 \| 2306 \| 0.6596 \| 0.2355 \| 0.6596 \| 0.8122 \| \| 0.049 \| 9.4979 \| 2308 \| 0.6603 \| 0.2355 \| 0.6603 \| 0.8126 \| \| 0.049 \| 9.5062 \| 2310 \| 0.6614 \| 0.2355 \| 0.6614 \| 0.8133 \| \| 0.049 \| 9.5144 \| 2312 \| 0.6614 \| 0.2355 \| 0.6614 \| 0.8133 \| \| 0.049 \| 9.5226 \| 2314 \| 0.6610 \| 0.2355 \| 0.6610 \| 0.8130 \| \| 0.049 \| 9.5309 \| 2316 \| 0.6613 \| 0.2355 \| 0.6613 \| 0.8132 \| \| 0.049 \| 9.5391 \| 2318 \| 0.6608 \| 0.2355 \| 0.6608 \| 0.8129 \| \| 0.049 \| 9.5473 \| 2320 \| 0.6601 \| 0.2355 \| 0.6601 \| 0.8124 \| \| 0.049 \| 9.5556 \| 2322 \| 0.6588 \| 0.2355 \| 0.6588 \| 0.8117 \| \| 0.049 \| 9.5638 \| 2324 \| 0.6564 \| 0.2355 \| 0.6564 \| 0.8102 \| \| 0.049 \| 9.5720 \| 2326 \| 0.6540 \| 0.2355 \| 0.6540 \| 0.8087 \| \| 0.049 \| 9.5802 \| 2328 \| 0.6524 \| 0.2355 \| 0.6524 \| 0.8077 \| \| 0.049 \| 9.5885 \| 2330 \| 0.6524 \| 0.2355 \| 0.6524 \| 0.8077 \| \| 0.049 \| 9.5967 \| 2332 \| 0.6513 \| 0.3771 \| 0.6513 \| 0.8070 \| \| 0.049 \| 9.6049 \| 2334 \| 0.6504 \| 0.3771 \| 0.6504 \| 0.8065 \| \| 0.049 \| 9.6132 \| 2336 \| 0.6489 \| 0.3771 \| 0.6489 \| 0.8055 \| \| 0.049 \| 9.6214 \| 2338 \| 0.6471 \| 0.3771 \| 0.6471 \| 0.8044 \| \| 0.049 \| 9.6296 \| 2340 \| 0.6455 \| 0.3771 \| 0.6455 \| 0.8034 \| \| 0.049 \| 9.6379 \| 2342 \| 0.6439 \| 0.3771 \| 0.6439 \| 0.8024 \| \| 0.049 \| 9.6461 \| 2344 \| 0.6426 \| 0.3771 \| 0.6426 \| 0.8016 \| \| 0.049 \| 9.6543 \| 2346 \| 0.6421 \| 0.3771 \| 0.6421 \| 0.8013 \| \| 0.049 \| 9.6626 \| 2348 \| 0.6419 \| 0.3396 \| 0.6419 \| 0.8012 \| \| 0.049 \| 9.6708 \| 2350 \| 0.6421 \| 0.3771 \| 0.6421 \| 0.8013 \| \| 0.049 \| 9.6790 \| 2352 \| 0.6414 \| 0.3771 \| 0.6414 \| 0.8009 \| \| 0.049 \| 9.6872 \| 2354 \| 0.6410 \| 0.3771 \| 0.6410 \| 0.8006 \| \| 0.049 \| 9.6955 \| 2356 \| 0.6405 \| 0.3771 \| 0.6405 \| 0.8003 \| \| 0.049 \| 9.7037 \| 2358 \| 0.6402 \| 0.3771 \| 0.6402 \| 0.8001 \| \| 0.049 \| 9.7119 \| 2360 \| 0.6400 \| 0.3771 \| 0.6400 \| 0.8000 \| \| 0.049 \| 9.7202 \| 2362 \| 0.6402 \| 0.3771 \| 0.6402 \| 0.8001 \| \| 0.049 \| 9.7284 \| 2364 \| 0.6404 \| 0.3771 \| 0.6404 \| 0.8003 \| \| 0.049 \| 9.7366 \| 2366 \| 0.6406 \| 0.3771 \| 0.6406 \| 0.8004 \| \| 0.049 \| 9.7449 \| 2368 \| 0.6408 \| 0.3771 \| 0.6408 \| 0.8005 \| \| 0.049 \| 9.7531 \| 2370 \| 0.6414 \| 0.3771 \| 0.6414 \| 0.8008 \| \| 0.049 \| 9.7613 \| 2372 \| 0.6420 \| 0.3771 \| 0.6420 \| 0.8013 \| \| 0.049 \| 9.7695 \| 2374 \| 0.6425 \| 0.3771 \| 0.6425 \| 0.8016 \| \| 0.049 \| 9.7778 \| 2376 \| 0.6432 \| 0.3771 \| 0.6432 \| 0.8020 \| \| 0.049 \| 9.7860 \| 2378 \| 0.6438 \| 0.3771 \| 0.6438 \| 0.8024 \| \| 0.049 \| 9.7942 \| 2380 \| 0.6444 \| 0.3771 \| 0.6444 \| 0.8027 \| \| 0.049 \| 9.8025 \| 2382 \| 0.6450 \| 0.3771 \| 0.6450 \| 0.8031 \| \| 0.049 \| 9.8107 \| 2384 \| 0.6448 \| 0.3771 \| 0.6448 \| 0.8030 \| \| 0.049 \| 9.8189 \| 2386 \| 0.6444 \| 0.3771 \| 0.6444 \| 0.8027 \| \| 0.049 \| 9.8272 \| 2388 \| 0.6439 \| 0.3771 \| 0.6439 \| 0.8025 \| \| 0.049 \| 9.8354 \| 2390 \| 0.6433 \| 0.3771 \| 0.6433 \| 0.8021 \| \| 0.049 \| 9.8436 \| 2392 \| 0.6433 \| 0.3771 \| 0.6433 \| 0.8020 \| \| 0.049 \| 9.8519 \| 2394 \| 0.6430 \| 0.3771 \| 0.6430 \| 0.8019 \| \| 0.049 \| 9.8601 \| 2396 \| 0.6428 \| 0.3771 \| 0.6428 \| 0.8018 \| \| 0.049 \| 9.8683 \| 2398 \| 0.6428 \| 0.3771 \| 0.6428 \| 0.8018 \| \| 0.049 \| 9.8765 \| 2400 \| 0.6432 \| 0.3771 \| 0.6432 \| 0.8020 \| \| 0.049 \| 9.8848 \| 2402 \| 0.6432 \| 0.3771 \| 0.6432 \| 0.8020 \| \| 0.049 \| 9.8930 \| 2404 \| 0.6433 \| 0.3771 \| 0.6433 \| 0.8020 \| \| 0.049 \| 9.9012 \| 2406 \| 0.6436 \| 0.3771 \| 0.6436 \| 0.8023 \| \| 0.049 \| 9.9095 \| 2408 \| 0.6437 \| 0.3771 \| 0.6437 \| 0.8023 \| \| 0.049 \| 9.9177 \| 2410 \| 0.6438 \| 0.3771 \| 0.6438 \| 0.8023 \| \| 0.049 \| 9.9259 \| 2412 \| 0.6436 \| 0.3771 \| 0.6436 \| 0.8023 \| \| 0.049 \| 9.9342 \| 2414 \| 0.6437 \| 0.3771 \| 0.6437 \| 0.8023 \| \| 0.049 \| 9.9424 \| 2416 \| 0.6437 \| 0.3771 \| 0.6437 \| 0.8023 \| \| 0.049 \| 9.9506 \| 2418 \| 0.6439 \| 0.3771 \| 0.6439 \| 0.8024 \| \| 0.049 \| 9.9588 \| 2420 \| 0.6439 \| 0.3771 \| 0.6439 \| 0.8024 \| \| 0.049 \| 9.9671 \| 2422 \| 0.6439 \| 0.3771 \| 0.6439 \| 0.8024 \| \| 0.049 \| 9.9753 \| 2424 \| 0.6438 \| 0.3771 \| 0.6438 \| 0.8024 \| \| 0.049 \| 9.9835 \| 2426 \| 0.6438 \| 0.3771 \| 0.6438 \| 0.8023 \| \| 0.049 \| 9.9918 \| 2428 \| 0.6437 \| 0.3771 \| 0.6437 \| 0.8023 \| \| 0.049 \| 10.0 \| 2430 \| 0.6437 \| 0.3771 \| 0.6437 \| 0.8023 \| ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1
DoeyLLM/OneLLM-Doey-ChatQA-V1-Llama-3.2-1B	DoeyLLM	2024-11-25T07:17:32Z	89	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:nvidia/ChatQA-Training-Data", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T05:02:44Z	--- license: apache-2.0 datasets: - nvidia/ChatQA-Training-Data language: - en base_model: - meta-llama/Llama-3.2-1B-Instruct pipeline_tag: text-generation library_name: transformers --- ## Model Summary This model is a fine-tuned version of LLaMA 3.2-1B, optimized using LoRA (Low-Rank Adaptation) on the [NVIDIA ChatQA-Training-Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data). It is tailored for conversational AI, question answering, and other instruction-following tasks, with support for sequences up to 1024 tokens. --- ## Key Features - Base Model: LLaMA 3.2-1B - Fine-Tuning Framework: LoRA - Dataset: NVIDIA ChatQA-Training-Data - Max Sequence Length: 1024 tokens - Use Case: Instruction-based tasks, question answering, conversational AI. ## Model Usage This fine-tuned model is suitable for: - Conversational AI: Chatbots and dialogue agents with improved contextual understanding. - Question Answering: Generating concise and accurate answers to user queries. - Instruction Following: Responding to structured prompts. - Long-Context Tasks: Processing sequences up to 1024 tokens for long-text reasoning. # How to Use DoeyLLM / OneLLM-Doey-V1-Llama-3.2-1B-Instruct This guide explains how to use the DoeyLLM model on both app (iOS) and PC platforms. --- ## App: Use with OneLLM OneLLM brings versatile large language models (LLMs) to your device—Llama, Gemma, Qwen, Mistral, and more. Enjoy private, offline GPT and AI tools tailored to your needs. With OneLLM, experience the capabilities of leading-edge language models directly on your device, all without an internet connection. Get fast, reliable, and intelligent responses, while keeping your data secure with local processing. ### Quick Start for mobile ![OneLLM](./OneLLM.png) Follow these steps to integrate the DoeyLLM model using the OneLLM app: 1. Download OneLLM Get the app from the [App Store](https://apps.apple.com/us/app/onellm-private-ai-gpt-llm/id6737907910) and install it on your iOS device. Or get the app from the [Play Store](https://play.google.com/store/apps/details?id=com.esotech.onellm) and install it on your Android device. 3. Load the DoeyLLM Model Use the OneLLM interface to load the DoeyLLM model directly into the app: - Navigate to the Model Library. - Search for `DoeyLLM`. - Select the model and tap Download to store it locally on your device. 4. Start Conversing Once the model is loaded, you can begin interacting with it through the app's chat interface. For example: - Tap the Chat tab. - Type your question or prompt, such as: > "Explain the significance of AI in education." - Receive real-time, intelligent responses generated locally. ### Key Features of OneLLM - Versatile Models: Supports various LLMs, including Llama, Gemma, and Qwen. - Private & Secure: All processing occurs locally on your device, ensuring data privacy. - Offline Capability: Use the app without requiring an internet connection. - Fast Performance: Optimized for mobile devices, delivering low-latency responses. For more details or support, visit the [OneLLM App Store page](https://apps.apple.com/us/app/onellm-private-ai-gpt-llm/id6737907910) and [Play Store](https://play.google.com/store/apps/details?id=com.esotech.onellm). ## PC: Use with Transformers The DoeyLLM model can also be used on PC platforms through the `transformers` library, enabling robust and scalable inference for various NLP tasks. ### Quick Start for PC Follow these steps to use the model with Transformers: 1. Install Transformers Ensure you have `transformers >= 4.43.0` installed. Update or install it via pip: ```bash pip install --upgrade transformers 2. Load the Model Use the transformers library to load the model and tokenizer: Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "OneLLM-Doey-V1-Llama-3.2-1B" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` ## Responsibility & Safety As part of our responsible release strategy, we adopted a three-pronged approach to managing trust and safety risks: Enable developers to deploy helpful, safe, and flexible experiences for their target audience and the use cases supported by the model. Protect developers from adversarial users attempting to exploit the model’s capabilities to potentially cause harm. Provide safeguards for the community to help prevent the misuse of the model.
tl81092/my-drug-model	tl81092	2024-11-25T07:17:11Z	105	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-11-25T07:16:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
JosephNguyen/Qwen2.5-7B-Instruct-finetuned	JosephNguyen	2024-11-25T07:12:02Z	83	1	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-11-25T06:36:15Z	--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - Developed by: JosephNguyen - License: apache-2.0 - Finetuned from model : unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
xuYiLi/vit-base-patch16-224-finetuned-flower	xuYiLi	2024-11-25T07:09:06Z	5	0	null	[ "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "region:us" ]	image-classification	2024-11-25T07:08:50Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-flower results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.907216489315033 --- # vit-base-patch16-224-finetuned-flower Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### crape myrtle ![crape myrtle](images/crape_myrtle.jpg) #### iris ![iris](images/iris.jpg) #### narcissus ![narcissus](images/narcissus.jpg) #### osmanthus ![osmanthus](images/osmanthus.jpg) #### peony ![peony](images/peony.jpg)
Keltezaa/zooey-deschanel	Keltezaa	2024-11-25T07:08:38Z	10	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "migrated", "celebrity", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2024-11-25T07:08:30Z	--- license: other license_name: bespoke-lora-trained-license license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=RentCivit&allowDerivatives=True&allowDifferentLicense=False tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - migrated - celebrity base_model: black-forest-labs/FLUX.1-dev instance_prompt: Zooey Deschanel widget: - text: ' ' output: url: >- 28183142.jpeg - text: ' ' output: url: >- 28183139.jpeg - text: ' ' output: url: >- 28183138.jpeg - text: ' ' output: url: >- 28183140.jpeg - text: ' ' output: url: >- 28183141.jpeg - text: ' ' output: url: >- 28183143.jpeg - text: ' ' output: url: >- 28183144.jpeg - text: ' ' output: url: >- 28183145.jpeg - text: ' ' output: url: >- 28183146.jpeg - text: ' ' output: url: >- 28183147.jpeg - text: ' ' output: url: >- 28183148.jpeg - text: ' ' output: url: >- 28183150.jpeg - text: 'portrait of Zooey Deschanel shot on a Hasselblad H3D-39. she is wearing a light summer dress at the beach in Italy' output: url: >- 28184076.jpeg --- # Zooey Deschanel <Gallery /> ## Model description <p>Trained on 15 Images for 2500 Steps</p> ## Trigger words You should use `Zooey Deschanel` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Keltezaa/zooey-deschanel/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device) pipeline.load_lora_weights('Keltezaa/zooey-deschanel', weight_name='Zooey_Deschanel_v1.safetensors') image = pipeline('portrait of Zooey Deschanel shot on a Hasselblad H3D-39. she is wearing a light summer dress at the beach in Italy').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
ahmedheakl/asm2asm-qwen2.5coder-0.5b-500k-2ep-tokenizer	ahmedheakl	2024-11-25T07:07:36Z	264	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen2.5-Coder-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-11-23T15:01:30Z	--- base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct library_name: transformers model_name: asm2asm-qwen2.5coder-0.5b-500k-2ep-tokenizer tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for asm2asm-qwen2.5coder-0.5b-500k-2ep-tokenizer This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ahmedheakl/asm2asm-qwen2.5coder-0.5b-500k-2ep-tokenizer", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ahmed-heakl/huggingface/runs/eppp58b2) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.5.1+cu124 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
chy2207/robot_care_8b_ver4	chy2207	2024-11-25T07:06:28Z	9	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-11-25T06:50:34Z	--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: chy2207 - License: apache-2.0 - Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mini1013/master_cate_ac8

mini1013

2024-11-25T10:14:43Z

77

0

setfit

[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]

text-classification

2024-11-25T10:14:16Z

--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: '[10%+복수620]국내생산 남자여자 최대15켤레 페이크삭스 실리콘 덧신 양말 학생 무지 25_챠밍레이스실리콘_여성_베이지(4켤레) 발장난양말' - text: W616 따뜻한 두꺼운 순면 통파일 무지 긴 양말 여자 남자 빅사이즈 수면 겨울 덧신 니삭스 W432 골지 통파일 덧신_S(225-245mm)_블랙 삭스에이 - text: '[2차 11/14 예약배송][23FW] HEMISH LEG WARMER - MELANGE GREY MELANGE GREY_FREE 주식회사 타입스(Types Co.,Ltd)' - text: 도톰한 면두올 양말 국내생산/중목/장목/스니커즈/패션/학생 25~26_26.남녀 기모덧신_여)2켤레 / 블랙 투투삭스 - text: 도톰 엄지 양말 발가락 여 타비 삭스 기모 보온 컬러 여자 두꺼운 무지 연브라운 김민주 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.7735123253257968 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 2 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1.0 | <ul><li>'자전거 등산 골프 겨울 발 다리토시 레그워머 브라운 디플코리아 (Digital Plus Korea)'</li><li>'국산 면 탁텔 겨울 방한 팔 다리 수면 토시 발 임산부 산후용품 수족냉증 겨울 방한 보온 기본 수면토시 그레이 세자매 양말'</li><li>'세븐다스 여자 레그워머 수면 여성 발토시 겨울 보온 SD001 그레이_FREE 아이보리'</li></ul> | | 0.0 | <ul><li>'[매장발송] 마리떼 11/6 배송 3PACK EMBROIDERY SOCKS multi OS 와이에스마켓'</li><li>'에브리데이 플러스 쿠션 트레이닝 크루 삭스(3켤레) SX6888-100 024 '</li><li>'[롯데백화점]언더아머(백) 유니섹스 UA 코어 쿼터 양말 - 3켤레 1358344-100 1.LG 롯데백화점_'</li></ul> | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.7735 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac8") # Run inference preds = model("도톰 엄지 양말 발가락 여 타비 삭스 기모 보온 컬러 여자 두꺼운 무지 연브라운 김민주") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 4 | 10.82 | 24 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0625 | 1 | 0.4226 | - | | 3.125 | 50 | 0.0022 | - | | 6.25 | 100 | 0.0001 | - | | 9.375 | 150 | 0.0001 | - | | 12.5 | 200 | 0.0001 | - | | 15.625 | 250 | 0.0001 | - | | 18.75 | 300 | 0.0001 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

mini1013/master_cate_ac7

mini1013

2024-11-25T10:12:50Z

137

0

setfit

[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]

text-classification

2024-11-25T10:12:28Z

--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 가스코 가죽전용염색약 소파 카시트 스니커즈 33색상 100ml 다크브라운 주식회사가스코 - text: 레인슈즈 장화 방수 부츠 수중작업 신발보호 고무 미끄럼방지 여성용 H_M 34-36 지에스 - text: 가스코 가죽전용염색약 도구 풀세트 가죽옷 100ml 브라운_무광 주식회사 가스코 - text: 엑스솔 에어슬림 인솔 기능성 신발 깔창 245mm 주식회사 영창에코 - text: 깁스 양말 싸개 발 보호 보온 방한 편한 이쁜 롱 부츠형 여성 방수커버 샤워 팔 남성용 플러시 슬리브/두꺼운 버전 높이 35_45 핑크고릴라 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.9254610935283204 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 7 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 6.0 | <ul><li>'등산화끈 1+1 통끈_라인네이비 신세계몰'</li><li>'등산화끈 1+1 트위스트_브라운 신세계몰'</li><li>'몽벨 슈레이스 플랫 4MM YELLOW JBSXXUZZ105 신발끈 평끈 등산화끈 140 (주)코어밸류'</li></ul> | | 2.0 | <ul><li>'고급 강남스타힐 구두굽/소음방지/충격완화/하이힐굽 블랙_DD-107 슈미즈'</li><li>'발 뒤꿈치 패드 쿠션 신발 구두 운동화 사이즈 클때 줄이기 패드 6-피넛_아이보리화이트_One Size(2P) 저스트에잇'</li><li>'고급 강남스타힐 구두굽/소음방지/충격완화/하이힐굽 블랙_DD-092 슈미즈'</li></ul> | | 5.0 | <ul><li>'[웰럽] 시그니처 깔창 아치 운동화 등산화 군대 군인 군화 안전화 평발 기능성 키높이 [0008]그린 M(255 270) CJONSTYLE'</li><li>'[롯데백화점]에코(슈즈) 컴포트 에브리데이 인솔 멘즈 9059029-00101 블랙_EU 39 롯데백화점_'</li><li>'등산화 깔창 기능성 운동화 특수 스포츠 신발 키높이 골프화 XL275-295 마켓퀸즈'</li></ul> | | 0.0 | <ul><li>'[현대백화점]금강제화 랜드로바 SHOSC0150SAM 휴대용 미니 구두헤라 [00001] 휴대용 구두칼 (주)현대홈쇼핑'</li><li>'에드가 체크 소가죽 휴대용 슈혼 navy 000 (주)트라이본즈'</li><li>'[금강제화](광주신세계) 콜렉션 휴대용 슈혼 스틸 미니 구두 헤라 N8MKA150/SHOSC0150SAM 10.5cm 신세계백화점'</li></ul> | | 4.0 | <ul><li>'비오는날 남성 여성 1회용비닐덧신 S M L 비올때신발 여름필수품 신발우비 색상_레인신발커버 투명블루M 오픈리빙'</li><li>'비올때 이색적인 여성용 싱글 슈즈 가죽 신발 여성 패션 레인신발커버 멋스러운코디 13_39 스톰브랜상범'</li><li>'투명 슈즈 패션 워터 장마 여성장화 미끄럼방지 학생 XXL(43-45 적합)_블루-하이 [미끄럼방지창x2년 품질] 구룡글로벌'</li></ul> | | 1.0 | <ul><li>'곰돌이 블랙 검정하트 화이트 남자 성인 커플 지비추 자비츠 심플 파츠 클로그 참 장식신발 A set (블랙-화이트) 뉴지(NYUZY)'</li><li>'슈팁 금속팁 메탈락팁 듀브레 메탈밴드 악어클립 메탈고정핀 플라스틱고정핀 골드슈팁 황동슈팁 실버슈팁 금속슈팁 굵은골드(4개) 슈레이스'</li><li>'(SAPHIR) 사피르 레노베이팅 컬러 재생크림 / 가죽 염색제 리노베이팅 미디엄브라운 제이엠컴퍼니'</li></ul> | | 3.0 | <ul><li>'STRATTON 남성용 삼나무 슈트리- 미국산, m / 9 - 10 알쓰리컴퍼니'</li><li>'발볼 여자 신발 남자 제골기 발등 여성하이힐 발등 코코나라'</li><li>'슈샤이너 전기 금속제골기 경첩타입 전문가용 레지가다 업소용 여성용 js9997'</li></ul> | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.9255 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac7") # Run inference preds = model("엑스솔 에어슬림 인솔 기능성 신발 깔창 245mm 주식회사 영창에코") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 3 | 10.4257 | 27 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | | 2.0 | 50 | | 3.0 | 50 | | 4.0 | 50 | | 5.0 | 50 | | 6.0 | 50 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:----:|:-------------:|:---------------:| | 0.0182 | 1 | 0.3761 | - | | 0.9091 | 50 | 0.2291 | - | | 1.8182 | 100 | 0.033 | - | | 2.7273 | 150 | 0.018 | - | | 3.6364 | 200 | 0.0001 | - | | 4.5455 | 250 | 0.0001 | - | | 5.4545 | 300 | 0.0001 | - | | 6.3636 | 350 | 0.0001 | - | | 7.2727 | 400 | 0.0001 | - | | 8.1818 | 450 | 0.0 | - | | 9.0909 | 500 | 0.0 | - | | 10.0 | 550 | 0.0 | - | | 10.9091 | 600 | 0.0 | - | | 11.8182 | 650 | 0.0 | - | | 12.7273 | 700 | 0.0 | - | | 13.6364 | 750 | 0.0 | - | | 14.5455 | 800 | 0.0 | - | | 15.4545 | 850 | 0.0 | - | | 16.3636 | 900 | 0.0 | - | | 17.2727 | 950 | 0.0001 | - | | 18.1818 | 1000 | 0.0 | - | | 19.0909 | 1050 | 0.0 | - | | 20.0 | 1100 | 0.0 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k3_task3_organization_fold1

MayBashendy

2024-11-25T10:12:48Z

161

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T10:11:10Z

--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k3_task3_organization_fold1 results: [] ---  # Arabic_FineTuningAraBERT_AugV5_k3_task3_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6023 - Qwk: 0.1852 - Mse: 0.6023 - Rmse: 0.7761 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.1111 | 2 | 3.2691 | 0.0041 | 3.2691 | 1.8081 | | No log | 0.2222 | 4 | 1.5440 | -0.0168 | 1.5440 | 1.2426 | | No log | 0.3333 | 6 | 1.0494 | -0.4426 | 1.0494 | 1.0244 | | No log | 0.4444 | 8 | 0.7627 | 0.0 | 0.7627 | 0.8733 | | No log | 0.5556 | 10 | 0.7207 | -0.0421 | 0.7207 | 0.8489 | | No log | 0.6667 | 12 | 1.1277 | -0.1000 | 1.1277 | 1.0619 | | No log | 0.7778 | 14 | 0.7978 | -0.2791 | 0.7978 | 0.8932 | | No log | 0.8889 | 16 | 0.6799 | 0.0 | 0.6799 | 0.8246 | | No log | 1.0 | 18 | 0.6912 | 0.0 | 0.6912 | 0.8314 | | No log | 1.1111 | 20 | 0.7478 | -0.0233 | 0.7478 | 0.8647 | | No log | 1.2222 | 22 | 0.7894 | -0.2692 | 0.7894 | 0.8885 | | No log | 1.3333 | 24 | 0.8841 | -0.1074 | 0.8841 | 0.9403 | | No log | 1.4444 | 26 | 0.9616 | 0.1646 | 0.9616 | 0.9806 | | No log | 1.5556 | 28 | 0.8752 | 0.0403 | 0.8752 | 0.9355 | | No log | 1.6667 | 30 | 0.7599 | -0.2655 | 0.7599 | 0.8717 | | No log | 1.7778 | 32 | 0.8963 | 0.0571 | 0.8963 | 0.9467 | | No log | 1.8889 | 34 | 1.2057 | 0.0 | 1.2057 | 1.0980 | | No log | 2.0 | 36 | 1.1424 | 0.0 | 1.1424 | 1.0688 | | No log | 2.1111 | 38 | 0.9274 | 0.0 | 0.9274 | 0.9630 | | No log | 2.2222 | 40 | 0.6915 | 0.0 | 0.6915 | 0.8316 | | No log | 2.3333 | 42 | 0.6934 | -0.0577 | 0.6934 | 0.8327 | | No log | 2.4444 | 44 | 1.0658 | 0.0120 | 1.0658 | 1.0324 | | No log | 2.5556 | 46 | 2.2327 | -0.0267 | 2.2327 | 1.4942 | | No log | 2.6667 | 48 | 1.8881 | 0.0 | 1.8881 | 1.3741 | | No log | 2.7778 | 50 | 1.3046 | 0.0 | 1.3046 | 1.1422 | | No log | 2.8889 | 52 | 0.8297 | 0.1879 | 0.8297 | 0.9109 | | No log | 3.0 | 54 | 0.6838 | 0.0 | 0.6838 | 0.8269 | | No log | 3.1111 | 56 | 0.6598 | 0.0 | 0.6598 | 0.8123 | | No log | 3.2222 | 58 | 0.6455 | 0.0 | 0.6455 | 0.8034 | | No log | 3.3333 | 60 | 0.6495 | 0.0 | 0.6495 | 0.8059 | | No log | 3.4444 | 62 | 0.6547 | 0.0 | 0.6547 | 0.8092 | | No log | 3.5556 | 64 | 0.6983 | 0.0 | 0.6983 | 0.8357 | | No log | 3.6667 | 66 | 0.8622 | -0.1074 | 0.8622 | 0.9286 | | No log | 3.7778 | 68 | 1.0773 | 0.0 | 1.0773 | 1.0379 | | No log | 3.8889 | 70 | 1.0490 | 0.0 | 1.0490 | 1.0242 | | No log | 4.0 | 72 | 1.0322 | 0.0 | 1.0322 | 1.0160 | | No log | 4.1111 | 74 | 0.9420 | 0.0 | 0.9420 | 0.9706 | | No log | 4.2222 | 76 | 0.8430 | 0.0571 | 0.8430 | 0.9181 | | No log | 4.3333 | 78 | 0.7792 | -0.0577 | 0.7792 | 0.8827 | | No log | 4.4444 | 80 | 0.7126 | -0.0233 | 0.7126 | 0.8441 | | No log | 4.5556 | 82 | 0.6694 | 0.0 | 0.6694 | 0.8182 | | No log | 4.6667 | 84 | 0.6598 | 0.0 | 0.6598 | 0.8123 | | No log | 4.7778 | 86 | 0.6827 | 0.0 | 0.6827 | 0.8263 | | No log | 4.8889 | 88 | 0.7243 | 0.1239 | 0.7243 | 0.8511 | | No log | 5.0 | 90 | 0.7197 | 0.1239 | 0.7197 | 0.8484 | | No log | 5.1111 | 92 | 0.7406 | 0.0984 | 0.7406 | 0.8606 | | No log | 5.2222 | 94 | 0.7973 | 0.0984 | 0.7973 | 0.8929 | | No log | 5.3333 | 96 | 0.7799 | 0.0984 | 0.7799 | 0.8831 | | No log | 5.4444 | 98 | 0.7187 | 0.1239 | 0.7187 | 0.8478 | | No log | 5.5556 | 100 | 0.6871 | 0.1239 | 0.6871 | 0.8289 | | No log | 5.6667 | 102 | 0.6141 | 0.0 | 0.6141 | 0.7836 | | No log | 5.7778 | 104 | 0.5736 | 0.0 | 0.5736 | 0.7574 | | No log | 5.8889 | 106 | 0.5768 | 0.0 | 0.5768 | 0.7595 | | No log | 6.0 | 108 | 0.5742 | 0.0 | 0.5742 | 0.7577 | | No log | 6.1111 | 110 | 0.5836 | 0.0 | 0.5836 | 0.7640 | | No log | 6.2222 | 112 | 0.5875 | 0.0 | 0.5875 | 0.7665 | | No log | 6.3333 | 114 | 0.5939 | 0.0 | 0.5939 | 0.7706 | | No log | 6.4444 | 116 | 0.6115 | 0.0 | 0.6115 | 0.7820 | | No log | 6.5556 | 118 | 0.6120 | 0.0 | 0.6120 | 0.7823 | | No log | 6.6667 | 120 | 0.6020 | -0.0233 | 0.6020 | 0.7759 | | No log | 6.7778 | 122 | 0.6050 | 0.1895 | 0.6050 | 0.7778 | | No log | 6.8889 | 124 | 0.6034 | -0.0233 | 0.6034 | 0.7768 | | No log | 7.0 | 126 | 0.6037 | -0.0233 | 0.6037 | 0.7770 | | No log | 7.1111 | 128 | 0.6084 | -0.0233 | 0.6084 | 0.7800 | | No log | 7.2222 | 130 | 0.6394 | 0.0 | 0.6394 | 0.7996 | | No log | 7.3333 | 132 | 0.6494 | 0.0 | 0.6494 | 0.8059 | | No log | 7.4444 | 134 | 0.6215 | -0.0233 | 0.6215 | 0.7884 | | No log | 7.5556 | 136 | 0.6098 | 0.1895 | 0.6098 | 0.7809 | | No log | 7.6667 | 138 | 0.6302 | 0.1538 | 0.6302 | 0.7939 | | No log | 7.7778 | 140 | 0.6763 | 0.1239 | 0.6763 | 0.8224 | | No log | 7.8889 | 142 | 0.6795 | 0.1239 | 0.6795 | 0.8243 | | No log | 8.0 | 144 | 0.6233 | 0.1538 | 0.6233 | 0.7895 | | No log | 8.1111 | 146 | 0.5968 | 0.1895 | 0.5968 | 0.7725 | | No log | 8.2222 | 148 | 0.6159 | 0.0 | 0.6159 | 0.7848 | | No log | 8.3333 | 150 | 0.6337 | 0.0 | 0.6337 | 0.7961 | | No log | 8.4444 | 152 | 0.6319 | 0.0 | 0.6319 | 0.7949 | | No log | 8.5556 | 154 | 0.6303 | 0.0 | 0.6303 | 0.7939 | | No log | 8.6667 | 156 | 0.6209 | 0.0 | 0.6209 | 0.7880 | | No log | 8.7778 | 158 | 0.6141 | 0.1852 | 0.6141 | 0.7837 | | No log | 8.8889 | 160 | 0.6148 | 0.1852 | 0.6148 | 0.7841 | | No log | 9.0 | 162 | 0.6149 | 0.1852 | 0.6149 | 0.7842 | | No log | 9.1111 | 164 | 0.6135 | 0.1852 | 0.6135 | 0.7832 | | No log | 9.2222 | 166 | 0.6099 | 0.1852 | 0.6099 | 0.7810 | | No log | 9.3333 | 168 | 0.6068 | 0.1852 | 0.6068 | 0.7790 | | No log | 9.4444 | 170 | 0.6060 | 0.1852 | 0.6060 | 0.7784 | | No log | 9.5556 | 172 | 0.6050 | 0.1852 | 0.6050 | 0.7778 | | No log | 9.6667 | 174 | 0.6033 | 0.1852 | 0.6033 | 0.7767 | | No log | 9.7778 | 176 | 0.6029 | 0.1852 | 0.6029 | 0.7765 | | No log | 9.8889 | 178 | 0.6023 | 0.1852 | 0.6023 | 0.7761 | | No log | 10.0 | 180 | 0.6023 | 0.1852 | 0.6023 | 0.7761 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

braindao/iq-code-evmind-14b-instruct-v0.2411.1

braindao

2024-11-25T10:10:34Z

8

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T10:05:01Z

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k2_task3_organization_fold1

MayBashendy

2024-11-25T10:07:49Z

162

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T10:05:33Z

--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k2_task3_organization_fold1 results: [] ---  # Arabic_FineTuningAraBERT_AugV5_k2_task3_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6027 - Qwk: 0.0222 - Mse: 0.6027 - Rmse: 0.7763 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.1429 | 2 | 3.6943 | 0.0 | 3.6943 | 1.9220 | | No log | 0.2857 | 4 | 1.8695 | 0.0 | 1.8695 | 1.3673 | | No log | 0.4286 | 6 | 1.0940 | 0.0 | 1.0940 | 1.0459 | | No log | 0.5714 | 8 | 0.7397 | 0.3654 | 0.7397 | 0.8601 | | No log | 0.7143 | 10 | 0.6617 | -0.0233 | 0.6617 | 0.8135 | | No log | 0.8571 | 12 | 0.6396 | -0.0233 | 0.6396 | 0.7997 | | No log | 1.0 | 14 | 0.7214 | -0.0577 | 0.7214 | 0.8493 | | No log | 1.1429 | 16 | 0.8774 | 0.2143 | 0.8774 | 0.9367 | | No log | 1.2857 | 18 | 0.7807 | 0.0984 | 0.7807 | 0.8836 | | No log | 1.4286 | 20 | 0.6395 | 0.0 | 0.6395 | 0.7997 | | No log | 1.5714 | 22 | 0.6691 | 0.0 | 0.6691 | 0.8180 | | No log | 1.7143 | 24 | 0.8455 | -0.0916 | 0.8455 | 0.9195 | | No log | 1.8571 | 26 | 1.2566 | 0.0 | 1.2566 | 1.1210 | | No log | 2.0 | 28 | 1.9493 | 0.0 | 1.9493 | 1.3962 | | No log | 2.1429 | 30 | 1.9458 | 0.0 | 1.9458 | 1.3949 | | No log | 2.2857 | 32 | 1.4166 | 0.0 | 1.4166 | 1.1902 | | No log | 2.4286 | 34 | 1.0109 | 0.0 | 1.0109 | 1.0054 | | No log | 2.5714 | 36 | 0.7919 | 0.2143 | 0.7919 | 0.8899 | | No log | 2.7143 | 38 | 0.8311 | 0.1879 | 0.8311 | 0.9117 | | No log | 2.8571 | 40 | 0.9516 | 0.0 | 0.9516 | 0.9755 | | No log | 3.0 | 42 | 0.9142 | 0.0 | 0.9142 | 0.9562 | | No log | 3.1429 | 44 | 0.7883 | 0.0763 | 0.7883 | 0.8879 | | No log | 3.2857 | 46 | 0.8340 | 0.0571 | 0.8340 | 0.9132 | | No log | 3.4286 | 48 | 0.8201 | 0.0571 | 0.8201 | 0.9056 | | No log | 3.5714 | 50 | 0.7335 | 0.1239 | 0.7335 | 0.8565 | | No log | 3.7143 | 52 | 0.6410 | 0.0 | 0.6410 | 0.8006 | | No log | 3.8571 | 54 | 0.6221 | 0.0 | 0.6221 | 0.7887 | | No log | 4.0 | 56 | 0.6607 | 0.0 | 0.6607 | 0.8128 | | No log | 4.1429 | 58 | 0.8841 | -0.0820 | 0.8841 | 0.9403 | | No log | 4.2857 | 60 | 0.9114 | -0.0820 | 0.9114 | 0.9547 | | No log | 4.4286 | 62 | 0.7034 | -0.0421 | 0.7034 | 0.8387 | | No log | 4.5714 | 64 | 0.6314 | 0.0 | 0.6314 | 0.7946 | | No log | 4.7143 | 66 | 0.5949 | 0.0 | 0.5949 | 0.7713 | | No log | 4.8571 | 68 | 0.5761 | 0.0 | 0.5761 | 0.7590 | | No log | 5.0 | 70 | 0.5803 | 0.0 | 0.5803 | 0.7618 | | No log | 5.1429 | 72 | 0.6058 | 0.1895 | 0.6058 | 0.7783 | | No log | 5.2857 | 74 | 0.5757 | 0.1895 | 0.5757 | 0.7588 | | No log | 5.4286 | 76 | 0.5368 | 0.0 | 0.5368 | 0.7327 | | No log | 5.5714 | 78 | 0.5330 | 0.0 | 0.5330 | 0.7301 | | No log | 5.7143 | 80 | 0.5448 | 0.0 | 0.5448 | 0.7381 | | No log | 5.8571 | 82 | 0.5658 | 0.0 | 0.5658 | 0.7522 | | No log | 6.0 | 84 | 0.6706 | 0.0 | 0.6706 | 0.8189 | | No log | 6.1429 | 86 | 0.8092 | 0.0222 | 0.8092 | 0.8996 | | No log | 6.2857 | 88 | 0.8005 | 0.0222 | 0.8005 | 0.8947 | | No log | 6.4286 | 90 | 0.6768 | 0.0222 | 0.6768 | 0.8227 | | No log | 6.5714 | 92 | 0.5966 | 0.0 | 0.5966 | 0.7724 | | No log | 6.7143 | 94 | 0.6005 | -0.0233 | 0.6005 | 0.7749 | | No log | 6.8571 | 96 | 0.5849 | -0.0233 | 0.5849 | 0.7648 | | No log | 7.0 | 98 | 0.5442 | 0.0 | 0.5442 | 0.7377 | | No log | 7.1429 | 100 | 0.5926 | 0.0222 | 0.5926 | 0.7698 | | No log | 7.2857 | 102 | 0.5723 | 0.0222 | 0.5723 | 0.7565 | | No log | 7.4286 | 104 | 0.5054 | 0.0 | 0.5054 | 0.7109 | | No log | 7.5714 | 106 | 0.4898 | 0.0 | 0.4898 | 0.6999 | | No log | 7.7143 | 108 | 0.5001 | 0.0 | 0.5001 | 0.7072 | | No log | 7.8571 | 110 | 0.5214 | -0.0421 | 0.5214 | 0.7221 | | No log | 8.0 | 112 | 0.5205 | 0.0 | 0.5205 | 0.7215 | | No log | 8.1429 | 114 | 0.5150 | 0.0 | 0.5150 | 0.7177 | | No log | 8.2857 | 116 | 0.5367 | 0.2667 | 0.5367 | 0.7326 | | No log | 8.4286 | 118 | 0.6132 | 0.2414 | 0.6132 | 0.7830 | | No log | 8.5714 | 120 | 0.6227 | 0.2326 | 0.6227 | 0.7891 | | No log | 8.7143 | 122 | 0.6027 | 0.2524 | 0.6027 | 0.7764 | | No log | 8.8571 | 124 | 0.5833 | 0.0222 | 0.5833 | 0.7637 | | No log | 9.0 | 126 | 0.5827 | 0.0222 | 0.5827 | 0.7634 | | No log | 9.1429 | 128 | 0.5799 | 0.0 | 0.5799 | 0.7615 | | No log | 9.2857 | 130 | 0.5873 | 0.0 | 0.5873 | 0.7664 | | No log | 9.4286 | 132 | 0.5931 | 0.0222 | 0.5931 | 0.7701 | | No log | 9.5714 | 134 | 0.5958 | 0.0222 | 0.5958 | 0.7719 | | No log | 9.7143 | 136 | 0.5963 | 0.0222 | 0.5963 | 0.7722 | | No log | 9.8571 | 138 | 0.6013 | 0.0222 | 0.6013 | 0.7754 | | No log | 10.0 | 140 | 0.6027 | 0.0222 | 0.6027 | 0.7763 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF

QuantFactory

2024-11-25T10:05:13Z

406

2

transformers

[ "transformers", "gguf", "Llama", "Llama-Cpp", "Llama3.2", "Instruct", "3B", "bin", "Sentient", "text-generation", "en", "dataset:mlabonne/lmsys-arena-human-preference-55k-sharegpt", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:quantized:meta-llama/Llama-3.2-3B-Instruct", "license:creativeml-openrail-m", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-11-22T04:56:16Z

--- license: creativeml-openrail-m datasets: - mlabonne/lmsys-arena-human-preference-55k-sharegpt language: - en base_model: - meta-llama/Llama-3.2-3B-Instruct pipeline_tag: text-generation library_name: transformers tags: - Llama - Llama-Cpp - Llama3.2 - Instruct - 3B - bin - Sentient --- [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory) # QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF This is quantized version of [prithivMLmods/Llama-Sentient-3.2-3B-Instruct](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct) created using llama.cpp # Original Model Card ## Llama-Sentient-3.2-3B-Instruct Modelfile | File Name | Size | Description | Upload Status | |-----------------------------------------|--------------|-----------------------------------------|----------------| | `.gitattributes` | 1.57 kB | Git attributes configuration file | Uploaded | | `README.md` | 42 Bytes | Initial commit README | Uploaded | | `config.json` | 1.04 kB | Configuration file | Uploaded | | `generation_config.json` | 248 Bytes | Generation configuration file | Uploaded | | `pytorch_model-00001-of-00002.bin` | 4.97 GB | PyTorch model file (part 1) | Uploaded (LFS) | | `pytorch_model-00002-of-00002.bin` | 1.46 GB | PyTorch model file (part 2) | Uploaded (LFS) | | `pytorch_model.bin.index.json` | 21.2 kB | Model index file | Uploaded | | `special_tokens_map.json` | 477 Bytes | Special tokens mapping | Uploaded | | `tokenizer.json` | 17.2 MB | Tokenizer JSON file | Uploaded (LFS) | | `tokenizer_config.json` | 57.4 kB | Tokenizer configuration file | Uploaded | | Model Type | Size | Context Length | Link | |------------|------|----------------|------| | GGUF | 3B | - | [🤗 Llama-Sentient-3.2-3B-Instruct-GGUF](https://huggingface.co/prithivMLmods/Llama-Sentient-3.2-3B-Instruct-GGUF) | The **Llama-Sentient-3.2-3B-Instruct** model is a fine-tuned version of the **Llama-3.2-3B-Instruct** model, optimized for **text generation** tasks, particularly where instruction-following abilities are critical. This model is trained on the **mlabonne/lmsys-arena-human-preference-55k-sharegpt** dataset, which enhances its performance in conversational and advisory contexts, making it suitable for a wide range of applications. ### Key Use Cases: 1. **Conversational AI**: Engage in intelligent dialogue, offering coherent responses and following instructions, useful for customer support and virtual assistants. 2. **Text Generation**: Generate high-quality, contextually appropriate content such as articles, summaries, explanations, and other forms of written communication based on user prompts. 3. **Instruction Following**: Follow specific instructions with accuracy, making it ideal for tasks that require structured guidance, such as technical troubleshooting or educational assistance. The model uses a **PyTorch-based architecture** and includes a range of necessary files such as configuration files, tokenizer files, and model weight files for deployment. ### Intended Applications: - **Chatbots** for virtual assistance, customer support, or as personal digital assistants. - **Content Creation Tools**, aiding in the generation of written materials, blog posts, or automated responses based on user inputs. - **Educational and Training Systems**, providing explanations and guided learning experiences in various domains. - **Human-AI Interaction** platforms, where the model can follow user instructions to provide personalized assistance or perform specific tasks. With its strong foundation in instruction-following and conversational contexts, the **Llama-Sentient-3.2-3B-Instruct** model offers versatile applications for both general and specialized domains.

PotatoB/task_3-exp

PotatoB

2024-11-25T10:03:32Z

5

0

null

[ "safetensors", "mistral", "merge", "mergekit", "potatoB/task_2-1", "potatoB/task_1-2", "license:apache-2.0", "region:us" ]

null

2024-11-25T10:00:36Z

--- license: apache-2.0 tags: - merge - mergekit - potatoB/task_2-1 - potatoB/task_1-2 --- # task_3-exp task_3-exp is a merged model generated for Model Kinship experiments, originating from * [potatoB/task_2-1](https://huggingface.co/potatoB/task_2-1) * [potatoB/task_1-2](https://huggingface.co/potatoB/task_1-2) ## 🧩 Configuration ```yaml slices: - sources: - model: potatoB/task_2-1 layer_range: [0, 32] - model: potatoB/task_1-2 layer_range: [0, 32] merge_method: slerp base_model: potatoB/task_2-1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ```

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold1

MayBashendy

2024-11-25T10:02:13Z

181

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T10:00:46Z

--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold1 results: [] ---  # Arabic_FineTuningAraBERT_AugV5_k1_task3_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4881 - Qwk: 0.3654 - Mse: 0.4881 - Rmse: 0.6987 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.2 | 2 | 4.3733 | 0.0 | 4.3733 | 2.0912 | | No log | 0.4 | 4 | 3.0656 | 0.0041 | 3.0656 | 1.7509 | | No log | 0.6 | 6 | 1.9187 | 0.0 | 1.9187 | 1.3852 | | No log | 0.8 | 8 | 1.2784 | 0.0 | 1.2784 | 1.1307 | | No log | 1.0 | 10 | 1.0288 | 0.0 | 1.0288 | 1.0143 | | No log | 1.2 | 12 | 0.8073 | 0.2143 | 0.8073 | 0.8985 | | No log | 1.4 | 14 | 0.8244 | -0.0708 | 0.8244 | 0.9080 | | No log | 1.6 | 16 | 0.8426 | -0.0708 | 0.8426 | 0.9179 | | No log | 1.8 | 18 | 0.8425 | -0.2623 | 0.8425 | 0.9179 | | No log | 2.0 | 20 | 0.7834 | -0.2737 | 0.7834 | 0.8851 | | No log | 2.2 | 22 | 0.7676 | -0.0233 | 0.7676 | 0.8761 | | No log | 2.4 | 24 | 0.7458 | -0.0233 | 0.7458 | 0.8636 | | No log | 2.6 | 26 | 0.7393 | -0.0233 | 0.7393 | 0.8598 | | No log | 2.8 | 28 | 0.7465 | -0.0233 | 0.7465 | 0.8640 | | No log | 3.0 | 30 | 0.7807 | -0.0708 | 0.7807 | 0.8836 | | No log | 3.2 | 32 | 0.7686 | -0.0708 | 0.7686 | 0.8767 | | No log | 3.4 | 34 | 0.7733 | 0.1239 | 0.7733 | 0.8794 | | No log | 3.6 | 36 | 0.7919 | -0.0820 | 0.7919 | 0.8899 | | No log | 3.8 | 38 | 0.8260 | 0.0571 | 0.8260 | 0.9088 | | No log | 4.0 | 40 | 0.8921 | 0.0253 | 0.8921 | 0.9445 | | No log | 4.2 | 42 | 0.9072 | 0.0253 | 0.9072 | 0.9524 | | No log | 4.4 | 44 | 0.8748 | 0.0403 | 0.8748 | 0.9353 | | No log | 4.6 | 46 | 0.7669 | 0.1538 | 0.7669 | 0.8757 | | No log | 4.8 | 48 | 0.7034 | 0.0 | 0.7034 | 0.8387 | | No log | 5.0 | 50 | 0.7061 | 0.0 | 0.7061 | 0.8403 | | No log | 5.2 | 52 | 0.7177 | -0.0233 | 0.7177 | 0.8472 | | No log | 5.4 | 54 | 0.7338 | -0.0233 | 0.7338 | 0.8566 | | No log | 5.6 | 56 | 0.7832 | 0.4590 | 0.7832 | 0.8850 | | No log | 5.8 | 58 | 0.8263 | 0.0571 | 0.8263 | 0.9090 | | No log | 6.0 | 60 | 0.9227 | 0.0253 | 0.9227 | 0.9606 | | No log | 6.2 | 62 | 0.8976 | 0.0253 | 0.8976 | 0.9474 | | No log | 6.4 | 64 | 0.8543 | 0.0403 | 0.8543 | 0.9243 | | No log | 6.6 | 66 | 0.7962 | 0.2443 | 0.7962 | 0.8923 | | No log | 6.8 | 68 | 0.7551 | 0.1239 | 0.7551 | 0.8690 | | No log | 7.0 | 70 | 0.7269 | 0.0984 | 0.7269 | 0.8526 | | No log | 7.2 | 72 | 0.6806 | 0.0984 | 0.6806 | 0.8250 | | No log | 7.4 | 74 | 0.6234 | 0.1239 | 0.6234 | 0.7896 | | No log | 7.6 | 76 | 0.5757 | 0.0984 | 0.5757 | 0.7587 | | No log | 7.8 | 78 | 0.5394 | 0.3529 | 0.5394 | 0.7345 | | No log | 8.0 | 80 | 0.4984 | 0.1239 | 0.4984 | 0.7059 | | No log | 8.2 | 82 | 0.4521 | 0.3654 | 0.4521 | 0.6724 | | No log | 8.4 | 84 | 0.4245 | 0.4884 | 0.4245 | 0.6516 | | No log | 8.6 | 86 | 0.4220 | 0.4884 | 0.4220 | 0.6496 | | No log | 8.8 | 88 | 0.4231 | 0.4884 | 0.4231 | 0.6505 | | No log | 9.0 | 90 | 0.4275 | 0.4211 | 0.4275 | 0.6538 | | No log | 9.2 | 92 | 0.4397 | 0.4211 | 0.4397 | 0.6631 | | No log | 9.4 | 94 | 0.4567 | 0.4211 | 0.4567 | 0.6758 | | No log | 9.6 | 96 | 0.4734 | 0.4211 | 0.4734 | 0.6880 | | No log | 9.8 | 98 | 0.4866 | 0.3654 | 0.4866 | 0.6975 | | No log | 10.0 | 100 | 0.4881 | 0.3654 | 0.4881 | 0.6987 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

mini1013/master_cate_ac4

mini1013

2024-11-25T10:01:43Z

89

0

setfit

[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]

text-classification

2024-11-25T10:01:22Z

--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 길이조절 안경고정 밴드 코받침 패드 운동 캠핑 등산 진브라운 알리몽드 - text: 레이밴 안경테 RB3691VF 2509 남자 여자 동그란안경 아시안핏 시온아이엔티 - text: 밀착 스포츠안경줄 흔들림방지 안경스트랩 비앤비 - text: '[텐바이텐]바체타팩토리 가죽 안경 케이스 08 오렌지_추가 안 함_추가 안 함 신세계몰' - text: TUMI 투미 카본 티타늄 명품 안경테 메탈 스퀘어 남자 여자 공용 안경 04.TU10-0003-01 LFmall02 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.9104360692836626 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 6 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 5.0 | <ul><li>'초경량 국산 안경테 베타 울템 카본 티타늄 뿔테안경 551-599_S571-2 브라운투톤 ENA아이웨어'</li><li>'B019 ORIGINAL GLASS CRYSTAL GREEN '</li><li>'니시데카즈오 BROWLINE2 하금테 근적외선 차단렌즈 아이라이크(EYE LIKE)'</li></ul> | | 1.0 | <ul><li>'레더렛소가죽선글라스파우치휴대용안경케이스 이정민'</li><li>'위에 안경 쓰는 파우치 편광 끼우는 선글라스 3종 세트 선그라스 클립 에끼우는 플립 온 클립선글라스3종세트_일반블랙 홉포엘'</li><li>'휴대용 가죽 선글라스 안경 파우치 케이스 보관함 안 PU안경케이스_그레이 라이프패션'</li></ul> | | 3.0 | <ul><li>'아이업꽈배기인조가죽안경줄10p세트선글라스줄 유어드림커머스'</li><li>'스트랩 캐주얼디자인줄 스토퍼줄 안경걸이 끈 B 더펭귄샵'</li><li>'천연 크리스탈 안경 선글라스 걸이 줄 원석 비즈 빈티지 에스닉 마스크 스트랩 겸용 블루 3mm 70-75CM nouville'</li></ul> | | 0.0 | <ul><li>'갤러리아 NIRNIR SUNGLASS 5 COLOR GREEN 갤러리아몰'</li><li>'여자 켓아이 뿔테 선그라스 썬그라스 남자 RORGGE 2111 상품선택_2유광블랙 온달이'</li><li>'뮤즈 서클 뿔테선글라스 코코아 푸치백'</li></ul> | | 2.0 | <ul><li>'로에드 안경 자국 코패드 코받침 눌림 선글라스 코 통증 방지 패드 교체 스티커 안경코패드 1.8mm（화이트）_2.8mm(화이트) 로에드'</li><li>'[힐포]국산 고급 초극세사 렌즈 안경닦이 김서림방지 클리너 크리너 악기수건 안경천 융s 05. knit 안경닦이30매 15x18cm_블루 모아텍스'</li><li>'자우버 렌즈 케어 클리닝 티슈 200매 메디위'</li></ul> | | 4.0 | <ul><li>'산리오 안경정리함 안경케이스 세트 6종 안경케이스시나모롤 지에이치글로벌'</li><li>'(이거찜) 프리미엄 가죽 안경집 안경케이스 가죽안경집 스카이 제이케이'</li><li>'스트랩 안경케이스 휴대용 안경파우치 가죽안경보관집 선글라스보관케이스 No.01 스트랩 안경케이스 블랙 여선영'</li></ul> | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.9104 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac4") # Run inference preds = model("밀착 스포츠안경줄 흔들림방지 안경스트랩 비앤비") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.53 | 20 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | | 2.0 | 50 | | 3.0 | 50 | | 4.0 | 50 | | 5.0 | 50 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:----:|:-------------:|:---------------:| | 0.0213 | 1 | 0.4524 | - | | 1.0638 | 50 | 0.2583 | - | | 2.1277 | 100 | 0.0642 | - | | 3.1915 | 150 | 0.0781 | - | | 4.2553 | 200 | 0.0806 | - | | 5.3191 | 250 | 0.0391 | - | | 6.3830 | 300 | 0.0011 | - | | 7.4468 | 350 | 0.0003 | - | | 8.5106 | 400 | 0.0001 | - | | 9.5745 | 450 | 0.0001 | - | | 10.6383 | 500 | 0.0 | - | | 11.7021 | 550 | 0.0 | - | | 12.7660 | 600 | 0.0 | - | | 13.8298 | 650 | 0.0 | - | | 14.8936 | 700 | 0.0 | - | | 15.9574 | 750 | 0.0 | - | | 17.0213 | 800 | 0.0 | - | | 18.0851 | 850 | 0.0 | - | | 19.1489 | 900 | 0.0 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

mini1013/master_cate_ac3

mini1013

2024-11-25T09:57:32Z

85

0

setfit

[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]

text-classification

2024-11-25T09:57:06Z

--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: 옴므 교체용 가죽 벨트끈 벨트줄 허리띠 벨트 가죽 수동 자동용 22_수동벨트용 이태리가죽 3.3cm_카멜(42인치) 에스컴퍼니 - text: 여성 여자 패션 와이드 밴딩 벨트 패딩 코트 허리 허리띠 원피스 가디건 코디 패딩벨트 088_(SH30)_아이보리 {SH30-Ivory} 스웰swell - text: '[1 + 1]쭉쭉스판 늘어나는 밴딩 벨트 남여공용 캐쥬얼 데일리 군용 텍티컬 벨트 01. 늘어나는 벨트 1+1_05. 다크브라운_라이트브라운 스토리몰2' - text: '[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free ' - text: 모두샵 남자 가죽 청바지벨트 캐주얼벨트 허리띠 이니셜각인 7. 브라운 D107_한글(정자체)_보통길이(36까지착용가능) 모두샾 inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.9649836541954232 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 3 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 1.0 | <ul><li>'고리 집게 가방 여행용 멜빵 클립 다용도 삼각버클 후크 옐로우몰'</li><li>'패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 흰색 폭 2.5cm 120cm 맴매2'</li><li>'패션 여성서스펜더 스트랩 양복 출근룩 정장 코스튬 파란색 흰색 빨간색 줄무늬 폭2.5 120cm 맴매2'</li></ul> | | 2.0 | <ul><li>'Basic Leather Belt 네이비_100cm 만달문화여행사'</li><li>'다이에나롤랑 러블리 여자벨트 146276 은장 브라운 FCB0012CM_L 105 네잎클로버마켓'</li><li>'[갤러리아] 헤지스핸드백HJBE2F406W2브라운 스티치장식 소가죽 여성 벨트(타임월드) 한화갤러리아(주)'</li></ul> | | 0.0 | <ul><li>'(아크테릭스)(공식판매처)(23SS) 컨베이어 벨트 32mm (AENSUX5577) BLACK_SM '</li><li>'[갤러리아] 헤지스핸드백 HJBE2F775BK_ 블랙 빅로고 버클 가죽 자동벨트(타임월드) 한화갤러리아(주)'</li><li>'닥스_핸드백 (선물포장/쇼핑백동봉) 블랙 체크배색 가죽 자동벨트 DBBE3E990BK 롯데백화점2관'</li></ul> | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.9650 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac3") # Run inference preds = model("[로제이] 정장 캐주얼 가죽 더블 서스펜더 멜빵 NRMGSN011_BL 블랙_free ") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.6133 | 17 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | | 2.0 | 50 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:----:|:-------------:|:---------------:| | 0.0417 | 1 | 0.394 | - | | 2.0833 | 50 | 0.0731 | - | | 4.1667 | 100 | 0.0 | - | | 6.25 | 150 | 0.0 | - | | 8.3333 | 200 | 0.0 | - | | 10.4167 | 250 | 0.0 | - | | 12.5 | 300 | 0.0 | - | | 14.5833 | 350 | 0.0 | - | | 16.6667 | 400 | 0.0 | - | | 18.75 | 450 | 0.0 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

omarelsayeed/LayoutReader90Small

omarelsayeed

2024-11-25T09:55:03Z

131

0

transformers

[ "transformers", "safetensors", "layoutlmv3", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-11-25T09:54:58Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mini1013/master_cate_ac2

mini1013

2024-11-25T09:55:02Z

95

0

setfit

[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]

text-classification

2024-11-25T09:54:41Z

--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: MLB [MLB] 루키 언스트럭쳐 볼캡 24종 택1 203993 선택 20) 3ACP7701N-07ORL_F 위드홀리투 - text: 남여공용 기본군모 4컬러 EVE 카키 에브리씽굿 - text: 골덴와이어버킷햇(T)7252 브라운 모티브코리아 - text: 패션울벙거지97 베이지 디플코리아 (Digital Plus Korea) - text: '[닥스](강남점)DBHE4EL01W2 브라운 체크 면 헌팅캡 신세계백화점' inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.8489339496048904 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 13 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 10.0 | <ul><li>'밀로 [Exclusive] Holiday Signature Ball Cap (20Colors) MINT GRAY 포챌린지'</li><li>'(골라) 남녀공용 (GL)CONTRAST STITCHED CAP (3 COLOR) WW9G3SAAC101 연핑크_FRE '</li><li>'밀로 [Exclusive] Holiday Signature Ball Cap (20Colors) STONE BLACK 포챌린지'</li></ul> | | 4.0 | <ul><li>'꽈배기 비니 모자 두꺼운 골무 털 뜨개 여성 겨울 캡 알파카 남자 커플 니트 주황색_S(아이 32-52 cm) 앤디일레븐'</li><li>'패션모자 방한 남자 니트 후드 겨울 장갑 가을 워머 도톰한 3종세트 기모 블랙 마이클로드'</li><li>'털모자 따뜻한 낚시 모자 아빠 중년남성 노인 겨울 옵션06 에스앤지샵'</li></ul> | | 7.0 | <ul><li>'[하프클럽/구김스]구김스 모자(스포츠/등산/여행/방수) BEST 7종 균일가 763_블랙_D type 하프클럽'</li><li>'캉골 아웃도어 액티비티 버켓 4480 에크루 M AK플라자1관'</li><li>'[벤시몽](신세계센텀점)[23FW] WINTER BUCKET HAT - 2color NAVY_FREE 주식회사 에스에스지닷컴'</li></ul> | | 3.0 | <ul><li>'고탄성 부드러운 메쉬 원단 운동야외활동 스카프 두건 연그레이 드림픽쳐스'</li><li>'[로스코]반다나 스카프 헤어밴드 페이즐리 손수건 OLIVE DRAB_4051/Freesize 패션플러스'</li><li>'페이즐리 반다나 헤어 머리두건 비 손수건 스카프 그린 보물삼'</li></ul> | | 1.0 | <ul><li>'방한모자2종 귀달이 털모자 군밤 스키 용품 트래퍼햇 마스크 캡방한모자 01.불구덩이군방모자 제이케이 아트 갤러리'</li><li>'[MLB] 패딩 트루퍼 귀달이 햇(3AWMPH136-50BKS) 블랙-50BKS/59H 에이케이에스앤디(주) AK플라자 평택점'</li><li>'겨울 곰돌이 후드 귀달이 모자 목돌이 동물 털모자 05.브라운 석진케이 주식회사'</li></ul> | | 9.0 | <ul><li>'스냅백 패션모자 snapback (투톤)그레이오렌지 루나마켓'</li><li>'스냅백 패션모자 snapback 레드 루나마켓'</li><li>'공용 메탈 원포인트 스냅백 뉴욕양키스 (32CP57111-50L) '</li></ul> | | 0.0 | <ul><li>'기본 군모 버킷햇 밀리터리 여자 빈티지군모 모자 남자 버캣햇 블랙 카키 / FREE 체인지비'</li><li>'빈티지 워싱 느낌 영문 레터링 장식 포인트 엣지 군모 그레이 (주)오너클랜'</li><li>'질좋은 군모 모자(차콜/국내생산) 네이비 프리마켓'</li></ul> | | 2.0 | <ul><li>'여자 겨울템 따뜻 극세사 양털곰돌이머리띠 귀마개 A24973_베이지_FREE 세븐제이스(7JS)'</li><li>'양털 곰돌이귀마개 귀도리 뽀글이 귀마개 방한귀마개 목도리 화이트 현성마켓'</li><li>'스타일 더하기-36-꽈배기방한귀마개 핑크 이미연'</li></ul> | | 6.0 | <ul><li>'국내발송 MARITHE FRANCOIS GIRBAUD 마리떼 CABLE KNIT BEANIE blue 1MG23SHG112 ONE SIZE 씨이랩'</li><li>'[매장발송] 마리떼 CLASSIC LOGO BEANIE black OS 와이에스마켓'</li><li>'MARITHE FRANCOIS GIRBAUD CABLE KNIT BEANIE gray 1MG23SHG112 227185 ONE SIZE 원플렉스'</li></ul> | | 8.0 | <ul><li>'비앙카 BIANCA (여성용) 누가/내추럴로고_OS '</li><li>'[롯데백화점]화이트샌즈 공용 UV 프로텍션 바이저 소니아 2.아이보리 롯데백화점_'</li><li>'화이트샌즈 소니아 UV 프로텍션 썬바이저 1종 [00003] 아이보리 현대홈쇼핑'</li></ul> | | 12.0 | <ul><li>'캉골 헌팅캡 울 플렉스핏 504 K0873 심리스 울 507 K0875 3107 남녀공용 베레모 3. K3107ST (Black)_SMALL 어썸우즈'</li><li>'다용도 활용 직원 종업원 단체 패션 모자 헌팅캡 화이트 가온'</li><li>'앨리 카페 바리스타 모자 베이커 캡 마도로스햇[루즈루나주얼리] 블랙 주식회사 웹이즈'</li></ul> | | 11.0 | <ul><li>'1631뉴욕 볼캡 6color / 남녀공용모자 캡모자 그린 레이어드컴퍼니'</li><li>'패션벙거지0009 벙거지 가을 모자 여성 패션 밤색 골드코스트'</li><li>'꽈배기니트벙거지모자B28016 검정 프레임바이브'</li></ul> | | 5.0 | <ul><li>'니트 베레모 S1450 진주방울 핑크 지에이치글로벌'</li><li>'[박민영, 라이즈 원빈 착용] 스터드 로고 울 베레모 블랙 '</li><li>'/ 베이직 레더 뉴스보이캡 빵모자 (2color) 아이보리_one size 롭스(robs)'</li></ul> | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.8489 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac2") # Run inference preds = model("남여공용 기본군모 4컬러 EVE 카키 에브리씽굿") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 3 | 9.5523 | 21 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | | 2.0 | 50 | | 3.0 | 50 | | 4.0 | 50 | | 5.0 | 50 | | 6.0 | 50 | | 7.0 | 50 | | 8.0 | 50 | | 9.0 | 50 | | 10.0 | 50 | | 11.0 | 50 | | 12.0 | 50 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:----:|:-------------:|:---------------:| | 0.0098 | 1 | 0.4348 | - | | 0.4902 | 50 | 0.3427 | - | | 0.9804 | 100 | 0.1921 | - | | 1.4706 | 150 | 0.1061 | - | | 1.9608 | 200 | 0.0544 | - | | 2.4510 | 250 | 0.0384 | - | | 2.9412 | 300 | 0.0155 | - | | 3.4314 | 350 | 0.0128 | - | | 3.9216 | 400 | 0.0177 | - | | 4.4118 | 450 | 0.0082 | - | | 4.9020 | 500 | 0.005 | - | | 5.3922 | 550 | 0.0007 | - | | 5.8824 | 600 | 0.0004 | - | | 6.3725 | 650 | 0.0003 | - | | 6.8627 | 700 | 0.0003 | - | | 7.3529 | 750 | 0.0003 | - | | 7.8431 | 800 | 0.0003 | - | | 8.3333 | 850 | 0.0003 | - | | 8.8235 | 900 | 0.0002 | - | | 9.3137 | 950 | 0.0002 | - | | 9.8039 | 1000 | 0.0001 | - | | 10.2941 | 1050 | 0.0001 | - | | 10.7843 | 1100 | 0.0001 | - | | 11.2745 | 1150 | 0.0001 | - | | 11.7647 | 1200 | 0.0001 | - | | 12.2549 | 1250 | 0.0001 | - | | 12.7451 | 1300 | 0.0001 | - | | 13.2353 | 1350 | 0.0001 | - | | 13.7255 | 1400 | 0.0001 | - | | 14.2157 | 1450 | 0.0001 | - | | 14.7059 | 1500 | 0.0001 | - | | 15.1961 | 1550 | 0.0001 | - | | 15.6863 | 1600 | 0.0001 | - | | 16.1765 | 1650 | 0.0001 | - | | 16.6667 | 1700 | 0.0001 | - | | 17.1569 | 1750 | 0.0001 | - | | 17.6471 | 1800 | 0.0001 | - | | 18.1373 | 1850 | 0.0001 | - | | 18.6275 | 1900 | 0.0001 | - | | 19.1176 | 1950 | 0.0001 | - | | 19.6078 | 2000 | 0.0001 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

ClaudeRitchie/tinyllama-vels-v1

ClaudeRitchie

2024-11-25T09:52:51Z

130

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T09:50:35Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BoltMonkey/SuperNeuralDreadDevil-8b

BoltMonkey

2024-11-25T09:46:12Z

43

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated", "BoltMonkey/DreadMix", "conversational", "base_model:BoltMonkey/DreadMix", "base_model:merge:BoltMonkey/DreadMix", "base_model:BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated", "base_model:merge:BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-10-13T03:25:41Z

--- library_name: transformers base_model: - BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated - BoltMonkey/DreadMix tags: - merge - mergekit - lazymergekit - BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated - BoltMonkey/DreadMix pipeline_tag: text-generation --- # SuperNeuralDreadDevil-8b SuperNeuralDreadDevil-8b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated](https://huggingface.co/BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated) * [BoltMonkey/DreadMix](https://huggingface.co/BoltMonkey/DreadMix) ## 🧩 Configuration ```yamlmodels: - model: NousResearch/Meta-Llama-3.1-8B-Instruct - model: BoltMonkey/NeuralDaredevil-SuperNova-Lite-7B-DARETIES-abliterated parameters: density: 0.53 weight: 0.55 - model: BoltMonkey/DreadMix parameters: density: 0.53 weight: 0.45 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3.1-8B-Instruct parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "BoltMonkey/SuperNeuralDreadDevil-8b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

PrunaAI/AmberYifan-Mistral-7B-v0.1-sft-dpo-10k-bnb-8bit-smashed

PrunaAI

2024-11-25T09:42:42Z

6

0

null

[ "safetensors", "mistral", "pruna-ai", "base_model:AmberYifan/Mistral-7B-v0.1-sft-dpo-10k", "base_model:quantized:AmberYifan/Mistral-7B-v0.1-sft-dpo-10k", "8-bit", "bitsandbytes", "region:us" ]

null

2024-11-25T09:33:11Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: AmberYifan/Mistral-7B-v0.1-sft-dpo-10k metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer"> <img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with llm-int8. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo AmberYifan/Mistral-7B-v0.1-sft-dpo-10k installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/AmberYifan-Mistral-7B-v0.1-sft-dpo-10k-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("AmberYifan/Mistral-7B-v0.1-sft-dpo-10k") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model AmberYifan/Mistral-7B-v0.1-sft-dpo-10k before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Do it by yourself [here](https://docs.pruna.ai/en/latest/setup/pip.html).

glif-loradex-trainer/fabian3000_chillguy

glif-loradex-trainer

2024-11-25T09:40:56Z

846

1

diffusers

[ "diffusers", "text-to-image", "template:sd-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:finetune:black-forest-labs/FLUX.1-dev", "license:other", "region:us", "flux", "lora", "base_model:adapter:black-forest-labs/FLUX.1-dev" ]

text-to-image

2024-11-25T09:40:37Z

--- tags: - diffusers - text-to-image - template:sd-lora - base_model:black-forest-labs/FLUX.1-dev - base_model:finetune:black-forest-labs/FLUX.1-dev - license:other - region:us - flux - lora widget: - output: url: samples/1732527571108__000001500_0.jpg text: chillguy as a spartan warrior - output: url: samples/1732527596137__000001500_1.jpg text: chillguy with text saying I AM CHILL - output: url: samples/1732527621130__000001500_2.jpg text: black and white photographic portrait of chillguy base_model: black-forest-labs/FLUX.1-dev trigger: chillguy instance_prompt: chillguy license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md --- # chillguy Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `fabian3000`. <Gallery /> ## Trigger words You should use `chillguy` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/glif-loradex-trainer/fabian3000_chillguy/tree/main) them in the Files & versions tab. ## License This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

huihui-ai/Llama-3.2-3B-Instruct-abliterated

huihui-ai

2024-11-25T09:39:09Z

3,315

50

transformers

[ "transformers", "safetensors", "llama", "text-generation", "abliterated", "uncensored", "conversational", "base_model:meta-llama/Llama-3.2-3B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-3B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-09-28T05:20:02Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-3B-Instruct tags: - abliterated - uncensored --- # 🦙 Llama-3.2-3B-Instruct-abliterated This is an uncensored version of Llama 3.2 3B Instruct created with abliteration (see [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about it). Special thanks to [@FailSpy](https://huggingface.co/failspy) for the original code and technique. Please follow him if you're interested in abliterated models. ## ollama You can use [huihui_ai/llama3.2-abliterate:3b](https://ollama.com/huihui_ai/llama3.2-abliterate:3b) directly, ``` ollama run huihui_ai/llama3.2-abliterate ``` or create your own model using the following methods. 1. Download this model. ``` huggingface-cli download huihui-ai/Llama-3.2-3B-Instruct-abliterated --local-dir ./huihui-ai/Llama-3.2-3B-Instruct-abliterated ``` 2. Get Llama-3.2-3B-Instruct model for reference. ``` ollama pull llama3.2 ``` 3. Export Llama-3.2-3B-Instruct model parameters. ``` ollama show llama3.2 --modelfile > Modelfile ``` 4. Modify Modelfile, Remove all comment lines (indicated by #) before the "FROM" keyword. Replace the "FROM" with the following content. ``` FROM huihui-ai/Llama-3.2-3B-Instruct-abliterated ``` 5. Use ollama create to then create the quantized model. ``` ollama create --quantize q4_K_M -f Modelfile Llama-3.2-3B-Instruct-abliterated-q4_K_M ``` 6. Run model ``` ollama run Llama-3.2-3B-Instruct-abliterated-q4_K_M ``` The running architecture is llama. ## Evaluations The following data has been re-evaluated and calculated as the average for each test. | Benchmark | Llama-3.2-3B-Instruct | Llama-3.2-3B-Instruct-abliterated | |-------------|-----------------------|-----------------------------------| | IF_Eval | 76.55 | **76.76** | | MMLU Pro | 27.88 | **28.00** | | TruthfulQA | 50.55 | **50.73** | | BBH | 41.81 | **41.86** | | GPQA | 28.39 | **28.41** | The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/huihui-ai/Llama-3.2-3B-Instruct-abliterated/blob/main/eval.sh)

huihui-ai/Llama-3.2-1B-Instruct-abliterated

huihui-ai

2024-11-25T09:36:23Z

632

6

transformers

[ "transformers", "safetensors", "llama", "text-generation", "abliterated", "uncensored", "conversational", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "license:llama3.2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-10-01T18:46:39Z

--- library_name: transformers license: llama3.2 base_model: meta-llama/Llama-3.2-1B-Instruct tags: - abliterated - uncensored --- # 🦙 Llama-3.2-1B-Instruct-abliterated This is an uncensored version of Llama 3.2 1B Instruct created with abliteration (see [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about it). Special thanks to [@FailSpy](https://huggingface.co/failspy) for the original code and technique. Please follow him if you're interested in abliterated models. ## ollama You can use [huihui_ai/llama3.2-abliterate:1b](https://ollama.com/huihui_ai/llama3.2-abliterate:1b) directly, ``` ollama run huihui_ai/llama3.2-abliterate:1b ``` ## Evaluations The following data has been re-evaluated and calculated as the average for each test. | Benchmark | Llama-3.2-1B-Instruct | Llama-3.2-1B-Instruct-abliterated | |-------------|-----------------------|-----------------------------------| | IF_Eval | **58.50** | 56.88 | | MMLU Pro | **16.35** | 14.35 | | TruthfulQA | **43.08** | 38.96 | | BBH | **33.75** | 31.83 | | GPQA | 25.96 | **26.39** | The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/huihui-ai/Llama-3.2-1B-Instruct-abliterated/blob/main/eval.sh)

mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF

mradermacher

2024-11-25T09:34:00Z

34

0

transformers

[ "transformers", "gguf", "text-generation-inference", "sft", "chocolatine", "fr", "dataset:jpacifico/french-orca-pairs-culinary-9865", "dataset:jpacifico/finetome_french_cook_definitions_v2", "base_model:jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1", "base_model:quantized:jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1", "license:mit", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-25T09:10:57Z

--- base_model: jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 datasets: - jpacifico/french-orca-pairs-culinary-9865 - jpacifico/finetome_french_cook_definitions_v2 language: - fr library_name: transformers license: mit quantized_by: mradermacher tags: - text-generation-inference - transformers - sft - chocolatine --- ## About      weighted/imatrix quants of https://huggingface.co/jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1  static quants are available at https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 1.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 1.1 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.2 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.3 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 1.4 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 1.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.7 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 1.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 1.9 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.0 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.1 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.2 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 2.3 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 2.3 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 2.3 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 2.3 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.3 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.i1-Q6_K.gguf) | i1-Q6_K | 3.2 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mini1013/master_cate_ac0

mini1013

2024-11-25T09:33:58Z

154

0

setfit

[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:mini1013/master_domain", "base_model:finetune:mini1013/master_domain", "model-index", "region:us" ]

text-classification

2024-11-25T09:33:32Z

--- base_model: mini1013/master_domain library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: '[헤지스ACC]HJBA3F885BK[13인치 노트북 수납가능][KEVIN]블랙 참장식 크로스 겸용 미니 토트백 에이케이에스앤디 (주) AK인터넷쇼핑몰' - text: 마젤란 메신저백 크로스백 슬링백 힙색 힙쌕 학생 여성 남자 캐주얼 크로스 여행용 여권 핸드폰 보조 학원 가방 LKHS-304_B-연핑크(+키홀더) 더블유팝 - text: 마젤란 메신저백 크로스백 슬링백 힙색 힙쌕 학생 여성 남자 캐주얼 크로스 여행용 여권 핸드폰 보조 학원 가방 ML-1928_연그레이 더블유팝 - text: '[갤러리아] JUBA4E021G2 [MATEO] 그레이 로고프린트 숄더백 JUBA4E021G2 [MATEO] 그레이 로고프린트 숄더백 NS홈쇼핑_NS몰' - text: '[디스커버리](신세계강남점)[23N] 디스커버리 미니 슬링백 (DXSG0043N) IVD 다크 아이보리_F 주식회사 에스에스지닷컴' inference: true model-index: - name: SetFit with mini1013/master_domain results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.8488667448221962 name: Metric --- # SetFit with mini1013/master_domain This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [mini1013/master_domain](https://huggingface.co/mini1013/master_domain) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 9 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 6.0 | <ul><li>'[질스튜어트](광주신세계)블랙 클래식 클러치백 [JUWA2F392BK] 주식회사 에스에스지닷컴'</li><li>'심플 클러치백 EOCFHX257BK/에스콰이아 블랙 롯데쇼핑(주)'</li><li>'[듀퐁] 소프트그레인 파우치 베이지 CG180263CL 베이지 (주)씨제이이엔엠'</li></ul> | | 3.0 | <ul><li>'엔지니어드가먼츠 블랙 나일론 토트백 23F1H034BLACK 주식회사 어도어럭스'</li><li>'[가이거] 퀼팅 레더 체인 숄더백 (+플랩지갑) 캐러멜 브라운 (주)우리홈쇼핑'</li><li>'토트 브리프 크로스백 FT8570 블랙 글로리홈'</li></ul> | | 4.0 | <ul><li>'여자캔버스 가방 코디 크로스백 남자에코백 신발 BLUE 고앤런'</li><li>'여학생 에코백 아이보리 가방 남녀공용 캐주얼 쇼퍼백 엘케이엠'</li><li>'패션 에코백 데일리 가방 캐주얼 숄더백 브라운 심정'</li></ul> | | 7.0 | <ul><li>'[갤러리아] 644040 2BKPI 1000 ONE SIZE 한화갤러리아(주)'</li><li>'[갤러리아] 헤지스핸드백 그린 워싱가죽 크로스 겸용 토트백 HJBA3E301E2(타임월드) 한화갤러리아(주)'</li><li>'[메종키츠네] 로고 프린트 코튼 토트백 블루 LW05102WW0008 BLUE_FREE 신세계몰'</li></ul> | | 2.0 | <ul><li>'바버 가죽 코팅 서류 가방 브리프 케이스 UBA0004 NAVY 뉴욕트레이딩'</li><li>'[롯데백화점]에스콰이아 23FW 신상 경량 나일론 노트북 수납 남여 데일리 토트 크로스백 EOCFHX258BK 롯데백화점_'</li><li>'22FW 신상 뉴 포멀 슬림 스퀘어 심플 비즈니스 캐주얼 서류가방 ECBFHX227GY 롯데백화점1관'</li></ul> | | 1.0 | <ul><li>'NATIONALGEOGRAPHIC N225USD340 다이브 플러스 V3 BLACK 240 맥스투'</li><li>'레스포삭 보이저 백팩 경량 나일론 보부상 복조리 가방 7839 플라워 행운샵'</li><li>'레스포삭 보이저 백팩 경량 Voyager Backpack 7839 블랙 하하대행'</li></ul> | | 0.0 | <ul><li>'[갤러리아] 헤지스핸드백HJBA2F770BK_ 블랙 로고 장식 솔리드 메신져백(타임월드) 한화갤러리아(주)'</li><li>'로아드로아 허쉬 메쉬 포켓 크로스 메신저백 (아이보리) 크로스백 FREE 가방팝'</li><li>'[본사공식] 타프 메신저백 사첼 S EOCBS04 008 롯데아이몰'</li></ul> | | 5.0 | <ul><li>'팩세이프 가방 GO 크로스바디 백 2.5L / PACSAFE URBAN 도난방지 유럽 해외 여행 등산 슬링백 크로스백 RFID차단 1. 제트 블랙 (JET BLACK) 시계1위팝워치'</li><li>'샨타코[Chantaco] 레더 크로스백 BB NH3271C53N 000/라코스테 롯데쇼핑(주)'</li><li>'팩세이프 가방 GO 크로스바디 백 2.5L / PACSAFE URBAN 도난방지 유럽 해외 여행 등산 슬링백 크로스백 RFID차단 2. 로즈 (ROSE) 시계1위팝워치'</li></ul> | | 8.0 | <ul><li>'[기회공작소] 데일리 슬링백 크로스 힙색 허리가방 스포츠 등산 힙색 허리색 슬링백 보조가방 글로리커머스'</li><li>'구찌 GG 캔버스 투웨이 밸트백 힙색 630915 KY9KN 9886 쏠나인'</li><li>'벨트형 핸드폰 허리가방 남자 벨트백 세로형 가죽 벨트파우치 지갑 허리벨트케이스 브라운 자주구매'</li></ul> | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.8489 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_cate_ac0") # Run inference preds = model("[디스커버리](신세계강남점)[23N] 디스커버리 미니 슬링백 (DXSG0043N) IVD 다크 아이보리_F 주식회사 에스에스지닷컴") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 4 | 9.2289 | 29 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 50 | | 1.0 | 50 | | 2.0 | 50 | | 3.0 | 50 | | 4.0 | 50 | | 5.0 | 50 | | 6.0 | 50 | | 7.0 | 50 | | 8.0 | 50 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:----:|:-------------:|:---------------:| | 0.0141 | 1 | 0.3958 | - | | 0.7042 | 50 | 0.3012 | - | | 1.4085 | 100 | 0.1811 | - | | 2.1127 | 150 | 0.0599 | - | | 2.8169 | 200 | 0.0333 | - | | 3.5211 | 250 | 0.0169 | - | | 4.2254 | 300 | 0.0005 | - | | 4.9296 | 350 | 0.0003 | - | | 5.6338 | 400 | 0.0002 | - | | 6.3380 | 450 | 0.0003 | - | | 7.0423 | 500 | 0.0001 | - | | 7.7465 | 550 | 0.0001 | - | | 8.4507 | 600 | 0.0001 | - | | 9.1549 | 650 | 0.0001 | - | | 9.8592 | 700 | 0.0001 | - | | 10.5634 | 750 | 0.0 | - | | 11.2676 | 800 | 0.0001 | - | | 11.9718 | 850 | 0.0001 | - | | 12.6761 | 900 | 0.0001 | - | | 13.3803 | 950 | 0.0 | - | | 14.0845 | 1000 | 0.0 | - | | 14.7887 | 1050 | 0.0 | - | | 15.4930 | 1100 | 0.0 | - | | 16.1972 | 1150 | 0.0 | - | | 16.9014 | 1200 | 0.0 | - | | 17.6056 | 1250 | 0.0 | - | | 18.3099 | 1300 | 0.0 | - | | 19.0141 | 1350 | 0.0 | - | | 19.7183 | 1400 | 0.0 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF

mradermacher

2024-11-25T09:31:34Z

83

0

transformers

[ "transformers", "gguf", "text-generation-inference", "sft", "chocolatine", "fr", "dataset:jpacifico/french-orca-pairs-culinary-9865", "dataset:jpacifico/finetome_french_cook_definitions_v2", "base_model:jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1", "base_model:quantized:jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1", "license:mit", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-25T09:00:57Z

--- base_model: jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1 datasets: - jpacifico/french-orca-pairs-culinary-9865 - jpacifico/finetome_french_cook_definitions_v2 language: - fr library_name: transformers license: mit quantized_by: mradermacher tags: - text-generation-inference - transformers - sft - chocolatine --- ## About      static quants of https://huggingface.co/jpacifico/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q2_K.gguf) | Q2_K | 1.5 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q3_K_S.gguf) | Q3_K_S | 1.8 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q3_K_M.gguf) | Q3_K_M | 2.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q3_K_L.gguf) | Q3_K_L | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.IQ4_XS.gguf) | IQ4_XS | 2.2 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 2.3 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q4_K_S.gguf) | Q4_K_S | 2.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q4_K_M.gguf) | Q4_K_M | 2.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q5_K_S.gguf) | Q5_K_S | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q5_K_M.gguf) | Q5_K_M | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q6_K.gguf) | Q6_K | 3.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1-GGUF/resolve/main/Chocolatine-Cook-3B-combined-SFT-DPO-v0.1.f16.gguf) | f16 | 7.7 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

leinad-deinor/Llama3.2-3b-redeIT-XML-GGUF

leinad-deinor

2024-11-25T09:22:10Z

9

0

null

[ "gguf", "llama", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-25T08:13:52Z

--- license: apache-2.0 ---

mini1013/master_item_ac

mini1013

2024-11-25T09:19:32Z

423

0

setfit

[ "setfit", "safetensors", "roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:klue/roberta-base", "base_model:finetune:klue/roberta-base", "model-index", "region:us" ]

text-classification

2024-11-25T09:19:09Z

--- base_model: klue/roberta-base library_name: setfit metrics: - metric pipeline_tag: text-classification tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer widget: - text: '[자체제작] 14k 콩사다리 체인 반지 핑크_D style(1푼 굵기)_10호 (주)제이디아이인터내셔널' - text: 실리콘 동전 지갑 심플 캐릭터 [on] 블랙캣(동전지갑) 비150 - text: 체크 남자 베레모 아빠 모자 헌팅캡 패션 빵모자 외출 베이지체크 (4JS) 포제이스 - text: TIMBERLAND 남성 앨번 6인치 워터프루프 워커부츠_TB0A1OIZC641 070(250) 비츠컴퍼니 - text: 라인댄스화 헬스화 스포츠 여성 재즈화 댄스화 볼룸 모던 미드힐 37_블랙 스트레이트 3.5cm/굽(메쉬) 사랑옵다 inference: true model-index: - name: SetFit with klue/roberta-base results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: metric value: 0.9385943021823656 name: Metric --- # SetFit with klue/roberta-base This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [klue/roberta-base](https://huggingface.co/klue/roberta-base) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 17 classes    ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels | Label | Examples | |:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | 2.0 | <ul><li>'남녀공용 멀티스카프 목토시 반다나 헤어밴드 두건 블랙 비오는밤'</li><li>'후드 모자 귀달이 겨울 털모자 동물 목돌이 03.브라운 뿔샵'</li><li>'햇빛 뒷목가리개 메쉬 통풍 선가드 자외선차단썬캡가드 그늘모자 쿨메쉬모자_그레이 에스더블유컴퍼니'</li></ul> | | 9.0 | <ul><li>'[LAP](강남점)아델라 핸들 미니 크로스백 (AP7AB208) 제트블랙(ZB)_FREE 신세계백화점'</li><li>'파스텔슬링백 힙색 미니 크로스 숄더백 그린 김후철'</li><li>'[메트로시티]봉봉백 클러치백 미듐 M233MQ3852Z 에이케이에스앤디 (주) AK인터넷쇼핑몰'</li></ul> | | 15.0 | <ul><li>'크리스마스 뱃지 브로치 배지 19종 세트 및 낱개 봉제 사슴 5 구매대행 이음'</li><li>'오드스튜디오 ODDSTUDIO 베이직 니트 체크 머플러 - 21COLOR 블랙 CS스페이스'</li><li>'넥케이프 넥커프스 페이크카라 레이어드카라 셔츠카라 1-카라-화이트 행복나라'</li></ul> | | 13.0 | <ul><li>'펄 쥬얼리 보석함 여행용 포켓 미니 악세사리 보관함 케이스 C타입-베이비핑크 제일사'</li><li>'[갤러리아] [비앤비골드] 14K 촘촘볼 블루큐빅 도넛링 반지 SRS39135 14K 화이트골드_1호 한화갤러리아(주)'</li><li>'미니골드 김천점 14K 18K 트레버 커플링 남자 여자 금반지 RJUC4047 RJUC4048 베이직하고 심플한 디자인 여자_14K옐로우골드 미니골드 김천점'</li></ul> | | 1.0 | <ul><li>'[베어파우](신세계강남점)(BEARPAW) 남성 털 슬리퍼 MARY MENS 블랙 K814001ND-M BLACK (K814001ND)_280 주식회사 에스에스지닷컴'</li><li>'노스페이스 뮬 슬립온 브이모션 - NS93P53A 블랙_290 롯데백화점2관'</li><li>'사무실 남자 슬리퍼 가죽 남성 빅 사이즈 48 47 사무용 신입생코디실내화 blue_38 리마106'</li></ul> | | 7.0 | <ul><li>'부드러운 슈트리 신발주름방지 신발모양유지 신발지탱 225 245 mm 커피와 기저귀'</li><li>'[갓성비] 꿀조합 애니비츠 세트 캐릭터 신발 악세사리 포켓몬 스누피 커비편의점SET 애니팝'</li><li>'MSMAX Jazz Dance Shoes Split Sole Men Dancing Sneakers High Top Boots for Women Silver 10.5 M Silver_11 Narrow 디아트479'</li></ul> | | 11.0 | <ul><li>'캐리어 수트케이스 양면 개방형 기내용 바퀴가방 화이트_26인치 피스온트레이드'</li><li>'클래시 패스 커버여권 포트월렛 포트파우치 파우치 여행지갑 포트 케이스 (01 레모니) 주식회사유마켓'</li><li>'클래시패스커버 (안티스키밍 여권케이스) (10블랙) JTEC'</li></ul> | | 4.0 | <ul><li>'고급 안경집 선글라스집 휴대용 케이스 파우치 하드 보관함 블랙 다온마켓'</li><li>'고급 올 칼라 크리스탈 다중 비즈 안경 줄 마스크 걸이 상품선택_블랙(골드) 리미몰'</li><li>'아이업꽈배기인조가죽안경줄10p세트선글라스줄 마니또야'</li></ul> | | 14.0 | <ul><li>'[갤러리아] [Prada]프라다 23FW 사피아노 반지갑 블랙 2MO004 QME F0002 2MO004 QME F0002 FREE 한화갤러리아(주)'</li><li>'닥스 액세서리 [OSCAR][오스카][제네시스 전용] 네이비 프리미엄 토고 수입 가죽 차키케이스 DBHO2F573N2 XXX 주식회사 LF'</li><li>'톰브라운 23SS 남성 페블그레인 머니클립 블랙 MAW025L 00198 001 ONE SIZE 주식회사 이지겟인터내셔널'</li></ul> | | 0.0 | <ul><li>'[롯데백화점]닥스ACC [선물포장/쇼핑백동봉] [GRIDⅡ] 브라운 패턴배색 소가죽 클러치백 DBBA2F266W3 롯데백화점_'</li><li>'만다리나덕 토트백 PIETRO P4T05163 은하수몰'</li><li>'내셔널지오그래픽 N245ATO510 베이직 에코백 BLACK TNSC'</li></ul> | | 16.0 | <ul><li>'올림머리 메탈프레임 반머리 꼬임 집게핀 114 유광스틸 7cm 이지 아트 프로덕션 (EG ART PRODUCTION)'</li><li>'꼬임 메탈프레임 반머리 올림머리 집게핀 114 무광로즈 7cm 네오몰'</li><li>'폼폼 방울털 장식 미니 머리끈 포인트 헤어끈 퍼플 1P 은강'</li></ul> | | 8.0 | <ul><li>'기모 롱 오버 니삭스 겨울 스타킹 다리 워머 롱삭스 롱양말 무릎 니하이 브라운 린이팸'</li><li>'최대12켤레 남여 국산양말 장목/니트/균일가/신상/중목/발목/수면/학생 37~38_37.여)털실 중목_4켤레 / 버건디 투투삭스'</li><li>'NY코튼클럽 5켤레 국산 극세사 기모 롱 무압박 임산부 수면양말 W8001-여성-카멜5족 GSSHOP_'</li></ul> | | 5.0 | <ul><li>'[한국금거래소] 순금 카네이션 배지 1.875g 부모님 추석 명절 생신 생일 기념일 기념 축하 감사선물 주식회사 한국금거래소디지털에셋'</li><li>'[한국금거래소]한국금거래소 순금 용 37.5g [순금24K] 롯데아이몰'</li><li>'한국금거래소 실버바 1kg(1000g) 주식회사 한국금거래소디지털에셋'</li></ul> | | 10.0 | <ul><li>'캠퍼 브루투스 트렉 첼시 앵클부츠 346335 EU 39 주식회사 수비르글로벌커머스(SUBIR Global Commerce)'</li><li>'슈콤마보니 워커 부츠 DG3CW22519BLK 블랙_250 롯데쇼핑(주) 프리미엄아울렛 타임빌라스'</li><li>'말랑 쿠키 거실화 실내화 거실슬리퍼 실내슬리퍼 LWS 그레이265mm 생활공작소365'</li></ul> | | 6.0 | <ul><li>'BOXY 박시 워치와인더 BWS-S / BWS-F 1구 아답터1개로 쌓아서 사용가능 BWS-S(DG)아답터미포함 와치닷컴'</li><li>'지샥 GA-2100 2110 지얄오크 베젤 밴드 일체형 용두 메탈 우레탄밴드 커스텀 옵션5:실버+블랙베젤_1.일반버클_화이트 방울방울'</li><li>'스타샵 카시오 MRW-200H-2B2 남성 손목시계 c57 선택19. AW-49H-1B 스타샵'</li></ul> | | 3.0 | <ul><li>'남자 멜빵 2 5CM 남성 및 여성 서스펜더 클립 사이드 홀스터 스타일 탄성 백 서스펜더 05 밝은 빨간색 헬로우스토어'</li><li>'멜빵 소형멜빵 용 멜빵 어린이멜빵 멜빵 맬빵 MinSellAmount 모루모루'</li><li>'[닥스 액세서리] [23FW] DBBE3F097BK 여성벨트DD Symbol 블랙 DD메탈릭 골드 버클 소 XXX '</li></ul> | | 12.0 | <ul><li>'미니 토시 사무용 광목 자수 팔토시 레드로즈 다솜이네'</li><li>'백화점 여성 남성 천연 양가죽 장갑 스마트폰 터치 털 손가락 겨울 방한 가죽 커플 장갑 2.여성용/스웨이드/차콜 힐렉스'</li><li>'[선물포장] 울 캐시미어혼방 핑거홀 장갑 JAGV2F310G2,JAGV2F311W2,JAGV2F312E2,JAGV2F313/질스튜어트 그린 롯데쇼핑(주)'</li></ul> | ## Evaluation ### Metrics | Label | Metric | |:--------|:-------| | **all** | 0.9386 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("mini1013/master_item_ac") # Run inference preds = model("실리콘 동전 지갑 심플 캐릭터 [on] 블랙캣(동전지갑) 비150") ```     ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:--------|:----| | Word count | 3 | 10.2537 | 30 | | Label | Training Sample Count | |:------|:----------------------| | 0.0 | 450 | | 1.0 | 650 | | 2.0 | 650 | | 3.0 | 150 | | 4.0 | 300 | | 5.0 | 120 | | 6.0 | 224 | | 7.0 | 350 | | 8.0 | 100 | | 9.0 | 467 | | 10.0 | 500 | | 11.0 | 600 | | 12.0 | 150 | | 13.0 | 450 | | 14.0 | 400 | | 15.0 | 1000 | | 16.0 | 250 | ### Training Hyperparameters - batch_size: (512, 512) - num_epochs: (20, 20) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 40 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:-------:|:-----:|:-------------:|:---------------:| | 0.0009 | 1 | 0.407 | - | | 0.0469 | 50 | 0.3772 | - | | 0.0939 | 100 | 0.3062 | - | | 0.1408 | 150 | 0.2861 | - | | 0.1878 | 200 | 0.2513 | - | | 0.2347 | 250 | 0.2284 | - | | 0.2817 | 300 | 0.1952 | - | | 0.3286 | 350 | 0.149 | - | | 0.3756 | 400 | 0.1154 | - | | 0.4225 | 450 | 0.1042 | - | | 0.4695 | 500 | 0.0802 | - | | 0.5164 | 550 | 0.0765 | - | | 0.5634 | 600 | 0.0767 | - | | 0.6103 | 650 | 0.0475 | - | | 0.6573 | 700 | 0.0535 | - | | 0.7042 | 750 | 0.0293 | - | | 0.7512 | 800 | 0.0388 | - | | 0.7981 | 850 | 0.0156 | - | | 0.8451 | 900 | 0.0348 | - | | 0.8920 | 950 | 0.0241 | - | | 0.9390 | 1000 | 0.023 | - | | 0.9859 | 1050 | 0.0166 | - | | 1.0329 | 1100 | 0.0124 | - | | 1.0798 | 1150 | 0.0139 | - | | 1.1268 | 1200 | 0.0122 | - | | 1.1737 | 1250 | 0.0111 | - | | 1.2207 | 1300 | 0.0062 | - | | 1.2676 | 1350 | 0.0106 | - | | 1.3146 | 1400 | 0.0112 | - | | 1.3615 | 1450 | 0.0137 | - | | 1.4085 | 1500 | 0.0154 | - | | 1.4554 | 1550 | 0.0185 | - | | 1.5023 | 1600 | 0.0248 | - | | 1.5493 | 1650 | 0.0128 | - | | 1.5962 | 1700 | 0.018 | - | | 1.6432 | 1750 | 0.0013 | - | | 1.6901 | 1800 | 0.0151 | - | | 1.7371 | 1850 | 0.0208 | - | | 1.7840 | 1900 | 0.0076 | - | | 1.8310 | 1950 | 0.0138 | - | | 1.8779 | 2000 | 0.0133 | - | | 1.9249 | 2050 | 0.0131 | - | | 1.9718 | 2100 | 0.0123 | - | | 2.0188 | 2150 | 0.0165 | - | | 2.0657 | 2200 | 0.0084 | - | | 2.1127 | 2250 | 0.0062 | - | | 2.1596 | 2300 | 0.0068 | - | | 2.2066 | 2350 | 0.0023 | - | | 2.2535 | 2400 | 0.006 | - | | 2.3005 | 2450 | 0.0048 | - | | 2.3474 | 2500 | 0.0016 | - | | 2.3944 | 2550 | 0.0046 | - | | 2.4413 | 2600 | 0.001 | - | | 2.4883 | 2650 | 0.0022 | - | | 2.5352 | 2700 | 0.0014 | - | | 2.5822 | 2750 | 0.0004 | - | | 2.6291 | 2800 | 0.0002 | - | | 2.6761 | 2850 | 0.0004 | - | | 2.7230 | 2900 | 0.0016 | - | | 2.7700 | 2950 | 0.0018 | - | | 2.8169 | 3000 | 0.0004 | - | | 2.8638 | 3050 | 0.0001 | - | | 2.9108 | 3100 | 0.0002 | - | | 2.9577 | 3150 | 0.0018 | - | | 3.0047 | 3200 | 0.0019 | - | | 3.0516 | 3250 | 0.0001 | - | | 3.0986 | 3300 | 0.0011 | - | | 3.1455 | 3350 | 0.0001 | - | | 3.1925 | 3400 | 0.0001 | - | | 3.2394 | 3450 | 0.0002 | - | | 3.2864 | 3500 | 0.0007 | - | | 3.3333 | 3550 | 0.0001 | - | | 3.3803 | 3600 | 0.0002 | - | | 3.4272 | 3650 | 0.0001 | - | | 3.4742 | 3700 | 0.0011 | - | | 3.5211 | 3750 | 0.0013 | - | | 3.5681 | 3800 | 0.0014 | - | | 3.6150 | 3850 | 0.0001 | - | | 3.6620 | 3900 | 0.0001 | - | | 3.7089 | 3950 | 0.0002 | - | | 3.7559 | 4000 | 0.0001 | - | | 3.8028 | 4050 | 0.0014 | - | | 3.8498 | 4100 | 0.0002 | - | | 3.8967 | 4150 | 0.0001 | - | | 3.9437 | 4200 | 0.0 | - | | 3.9906 | 4250 | 0.0 | - | | 4.0376 | 4300 | 0.0001 | - | | 4.0845 | 4350 | 0.0002 | - | | 4.1315 | 4400 | 0.0 | - | | 4.1784 | 4450 | 0.0001 | - | | 4.2254 | 4500 | 0.0 | - | | 4.2723 | 4550 | 0.0 | - | | 4.3192 | 4600 | 0.0003 | - | | 4.3662 | 4650 | 0.0007 | - | | 4.4131 | 4700 | 0.0 | - | | 4.4601 | 4750 | 0.0001 | - | | 4.5070 | 4800 | 0.0011 | - | | 4.5540 | 4850 | 0.0003 | - | | 4.6009 | 4900 | 0.0005 | - | | 4.6479 | 4950 | 0.0001 | - | | 4.6948 | 5000 | 0.0001 | - | | 4.7418 | 5050 | 0.0001 | - | | 4.7887 | 5100 | 0.0001 | - | | 4.8357 | 5150 | 0.0 | - | | 4.8826 | 5200 | 0.0 | - | | 4.9296 | 5250 | 0.0 | - | | 4.9765 | 5300 | 0.0001 | - | | 5.0235 | 5350 | 0.0 | - | | 5.0704 | 5400 | 0.0 | - | | 5.1174 | 5450 | 0.0 | - | | 5.1643 | 5500 | 0.0 | - | | 5.2113 | 5550 | 0.0 | - | | 5.2582 | 5600 | 0.0001 | - | | 5.3052 | 5650 | 0.0 | - | | 5.3521 | 5700 | 0.0 | - | | 5.3991 | 5750 | 0.0 | - | | 5.4460 | 5800 | 0.0 | - | | 5.4930 | 5850 | 0.0 | - | | 5.5399 | 5900 | 0.0 | - | | 5.5869 | 5950 | 0.0 | - | | 5.6338 | 6000 | 0.0 | - | | 5.6808 | 6050 | 0.0 | - | | 5.7277 | 6100 | 0.0 | - | | 5.7746 | 6150 | 0.0 | - | | 5.8216 | 6200 | 0.0 | - | | 5.8685 | 6250 | 0.0 | - | | 5.9155 | 6300 | 0.0001 | - | | 5.9624 | 6350 | 0.0004 | - | | 6.0094 | 6400 | 0.0007 | - | | 6.0563 | 6450 | 0.0 | - | | 6.1033 | 6500 | 0.0001 | - | | 6.1502 | 6550 | 0.0 | - | | 6.1972 | 6600 | 0.0001 | - | | 6.2441 | 6650 | 0.0 | - | | 6.2911 | 6700 | 0.0 | - | | 6.3380 | 6750 | 0.0009 | - | | 6.3850 | 6800 | 0.0 | - | | 6.4319 | 6850 | 0.0001 | - | | 6.4789 | 6900 | 0.0 | - | | 6.5258 | 6950 | 0.0001 | - | | 6.5728 | 7000 | 0.0 | - | | 6.6197 | 7050 | 0.0 | - | | 6.6667 | 7100 | 0.0 | - | | 6.7136 | 7150 | 0.0 | - | | 6.7606 | 7200 | 0.0001 | - | | 6.8075 | 7250 | 0.0 | - | | 6.8545 | 7300 | 0.0 | - | | 6.9014 | 7350 | 0.0 | - | | 6.9484 | 7400 | 0.0 | - | | 6.9953 | 7450 | 0.0 | - | | 7.0423 | 7500 | 0.0 | - | | 7.0892 | 7550 | 0.0 | - | | 7.1362 | 7600 | 0.0 | - | | 7.1831 | 7650 | 0.0 | - | | 7.2300 | 7700 | 0.0 | - | | 7.2770 | 7750 | 0.0001 | - | | 7.3239 | 7800 | 0.0 | - | | 7.3709 | 7850 | 0.0 | - | | 7.4178 | 7900 | 0.0 | - | | 7.4648 | 7950 | 0.0 | - | | 7.5117 | 8000 | 0.0 | - | | 7.5587 | 8050 | 0.0 | - | | 7.6056 | 8100 | 0.0 | - | | 7.6526 | 8150 | 0.0024 | - | | 7.6995 | 8200 | 0.0 | - | | 7.7465 | 8250 | 0.0 | - | | 7.7934 | 8300 | 0.0 | - | | 7.8404 | 8350 | 0.0 | - | | 7.8873 | 8400 | 0.0 | - | | 7.9343 | 8450 | 0.0 | - | | 7.9812 | 8500 | 0.0 | - | | 8.0282 | 8550 | 0.0 | - | | 8.0751 | 8600 | 0.0 | - | | 8.1221 | 8650 | 0.0 | - | | 8.1690 | 8700 | 0.0 | - | | 8.2160 | 8750 | 0.0 | - | | 8.2629 | 8800 | 0.0 | - | | 8.3099 | 8850 | 0.0 | - | | 8.3568 | 8900 | 0.0 | - | | 8.4038 | 8950 | 0.0 | - | | 8.4507 | 9000 | 0.0 | - | | 8.4977 | 9050 | 0.0 | - | | 8.5446 | 9100 | 0.0 | - | | 8.5915 | 9150 | 0.0 | - | | 8.6385 | 9200 | 0.0002 | - | | 8.6854 | 9250 | 0.0003 | - | | 8.7324 | 9300 | 0.0005 | - | | 8.7793 | 9350 | 0.0001 | - | | 8.8263 | 9400 | 0.0001 | - | | 8.8732 | 9450 | 0.0001 | - | | 8.9202 | 9500 | 0.0 | - | | 8.9671 | 9550 | 0.0 | - | | 9.0141 | 9600 | 0.0001 | - | | 9.0610 | 9650 | 0.0001 | - | | 9.1080 | 9700 | 0.0 | - | | 9.1549 | 9750 | 0.0 | - | | 9.2019 | 9800 | 0.0001 | - | | 9.2488 | 9850 | 0.0 | - | | 9.2958 | 9900 | 0.0 | - | | 9.3427 | 9950 | 0.0 | - | | 9.3897 | 10000 | 0.0 | - | | 9.4366 | 10050 | 0.0 | - | | 9.4836 | 10100 | 0.0 | - | | 9.5305 | 10150 | 0.0 | - | | 9.5775 | 10200 | 0.0 | - | | 9.6244 | 10250 | 0.0 | - | | 9.6714 | 10300 | 0.0 | - | | 9.7183 | 10350 | 0.0 | - | | 9.7653 | 10400 | 0.0 | - | | 9.8122 | 10450 | 0.0 | - | | 9.8592 | 10500 | 0.0016 | - | | 9.9061 | 10550 | 0.0 | - | | 9.9531 | 10600 | 0.0 | - | | 10.0 | 10650 | 0.0 | - | | 10.0469 | 10700 | 0.0003 | - | | 10.0939 | 10750 | 0.0 | - | | 10.1408 | 10800 | 0.0 | - | | 10.1878 | 10850 | 0.0 | - | | 10.2347 | 10900 | 0.0 | - | | 10.2817 | 10950 | 0.0 | - | | 10.3286 | 11000 | 0.0 | - | | 10.3756 | 11050 | 0.0 | - | | 10.4225 | 11100 | 0.0 | - | | 10.4695 | 11150 | 0.0 | - | | 10.5164 | 11200 | 0.0 | - | | 10.5634 | 11250 | 0.0 | - | | 10.6103 | 11300 | 0.0 | - | | 10.6573 | 11350 | 0.0 | - | | 10.7042 | 11400 | 0.0 | - | | 10.7512 | 11450 | 0.0 | - | | 10.7981 | 11500 | 0.0 | - | | 10.8451 | 11550 | 0.0 | - | | 10.8920 | 11600 | 0.0 | - | | 10.9390 | 11650 | 0.0 | - | | 10.9859 | 11700 | 0.0 | - | | 11.0329 | 11750 | 0.0 | - | | 11.0798 | 11800 | 0.0 | - | | 11.1268 | 11850 | 0.0 | - | | 11.1737 | 11900 | 0.0 | - | | 11.2207 | 11950 | 0.0 | - | | 11.2676 | 12000 | 0.0 | - | | 11.3146 | 12050 | 0.0 | - | | 11.3615 | 12100 | 0.0 | - | | 11.4085 | 12150 | 0.0 | - | | 11.4554 | 12200 | 0.0 | - | | 11.5023 | 12250 | 0.0015 | - | | 11.5493 | 12300 | 0.0 | - | | 11.5962 | 12350 | 0.0 | - | | 11.6432 | 12400 | 0.0 | - | | 11.6901 | 12450 | 0.0 | - | | 11.7371 | 12500 | 0.0 | - | | 11.7840 | 12550 | 0.0002 | - | | 11.8310 | 12600 | 0.0 | - | | 11.8779 | 12650 | 0.0 | - | | 11.9249 | 12700 | 0.0 | - | | 11.9718 | 12750 | 0.0001 | - | | 12.0188 | 12800 | 0.0 | - | | 12.0657 | 12850 | 0.0 | - | | 12.1127 | 12900 | 0.0 | - | | 12.1596 | 12950 | 0.0001 | - | | 12.2066 | 13000 | 0.0001 | - | | 12.2535 | 13050 | 0.0 | - | | 12.3005 | 13100 | 0.0 | - | | 12.3474 | 13150 | 0.0001 | - | | 12.3944 | 13200 | 0.0 | - | | 12.4413 | 13250 | 0.0 | - | | 12.4883 | 13300 | 0.0 | - | | 12.5352 | 13350 | 0.0 | - | | 12.5822 | 13400 | 0.0 | - | | 12.6291 | 13450 | 0.0 | - | | 12.6761 | 13500 | 0.0 | - | | 12.7230 | 13550 | 0.0 | - | | 12.7700 | 13600 | 0.0 | - | | 12.8169 | 13650 | 0.0 | - | | 12.8638 | 13700 | 0.0 | - | | 12.9108 | 13750 | 0.0 | - | | 12.9577 | 13800 | 0.0 | - | | 13.0047 | 13850 | 0.0 | - | | 13.0516 | 13900 | 0.0 | - | | 13.0986 | 13950 | 0.0 | - | | 13.1455 | 14000 | 0.0 | - | | 13.1925 | 14050 | 0.0 | - | | 13.2394 | 14100 | 0.0 | - | | 13.2864 | 14150 | 0.0 | - | | 13.3333 | 14200 | 0.0 | - | | 13.3803 | 14250 | 0.0 | - | | 13.4272 | 14300 | 0.0 | - | | 13.4742 | 14350 | 0.0 | - | | 13.5211 | 14400 | 0.0 | - | | 13.5681 | 14450 | 0.0 | - | | 13.6150 | 14500 | 0.0 | - | | 13.6620 | 14550 | 0.0 | - | | 13.7089 | 14600 | 0.0 | - | | 13.7559 | 14650 | 0.0 | - | | 13.8028 | 14700 | 0.0 | - | | 13.8498 | 14750 | 0.0 | - | | 13.8967 | 14800 | 0.0 | - | | 13.9437 | 14850 | 0.0 | - | | 13.9906 | 14900 | 0.0 | - | | 14.0376 | 14950 | 0.0 | - | | 14.0845 | 15000 | 0.0 | - | | 14.1315 | 15050 | 0.0 | - | | 14.1784 | 15100 | 0.0001 | - | | 14.2254 | 15150 | 0.0 | - | | 14.2723 | 15200 | 0.0 | - | | 14.3192 | 15250 | 0.0 | - | | 14.3662 | 15300 | 0.0 | - | | 14.4131 | 15350 | 0.0 | - | | 14.4601 | 15400 | 0.0 | - | | 14.5070 | 15450 | 0.0 | - | | 14.5540 | 15500 | 0.0 | - | | 14.6009 | 15550 | 0.0 | - | | 14.6479 | 15600 | 0.0 | - | | 14.6948 | 15650 | 0.0 | - | | 14.7418 | 15700 | 0.0 | - | | 14.7887 | 15750 | 0.0 | - | | 14.8357 | 15800 | 0.0 | - | | 14.8826 | 15850 | 0.0 | - | | 14.9296 | 15900 | 0.0 | - | | 14.9765 | 15950 | 0.0 | - | | 15.0235 | 16000 | 0.0 | - | | 15.0704 | 16050 | 0.0 | - | | 15.1174 | 16100 | 0.0 | - | | 15.1643 | 16150 | 0.0 | - | | 15.2113 | 16200 | 0.0 | - | | 15.2582 | 16250 | 0.0 | - | | 15.3052 | 16300 | 0.0 | - | | 15.3521 | 16350 | 0.0 | - | | 15.3991 | 16400 | 0.0 | - | | 15.4460 | 16450 | 0.0 | - | | 15.4930 | 16500 | 0.0 | - | | 15.5399 | 16550 | 0.0 | - | | 15.5869 | 16600 | 0.0 | - | | 15.6338 | 16650 | 0.0 | - | | 15.6808 | 16700 | 0.0 | - | | 15.7277 | 16750 | 0.0 | - | | 15.7746 | 16800 | 0.0 | - | | 15.8216 | 16850 | 0.0 | - | | 15.8685 | 16900 | 0.0 | - | | 15.9155 | 16950 | 0.0 | - | | 15.9624 | 17000 | 0.0 | - | | 16.0094 | 17050 | 0.0 | - | | 16.0563 | 17100 | 0.0 | - | | 16.1033 | 17150 | 0.0 | - | | 16.1502 | 17200 | 0.0 | - | | 16.1972 | 17250 | 0.0 | - | | 16.2441 | 17300 | 0.0 | - | | 16.2911 | 17350 | 0.0 | - | | 16.3380 | 17400 | 0.0 | - | | 16.3850 | 17450 | 0.0 | - | | 16.4319 | 17500 | 0.0 | - | | 16.4789 | 17550 | 0.0 | - | | 16.5258 | 17600 | 0.0 | - | | 16.5728 | 17650 | 0.0 | - | | 16.6197 | 17700 | 0.0 | - | | 16.6667 | 17750 | 0.0 | - | | 16.7136 | 17800 | 0.0 | - | | 16.7606 | 17850 | 0.0 | - | | 16.8075 | 17900 | 0.0 | - | | 16.8545 | 17950 | 0.0 | - | | 16.9014 | 18000 | 0.0 | - | | 16.9484 | 18050 | 0.0 | - | | 16.9953 | 18100 | 0.0 | - | | 17.0423 | 18150 | 0.0 | - | | 17.0892 | 18200 | 0.0 | - | | 17.1362 | 18250 | 0.0 | - | | 17.1831 | 18300 | 0.0 | - | | 17.2300 | 18350 | 0.0 | - | | 17.2770 | 18400 | 0.0 | - | | 17.3239 | 18450 | 0.0 | - | | 17.3709 | 18500 | 0.0 | - | | 17.4178 | 18550 | 0.0 | - | | 17.4648 | 18600 | 0.0 | - | | 17.5117 | 18650 | 0.0 | - | | 17.5587 | 18700 | 0.0 | - | | 17.6056 | 18750 | 0.0 | - | | 17.6526 | 18800 | 0.0 | - | | 17.6995 | 18850 | 0.0 | - | | 17.7465 | 18900 | 0.0 | - | | 17.7934 | 18950 | 0.0 | - | | 17.8404 | 19000 | 0.0 | - | | 17.8873 | 19050 | 0.0 | - | | 17.9343 | 19100 | 0.0 | - | | 17.9812 | 19150 | 0.0 | - | | 18.0282 | 19200 | 0.0 | - | | 18.0751 | 19250 | 0.0 | - | | 18.1221 | 19300 | 0.0 | - | | 18.1690 | 19350 | 0.0 | - | | 18.2160 | 19400 | 0.0 | - | | 18.2629 | 19450 | 0.0 | - | | 18.3099 | 19500 | 0.0 | - | | 18.3568 | 19550 | 0.0 | - | | 18.4038 | 19600 | 0.0 | - | | 18.4507 | 19650 | 0.0 | - | | 18.4977 | 19700 | 0.0 | - | | 18.5446 | 19750 | 0.0 | - | | 18.5915 | 19800 | 0.0 | - | | 18.6385 | 19850 | 0.0 | - | | 18.6854 | 19900 | 0.0 | - | | 18.7324 | 19950 | 0.0 | - | | 18.7793 | 20000 | 0.0 | - | | 18.8263 | 20050 | 0.0 | - | | 18.8732 | 20100 | 0.0 | - | | 18.9202 | 20150 | 0.0 | - | | 18.9671 | 20200 | 0.0 | - | | 19.0141 | 20250 | 0.0 | - | | 19.0610 | 20300 | 0.0 | - | | 19.1080 | 20350 | 0.0 | - | | 19.1549 | 20400 | 0.0 | - | | 19.2019 | 20450 | 0.0 | - | | 19.2488 | 20500 | 0.0 | - | | 19.2958 | 20550 | 0.0 | - | | 19.3427 | 20600 | 0.0 | - | | 19.3897 | 20650 | 0.0 | - | | 19.4366 | 20700 | 0.0 | - | | 19.4836 | 20750 | 0.0 | - | | 19.5305 | 20800 | 0.0 | - | | 19.5775 | 20850 | 0.0 | - | | 19.6244 | 20900 | 0.0 | - | | 19.6714 | 20950 | 0.0 | - | | 19.7183 | 21000 | 0.0 | - | | 19.7653 | 21050 | 0.0 | - | | 19.8122 | 21100 | 0.0 | - | | 19.8592 | 21150 | 0.0 | - | | 19.9061 | 21200 | 0.0 | - | | 19.9531 | 21250 | 0.0 | - | | 20.0 | 21300 | 0.0 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.1.0.dev0 - Sentence Transformers: 3.1.1 - Transformers: 4.46.1 - PyTorch: 2.4.0+cu121 - Datasets: 2.20.0 - Tokenizers: 0.20.0 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ```

mradermacher/Ice0.41-22.11-RP-i1-GGUF

mradermacher

2024-11-25T09:18:22Z

179

3

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:icefog72/Ice0.41-22.11-RP", "base_model:quantized:icefog72/Ice0.41-22.11-RP", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T09:21:22Z

--- base_model: icefog72/Ice0.41-22.11-RP language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/icefog72/Ice0.41-22.11-RP  static quants are available at https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF/resolve/main/Ice0.41-22.11-RP.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF

mradermacher

2024-11-25T09:17:42Z

263

2

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:bunnycore/Tulu-3.1-8B-SuperNova", "base_model:quantized:bunnycore/Tulu-3.1-8B-SuperNova", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T04:51:17Z

--- base_model: bunnycore/Tulu-3.1-8B-SuperNova language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/bunnycore/Tulu-3.1-8B-SuperNova  static quants are available at https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF/resolve/main/Tulu-3.1-8B-SuperNova.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF

mradermacher

2024-11-25T09:17:34Z

38

2

transformers

[ "transformers", "gguf", "en", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T06:30:46Z

--- base_model: ddh0/Qwen2.5-14B-Mixed-Instruct language: - en library_name: transformers quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/ddh0/Qwen2.5-14B-Mixed-Instruct  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q2_K.gguf) | Q2_K | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q3_K_S.gguf) | Q3_K_S | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q3_K_L.gguf) | Q3_K_L | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.IQ4_XS.gguf) | IQ4_XS | 8.3 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q4_0_4_4.gguf) | Q4_0_4_4 | 8.6 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q5_K_S.gguf) | Q5_K_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q5_K_M.gguf) | Q5_K_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q6_K.gguf) | Q6_K | 12.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF/resolve/main/Qwen2.5-14B-Mixed-Instruct.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

Sakalti/Kan1-2.5b

Sakalti

2024-11-25T09:17:28Z

129

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2403.19522", "base_model:Qwen/Qwen2.5-7B-Instruct", "base_model:finetune:Qwen/Qwen2.5-7B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T09:15:02Z

--- base_model: - Qwen/Qwen2.5-7B-Instruct library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) as a base. ### Models Merged The following models were included in the merge: ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: Qwen/Qwen2.5-7B-Instruct layer_range: [0, 6] - model: Qwen/Qwen2.5-7B-Instruct layer_range: [7, 13] - model: Qwen/Qwen2.5-7B-Instruct layer_range: [14, 20] - model: Qwen/Qwen2.5-7B-Instruct layer_range: [21, 27] merge_method: model_stock base_model: Qwen/Qwen2.5-7B-Instruct dtype: bfloat16 ```

mradermacher/NeuralDaredevil-12b-32k-GGUF

mradermacher

2024-11-25T09:17:19Z

121

2

transformers

[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "mlabonne/NeuralDaredevil-7B", "en", "base_model:mvpmaster/NeuralDaredevil-12b-32k", "base_model:quantized:mvpmaster/NeuralDaredevil-12b-32k", "endpoints_compatible", "region:us" ]

null

2024-11-23T18:19:56Z

--- base_model: mvpmaster/NeuralDaredevil-12b-32k language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B - mlabonne/NeuralDaredevil-7B --- ## About      static quants of https://huggingface.co/mvpmaster/NeuralDaredevil-12b-32k  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q2_K.gguf) | Q2_K | 4.7 | | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q3_K_S.gguf) | Q3_K_S | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q3_K_L.gguf) | Q3_K_L | 6.7 | | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.IQ4_XS.gguf) | IQ4_XS | 6.9 | | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q4_K_S.gguf) | Q4_K_S | 7.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q4_K_M.gguf) | Q4_K_M | 7.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q5_K_S.gguf) | Q5_K_S | 8.7 | | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q5_K_M.gguf) | Q5_K_M | 8.9 | | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q6_K.gguf) | Q6_K | 10.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/NeuralDaredevil-12b-32k-GGUF/resolve/main/NeuralDaredevil-12b-32k.Q8_0.gguf) | Q8_0 | 13.4 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

SeppeV/xlnet_ft_pref_10pc

SeppeV

2024-11-25T09:10:11Z

89

0

transformers

[ "transformers", "safetensors", "xlnet", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-23T15:02:28Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

PrunaAI/ahmedheakl-asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer-bnb-8bit-smashed

PrunaAI

2024-11-25T09:09:29Z

6

0

null

[ "safetensors", "qwen2", "pruna-ai", "base_model:ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer", "base_model:quantized:ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer", "8-bit", "bitsandbytes", "region:us" ]

null

2024-11-24T09:08:22Z

--- thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg" base_model: ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer metrics: - memory_disk - memory_inference - inference_latency - inference_throughput - inference_CO2_emissions - inference_energy_consumption tags: - pruna-ai ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer"> <img src="https://imgur.com/rVAgqMY.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div>  [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI) [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI) [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx) # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help. ## Results ![image info](./plots.png) **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with llm-int8. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you. - ***What is the model format?*** We use safetensors. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads. - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases. ## Setup You can run the smashed model with these steps: 0. Check requirements from the original repo ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer installed. In particular, check python, cuda, and transformers versions. 1. Make sure that you have installed quantization related packages. ```bash pip install transformers accelerate bitsandbytes>0.37.0 ``` 2. Load & run the model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("PrunaAI/ahmedheakl-asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer-bnb-8bit-smashed", trust_remote_code=True, device_map='auto') tokenizer = AutoTokenizer.from_pretrained("ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer") input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"] outputs = model.generate(input_ids, max_new_tokens=216) tokenizer.decode(outputs[0]) ``` ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model ahmedheakl/asm2asm-qwen2.5coder-0.5b-100k-2ep-tokenizer before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Do it by yourself [here](https://docs.pruna.ai/en/latest/setup/pip.html).

July-Tokyo/xlm-roberta-base-finetuned-panx-de

July-Tokyo

2024-11-25T09:09:10Z

133

0

transformers

[ "transformers", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-base", "base_model:finetune:FacebookAI/xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-11-25T07:10:45Z

--- library_name: transformers license: mit base_model: xlm-roberta-base tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: [] ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1377 - F1: 0.8627 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2592 | 1.0 | 525 | 0.1594 | 0.8243 | | 0.126 | 2.0 | 1050 | 0.1390 | 0.8513 | | 0.0802 | 3.0 | 1575 | 0.1377 | 0.8627 | ### Framework versions - Transformers 4.46.1 - Pytorch 2.5.1+cu118 - Datasets 3.1.0 - Tokenizers 0.20.1

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k60_task2_organization_fold0

MayBashendy

2024-11-25T09:09:00Z

164

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T08:30:51Z

--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k60_task2_organization_fold0 results: [] ---  # Arabic_FineTuningAraBERT_AugV5_k60_task2_organization_fold0 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5654 - Qwk: 0.4661 - Mse: 0.5654 - Rmse: 0.7519 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.0049 | 2 | 3.7329 | 0.0 | 3.7329 | 1.9321 | | No log | 0.0098 | 4 | 3.0481 | 0.0233 | 3.0481 | 1.7459 | | No log | 0.0147 | 6 | 1.8078 | -0.0157 | 1.8078 | 1.3445 | | No log | 0.0196 | 8 | 1.2965 | 0.0 | 1.2965 | 1.1386 | | No log | 0.0245 | 10 | 1.2090 | 0.0 | 1.2090 | 1.0996 | | No log | 0.0294 | 12 | 1.2281 | -0.0302 | 1.2281 | 1.1082 | | No log | 0.0343 | 14 | 1.4012 | -0.0302 | 1.4012 | 1.1837 | | No log | 0.0392 | 16 | 1.3026 | 0.0 | 1.3026 | 1.1413 | | No log | 0.0441 | 18 | 1.2453 | 0.0 | 1.2453 | 1.1159 | | No log | 0.0490 | 20 | 1.0166 | 0.0 | 1.0166 | 1.0083 | | No log | 0.0539 | 22 | 0.7326 | 0.1213 | 0.7326 | 0.8559 | | No log | 0.0588 | 24 | 0.8443 | 0.0 | 0.8443 | 0.9188 | | No log | 0.0637 | 26 | 1.5283 | -0.0302 | 1.5283 | 1.2362 | | No log | 0.0686 | 28 | 1.8904 | -0.1442 | 1.8904 | 1.3749 | | No log | 0.0735 | 30 | 1.7874 | -0.1282 | 1.7874 | 1.3369 | | No log | 0.0784 | 32 | 1.5789 | 0.0 | 1.5789 | 1.2566 | | No log | 0.0833 | 34 | 1.5011 | 0.0 | 1.5011 | 1.2252 | | No log | 0.0882 | 36 | 1.2510 | 0.0 | 1.2510 | 1.1185 | | No log | 0.0931 | 38 | 1.0157 | 0.0 | 1.0157 | 1.0078 | | No log | 0.0980 | 40 | 1.1595 | 0.0 | 1.1595 | 1.0768 | | No log | 0.1029 | 42 | 1.1783 | 0.0 | 1.1783 | 1.0855 | | No log | 0.1078 | 44 | 1.3290 | 0.0 | 1.3290 | 1.1528 | | No log | 0.1127 | 46 | 1.7079 | 0.1811 | 1.7079 | 1.3069 | | No log | 0.1176 | 48 | 1.5984 | 0.0577 | 1.5984 | 1.2643 | | No log | 0.1225 | 50 | 1.5642 | 0.1550 | 1.5642 | 1.2507 | | No log | 0.1275 | 52 | 1.5572 | 0.1811 | 1.5572 | 1.2479 | | No log | 0.1324 | 54 | 1.3567 | 0.0491 | 1.3567 | 1.1648 | | No log | 0.1373 | 56 | 1.5691 | 0.1811 | 1.5691 | 1.2526 | | No log | 0.1422 | 58 | 1.4052 | 0.0924 | 1.4052 | 1.1854 | | No log | 0.1471 | 60 | 0.8369 | 0.0 | 0.8369 | 0.9148 | | No log | 0.1520 | 62 | 0.7136 | 0.2452 | 0.7136 | 0.8448 | | No log | 0.1569 | 64 | 0.7666 | 0.1213 | 0.7666 | 0.8756 | | No log | 0.1618 | 66 | 1.0461 | 0.0 | 1.0461 | 1.0228 | | No log | 0.1667 | 68 | 1.6175 | 0.0641 | 1.6175 | 1.2718 | | No log | 0.1716 | 70 | 1.8439 | 0.0265 | 1.8439 | 1.3579 | | No log | 0.1765 | 72 | 1.6212 | 0.0924 | 1.6212 | 1.2733 | | No log | 0.1814 | 74 | 1.2282 | 0.0 | 1.2282 | 1.1082 | | No log | 0.1863 | 76 | 1.0309 | 0.0 | 1.0309 | 1.0153 | | No log | 0.1912 | 78 | 1.2423 | 0.0491 | 1.2423 | 1.1146 | | No log | 0.1961 | 80 | 1.8788 | 0.1170 | 1.8788 | 1.3707 | | No log | 0.2010 | 82 | 2.6548 | 0.0 | 2.6548 | 1.6294 | | No log | 0.2059 | 84 | 2.5991 | 0.0 | 2.5991 | 1.6122 | | No log | 0.2108 | 86 | 2.1897 | -0.0073 | 2.1897 | 1.4798 | | No log | 0.2157 | 88 | 1.4933 | 0.0491 | 1.4933 | 1.2220 | | No log | 0.2206 | 90 | 0.9251 | 0.0 | 0.9251 | 0.9618 | | No log | 0.2255 | 92 | 0.7122 | 0.2821 | 0.7122 | 0.8439 | | No log | 0.2304 | 94 | 0.6805 | 0.2289 | 0.6805 | 0.8249 | | No log | 0.2353 | 96 | 0.7859 | -0.0544 | 0.7859 | 0.8865 | | No log | 0.2402 | 98 | 1.3142 | 0.0 | 1.3142 | 1.1464 | | No log | 0.2451 | 100 | 1.8197 | 0.0955 | 1.8197 | 1.3490 | | No log | 0.25 | 102 | 2.1004 | 0.0499 | 2.1004 | 1.4493 | | No log | 0.2549 | 104 | 1.9572 | 0.1170 | 1.9572 | 1.3990 | | No log | 0.2598 | 106 | 1.6868 | 0.125 | 1.6868 | 1.2988 | | No log | 0.2647 | 108 | 1.4735 | 0.0 | 1.4735 | 1.2139 | | No log | 0.2696 | 110 | 1.1563 | 0.0 | 1.1563 | 1.0753 | | No log | 0.2745 | 112 | 1.0011 | 0.0 | 1.0011 | 1.0006 | | No log | 0.2794 | 114 | 1.1042 | 0.0 | 1.1042 | 1.0508 | | No log | 0.2843 | 116 | 1.0354 | 0.0 | 1.0354 | 1.0176 | | No log | 0.2892 | 118 | 0.9569 | 0.0 | 0.9569 | 0.9782 | | No log | 0.2941 | 120 | 1.0087 | 0.0 | 1.0087 | 1.0043 | | No log | 0.2990 | 122 | 0.9712 | 0.0 | 0.9712 | 0.9855 | | No log | 0.3039 | 124 | 0.8387 | -0.0544 | 0.8387 | 0.9158 | | No log | 0.3088 | 126 | 0.8373 | -0.1667 | 0.8373 | 0.9150 | | No log | 0.3137 | 128 | 0.9305 | 0.0335 | 0.9305 | 0.9646 | | No log | 0.3186 | 130 | 1.0780 | 0.0 | 1.0780 | 1.0383 | | No log | 0.3235 | 132 | 1.2888 | 0.0 | 1.2888 | 1.1352 | | No log | 0.3284 | 134 | 1.3256 | 0.0 | 1.3256 | 1.1513 | | No log | 0.3333 | 136 | 1.4511 | 0.0 | 1.4511 | 1.2046 | | No log | 0.3382 | 138 | 1.4020 | 0.0 | 1.4020 | 1.1841 | | No log | 0.3431 | 140 | 1.2453 | 0.0 | 1.2453 | 1.1159 | | No log | 0.3480 | 142 | 1.0186 | 0.0 | 1.0186 | 1.0093 | | No log | 0.3529 | 144 | 0.9046 | -0.1099 | 0.9046 | 0.9511 | | No log | 0.3578 | 146 | 1.1096 | 0.0162 | 1.1096 | 1.0534 | | No log | 0.3627 | 148 | 1.9099 | 0.0808 | 1.9099 | 1.3820 | | No log | 0.3676 | 150 | 2.2249 | 0.0538 | 2.2249 | 1.4916 | | No log | 0.3725 | 152 | 1.9898 | 0.0455 | 1.9898 | 1.4106 | | No log | 0.3775 | 154 | 1.3677 | 0.0605 | 1.3677 | 1.1695 | | No log | 0.3824 | 156 | 0.8731 | -0.0548 | 0.8731 | 0.9344 | | No log | 0.3873 | 158 | 0.8028 | 0.2150 | 0.8028 | 0.8960 | | No log | 0.3922 | 160 | 0.8322 | -0.0548 | 0.8322 | 0.9123 | | No log | 0.3971 | 162 | 1.0129 | 0.0 | 1.0129 | 1.0064 | | No log | 0.4020 | 164 | 1.3319 | 0.0491 | 1.3319 | 1.1541 | | No log | 0.4069 | 166 | 1.5114 | -0.0096 | 1.5114 | 1.2294 | | No log | 0.4118 | 168 | 1.4833 | -0.0328 | 1.4833 | 1.2179 | | No log | 0.4167 | 170 | 1.3613 | 0.0173 | 1.3613 | 1.1667 | | No log | 0.4216 | 172 | 1.1971 | 0.0491 | 1.1971 | 1.0941 | | No log | 0.4265 | 174 | 1.0106 | 0.0 | 1.0106 | 1.0053 | | No log | 0.4314 | 176 | 0.8181 | 0.1564 | 0.8181 | 0.9045 | | No log | 0.4363 | 178 | 0.7188 | -0.0312 | 0.7188 | 0.8478 | | No log | 0.4412 | 180 | 0.7157 | 0.0993 | 0.7157 | 0.8460 | | No log | 0.4461 | 182 | 0.7602 | 0.0265 | 0.7602 | 0.8719 | | No log | 0.4510 | 184 | 0.8625 | -0.0087 | 0.8625 | 0.9287 | | No log | 0.4559 | 186 | 1.0581 | 0.0 | 1.0581 | 1.0287 | | No log | 0.4608 | 188 | 1.1478 | 0.1213 | 1.1478 | 1.0714 | | No log | 0.4657 | 190 | 1.1894 | 0.1213 | 1.1894 | 1.0906 | | No log | 0.4706 | 192 | 1.1746 | 0.0436 | 1.1746 | 1.0838 | | No log | 0.4755 | 194 | 1.2390 | 0.0109 | 1.2390 | 1.1131 | | No log | 0.4804 | 196 | 1.1443 | 0.1939 | 1.1443 | 1.0697 | | No log | 0.4853 | 198 | 1.0193 | 0.1429 | 1.0193 | 1.0096 | | No log | 0.4902 | 200 | 0.9713 | -0.0185 | 0.9713 | 0.9855 | | No log | 0.4951 | 202 | 0.9346 | 0.0411 | 0.9346 | 0.9667 | | No log | 0.5 | 204 | 1.0566 | 0.1765 | 1.0566 | 1.0279 | | No log | 0.5049 | 206 | 1.1853 | 0.1600 | 1.1853 | 1.0887 | | No log | 0.5098 | 208 | 1.1745 | 0.0491 | 1.1745 | 1.0838 | | No log | 0.5147 | 210 | 1.0121 | 0.1923 | 1.0121 | 1.0060 | | No log | 0.5196 | 212 | 0.7318 | 0.2329 | 0.7318 | 0.8554 | | No log | 0.5245 | 214 | 0.6922 | 0.0957 | 0.6922 | 0.8320 | | No log | 0.5294 | 216 | 0.7221 | 0.0567 | 0.7221 | 0.8498 | | No log | 0.5343 | 218 | 0.7596 | 0.1600 | 0.7596 | 0.8716 | | No log | 0.5392 | 220 | 0.7837 | 0.1600 | 0.7837 | 0.8853 | | No log | 0.5441 | 222 | 0.7752 | 0.0567 | 0.7752 | 0.8804 | | No log | 0.5490 | 224 | 0.7915 | 0.2184 | 0.7915 | 0.8897 | | No log | 0.5539 | 226 | 0.7819 | 0.2184 | 0.7819 | 0.8842 | | No log | 0.5588 | 228 | 0.7789 | 0.1600 | 0.7789 | 0.8825 | | No log | 0.5637 | 230 | 1.0059 | 0.1800 | 1.0059 | 1.0030 | | No log | 0.5686 | 232 | 0.9666 | 0.0701 | 0.9666 | 0.9832 | | No log | 0.5735 | 234 | 0.7999 | 0.1370 | 0.7999 | 0.8944 | | No log | 0.5784 | 236 | 0.7399 | 0.1168 | 0.7399 | 0.8602 | | No log | 0.5833 | 238 | 0.7587 | 0.1168 | 0.7587 | 0.8711 | | No log | 0.5882 | 240 | 0.8089 | 0.0045 | 0.8089 | 0.8994 | | No log | 0.5931 | 242 | 0.8514 | 0.0045 | 0.8514 | 0.9227 | | No log | 0.5980 | 244 | 0.9308 | -0.0312 | 0.9308 | 0.9648 | | No log | 0.6029 | 246 | 0.9141 | -0.0312 | 0.9141 | 0.9561 | | No log | 0.6078 | 248 | 0.8402 | -0.0548 | 0.8402 | 0.9166 | | No log | 0.6127 | 250 | 0.7696 | -0.0048 | 0.7696 | 0.8773 | | No log | 0.6176 | 252 | 0.7865 | -0.0943 | 0.7865 | 0.8868 | | No log | 0.6225 | 254 | 0.8473 | 0.1356 | 0.8473 | 0.9205 | | No log | 0.6275 | 256 | 0.9396 | 0.1064 | 0.9396 | 0.9693 | | No log | 0.6324 | 258 | 1.0712 | -0.0483 | 1.0712 | 1.0350 | | No log | 0.6373 | 260 | 1.1024 | -0.0909 | 1.1024 | 1.0500 | | No log | 0.6422 | 262 | 1.0499 | 0.0297 | 1.0499 | 1.0246 | | No log | 0.6471 | 264 | 1.0232 | 0.1765 | 1.0232 | 1.0115 | | No log | 0.6520 | 266 | 1.0093 | -0.0553 | 1.0093 | 1.0046 | | No log | 0.6569 | 268 | 0.9457 | -0.0153 | 0.9457 | 0.9725 | | No log | 0.6618 | 270 | 0.8648 | -0.0153 | 0.8648 | 0.9300 | | No log | 0.6667 | 272 | 0.8738 | -0.0553 | 0.8738 | 0.9348 | | No log | 0.6716 | 274 | 0.9585 | 0.0903 | 0.9585 | 0.9790 | | No log | 0.6765 | 276 | 1.0674 | 0.1408 | 1.0674 | 1.0332 | | No log | 0.6814 | 278 | 1.1280 | 0.0455 | 1.1280 | 1.0621 | | No log | 0.6863 | 280 | 1.0904 | 0.1408 | 1.0904 | 1.0442 | | No log | 0.6912 | 282 | 1.0795 | 0.0455 | 1.0795 | 1.0390 | | No log | 0.6961 | 284 | 1.0543 | 0.0720 | 1.0543 | 1.0268 | | No log | 0.7010 | 286 | 0.9815 | 0.0473 | 0.9815 | 0.9907 | | No log | 0.7059 | 288 | 0.9048 | 0.1765 | 0.9048 | 0.9512 | | No log | 0.7108 | 290 | 0.8590 | 0.1765 | 0.8590 | 0.9268 | | No log | 0.7157 | 292 | 0.8301 | 0.1765 | 0.8301 | 0.9111 | | No log | 0.7206 | 294 | 0.8156 | 0.0567 | 0.8156 | 0.9031 | | No log | 0.7255 | 296 | 0.8623 | 0.0785 | 0.8623 | 0.9286 | | No log | 0.7304 | 298 | 0.8934 | 0.0375 | 0.8934 | 0.9452 | | No log | 0.7353 | 300 | 0.9323 | 0.0038 | 0.9323 | 0.9656 | | No log | 0.7402 | 302 | 0.9764 | 0.0038 | 0.9764 | 0.9882 | | No log | 0.7451 | 304 | 0.9810 | -0.0460 | 0.9810 | 0.9904 | | No log | 0.75 | 306 | 0.9550 | 0.0170 | 0.9550 | 0.9773 | | No log | 0.7549 | 308 | 0.9805 | 0.0375 | 0.9805 | 0.9902 | | No log | 0.7598 | 310 | 0.9342 | 0.0041 | 0.9342 | 0.9665 | | No log | 0.7647 | 312 | 0.9290 | -0.0286 | 0.9290 | 0.9638 | | No log | 0.7696 | 314 | 0.8797 | -0.0185 | 0.8797 | 0.9379 | | No log | 0.7745 | 316 | 0.8227 | -0.0794 | 0.8227 | 0.9070 | | No log | 0.7794 | 318 | 0.7853 | 0.0957 | 0.7853 | 0.8861 | | No log | 0.7843 | 320 | 0.7605 | 0.1356 | 0.7605 | 0.8721 | | No log | 0.7892 | 322 | 0.7714 | 0.1765 | 0.7714 | 0.8783 | | No log | 0.7941 | 324 | 0.7959 | 0.1765 | 0.7959 | 0.8921 | | No log | 0.7990 | 326 | 0.8596 | 0.1765 | 0.8596 | 0.9271 | | No log | 0.8039 | 328 | 0.9724 | 0.3029 | 0.9724 | 0.9861 | | No log | 0.8088 | 330 | 0.9962 | 0.3029 | 0.9962 | 0.9981 | | No log | 0.8137 | 332 | 0.9977 | 0.1635 | 0.9977 | 0.9988 | | No log | 0.8186 | 334 | 1.0736 | 0.0321 | 1.0736 | 1.0362 | | No log | 0.8235 | 336 | 1.0085 | 0.1818 | 1.0085 | 1.0042 | | No log | 0.8284 | 338 | 0.9455 | 0.0099 | 0.9455 | 0.9724 | | No log | 0.8333 | 340 | 0.9222 | 0.1356 | 0.9222 | 0.9603 | | No log | 0.8382 | 342 | 0.8597 | 0.0957 | 0.8597 | 0.9272 | | No log | 0.8431 | 344 | 0.8225 | 0.1356 | 0.8225 | 0.9069 | | No log | 0.8480 | 346 | 0.7537 | 0.1765 | 0.7537 | 0.8682 | | No log | 0.8529 | 348 | 0.7236 | 0.2184 | 0.7236 | 0.8506 | | No log | 0.8578 | 350 | 0.7374 | 0.0503 | 0.7374 | 0.8587 | | No log | 0.8627 | 352 | 0.8519 | 0.0099 | 0.8519 | 0.9230 | | No log | 0.8676 | 354 | 0.9807 | 0.1765 | 0.9807 | 0.9903 | | No log | 0.8725 | 356 | 1.0893 | 0.0516 | 1.0893 | 1.0437 | | No log | 0.8775 | 358 | 1.1002 | -0.0876 | 1.1002 | 1.0489 | | No log | 0.8824 | 360 | 1.0915 | 0.1429 | 1.0915 | 1.0448 | | No log | 0.8873 | 362 | 1.0676 | 0.1715 | 1.0676 | 1.0332 | | No log | 0.8922 | 364 | 0.9708 | 0.0833 | 0.9708 | 0.9853 | | No log | 0.8971 | 366 | 0.8791 | 0.0916 | 0.8791 | 0.9376 | | No log | 0.9020 | 368 | 0.7827 | 0.0099 | 0.7827 | 0.8847 | | No log | 0.9069 | 370 | 0.7366 | 0.1356 | 0.7366 | 0.8582 | | No log | 0.9118 | 372 | 0.7425 | 0.1168 | 0.7425 | 0.8617 | | No log | 0.9167 | 374 | 0.7658 | 0.0045 | 0.7658 | 0.8751 | | No log | 0.9216 | 376 | 0.7434 | 0.0567 | 0.7434 | 0.8622 | | No log | 0.9265 | 378 | 0.7418 | 0.1356 | 0.7418 | 0.8613 | | No log | 0.9314 | 380 | 0.8042 | 0.0099 | 0.8042 | 0.8968 | | No log | 0.9363 | 382 | 0.9116 | 0.0099 | 0.9116 | 0.9548 | | No log | 0.9412 | 384 | 0.9422 | 0.1765 | 0.9422 | 0.9707 | | No log | 0.9461 | 386 | 0.9646 | 0.1765 | 0.9646 | 0.9821 | | No log | 0.9510 | 388 | 0.9366 | 0.1765 | 0.9366 | 0.9678 | | No log | 0.9559 | 390 | 0.8935 | 0.1765 | 0.8935 | 0.9452 | | No log | 0.9608 | 392 | 0.8234 | 0.1765 | 0.8234 | 0.9074 | | No log | 0.9657 | 394 | 0.7723 | 0.1356 | 0.7723 | 0.8788 | | No log | 0.9706 | 396 | 0.7751 | 0.1356 | 0.7751 | 0.8804 | | No log | 0.9755 | 398 | 0.7900 | 0.1962 | 0.7900 | 0.8888 | | No log | 0.9804 | 400 | 0.8108 | 0.1962 | 0.8108 | 0.9005 | | No log | 0.9853 | 402 | 0.8253 | 0.1765 | 0.8253 | 0.9085 | | No log | 0.9902 | 404 | 0.8073 | 0.0099 | 0.8073 | 0.8985 | | No log | 0.9951 | 406 | 0.8569 | 0.0099 | 0.8569 | 0.9257 | | No log | 1.0 | 408 | 0.9797 | 0.0503 | 0.9797 | 0.9898 | | No log | 1.0049 | 410 | 1.0478 | 0.0503 | 1.0478 | 1.0236 | | No log | 1.0098 | 412 | 1.0258 | 0.0503 | 1.0258 | 1.0128 | | No log | 1.0147 | 414 | 0.9693 | 0.0503 | 0.9693 | 0.9845 | | No log | 1.0196 | 416 | 0.8774 | 0.0503 | 0.8774 | 0.9367 | | No log | 1.0245 | 418 | 0.8861 | 0.0099 | 0.8861 | 0.9413 | | No log | 1.0294 | 420 | 0.9214 | 0.0099 | 0.9214 | 0.9599 | | No log | 1.0343 | 422 | 0.8879 | 0.0099 | 0.8879 | 0.9423 | | No log | 1.0392 | 424 | 0.8490 | 0.1231 | 0.8490 | 0.9214 | | No log | 1.0441 | 426 | 0.8146 | 0.0870 | 0.8146 | 0.9025 | | No log | 1.0490 | 428 | 0.7657 | 0.1356 | 0.7657 | 0.8750 | | No log | 1.0539 | 430 | 0.7545 | 0.0503 | 0.7545 | 0.8686 | | No log | 1.0588 | 432 | 0.7598 | 0.0503 | 0.7598 | 0.8717 | | No log | 1.0637 | 434 | 0.7718 | -0.0825 | 0.7718 | 0.8785 | | No log | 1.0686 | 436 | 0.8087 | -0.0825 | 0.8086 | 0.8992 | | No log | 1.0735 | 438 | 0.8054 | -0.0825 | 0.8054 | 0.8974 | | No log | 1.0784 | 440 | 0.8101 | 0.0503 | 0.8101 | 0.9000 | | No log | 1.0833 | 442 | 0.8115 | 0.0503 | 0.8115 | 0.9008 | | No log | 1.0882 | 444 | 0.7931 | 0.2184 | 0.7931 | 0.8906 | | No log | 1.0931 | 446 | 0.7737 | 0.1765 | 0.7737 | 0.8796 | | No log | 1.0980 | 448 | 0.7484 | 0.1765 | 0.7484 | 0.8651 | | No log | 1.1029 | 450 | 0.7459 | 0.2184 | 0.7459 | 0.8637 | | No log | 1.1078 | 452 | 0.7613 | 0.2184 | 0.7613 | 0.8725 | | No log | 1.1127 | 454 | 0.7604 | 0.2184 | 0.7604 | 0.8720 | | No log | 1.1176 | 456 | 0.7483 | 0.2184 | 0.7483 | 0.8651 | | No log | 1.1225 | 458 | 0.7503 | 0.2613 | 0.7503 | 0.8662 | | No log | 1.1275 | 460 | 0.7585 | 0.0916 | 0.7585 | 0.8709 | | No log | 1.1324 | 462 | 0.7548 | 0.2613 | 0.7548 | 0.8688 | | No log | 1.1373 | 464 | 0.7606 | 0.1765 | 0.7606 | 0.8721 | | No log | 1.1422 | 466 | 0.7849 | 0.1765 | 0.7849 | 0.8859 | | No log | 1.1471 | 468 | 0.8086 | 0.1765 | 0.8086 | 0.8992 | | No log | 1.1520 | 470 | 0.8458 | 0.1765 | 0.8458 | 0.9197 | | No log | 1.1569 | 472 | 0.8762 | 0.1765 | 0.8762 | 0.9360 | | No log | 1.1618 | 474 | 0.9001 | 0.1356 | 0.9001 | 0.9487 | | No log | 1.1667 | 476 | 0.9094 | 0.1356 | 0.9094 | 0.9536 | | No log | 1.1716 | 478 | 0.9015 | 0.1356 | 0.9015 | 0.9495 | | No log | 1.1765 | 480 | 0.8821 | 0.1765 | 0.8821 | 0.9392 | | No log | 1.1814 | 482 | 0.8844 | 0.3029 | 0.8844 | 0.9404 | | No log | 1.1863 | 484 | 0.9158 | 0.1992 | 0.9158 | 0.9570 | | No log | 1.1912 | 486 | 0.9036 | 0.1600 | 0.9036 | 0.9506 | | No log | 1.1961 | 488 | 0.8359 | 0.0258 | 0.8359 | 0.9143 | | No log | 1.2010 | 490 | 0.7704 | 0.0258 | 0.7704 | 0.8777 | | No log | 1.2059 | 492 | 0.7179 | 0.0916 | 0.7179 | 0.8473 | | No log | 1.2108 | 494 | 0.6796 | 0.2184 | 0.6796 | 0.8244 | | No log | 1.2157 | 496 | 0.6828 | 0.1765 | 0.6828 | 0.8263 | | No log | 1.2206 | 498 | 0.6992 | 0.2184 | 0.6992 | 0.8362 | | 0.5578 | 1.2255 | 500 | 0.7241 | 0.2184 | 0.7241 | 0.8510 | | 0.5578 | 1.2304 | 502 | 0.7504 | 0.0503 | 0.7504 | 0.8663 | | 0.5578 | 1.2353 | 504 | 0.8165 | 0.0679 | 0.8165 | 0.9036 | | 0.5578 | 1.2402 | 506 | 0.8493 | 0.0679 | 0.8493 | 0.9216 | | 0.5578 | 1.2451 | 508 | 0.8073 | 0.0679 | 0.8073 | 0.8985 | | 0.5578 | 1.25 | 510 | 0.7283 | 0.0258 | 0.7283 | 0.8534 | | 0.5578 | 1.2549 | 512 | 0.6871 | 0.0503 | 0.6871 | 0.8289 | | 0.5578 | 1.2598 | 514 | 0.6995 | 0.1783 | 0.6995 | 0.8364 | | 0.5578 | 1.2647 | 516 | 0.6958 | 0.1783 | 0.6958 | 0.8341 | | 0.5578 | 1.2696 | 518 | 0.6776 | 0.1356 | 0.6776 | 0.8232 | | 0.5578 | 1.2745 | 520 | 0.6641 | 0.0503 | 0.6641 | 0.8149 | | 0.5578 | 1.2794 | 522 | 0.6771 | 0.0258 | 0.6771 | 0.8228 | | 0.5578 | 1.2843 | 524 | 0.7397 | 0.0258 | 0.7397 | 0.8601 | | 0.5578 | 1.2892 | 526 | 0.7622 | 0.0258 | 0.7622 | 0.8731 | | 0.5578 | 1.2941 | 528 | 0.7170 | 0.0258 | 0.7170 | 0.8468 | | 0.5578 | 1.2990 | 530 | 0.6831 | -0.0153 | 0.6831 | 0.8265 | | 0.5578 | 1.3039 | 532 | 0.6822 | -0.0153 | 0.6822 | 0.8259 | | 0.5578 | 1.3088 | 534 | 0.6797 | -0.0153 | 0.6797 | 0.8244 | | 0.5578 | 1.3137 | 536 | 0.6996 | -0.0153 | 0.6996 | 0.8364 | | 0.5578 | 1.3186 | 538 | 0.7385 | 0.0258 | 0.7385 | 0.8594 | | 0.5578 | 1.3235 | 540 | 0.7466 | -0.0153 | 0.7466 | 0.8641 | | 0.5578 | 1.3284 | 542 | 0.7164 | -0.0153 | 0.7164 | 0.8464 | | 0.5578 | 1.3333 | 544 | 0.7053 | -0.0153 | 0.7053 | 0.8398 | | 0.5578 | 1.3382 | 546 | 0.7006 | 0.0503 | 0.7006 | 0.8370 | | 0.5578 | 1.3431 | 548 | 0.7064 | 0.0099 | 0.7064 | 0.8405 | | 0.5578 | 1.3480 | 550 | 0.7086 | 0.1765 | 0.7086 | 0.8418 | | 0.5578 | 1.3529 | 552 | 0.7189 | 0.1765 | 0.7189 | 0.8479 | | 0.5578 | 1.3578 | 554 | 0.7168 | 0.1765 | 0.7168 | 0.8466 | | 0.5578 | 1.3627 | 556 | 0.7376 | 0.1765 | 0.7376 | 0.8588 | | 0.5578 | 1.3676 | 558 | 0.7608 | 0.0828 | 0.7608 | 0.8723 | | 0.5578 | 1.3725 | 560 | 0.7547 | 0.0503 | 0.7547 | 0.8688 | | 0.5578 | 1.3775 | 562 | 0.7262 | 0.1765 | 0.7262 | 0.8522 | | 0.5578 | 1.3824 | 564 | 0.7455 | 0.0503 | 0.7455 | 0.8634 | | 0.5578 | 1.3873 | 566 | 0.8094 | 0.1818 | 0.8094 | 0.8997 | | 0.5578 | 1.3922 | 568 | 0.7910 | 0.1209 | 0.7910 | 0.8894 | | 0.5578 | 1.3971 | 570 | 0.7395 | 0.0916 | 0.7395 | 0.8599 | | 0.5578 | 1.4020 | 572 | 0.6950 | 0.0503 | 0.6950 | 0.8336 | | 0.5578 | 1.4069 | 574 | 0.6585 | 0.1765 | 0.6585 | 0.8115 | | 0.5578 | 1.4118 | 576 | 0.6510 | 0.1765 | 0.6510 | 0.8068 | | 0.5578 | 1.4167 | 578 | 0.6428 | 0.1765 | 0.6428 | 0.8018 | | 0.5578 | 1.4216 | 580 | 0.6618 | 0.0503 | 0.6618 | 0.8135 | | 0.5578 | 1.4265 | 582 | 0.6488 | 0.0099 | 0.6488 | 0.8055 | | 0.5578 | 1.4314 | 584 | 0.6235 | -0.0294 | 0.6235 | 0.7896 | | 0.5578 | 1.4363 | 586 | 0.6235 | 0.0735 | 0.6235 | 0.7896 | | 0.5578 | 1.4412 | 588 | 0.6686 | 0.0099 | 0.6686 | 0.8177 | | 0.5578 | 1.4461 | 590 | 0.7365 | 0.0916 | 0.7365 | 0.8582 | | 0.5578 | 1.4510 | 592 | 0.7768 | 0.1793 | 0.7768 | 0.8814 | | 0.5578 | 1.4559 | 594 | 0.7998 | 0.1793 | 0.7998 | 0.8943 | | 0.5578 | 1.4608 | 596 | 0.8258 | 0.1635 | 0.8258 | 0.9087 | | 0.5578 | 1.4657 | 598 | 0.8195 | 0.1765 | 0.8195 | 0.9053 | | 0.5578 | 1.4706 | 600 | 0.8337 | 0.0455 | 0.8337 | 0.9131 | | 0.5578 | 1.4755 | 602 | 0.7986 | 0.0503 | 0.7986 | 0.8936 | | 0.5578 | 1.4804 | 604 | 0.7542 | 0.0099 | 0.7542 | 0.8684 | | 0.5578 | 1.4853 | 606 | 0.7238 | 0.0503 | 0.7238 | 0.8508 | | 0.5578 | 1.4902 | 608 | 0.7202 | 0.0503 | 0.7202 | 0.8486 | | 0.5578 | 1.4951 | 610 | 0.6943 | 0.0503 | 0.6943 | 0.8333 | | 0.5578 | 1.5 | 612 | 0.7182 | -0.0153 | 0.7182 | 0.8475 | | 0.5578 | 1.5049 | 614 | 0.7282 | 0.0258 | 0.7282 | 0.8534 | | 0.5578 | 1.5098 | 616 | 0.7412 | 0.0258 | 0.7412 | 0.8609 | | 0.5578 | 1.5147 | 618 | 0.7442 | 0.0258 | 0.7442 | 0.8627 | | 0.5578 | 1.5196 | 620 | 0.7727 | 0.0258 | 0.7727 | 0.8790 | | 0.5578 | 1.5245 | 622 | 0.7509 | -0.0153 | 0.7509 | 0.8666 | | 0.5578 | 1.5294 | 624 | 0.7460 | 0.0503 | 0.7460 | 0.8637 | | 0.5578 | 1.5343 | 626 | 0.7664 | 0.2184 | 0.7664 | 0.8754 | | 0.5578 | 1.5392 | 628 | 0.7797 | 0.0503 | 0.7797 | 0.8830 | | 0.5578 | 1.5441 | 630 | 0.8092 | 0.0503 | 0.8092 | 0.8996 | | 0.5578 | 1.5490 | 632 | 0.8579 | -0.0153 | 0.8579 | 0.9262 | | 0.5578 | 1.5539 | 634 | 0.8892 | -0.0153 | 0.8892 | 0.9430 | | 0.5578 | 1.5588 | 636 | 0.8795 | -0.0153 | 0.8795 | 0.9378 | | 0.5578 | 1.5637 | 638 | 0.8237 | 0.0503 | 0.8237 | 0.9076 | | 0.5578 | 1.5686 | 640 | 0.7623 | 0.0503 | 0.7623 | 0.8731 | | 0.5578 | 1.5735 | 642 | 0.7500 | 0.0503 | 0.7500 | 0.8660 | | 0.5578 | 1.5784 | 644 | 0.7688 | 0.0503 | 0.7688 | 0.8768 | | 0.5578 | 1.5833 | 646 | 0.7593 | 0.0503 | 0.7593 | 0.8714 | | 0.5578 | 1.5882 | 648 | 0.7676 | 0.0916 | 0.7676 | 0.8761 | | 0.5578 | 1.5931 | 650 | 0.7992 | 0.2355 | 0.7992 | 0.8940 | | 0.5578 | 1.5980 | 652 | 0.7329 | 0.0916 | 0.7329 | 0.8561 | | 0.5578 | 1.6029 | 654 | 0.6586 | 0.1765 | 0.6586 | 0.8116 | | 0.5578 | 1.6078 | 656 | 0.6617 | 0.2725 | 0.6617 | 0.8135 | | 0.5578 | 1.6127 | 658 | 0.7002 | 0.2329 | 0.7002 | 0.8368 | | 0.5578 | 1.6176 | 660 | 0.7021 | 0.2725 | 0.7021 | 0.8379 | | 0.5578 | 1.6225 | 662 | 0.6746 | 0.3131 | 0.6746 | 0.8214 | | 0.5578 | 1.6275 | 664 | 0.6984 | 0.2921 | 0.6984 | 0.8357 | | 0.5578 | 1.6324 | 666 | 0.8072 | 0.1992 | 0.8072 | 0.8984 | | 0.5578 | 1.6373 | 668 | 0.8233 | 0.1992 | 0.8233 | 0.9074 | | 0.5578 | 1.6422 | 670 | 0.8055 | 0.2355 | 0.8055 | 0.8975 | | 0.5578 | 1.6471 | 672 | 0.7299 | 0.2921 | 0.7299 | 0.8543 | | 0.5578 | 1.6520 | 674 | 0.6800 | 0.1765 | 0.6800 | 0.8246 | | 0.5578 | 1.6569 | 676 | 0.7202 | 0.1783 | 0.7202 | 0.8487 | | 0.5578 | 1.6618 | 678 | 0.7445 | 0.1797 | 0.7445 | 0.8629 | | 0.5578 | 1.6667 | 680 | 0.7278 | 0.1419 | 0.7278 | 0.8531 | | 0.5578 | 1.6716 | 682 | 0.6878 | 0.0957 | 0.6878 | 0.8293 | | 0.5578 | 1.6765 | 684 | 0.7109 | 0.1356 | 0.7109 | 0.8432 | | 0.5578 | 1.6814 | 686 | 0.7583 | 0.2921 | 0.7583 | 0.8708 | | 0.5578 | 1.6863 | 688 | 0.7626 | 0.2921 | 0.7626 | 0.8733 | | 0.5578 | 1.6912 | 690 | 0.7425 | 0.1765 | 0.7425 | 0.8617 | | 0.5578 | 1.6961 | 692 | 0.7350 | 0.1765 | 0.7350 | 0.8573 | | 0.5578 | 1.7010 | 694 | 0.7285 | 0.1765 | 0.7285 | 0.8535 | | 0.5578 | 1.7059 | 696 | 0.7034 | 0.1765 | 0.7034 | 0.8387 | | 0.5578 | 1.7108 | 698 | 0.6936 | 0.1765 | 0.6936 | 0.8328 | | 0.5578 | 1.7157 | 700 | 0.6978 | 0.1765 | 0.6978 | 0.8354 | | 0.5578 | 1.7206 | 702 | 0.7344 | 0.1765 | 0.7344 | 0.8570 | | 0.5578 | 1.7255 | 704 | 0.7590 | 0.2921 | 0.7590 | 0.8712 | | 0.5578 | 1.7304 | 706 | 0.7798 | 0.2921 | 0.7798 | 0.8831 | | 0.5578 | 1.7353 | 708 | 0.7821 | 0.1992 | 0.7821 | 0.8843 | | 0.5578 | 1.7402 | 710 | 0.7444 | 0.2921 | 0.7444 | 0.8628 | | 0.5578 | 1.7451 | 712 | 0.6936 | 0.2921 | 0.6936 | 0.8328 | | 0.5578 | 1.75 | 714 | 0.6691 | 0.1765 | 0.6691 | 0.8180 | | 0.5578 | 1.7549 | 716 | 0.6563 | 0.1356 | 0.6563 | 0.8101 | | 0.5578 | 1.7598 | 718 | 0.6508 | 0.1356 | 0.6508 | 0.8067 | | 0.5578 | 1.7647 | 720 | 0.6493 | 0.0099 | 0.6493 | 0.8058 | | 0.5578 | 1.7696 | 722 | 0.6629 | 0.0503 | 0.6629 | 0.8142 | | 0.5578 | 1.7745 | 724 | 0.7317 | 0.2186 | 0.7317 | 0.8554 | | 0.5578 | 1.7794 | 726 | 0.8393 | 0.2727 | 0.8393 | 0.9162 | | 0.5578 | 1.7843 | 728 | 0.8773 | 0.2846 | 0.8773 | 0.9367 | | 0.5578 | 1.7892 | 730 | 0.7873 | 0.2186 | 0.7873 | 0.8873 | | 0.5578 | 1.7941 | 732 | 0.7080 | 0.2184 | 0.7080 | 0.8414 | | 0.5578 | 1.7990 | 734 | 0.6906 | 0.1356 | 0.6906 | 0.8310 | | 0.5578 | 1.8039 | 736 | 0.6882 | 0.1356 | 0.6882 | 0.8296 | | 0.5578 | 1.8088 | 738 | 0.6746 | 0.1356 | 0.6746 | 0.8213 | | 0.5578 | 1.8137 | 740 | 0.6798 | 0.1765 | 0.6798 | 0.8245 | | 0.5578 | 1.8186 | 742 | 0.6784 | 0.1765 | 0.6784 | 0.8236 | | 0.5578 | 1.8235 | 744 | 0.6669 | 0.1765 | 0.6669 | 0.8167 | | 0.5578 | 1.8284 | 746 | 0.6734 | 0.2184 | 0.6734 | 0.8206 | | 0.5578 | 1.8333 | 748 | 0.6727 | 0.2184 | 0.6727 | 0.8202 | | 0.5578 | 1.8382 | 750 | 0.6770 | 0.2613 | 0.6770 | 0.8228 | | 0.5578 | 1.8431 | 752 | 0.6828 | 0.0916 | 0.6828 | 0.8263 | | 0.5578 | 1.8480 | 754 | 0.6833 | 0.0258 | 0.6833 | 0.8266 | | 0.5578 | 1.8529 | 756 | 0.7135 | 0.0258 | 0.7135 | 0.8447 | | 0.5578 | 1.8578 | 758 | 0.7219 | -0.0153 | 0.7219 | 0.8496 | | 0.5578 | 1.8627 | 760 | 0.7159 | -0.0153 | 0.7159 | 0.8461 | | 0.5578 | 1.8676 | 762 | 0.7267 | 0.0099 | 0.7267 | 0.8525 | | 0.5578 | 1.8725 | 764 | 0.7224 | 0.1356 | 0.7224 | 0.8499 | | 0.5578 | 1.8775 | 766 | 0.7271 | 0.1356 | 0.7271 | 0.8527 | | 0.5578 | 1.8824 | 768 | 0.7485 | 0.1765 | 0.7485 | 0.8651 | | 0.5578 | 1.8873 | 770 | 0.7799 | 0.1765 | 0.7799 | 0.8831 | | 0.5578 | 1.8922 | 772 | 0.7954 | 0.0099 | 0.7954 | 0.8919 | | 0.5578 | 1.8971 | 774 | 0.7923 | 0.0099 | 0.7923 | 0.8901 | | 0.5578 | 1.9020 | 776 | 0.7898 | 0.0099 | 0.7898 | 0.8887 | | 0.5578 | 1.9069 | 778 | 0.8181 | 0.1793 | 0.8181 | 0.9045 | | 0.5578 | 1.9118 | 780 | 0.8152 | 0.0503 | 0.8152 | 0.9029 | | 0.5578 | 1.9167 | 782 | 0.7926 | 0.1765 | 0.7926 | 0.8903 | | 0.5578 | 1.9216 | 784 | 0.7978 | 0.1765 | 0.7978 | 0.8932 | | 0.5578 | 1.9265 | 786 | 0.8013 | 0.2184 | 0.8013 | 0.8951 | | 0.5578 | 1.9314 | 788 | 0.8023 | 0.2184 | 0.8023 | 0.8957 | | 0.5578 | 1.9363 | 790 | 0.7933 | 0.1765 | 0.7933 | 0.8907 | | 0.5578 | 1.9412 | 792 | 0.7900 | 0.3318 | 0.7900 | 0.8888 | | 0.5578 | 1.9461 | 794 | 0.7859 | 0.3318 | 0.7859 | 0.8865 | | 0.5578 | 1.9510 | 796 | 0.7713 | 0.3318 | 0.7713 | 0.8782 | | 0.5578 | 1.9559 | 798 | 0.7542 | 0.1765 | 0.7542 | 0.8685 | | 0.5578 | 1.9608 | 800 | 0.7744 | 0.3318 | 0.7744 | 0.8800 | | 0.5578 | 1.9657 | 802 | 0.8383 | 0.2186 | 0.8383 | 0.9156 | | 0.5578 | 1.9706 | 804 | 0.8534 | 0.2186 | 0.8534 | 0.9238 | | 0.5578 | 1.9755 | 806 | 0.8328 | 0.1793 | 0.8328 | 0.9126 | | 0.5578 | 1.9804 | 808 | 0.7872 | 0.2921 | 0.7872 | 0.8872 | | 0.5578 | 1.9853 | 810 | 0.7593 | 0.1765 | 0.7593 | 0.8714 | | 0.5578 | 1.9902 | 812 | 0.7483 | 0.1765 | 0.7483 | 0.8651 | | 0.5578 | 1.9951 | 814 | 0.7205 | 0.1765 | 0.7205 | 0.8488 | | 0.5578 | 2.0 | 816 | 0.7090 | 0.0099 | 0.7090 | 0.8420 | | 0.5578 | 2.0049 | 818 | 0.7199 | 0.0503 | 0.7199 | 0.8485 | | 0.5578 | 2.0098 | 820 | 0.7702 | 0.1600 | 0.7702 | 0.8776 | | 0.5578 | 2.0147 | 822 | 0.7948 | 0.1600 | 0.7948 | 0.8915 | | 0.5578 | 2.0196 | 824 | 0.7714 | 0.1209 | 0.7714 | 0.8783 | | 0.5578 | 2.0245 | 826 | 0.7442 | 0.0503 | 0.7442 | 0.8626 | | 0.5578 | 2.0294 | 828 | 0.7438 | 0.0503 | 0.7438 | 0.8625 | | 0.5578 | 2.0343 | 830 | 0.7616 | 0.0099 | 0.7616 | 0.8727 | | 0.5578 | 2.0392 | 832 | 0.7911 | 0.0099 | 0.7911 | 0.8894 | | 0.5578 | 2.0441 | 834 | 0.8279 | 0.0099 | 0.8279 | 0.9099 | | 0.5578 | 2.0490 | 836 | 0.8528 | 0.0099 | 0.8528 | 0.9235 | | 0.5578 | 2.0539 | 838 | 0.8694 | 0.1409 | 0.8694 | 0.9324 | | 0.5578 | 2.0588 | 840 | 0.8550 | 0.0099 | 0.8550 | 0.9247 | | 0.5578 | 2.0637 | 842 | 0.8447 | 0.0099 | 0.8447 | 0.9191 | | 0.5578 | 2.0686 | 844 | 0.8239 | 0.0099 | 0.8239 | 0.9077 | | 0.5578 | 2.0735 | 846 | 0.7818 | 0.0099 | 0.7818 | 0.8842 | | 0.5578 | 2.0784 | 848 | 0.7487 | 0.0099 | 0.7487 | 0.8653 | | 0.5578 | 2.0833 | 850 | 0.7326 | 0.0099 | 0.7326 | 0.8559 | | 0.5578 | 2.0882 | 852 | 0.7200 | 0.0099 | 0.7200 | 0.8485 | | 0.5578 | 2.0931 | 854 | 0.7178 | 0.0099 | 0.7178 | 0.8472 | | 0.5578 | 2.0980 | 856 | 0.7123 | 0.0099 | 0.7123 | 0.8440 | | 0.5578 | 2.1029 | 858 | 0.7103 | 0.0503 | 0.7103 | 0.8428 | | 0.5578 | 2.1078 | 860 | 0.7040 | 0.0099 | 0.7040 | 0.8391 | | 0.5578 | 2.1127 | 862 | 0.7110 | 0.0099 | 0.7110 | 0.8432 | | 0.5578 | 2.1176 | 864 | 0.7208 | 0.0099 | 0.7208 | 0.8490 | | 0.5578 | 2.1225 | 866 | 0.7310 | 0.0099 | 0.7310 | 0.8550 | | 0.5578 | 2.1275 | 868 | 0.7427 | 0.0099 | 0.7427 | 0.8618 | | 0.5578 | 2.1324 | 870 | 0.7558 | 0.0099 | 0.7558 | 0.8694 | | 0.5578 | 2.1373 | 872 | 0.7712 | 0.0099 | 0.7712 | 0.8782 | | 0.5578 | 2.1422 | 874 | 0.8072 | 0.0099 | 0.8072 | 0.8984 | | 0.5578 | 2.1471 | 876 | 0.8080 | 0.1793 | 0.8080 | 0.8989 | | 0.5578 | 2.1520 | 878 | 0.7558 | 0.0099 | 0.7558 | 0.8694 | | 0.5578 | 2.1569 | 880 | 0.7004 | 0.0099 | 0.7004 | 0.8369 | | 0.5578 | 2.1618 | 882 | 0.6875 | 0.0099 | 0.6875 | 0.8292 | | 0.5578 | 2.1667 | 884 | 0.6856 | 0.0099 | 0.6856 | 0.8280 | | 0.5578 | 2.1716 | 886 | 0.6961 | 0.0099 | 0.6961 | 0.8343 | | 0.5578 | 2.1765 | 888 | 0.7183 | 0.0099 | 0.7183 | 0.8475 | | 0.5578 | 2.1814 | 890 | 0.7322 | 0.0099 | 0.7322 | 0.8557 | | 0.5578 | 2.1863 | 892 | 0.7432 | 0.0099 | 0.7432 | 0.8621 | | 0.5578 | 2.1912 | 894 | 0.7627 | 0.0099 | 0.7627 | 0.8733 | | 0.5578 | 2.1961 | 896 | 0.7871 | 0.0099 | 0.7871 | 0.8872 | | 0.5578 | 2.2010 | 898 | 0.8083 | 0.0099 | 0.8083 | 0.8990 | | 0.5578 | 2.2059 | 900 | 0.8043 | 0.0503 | 0.8043 | 0.8968 | | 0.5578 | 2.2108 | 902 | 0.7885 | 0.0916 | 0.7885 | 0.8880 | | 0.5578 | 2.2157 | 904 | 0.7669 | 0.2186 | 0.7669 | 0.8757 | | 0.5578 | 2.2206 | 906 | 0.7204 | 0.2186 | 0.7204 | 0.8488 | | 0.5578 | 2.2255 | 908 | 0.7269 | 0.2186 | 0.7269 | 0.8526 | | 0.5578 | 2.2304 | 910 | 0.7296 | 0.2186 | 0.7296 | 0.8542 | | 0.5578 | 2.2353 | 912 | 0.7265 | 0.1793 | 0.7265 | 0.8523 | | 0.5578 | 2.2402 | 914 | 0.7163 | 0.1793 | 0.7163 | 0.8464 | | 0.5578 | 2.2451 | 916 | 0.7555 | 0.1793 | 0.7555 | 0.8692 | | 0.5578 | 2.25 | 918 | 0.7513 | 0.1793 | 0.7513 | 0.8668 | | 0.5578 | 2.2549 | 920 | 0.7326 | 0.1793 | 0.7326 | 0.8559 | | 0.5578 | 2.2598 | 922 | 0.7222 | 0.1793 | 0.7222 | 0.8498 | | 0.5578 | 2.2647 | 924 | 0.7370 | 0.1793 | 0.7370 | 0.8585 | | 0.5578 | 2.2696 | 926 | 0.7920 | 0.1992 | 0.7920 | 0.8900 | | 0.5578 | 2.2745 | 928 | 0.8360 | 0.1992 | 0.8360 | 0.9143 | | 0.5578 | 2.2794 | 930 | 0.7819 | 0.1992 | 0.7819 | 0.8843 | | 0.5578 | 2.2843 | 932 | 0.7211 | 0.2533 | 0.7211 | 0.8492 | | 0.5578 | 2.2892 | 934 | 0.6942 | 0.1356 | 0.6942 | 0.8332 | | 0.5578 | 2.2941 | 936 | 0.6928 | 0.1356 | 0.6928 | 0.8323 | | 0.5578 | 2.2990 | 938 | 0.6936 | 0.2533 | 0.6936 | 0.8328 | | 0.5578 | 2.3039 | 940 | 0.6979 | 0.1409 | 0.6979 | 0.8354 | | 0.5578 | 2.3088 | 942 | 0.6968 | 0.1409 | 0.6968 | 0.8347 | | 0.5578 | 2.3137 | 944 | 0.6995 | 0.1409 | 0.6995 | 0.8363 | | 0.5578 | 2.3186 | 946 | 0.7280 | 0.1409 | 0.7280 | 0.8532 | | 0.5578 | 2.3235 | 948 | 0.7728 | 0.1635 | 0.7728 | 0.8791 | | 0.5578 | 2.3284 | 950 | 0.7729 | 0.1635 | 0.7729 | 0.8792 | | 0.5578 | 2.3333 | 952 | 0.7663 | 0.1635 | 0.7663 | 0.8754 | | 0.5578 | 2.3382 | 954 | 0.7735 | 0.1635 | 0.7735 | 0.8795 | | 0.5578 | 2.3431 | 956 | 0.7792 | 0.1409 | 0.7792 | 0.8827 | | 0.5578 | 2.3480 | 958 | 0.7592 | 0.2921 | 0.7592 | 0.8713 | | 0.5578 | 2.3529 | 960 | 0.7620 | 0.1409 | 0.7620 | 0.8729 | | 0.5578 | 2.3578 | 962 | 0.7656 | 0.1793 | 0.7656 | 0.8750 | | 0.5578 | 2.3627 | 964 | 0.7905 | 0.2588 | 0.7905 | 0.8891 | | 0.5578 | 2.3676 | 966 | 0.7927 | 0.2588 | 0.7927 | 0.8904 | | 0.5578 | 2.3725 | 968 | 0.7444 | 0.2588 | 0.7444 | 0.8628 | | 0.5578 | 2.3775 | 970 | 0.7016 | 0.2186 | 0.7016 | 0.8376 | | 0.5578 | 2.3824 | 972 | 0.6727 | 0.0503 | 0.6727 | 0.8202 | | 0.5578 | 2.3873 | 974 | 0.6824 | 0.0099 | 0.6824 | 0.8261 | | 0.5578 | 2.3922 | 976 | 0.6995 | 0.1409 | 0.6995 | 0.8364 | | 0.5578 | 2.3971 | 978 | 0.7122 | 0.1409 | 0.7122 | 0.8439 | | 0.5578 | 2.4020 | 980 | 0.7272 | 0.1793 | 0.7272 | 0.8528 | | 0.5578 | 2.4069 | 982 | 0.7788 | 0.1793 | 0.7788 | 0.8825 | | 0.5578 | 2.4118 | 984 | 0.8050 | 0.1793 | 0.8050 | 0.8972 | | 0.5578 | 2.4167 | 986 | 0.8236 | 0.2186 | 0.8236 | 0.9075 | | 0.5578 | 2.4216 | 988 | 0.7882 | 0.2588 | 0.7882 | 0.8878 | | 0.5578 | 2.4265 | 990 | 0.7090 | 0.1793 | 0.7090 | 0.8420 | | 0.5578 | 2.4314 | 992 | 0.6612 | 0.1793 | 0.6612 | 0.8132 | | 0.5578 | 2.4363 | 994 | 0.6352 | 0.0099 | 0.6352 | 0.7970 | | 0.5578 | 2.4412 | 996 | 0.6234 | 0.2553 | 0.6234 | 0.7895 | | 0.5578 | 2.4461 | 998 | 0.6244 | 0.2553 | 0.6244 | 0.7902 | | 0.1212 | 2.4510 | 1000 | 0.6264 | 0.2373 | 0.6264 | 0.7914 | | 0.1212 | 2.4559 | 1002 | 0.6482 | 0.1409 | 0.6482 | 0.8051 | | 0.1212 | 2.4608 | 1004 | 0.6848 | 0.1409 | 0.6848 | 0.8275 | | 0.1212 | 2.4657 | 1006 | 0.7350 | 0.1793 | 0.7350 | 0.8573 | | 0.1212 | 2.4706 | 1008 | 0.7820 | 0.1793 | 0.7820 | 0.8843 | | 0.1212 | 2.4755 | 1010 | 0.8048 | 0.1793 | 0.8048 | 0.8971 | | 0.1212 | 2.4804 | 1012 | 0.8372 | 0.2881 | 0.8372 | 0.9150 | | 0.1212 | 2.4853 | 1014 | 0.8292 | 0.2881 | 0.8292 | 0.9106 | | 0.1212 | 2.4902 | 1016 | 0.7600 | 0.1978 | 0.7600 | 0.8718 | | 0.1212 | 2.4951 | 1018 | 0.7176 | 0.1978 | 0.7176 | 0.8471 | | 0.1212 | 2.5 | 1020 | 0.6963 | 0.1978 | 0.6963 | 0.8344 | | 0.1212 | 2.5049 | 1022 | 0.7406 | 0.2364 | 0.7406 | 0.8606 | | 0.1212 | 2.5098 | 1024 | 0.7410 | 0.1793 | 0.7410 | 0.8608 | | 0.1212 | 2.5147 | 1026 | 0.6967 | 0.2364 | 0.6967 | 0.8347 | | 0.1212 | 2.5196 | 1028 | 0.6256 | 0.1978 | 0.6256 | 0.7910 | | 0.1212 | 2.5245 | 1030 | 0.6013 | 0.1978 | 0.6013 | 0.7754 | | 0.1212 | 2.5294 | 1032 | 0.6069 | 0.1141 | 0.6069 | 0.7790 | | 0.1212 | 2.5343 | 1034 | 0.6189 | 0.0503 | 0.6189 | 0.7867 | | 0.1212 | 2.5392 | 1036 | 0.6228 | 0.0503 | 0.6228 | 0.7892 | | 0.1212 | 2.5441 | 1038 | 0.6630 | 0.0099 | 0.6630 | 0.8143 | | 0.1212 | 2.5490 | 1040 | 0.6965 | 0.1765 | 0.6965 | 0.8346 | | 0.1212 | 2.5539 | 1042 | 0.7601 | 0.2921 | 0.7601 | 0.8718 | | 0.1212 | 2.5588 | 1044 | 0.8109 | 0.2921 | 0.8109 | 0.9005 | | 0.1212 | 2.5637 | 1046 | 0.8554 | 0.2921 | 0.8554 | 0.9249 | | 0.1212 | 2.5686 | 1048 | 0.9004 | 0.1503 | 0.9004 | 0.9489 | | 0.1212 | 2.5735 | 1050 | 0.9211 | 0.0928 | 0.9211 | 0.9597 | | 0.1212 | 2.5784 | 1052 | 0.9303 | 0.1463 | 0.9303 | 0.9645 | | 0.1212 | 2.5833 | 1054 | 0.9738 | 0.1463 | 0.9738 | 0.9868 | | 0.1212 | 2.5882 | 1056 | 0.9299 | 0.1463 | 0.9299 | 0.9643 | | 0.1212 | 2.5931 | 1058 | 0.8688 | 0.2186 | 0.8688 | 0.9321 | | 0.1212 | 2.5980 | 1060 | 0.7843 | 0.3318 | 0.7843 | 0.8856 | | 0.1212 | 2.6029 | 1062 | 0.7275 | 0.1765 | 0.7275 | 0.8529 | | 0.1212 | 2.6078 | 1064 | 0.7032 | 0.1356 | 0.7032 | 0.8386 | | 0.1212 | 2.6127 | 1066 | 0.7016 | 0.0957 | 0.7016 | 0.8376 | | 0.1212 | 2.6176 | 1068 | 0.7012 | 0.1356 | 0.7012 | 0.8374 | | 0.1212 | 2.6225 | 1070 | 0.7049 | 0.1765 | 0.7049 | 0.8396 | | 0.1212 | 2.6275 | 1072 | 0.7225 | 0.1765 | 0.7225 | 0.8500 | | 0.1212 | 2.6324 | 1074 | 0.7325 | 0.1765 | 0.7325 | 0.8558 | | 0.1212 | 2.6373 | 1076 | 0.7354 | 0.2921 | 0.7354 | 0.8575 | | 0.1212 | 2.6422 | 1078 | 0.7511 | 0.2921 | 0.7511 | 0.8667 | | 0.1212 | 2.6471 | 1080 | 0.7452 | 0.2921 | 0.7452 | 0.8633 | | 0.1212 | 2.6520 | 1082 | 0.7244 | 0.3318 | 0.7244 | 0.8511 | | 0.1212 | 2.6569 | 1084 | 0.7004 | 0.2921 | 0.7004 | 0.8369 | | 0.1212 | 2.6618 | 1086 | 0.6903 | 0.2533 | 0.6903 | 0.8308 | | 0.1212 | 2.6667 | 1088 | 0.6843 | 0.2533 | 0.6843 | 0.8272 | | 0.1212 | 2.6716 | 1090 | 0.6789 | 0.3318 | 0.6789 | 0.8240 | | 0.1212 | 2.6765 | 1092 | 0.6671 | 0.2533 | 0.6671 | 0.8168 | | 0.1212 | 2.6814 | 1094 | 0.6698 | 0.2921 | 0.6698 | 0.8184 | | 0.1212 | 2.6863 | 1096 | 0.6922 | 0.1793 | 0.6922 | 0.8320 | | 0.1212 | 2.6912 | 1098 | 0.7097 | 0.2186 | 0.7097 | 0.8424 | | 0.1212 | 2.6961 | 1100 | 0.7115 | 0.2588 | 0.7115 | 0.8435 | | 0.1212 | 2.7010 | 1102 | 0.6823 | 0.2588 | 0.6823 | 0.8260 | | 0.1212 | 2.7059 | 1104 | 0.6424 | 0.1793 | 0.6424 | 0.8015 | | 0.1212 | 2.7108 | 1106 | 0.6226 | 0.1765 | 0.6226 | 0.7891 | | 0.1212 | 2.7157 | 1108 | 0.6197 | 0.1765 | 0.6197 | 0.7872 | | 0.1212 | 2.7206 | 1110 | 0.6313 | 0.1765 | 0.6313 | 0.7946 | | 0.1212 | 2.7255 | 1112 | 0.6564 | 0.1793 | 0.6564 | 0.8102 | | 0.1212 | 2.7304 | 1114 | 0.7129 | 0.2588 | 0.7129 | 0.8443 | | 0.1212 | 2.7353 | 1116 | 0.7700 | 0.2588 | 0.7700 | 0.8775 | | 0.1212 | 2.7402 | 1118 | 0.8459 | 0.3636 | 0.8459 | 0.9197 | | 0.1212 | 2.7451 | 1120 | 0.8834 | 0.3687 | 0.8834 | 0.9399 | | 0.1212 | 2.75 | 1122 | 0.8543 | 0.3687 | 0.8543 | 0.9243 | | 0.1212 | 2.7549 | 1124 | 0.7792 | 0.1793 | 0.7792 | 0.8827 | | 0.1212 | 2.7598 | 1126 | 0.7107 | 0.2154 | 0.7107 | 0.8430 | | 0.1212 | 2.7647 | 1128 | 0.6777 | 0.2154 | 0.6777 | 0.8232 | | 0.1212 | 2.7696 | 1130 | 0.6717 | 0.2154 | 0.6717 | 0.8196 | | 0.1212 | 2.7745 | 1132 | 0.6612 | 0.0957 | 0.6612 | 0.8131 | | 0.1212 | 2.7794 | 1134 | 0.6444 | 0.1560 | 0.6444 | 0.8028 | | 0.1212 | 2.7843 | 1136 | 0.6364 | 0.1560 | 0.6364 | 0.7978 | | 0.1212 | 2.7892 | 1138 | 0.6291 | 0.1560 | 0.6291 | 0.7932 | | 0.1212 | 2.7941 | 1140 | 0.6268 | 0.1560 | 0.6268 | 0.7917 | | 0.1212 | 2.7990 | 1142 | 0.6319 | 0.1560 | 0.6319 | 0.7949 | | 0.1212 | 2.8039 | 1144 | 0.6426 | 0.0957 | 0.6426 | 0.8016 | | 0.1212 | 2.8088 | 1146 | 0.6633 | 0.0099 | 0.6633 | 0.8144 | | 0.1212 | 2.8137 | 1148 | 0.7144 | 0.2186 | 0.7144 | 0.8452 | | 0.1212 | 2.8186 | 1150 | 0.7613 | 0.2186 | 0.7613 | 0.8726 | | 0.1212 | 2.8235 | 1152 | 0.7801 | 0.1409 | 0.7801 | 0.8832 | | 0.1212 | 2.8284 | 1154 | 0.7979 | 0.2533 | 0.7979 | 0.8933 | | 0.1212 | 2.8333 | 1156 | 0.8013 | 0.2533 | 0.8013 | 0.8951 | | 0.1212 | 2.8382 | 1158 | 0.8101 | 0.2533 | 0.8101 | 0.9001 | | 0.1212 | 2.8431 | 1160 | 0.8162 | 0.1409 | 0.8162 | 0.9034 | | 0.1212 | 2.8480 | 1162 | 0.7858 | 0.1793 | 0.7858 | 0.8864 | | 0.1212 | 2.8529 | 1164 | 0.7463 | 0.1409 | 0.7463 | 0.8639 | | 0.1212 | 2.8578 | 1166 | 0.7142 | 0.1409 | 0.7142 | 0.8451 | | 0.1212 | 2.8627 | 1168 | 0.6781 | 0.0099 | 0.6781 | 0.8235 | | 0.1212 | 2.8676 | 1170 | 0.6558 | -0.0294 | 0.6558 | 0.8098 | | 0.1212 | 2.8725 | 1172 | 0.6414 | 0.1034 | 0.6414 | 0.8009 | | 0.1212 | 2.8775 | 1174 | 0.6365 | -0.0294 | 0.6365 | 0.7978 | | 0.1212 | 2.8824 | 1176 | 0.6352 | 0.1962 | 0.6352 | 0.7970 | | 0.1212 | 2.8873 | 1178 | 0.6246 | 0.1962 | 0.6246 | 0.7903 | | 0.1212 | 2.8922 | 1180 | 0.6277 | -0.0294 | 0.6277 | 0.7923 | | 0.1212 | 2.8971 | 1182 | 0.6271 | 0.0339 | 0.6271 | 0.7919 | | 0.1212 | 2.9020 | 1184 | 0.6218 | 0.1409 | 0.6218 | 0.7886 | | 0.1212 | 2.9069 | 1186 | 0.6191 | 0.1793 | 0.6191 | 0.7868 | | 0.1212 | 2.9118 | 1188 | 0.6122 | 0.0099 | 0.6122 | 0.7824 | | 0.1212 | 2.9167 | 1190 | 0.6226 | 0.1962 | 0.6226 | 0.7890 | | 0.1212 | 2.9216 | 1192 | 0.6327 | 0.1962 | 0.6327 | 0.7954 | | 0.1212 | 2.9265 | 1194 | 0.6284 | 0.1962 | 0.6284 | 0.7927 | | 0.1212 | 2.9314 | 1196 | 0.6272 | 0.0503 | 0.6272 | 0.7920 | | 0.1212 | 2.9363 | 1198 | 0.6282 | 0.0916 | 0.6282 | 0.7926 | | 0.1212 | 2.9412 | 1200 | 0.6260 | 0.0916 | 0.6260 | 0.7912 | | 0.1212 | 2.9461 | 1202 | 0.6322 | 0.0916 | 0.6322 | 0.7951 | | 0.1212 | 2.9510 | 1204 | 0.6453 | 0.2186 | 0.6453 | 0.8033 | | 0.1212 | 2.9559 | 1206 | 0.6579 | 0.1793 | 0.6579 | 0.8111 | | 0.1212 | 2.9608 | 1208 | 0.6697 | 0.2186 | 0.6697 | 0.8183 | | 0.1212 | 2.9657 | 1210 | 0.6697 | 0.1793 | 0.6697 | 0.8183 | | 0.1212 | 2.9706 | 1212 | 0.6698 | 0.1793 | 0.6698 | 0.8184 | | 0.1212 | 2.9755 | 1214 | 0.6784 | 0.1793 | 0.6784 | 0.8236 | | 0.1212 | 2.9804 | 1216 | 0.6913 | 0.1793 | 0.6913 | 0.8315 | | 0.1212 | 2.9853 | 1218 | 0.6965 | 0.1793 | 0.6965 | 0.8346 | | 0.1212 | 2.9902 | 1220 | 0.7009 | 0.1409 | 0.7009 | 0.8372 | | 0.1212 | 2.9951 | 1222 | 0.7186 | 0.1034 | 0.7186 | 0.8477 | | 0.1212 | 3.0 | 1224 | 0.7407 | 0.2154 | 0.7407 | 0.8606 | | 0.1212 | 3.0049 | 1226 | 0.7650 | 0.2154 | 0.7650 | 0.8746 | | 0.1212 | 3.0098 | 1228 | 0.7814 | 0.2154 | 0.7814 | 0.8840 | | 0.1212 | 3.0147 | 1230 | 0.7895 | 0.2154 | 0.7895 | 0.8885 | | 0.1212 | 3.0196 | 1232 | 0.7814 | 0.2154 | 0.7814 | 0.8840 | | 0.1212 | 3.0245 | 1234 | 0.7751 | 0.0957 | 0.7751 | 0.8804 | | 0.1212 | 3.0294 | 1236 | 0.7702 | 0.0957 | 0.7702 | 0.8776 | | 0.1212 | 3.0343 | 1238 | 0.7634 | 0.1034 | 0.7634 | 0.8737 | | 0.1212 | 3.0392 | 1240 | 0.7536 | 0.1034 | 0.7536 | 0.8681 | | 0.1212 | 3.0441 | 1242 | 0.7258 | 0.1793 | 0.7258 | 0.8519 | | 0.1212 | 3.0490 | 1244 | 0.6818 | 0.0503 | 0.6818 | 0.8257 | | 0.1212 | 3.0539 | 1246 | 0.6520 | -0.0678 | 0.6520 | 0.8075 | | 0.1212 | 3.0588 | 1248 | 0.6756 | 0.2150 | 0.6756 | 0.8219 | | 0.1212 | 3.0637 | 1250 | 0.7224 | 0.2150 | 0.7224 | 0.8500 | | 0.1212 | 3.0686 | 1252 | 0.7444 | 0.1755 | 0.7444 | 0.8628 | | 0.1212 | 3.0735 | 1254 | 0.7450 | 0.2150 | 0.7450 | 0.8631 | | 0.1212 | 3.0784 | 1256 | 0.7266 | 0.2150 | 0.7266 | 0.8524 | | 0.1212 | 3.0833 | 1258 | 0.7112 | 0.0957 | 0.7112 | 0.8433 | | 0.1212 | 3.0882 | 1260 | 0.7306 | -0.0294 | 0.7306 | 0.8548 | | 0.1212 | 3.0931 | 1262 | 0.7970 | 0.2186 | 0.7970 | 0.8928 | | 0.1212 | 3.0980 | 1264 | 0.8659 | 0.1162 | 0.8659 | 0.9305 | | 0.1212 | 3.1029 | 1266 | 0.8734 | 0.1162 | 0.8734 | 0.9346 | | 0.1212 | 3.1078 | 1268 | 0.8305 | 0.1992 | 0.8305 | 0.9113 | | 0.1212 | 3.1127 | 1270 | 0.7934 | 0.2317 | 0.7934 | 0.8907 | | 0.1212 | 3.1176 | 1272 | 0.7629 | 0.2696 | 0.7629 | 0.8735 | | 0.1212 | 3.1225 | 1274 | 0.7458 | 0.3226 | 0.7458 | 0.8636 | | 0.1212 | 3.1275 | 1276 | 0.7510 | 0.2696 | 0.7510 | 0.8666 | | 0.1212 | 3.1324 | 1278 | 0.7766 | 0.2317 | 0.7766 | 0.8813 | | 0.1212 | 3.1373 | 1280 | 0.8266 | 0.1848 | 0.8266 | 0.9092 | | 0.1212 | 3.1422 | 1282 | 0.8591 | 0.2734 | 0.8591 | 0.9269 | | 0.1212 | 3.1471 | 1284 | 0.8567 | 0.3037 | 0.8567 | 0.9256 | | 0.1212 | 3.1520 | 1286 | 0.8112 | 0.3687 | 0.8112 | 0.9007 | | 0.1212 | 3.1569 | 1288 | 0.7511 | 0.2588 | 0.7511 | 0.8666 | | 0.1212 | 3.1618 | 1290 | 0.6872 | 0.2588 | 0.6872 | 0.8290 | | 0.1212 | 3.1667 | 1292 | 0.6302 | 0.1978 | 0.6302 | 0.7939 | | 0.1212 | 3.1716 | 1294 | 0.6109 | 0.0735 | 0.6109 | 0.7816 | | 0.1212 | 3.1765 | 1296 | 0.6134 | 0.2553 | 0.6134 | 0.7832 | | 0.1212 | 3.1814 | 1298 | 0.6129 | 0.2553 | 0.6129 | 0.7829 | | 0.1212 | 3.1863 | 1300 | 0.6005 | 0.0339 | 0.6005 | 0.7749 | | 0.1212 | 3.1912 | 1302 | 0.6036 | 0.0735 | 0.6036 | 0.7769 | | 0.1212 | 3.1961 | 1304 | 0.6201 | 0.0503 | 0.6201 | 0.7875 | | 0.1212 | 3.2010 | 1306 | 0.6512 | 0.2588 | 0.6512 | 0.8070 | | 0.1212 | 3.2059 | 1308 | 0.7153 | 0.2588 | 0.7153 | 0.8457 | | 0.1212 | 3.2108 | 1310 | 0.7597 | 0.2588 | 0.7597 | 0.8716 | | 0.1212 | 3.2157 | 1312 | 0.7538 | 0.2588 | 0.7538 | 0.8682 | | 0.1212 | 3.2206 | 1314 | 0.7161 | 0.2588 | 0.7161 | 0.8462 | | 0.1212 | 3.2255 | 1316 | 0.6582 | 0.2588 | 0.6582 | 0.8113 | | 0.1212 | 3.2304 | 1318 | 0.6287 | 0.0099 | 0.6287 | 0.7929 | | 0.1212 | 3.2353 | 1320 | 0.6382 | 0.1560 | 0.6382 | 0.7989 | | 0.1212 | 3.2402 | 1322 | 0.6511 | 0.1560 | 0.6511 | 0.8069 | | 0.1212 | 3.2451 | 1324 | 0.6503 | 0.1356 | 0.6503 | 0.8064 | | 0.1212 | 3.25 | 1326 | 0.6509 | -0.0294 | 0.6509 | 0.8068 | | 0.1212 | 3.2549 | 1328 | 0.6657 | 0.1793 | 0.6657 | 0.8159 | | 0.1212 | 3.2598 | 1330 | 0.6928 | 0.2186 | 0.6928 | 0.8323 | | 0.1212 | 3.2647 | 1332 | 0.7036 | 0.2186 | 0.7036 | 0.8388 | | 0.1212 | 3.2696 | 1334 | 0.7117 | 0.2186 | 0.7117 | 0.8436 | | 0.1212 | 3.2745 | 1336 | 0.6872 | 0.1793 | 0.6872 | 0.8290 | | 0.1212 | 3.2794 | 1338 | 0.6692 | 0.1409 | 0.6692 | 0.8181 | | 0.1212 | 3.2843 | 1340 | 0.6413 | 0.0099 | 0.6413 | 0.8008 | | 0.1212 | 3.2892 | 1342 | 0.6219 | 0.0735 | 0.6219 | 0.7886 | | 0.1212 | 3.2941 | 1344 | 0.6120 | 0.1141 | 0.6120 | 0.7823 | | 0.1212 | 3.2990 | 1346 | 0.6147 | 0.1558 | 0.6147 | 0.7840 | | 0.1212 | 3.3039 | 1348 | 0.6333 | 0.1340 | 0.6333 | 0.7958 | | 0.1212 | 3.3088 | 1350 | 0.6374 | 0.1340 | 0.6374 | 0.7984 | | 0.1212 | 3.3137 | 1352 | 0.6323 | 0.1985 | 0.6323 | 0.7952 | | 0.1212 | 3.3186 | 1354 | 0.6119 | 0.1985 | 0.6119 | 0.7822 | | 0.1212 | 3.3235 | 1356 | 0.5826 | 0.1558 | 0.5826 | 0.7633 | | 0.1212 | 3.3284 | 1358 | 0.5638 | 0.1141 | 0.5638 | 0.7508 | | 0.1212 | 3.3333 | 1360 | 0.5615 | 0.2794 | 0.5615 | 0.7493 | | 0.1212 | 3.3382 | 1362 | 0.5737 | 0.2794 | 0.5737 | 0.7574 | | 0.1212 | 3.3431 | 1364 | 0.5984 | 0.2794 | 0.5984 | 0.7735 | | 0.1212 | 3.3480 | 1366 | 0.6173 | 0.2794 | 0.6173 | 0.7857 | | 0.1212 | 3.3529 | 1368 | 0.6508 | 0.3865 | 0.6508 | 0.8067 | | 0.1212 | 3.3578 | 1370 | 0.7077 | 0.3163 | 0.7077 | 0.8412 | | 0.1212 | 3.3627 | 1372 | 0.7377 | 0.3163 | 0.7377 | 0.8589 | | 0.1212 | 3.3676 | 1374 | 0.7257 | 0.3163 | 0.7257 | 0.8519 | | 0.1212 | 3.3725 | 1376 | 0.6912 | 0.3163 | 0.6912 | 0.8314 | | 0.1212 | 3.3775 | 1378 | 0.6567 | 0.3163 | 0.6567 | 0.8104 | | 0.1212 | 3.3824 | 1380 | 0.6102 | 0.3163 | 0.6102 | 0.7812 | | 0.1212 | 3.3873 | 1382 | 0.6045 | 0.3163 | 0.6045 | 0.7775 | | 0.1212 | 3.3922 | 1384 | 0.6079 | 0.3163 | 0.6079 | 0.7797 | | 0.1212 | 3.3971 | 1386 | 0.6079 | 0.3163 | 0.6079 | 0.7797 | | 0.1212 | 3.4020 | 1388 | 0.6066 | 0.3163 | 0.6066 | 0.7788 | | 0.1212 | 3.4069 | 1390 | 0.5949 | 0.3163 | 0.5949 | 0.7713 | | 0.1212 | 3.4118 | 1392 | 0.5952 | 0.3163 | 0.5952 | 0.7715 | | 0.1212 | 3.4167 | 1394 | 0.6242 | 0.2588 | 0.6242 | 0.7901 | | 0.1212 | 3.4216 | 1396 | 0.6646 | 0.2588 | 0.6646 | 0.8152 | | 0.1212 | 3.4265 | 1398 | 0.6828 | 0.2588 | 0.6828 | 0.8263 | | 0.1212 | 3.4314 | 1400 | 0.6958 | 0.2588 | 0.6958 | 0.8342 | | 0.1212 | 3.4363 | 1402 | 0.6845 | 0.2588 | 0.6845 | 0.8274 | | 0.1212 | 3.4412 | 1404 | 0.6459 | 0.2186 | 0.6459 | 0.8037 | | 0.1212 | 3.4461 | 1406 | 0.6205 | 0.2154 | 0.6205 | 0.7877 | | 0.1212 | 3.4510 | 1408 | 0.6152 | 0.2696 | 0.6152 | 0.7844 | | 0.1212 | 3.4559 | 1410 | 0.6115 | 0.1560 | 0.6115 | 0.7820 | | 0.1212 | 3.4608 | 1412 | 0.5999 | 0.2150 | 0.5999 | 0.7745 | | 0.1212 | 3.4657 | 1414 | 0.5983 | 0.1978 | 0.5983 | 0.7735 | | 0.1212 | 3.4706 | 1416 | 0.6313 | 0.3163 | 0.6313 | 0.7945 | | 0.1212 | 3.4755 | 1418 | 0.6568 | 0.2588 | 0.6568 | 0.8105 | | 0.1212 | 3.4804 | 1420 | 0.7026 | 0.2588 | 0.7026 | 0.8382 | | 0.1212 | 3.4853 | 1422 | 0.7229 | 0.2588 | 0.7229 | 0.8503 | | 0.1212 | 3.4902 | 1424 | 0.7069 | 0.2588 | 0.7069 | 0.8408 | | 0.1212 | 3.4951 | 1426 | 0.6812 | 0.2588 | 0.6812 | 0.8253 | | 0.1212 | 3.5 | 1428 | 0.6720 | 0.2588 | 0.6720 | 0.8197 | | 0.1212 | 3.5049 | 1430 | 0.6791 | 0.2588 | 0.6791 | 0.8241 | | 0.1212 | 3.5098 | 1432 | 0.6596 | 0.2588 | 0.6596 | 0.8121 | | 0.1212 | 3.5147 | 1434 | 0.6479 | 0.1340 | 0.6479 | 0.8049 | | 0.1212 | 3.5196 | 1436 | 0.6485 | -0.0048 | 0.6485 | 0.8053 | | 0.1212 | 3.5245 | 1438 | 0.6534 | 0.0503 | 0.6534 | 0.8083 | | 0.1212 | 3.5294 | 1440 | 0.6638 | 0.2588 | 0.6638 | 0.8147 | | 0.1212 | 3.5343 | 1442 | 0.6916 | 0.2588 | 0.6916 | 0.8316 | | 0.1212 | 3.5392 | 1444 | 0.7170 | 0.2588 | 0.7170 | 0.8468 | | 0.1212 | 3.5441 | 1446 | 0.7221 | 0.2588 | 0.7221 | 0.8498 | | 0.1212 | 3.5490 | 1448 | 0.7272 | 0.1793 | 0.7272 | 0.8528 | | 0.1212 | 3.5539 | 1450 | 0.7159 | 0.2154 | 0.7159 | 0.8461 | | 0.1212 | 3.5588 | 1452 | 0.7029 | 0.2696 | 0.7029 | 0.8384 | | 0.1212 | 3.5637 | 1454 | 0.6966 | 0.2696 | 0.6966 | 0.8346 | | 0.1212 | 3.5686 | 1456 | 0.6862 | 0.2696 | 0.6862 | 0.8284 | | 0.1212 | 3.5735 | 1458 | 0.6811 | 0.2696 | 0.6811 | 0.8253 | | 0.1212 | 3.5784 | 1460 | 0.6799 | 0.1978 | 0.6799 | 0.8246 | | 0.1212 | 3.5833 | 1462 | 0.7066 | 0.1793 | 0.7066 | 0.8406 | | 0.1212 | 3.5882 | 1464 | 0.7253 | 0.2588 | 0.7253 | 0.8517 | | 0.1212 | 3.5931 | 1466 | 0.7126 | 0.2588 | 0.7126 | 0.8442 | | 0.1212 | 3.5980 | 1468 | 0.7129 | 0.2588 | 0.7129 | 0.8443 | | 0.1212 | 3.6029 | 1470 | 0.7152 | 0.2588 | 0.7152 | 0.8457 | | 0.1212 | 3.6078 | 1472 | 0.7071 | 0.2588 | 0.7071 | 0.8409 | | 0.1212 | 3.6127 | 1474 | 0.6837 | 0.1793 | 0.6837 | 0.8269 | | 0.1212 | 3.6176 | 1476 | 0.6841 | 0.1978 | 0.6841 | 0.8271 | | 0.1212 | 3.6225 | 1478 | 0.6879 | 0.3077 | 0.6879 | 0.8294 | | 0.1212 | 3.6275 | 1480 | 0.6937 | 0.2696 | 0.6937 | 0.8329 | | 0.1212 | 3.6324 | 1482 | 0.6995 | 0.2696 | 0.6995 | 0.8364 | | 0.1212 | 3.6373 | 1484 | 0.7072 | 0.2696 | 0.7072 | 0.8409 | | 0.1212 | 3.6422 | 1486 | 0.7205 | 0.2533 | 0.7205 | 0.8488 | | 0.1212 | 3.6471 | 1488 | 0.7153 | 0.2533 | 0.7153 | 0.8457 | | 0.1212 | 3.6520 | 1490 | 0.7201 | 0.1409 | 0.7201 | 0.8486 | | 0.1212 | 3.6569 | 1492 | 0.7354 | 0.1793 | 0.7354 | 0.8576 | | 0.1212 | 3.6618 | 1494 | 0.7457 | 0.2186 | 0.7457 | 0.8635 | | 0.1212 | 3.6667 | 1496 | 0.7241 | 0.1793 | 0.7241 | 0.8510 | | 0.1212 | 3.6716 | 1498 | 0.6961 | 0.1409 | 0.6961 | 0.8343 | | 0.0791 | 3.6765 | 1500 | 0.6778 | 0.1356 | 0.6778 | 0.8233 | | 0.0791 | 3.6814 | 1502 | 0.6761 | 0.1356 | 0.6761 | 0.8222 | | 0.0791 | 3.6863 | 1504 | 0.6859 | 0.0957 | 0.6859 | 0.8282 | | 0.0791 | 3.6912 | 1506 | 0.6944 | 0.0957 | 0.6944 | 0.8333 | | 0.0791 | 3.6961 | 1508 | 0.7040 | 0.0957 | 0.7040 | 0.8390 | | 0.0791 | 3.7010 | 1510 | 0.7152 | 0.1356 | 0.7152 | 0.8457 | | 0.0791 | 3.7059 | 1512 | 0.7406 | 0.2588 | 0.7406 | 0.8606 | | 0.0791 | 3.7108 | 1514 | 0.7760 | 0.2588 | 0.7760 | 0.8809 | | 0.0791 | 3.7157 | 1516 | 0.7728 | 0.2588 | 0.7728 | 0.8791 | | 0.0791 | 3.7206 | 1518 | 0.7336 | 0.2588 | 0.7336 | 0.8565 | | 0.0791 | 3.7255 | 1520 | 0.7011 | 0.2588 | 0.7011 | 0.8373 | | 0.0791 | 3.7304 | 1522 | 0.6835 | 0.1793 | 0.6835 | 0.8267 | | 0.0791 | 3.7353 | 1524 | 0.6768 | 0.1409 | 0.6768 | 0.8227 | | 0.0791 | 3.7402 | 1526 | 0.6799 | 0.1409 | 0.6799 | 0.8245 | | 0.0791 | 3.7451 | 1528 | 0.6861 | 0.2921 | 0.6861 | 0.8283 | | 0.0791 | 3.75 | 1530 | 0.6959 | 0.2533 | 0.6959 | 0.8342 | | 0.0791 | 3.7549 | 1532 | 0.6940 | 0.2154 | 0.6940 | 0.8331 | | 0.0791 | 3.7598 | 1534 | 0.6892 | 0.2154 | 0.6892 | 0.8302 | | 0.0791 | 3.7647 | 1536 | 0.7028 | 0.2154 | 0.7028 | 0.8383 | | 0.0791 | 3.7696 | 1538 | 0.7101 | 0.0957 | 0.7101 | 0.8427 | | 0.0791 | 3.7745 | 1540 | 0.7086 | 0.2154 | 0.7086 | 0.8418 | | 0.0791 | 3.7794 | 1542 | 0.7038 | 0.2154 | 0.7038 | 0.8389 | | 0.0791 | 3.7843 | 1544 | 0.6954 | 0.2154 | 0.6954 | 0.8339 | | 0.0791 | 3.7892 | 1546 | 0.7015 | 0.3318 | 0.7015 | 0.8376 | | 0.0791 | 3.7941 | 1548 | 0.7003 | 0.1793 | 0.7003 | 0.8368 | | 0.0791 | 3.7990 | 1550 | 0.6815 | 0.1793 | 0.6815 | 0.8255 | | 0.0791 | 3.8039 | 1552 | 0.6841 | 0.2186 | 0.6841 | 0.8271 | | 0.0791 | 3.8088 | 1554 | 0.6907 | 0.2186 | 0.6907 | 0.8311 | | 0.0791 | 3.8137 | 1556 | 0.6936 | 0.2186 | 0.6936 | 0.8328 | | 0.0791 | 3.8186 | 1558 | 0.6869 | 0.2186 | 0.6869 | 0.8288 | | 0.0791 | 3.8235 | 1560 | 0.6744 | 0.2921 | 0.6744 | 0.8212 | | 0.0791 | 3.8284 | 1562 | 0.6651 | 0.2154 | 0.6651 | 0.8156 | | 0.0791 | 3.8333 | 1564 | 0.6676 | 0.2154 | 0.6676 | 0.8171 | | 0.0791 | 3.8382 | 1566 | 0.6710 | 0.2154 | 0.6710 | 0.8191 | | 0.0791 | 3.8431 | 1568 | 0.6762 | 0.2533 | 0.6762 | 0.8223 | | 0.0791 | 3.8480 | 1570 | 0.6898 | 0.1034 | 0.6898 | 0.8305 | | 0.0791 | 3.8529 | 1572 | 0.6950 | 0.2186 | 0.6950 | 0.8337 | | 0.0791 | 3.8578 | 1574 | 0.6865 | 0.2186 | 0.6865 | 0.8286 | | 0.0791 | 3.8627 | 1576 | 0.6698 | 0.1409 | 0.6698 | 0.8184 | | 0.0791 | 3.8676 | 1578 | 0.6508 | 0.2154 | 0.6508 | 0.8067 | | 0.0791 | 3.8725 | 1580 | 0.6741 | 0.2150 | 0.6741 | 0.8210 | | 0.0791 | 3.8775 | 1582 | 0.7014 | 0.1755 | 0.7014 | 0.8375 | | 0.0791 | 3.8824 | 1584 | 0.6867 | 0.2851 | 0.6867 | 0.8286 | | 0.0791 | 3.8873 | 1586 | 0.6669 | 0.3226 | 0.6669 | 0.8166 | | 0.0791 | 3.8922 | 1588 | 0.6563 | 0.3226 | 0.6563 | 0.8101 | | 0.0791 | 3.8971 | 1590 | 0.6450 | 0.3077 | 0.6450 | 0.8031 | | 0.0791 | 3.9020 | 1592 | 0.6484 | 0.3467 | 0.6484 | 0.8053 | | 0.0791 | 3.9069 | 1594 | 0.6492 | 0.3467 | 0.6492 | 0.8057 | | 0.0791 | 3.9118 | 1596 | 0.6567 | 0.3077 | 0.6567 | 0.8104 | | 0.0791 | 3.9167 | 1598 | 0.6630 | 0.3077 | 0.6630 | 0.8143 | | 0.0791 | 3.9216 | 1600 | 0.6768 | 0.3077 | 0.6768 | 0.8227 | | 0.0791 | 3.9265 | 1602 | 0.6933 | 0.3077 | 0.6933 | 0.8327 | | 0.0791 | 3.9314 | 1604 | 0.7030 | 0.1600 | 0.7030 | 0.8385 | | 0.0791 | 3.9363 | 1606 | 0.7104 | 0.1978 | 0.7104 | 0.8428 | | 0.0791 | 3.9412 | 1608 | 0.7090 | 0.1978 | 0.7090 | 0.8420 | | 0.0791 | 3.9461 | 1610 | 0.7204 | 0.2364 | 0.7204 | 0.8488 | | 0.0791 | 3.9510 | 1612 | 0.7198 | 0.2364 | 0.7198 | 0.8484 | | 0.0791 | 3.9559 | 1614 | 0.7037 | 0.2364 | 0.7037 | 0.8388 | | 0.0791 | 3.9608 | 1616 | 0.6855 | 0.2364 | 0.6855 | 0.8279 | | 0.0791 | 3.9657 | 1618 | 0.6595 | 0.1600 | 0.6595 | 0.8121 | | 0.0791 | 3.9706 | 1620 | 0.6500 | 0.1600 | 0.6500 | 0.8063 | | 0.0791 | 3.9755 | 1622 | 0.6526 | 0.1600 | 0.6526 | 0.8078 | | 0.0791 | 3.9804 | 1624 | 0.6712 | 0.2364 | 0.6712 | 0.8193 | | 0.0791 | 3.9853 | 1626 | 0.7021 | 0.2186 | 0.7021 | 0.8379 | | 0.0791 | 3.9902 | 1628 | 0.7048 | 0.2186 | 0.7048 | 0.8395 | | 0.0791 | 3.9951 | 1630 | 0.6990 | 0.2186 | 0.6990 | 0.8360 | | 0.0791 | 4.0 | 1632 | 0.7031 | 0.2186 | 0.7031 | 0.8385 | | 0.0791 | 4.0049 | 1634 | 0.6996 | 0.2186 | 0.6996 | 0.8364 | | 0.0791 | 4.0098 | 1636 | 0.6952 | 0.1409 | 0.6952 | 0.8338 | | 0.0791 | 4.0147 | 1638 | 0.7047 | 0.1034 | 0.7047 | 0.8395 | | 0.0791 | 4.0196 | 1640 | 0.7232 | 0.1034 | 0.7232 | 0.8504 | | 0.0791 | 4.0245 | 1642 | 0.7470 | 0.1034 | 0.7470 | 0.8643 | | 0.0791 | 4.0294 | 1644 | 0.7678 | 0.1409 | 0.7678 | 0.8762 | | 0.0791 | 4.0343 | 1646 | 0.8113 | 0.1793 | 0.8113 | 0.9007 | | 0.0791 | 4.0392 | 1648 | 0.8505 | 0.3255 | 0.8505 | 0.9222 | | 0.0791 | 4.0441 | 1650 | 0.8698 | 0.3255 | 0.8698 | 0.9326 | | 0.0791 | 4.0490 | 1652 | 0.8610 | 0.3255 | 0.8610 | 0.9279 | | 0.0791 | 4.0539 | 1654 | 0.8489 | 0.3255 | 0.8489 | 0.9213 | | 0.0791 | 4.0588 | 1656 | 0.8233 | 0.1409 | 0.8233 | 0.9073 | | 0.0791 | 4.0637 | 1658 | 0.8296 | 0.1409 | 0.8296 | 0.9108 | | 0.0791 | 4.0686 | 1660 | 0.8387 | 0.2186 | 0.8387 | 0.9158 | | 0.0791 | 4.0735 | 1662 | 0.8289 | 0.2186 | 0.8289 | 0.9104 | | 0.0791 | 4.0784 | 1664 | 0.8289 | 0.2186 | 0.8289 | 0.9104 | | 0.0791 | 4.0833 | 1666 | 0.8186 | 0.2186 | 0.8186 | 0.9048 | | 0.0791 | 4.0882 | 1668 | 0.8049 | 0.1793 | 0.8049 | 0.8972 | | 0.0791 | 4.0931 | 1670 | 0.7837 | 0.1793 | 0.7837 | 0.8853 | | 0.0791 | 4.0980 | 1672 | 0.7665 | 0.1409 | 0.7665 | 0.8755 | | 0.0791 | 4.1029 | 1674 | 0.7436 | 0.1600 | 0.7436 | 0.8623 | | 0.0791 | 4.1078 | 1676 | 0.7272 | 0.1600 | 0.7272 | 0.8528 | | 0.0791 | 4.1127 | 1678 | 0.7201 | 0.1978 | 0.7201 | 0.8486 | | 0.0791 | 4.1176 | 1680 | 0.7042 | 0.1409 | 0.7042 | 0.8392 | | 0.0791 | 4.1225 | 1682 | 0.6854 | 0.1409 | 0.6854 | 0.8279 | | 0.0791 | 4.1275 | 1684 | 0.6862 | 0.1409 | 0.6862 | 0.8284 | | 0.0791 | 4.1324 | 1686 | 0.6993 | 0.1793 | 0.6993 | 0.8362 | | 0.0791 | 4.1373 | 1688 | 0.7311 | 0.2186 | 0.7311 | 0.8550 | | 0.0791 | 4.1422 | 1690 | 0.7596 | 0.2186 | 0.7596 | 0.8715 | | 0.0791 | 4.1471 | 1692 | 0.7471 | 0.2186 | 0.7471 | 0.8644 | | 0.0791 | 4.1520 | 1694 | 0.7068 | 0.2186 | 0.7068 | 0.8407 | | 0.0791 | 4.1569 | 1696 | 0.6582 | 0.1034 | 0.6582 | 0.8113 | | 0.0791 | 4.1618 | 1698 | 0.6384 | 0.1962 | 0.6384 | 0.7990 | | 0.0791 | 4.1667 | 1700 | 0.6446 | 0.2150 | 0.6446 | 0.8029 | | 0.0791 | 4.1716 | 1702 | 0.6478 | 0.2150 | 0.6478 | 0.8048 | | 0.0791 | 4.1765 | 1704 | 0.6374 | 0.2150 | 0.6374 | 0.7983 | | 0.0791 | 4.1814 | 1706 | 0.6339 | 0.2553 | 0.6339 | 0.7962 | | 0.0791 | 4.1863 | 1708 | 0.6347 | 0.1356 | 0.6347 | 0.7967 | | 0.0791 | 4.1912 | 1710 | 0.6393 | 0.2921 | 0.6393 | 0.7996 | | 0.0791 | 4.1961 | 1712 | 0.6482 | 0.1793 | 0.6482 | 0.8051 | | 0.0791 | 4.2010 | 1714 | 0.6547 | 0.2186 | 0.6547 | 0.8091 | | 0.0791 | 4.2059 | 1716 | 0.6433 | 0.2186 | 0.6433 | 0.8021 | | 0.0791 | 4.2108 | 1718 | 0.6339 | 0.2186 | 0.6339 | 0.7962 | | 0.0791 | 4.2157 | 1720 | 0.6289 | 0.0503 | 0.6289 | 0.7930 | | 0.0791 | 4.2206 | 1722 | 0.6347 | 0.1793 | 0.6347 | 0.7967 | | 0.0791 | 4.2255 | 1724 | 0.6359 | 0.1765 | 0.6359 | 0.7974 | | 0.0791 | 4.2304 | 1726 | 0.6393 | 0.2921 | 0.6393 | 0.7996 | | 0.0791 | 4.2353 | 1728 | 0.6440 | 0.3318 | 0.6440 | 0.8025 | | 0.0791 | 4.2402 | 1730 | 0.6491 | 0.3318 | 0.6491 | 0.8056 | | 0.0791 | 4.2451 | 1732 | 0.6437 | 0.2921 | 0.6437 | 0.8023 | | 0.0791 | 4.25 | 1734 | 0.6366 | 0.1765 | 0.6366 | 0.7979 | | 0.0791 | 4.2549 | 1736 | 0.6388 | 0.1560 | 0.6388 | 0.7992 | | 0.0791 | 4.2598 | 1738 | 0.6462 | 0.2150 | 0.6462 | 0.8039 | | 0.0791 | 4.2647 | 1740 | 0.6544 | 0.2150 | 0.6544 | 0.8089 | | 0.0791 | 4.2696 | 1742 | 0.6589 | 0.2696 | 0.6589 | 0.8117 | | 0.0791 | 4.2745 | 1744 | 0.6620 | 0.2696 | 0.6620 | 0.8136 | | 0.0791 | 4.2794 | 1746 | 0.6786 | 0.3318 | 0.6786 | 0.8237 | | 0.0791 | 4.2843 | 1748 | 0.7055 | 0.2186 | 0.7055 | 0.8400 | | 0.0791 | 4.2892 | 1750 | 0.7199 | 0.2186 | 0.7199 | 0.8485 | | 0.0791 | 4.2941 | 1752 | 0.7248 | 0.2186 | 0.7248 | 0.8513 | | 0.0791 | 4.2990 | 1754 | 0.7232 | 0.2186 | 0.7232 | 0.8504 | | 0.0791 | 4.3039 | 1756 | 0.7045 | 0.1793 | 0.7045 | 0.8393 | | 0.0791 | 4.3088 | 1758 | 0.6700 | 0.2921 | 0.6700 | 0.8185 | | 0.0791 | 4.3137 | 1760 | 0.6459 | 0.3077 | 0.6459 | 0.8037 | | 0.0791 | 4.3186 | 1762 | 0.6375 | 0.2696 | 0.6375 | 0.7984 | | 0.0791 | 4.3235 | 1764 | 0.6266 | 0.3226 | 0.6266 | 0.7916 | | 0.0791 | 4.3284 | 1766 | 0.6143 | 0.3609 | 0.6143 | 0.7838 | | 0.0791 | 4.3333 | 1768 | 0.6055 | 0.3467 | 0.6055 | 0.7781 | | 0.0791 | 4.3382 | 1770 | 0.6156 | 0.1409 | 0.6156 | 0.7846 | | 0.0791 | 4.3431 | 1772 | 0.6485 | 0.2186 | 0.6485 | 0.8053 | | 0.0791 | 4.3480 | 1774 | 0.6657 | 0.2588 | 0.6657 | 0.8159 | | 0.0791 | 4.3529 | 1776 | 0.6797 | 0.2588 | 0.6797 | 0.8244 | | 0.0791 | 4.3578 | 1778 | 0.6674 | 0.2588 | 0.6674 | 0.8170 | | 0.0791 | 4.3627 | 1780 | 0.6352 | 0.2186 | 0.6352 | 0.7970 | | 0.0791 | 4.3676 | 1782 | 0.6177 | 0.1793 | 0.6177 | 0.7859 | | 0.0791 | 4.3725 | 1784 | 0.6099 | 0.2921 | 0.6099 | 0.7810 | | 0.0791 | 4.3775 | 1786 | 0.6069 | 0.3467 | 0.6069 | 0.7791 | | 0.0791 | 4.3824 | 1788 | 0.6065 | 0.2921 | 0.6065 | 0.7788 | | 0.0791 | 4.3873 | 1790 | 0.6111 | 0.3318 | 0.6111 | 0.7817 | | 0.0791 | 4.3922 | 1792 | 0.6225 | 0.2186 | 0.6225 | 0.7890 | | 0.0791 | 4.3971 | 1794 | 0.6230 | 0.2186 | 0.6230 | 0.7893 | | 0.0791 | 4.4020 | 1796 | 0.6145 | 0.2186 | 0.6145 | 0.7839 | | 0.0791 | 4.4069 | 1798 | 0.6213 | 0.2186 | 0.6213 | 0.7882 | | 0.0791 | 4.4118 | 1800 | 0.6351 | 0.2588 | 0.6351 | 0.7970 | | 0.0791 | 4.4167 | 1802 | 0.6360 | 0.2186 | 0.6360 | 0.7975 | | 0.0791 | 4.4216 | 1804 | 0.6447 | 0.2186 | 0.6447 | 0.8029 | | 0.0791 | 4.4265 | 1806 | 0.6423 | 0.2186 | 0.6423 | 0.8015 | | 0.0791 | 4.4314 | 1808 | 0.6185 | 0.3724 | 0.6185 | 0.7864 | | 0.0791 | 4.4363 | 1810 | 0.6105 | 0.3724 | 0.6105 | 0.7814 | | 0.0791 | 4.4412 | 1812 | 0.6064 | 0.3724 | 0.6064 | 0.7787 | | 0.0791 | 4.4461 | 1814 | 0.6078 | 0.3724 | 0.6078 | 0.7796 | | 0.0791 | 4.4510 | 1816 | 0.6086 | 0.3724 | 0.6086 | 0.7801 | | 0.0791 | 4.4559 | 1818 | 0.5950 | 0.3318 | 0.5950 | 0.7713 | | 0.0791 | 4.4608 | 1820 | 0.5914 | 0.1962 | 0.5914 | 0.7690 | | 0.0791 | 4.4657 | 1822 | 0.5938 | 0.1962 | 0.5938 | 0.7706 | | 0.0791 | 4.4706 | 1824 | 0.5969 | 0.1962 | 0.5969 | 0.7726 | | 0.0791 | 4.4755 | 1826 | 0.6043 | 0.1962 | 0.6043 | 0.7774 | | 0.0791 | 4.4804 | 1828 | 0.6081 | 0.1962 | 0.6081 | 0.7798 | | 0.0791 | 4.4853 | 1830 | 0.5997 | 0.1962 | 0.5997 | 0.7744 | | 0.0791 | 4.4902 | 1832 | 0.5905 | 0.1962 | 0.5905 | 0.7685 | | 0.0791 | 4.4951 | 1834 | 0.5845 | 0.2373 | 0.5845 | 0.7645 | | 0.0791 | 4.5 | 1836 | 0.5791 | 0.2373 | 0.5791 | 0.7610 | | 0.0791 | 4.5049 | 1838 | 0.5741 | 0.2373 | 0.5741 | 0.7577 | | 0.0791 | 4.5098 | 1840 | 0.5715 | 0.2373 | 0.5715 | 0.7560 | | 0.0791 | 4.5147 | 1842 | 0.5695 | 0.1765 | 0.5695 | 0.7547 | | 0.0791 | 4.5196 | 1844 | 0.5697 | 0.2184 | 0.5697 | 0.7548 | | 0.0791 | 4.5245 | 1846 | 0.5658 | 0.2184 | 0.5658 | 0.7522 | | 0.0791 | 4.5294 | 1848 | 0.5707 | 0.3724 | 0.5707 | 0.7555 | | 0.0791 | 4.5343 | 1850 | 0.5879 | 0.3724 | 0.5879 | 0.7667 | | 0.0791 | 4.5392 | 1852 | 0.6251 | 0.2186 | 0.6251 | 0.7907 | | 0.0791 | 4.5441 | 1854 | 0.6718 | 0.2186 | 0.6718 | 0.8197 | | 0.0791 | 4.5490 | 1856 | 0.6709 | 0.2186 | 0.6709 | 0.8191 | | 0.0791 | 4.5539 | 1858 | 0.6502 | 0.2186 | 0.6502 | 0.8064 | | 0.0791 | 4.5588 | 1860 | 0.6372 | 0.3724 | 0.6372 | 0.7982 | | 0.0791 | 4.5637 | 1862 | 0.6120 | 0.3467 | 0.6120 | 0.7823 | | 0.0791 | 4.5686 | 1864 | 0.5944 | 0.3077 | 0.5944 | 0.7710 | | 0.0791 | 4.5735 | 1866 | 0.5952 | 0.3077 | 0.5952 | 0.7715 | | 0.0791 | 4.5784 | 1868 | 0.6035 | 0.3077 | 0.6035 | 0.7769 | | 0.0791 | 4.5833 | 1870 | 0.6174 | 0.3077 | 0.6174 | 0.7857 | | 0.0791 | 4.5882 | 1872 | 0.6202 | 0.3077 | 0.6202 | 0.7875 | | 0.0791 | 4.5931 | 1874 | 0.6141 | 0.3077 | 0.6141 | 0.7836 | | 0.0791 | 4.5980 | 1876 | 0.6143 | 0.3077 | 0.6143 | 0.7838 | | 0.0791 | 4.6029 | 1878 | 0.6190 | 0.3077 | 0.6190 | 0.7868 | | 0.0791 | 4.6078 | 1880 | 0.6174 | 0.3609 | 0.6174 | 0.7858 | | 0.0791 | 4.6127 | 1882 | 0.6118 | 0.3609 | 0.6118 | 0.7822 | | 0.0791 | 4.6176 | 1884 | 0.5976 | 0.3077 | 0.5976 | 0.7731 | | 0.0791 | 4.6225 | 1886 | 0.5875 | 0.3609 | 0.5875 | 0.7665 | | 0.0791 | 4.6275 | 1888 | 0.5813 | 0.3609 | 0.5813 | 0.7624 | | 0.0791 | 4.6324 | 1890 | 0.5859 | 0.3609 | 0.5859 | 0.7655 | | 0.0791 | 4.6373 | 1892 | 0.5935 | 0.3787 | 0.5935 | 0.7704 | | 0.0791 | 4.6422 | 1894 | 0.5933 | 0.3609 | 0.5933 | 0.7703 | | 0.0791 | 4.6471 | 1896 | 0.5921 | 0.3609 | 0.5921 | 0.7695 | | 0.0791 | 4.6520 | 1898 | 0.5860 | 0.3077 | 0.5860 | 0.7655 | | 0.0791 | 4.6569 | 1900 | 0.5861 | 0.3467 | 0.5861 | 0.7655 | | 0.0791 | 4.6618 | 1902 | 0.5949 | 0.3865 | 0.5949 | 0.7713 | | 0.0791 | 4.6667 | 1904 | 0.5916 | 0.4273 | 0.5916 | 0.7692 | | 0.0791 | 4.6716 | 1906 | 0.5844 | 0.4273 | 0.5844 | 0.7645 | | 0.0791 | 4.6765 | 1908 | 0.5682 | 0.3163 | 0.5682 | 0.7538 | | 0.0791 | 4.6814 | 1910 | 0.5449 | 0.3163 | 0.5449 | 0.7382 | | 0.0791 | 4.6863 | 1912 | 0.5272 | 0.2759 | 0.5272 | 0.7261 | | 0.0791 | 4.6912 | 1914 | 0.5094 | 0.2364 | 0.5094 | 0.7137 | | 0.0791 | 4.6961 | 1916 | 0.4956 | 0.2794 | 0.4956 | 0.7040 | | 0.0791 | 4.7010 | 1918 | 0.4901 | 0.2794 | 0.4901 | 0.7001 | | 0.0791 | 4.7059 | 1920 | 0.4917 | 0.3390 | 0.4917 | 0.7012 | | 0.0791 | 4.7108 | 1922 | 0.4968 | 0.3390 | 0.4968 | 0.7048 | | 0.0791 | 4.7157 | 1924 | 0.5044 | 0.2967 | 0.5044 | 0.7102 | | 0.0791 | 4.7206 | 1926 | 0.5157 | 0.4400 | 0.5157 | 0.7181 | | 0.0791 | 4.7255 | 1928 | 0.5382 | 0.3865 | 0.5382 | 0.7336 | | 0.0791 | 4.7304 | 1930 | 0.5782 | 0.3318 | 0.5782 | 0.7604 | | 0.0791 | 4.7353 | 1932 | 0.6183 | 0.3318 | 0.6183 | 0.7863 | | 0.0791 | 4.7402 | 1934 | 0.6317 | 0.3318 | 0.6317 | 0.7948 | | 0.0791 | 4.7451 | 1936 | 0.6400 | 0.3318 | 0.6400 | 0.8000 | | 0.0791 | 4.75 | 1938 | 0.6419 | 0.3318 | 0.6419 | 0.8012 | | 0.0791 | 4.7549 | 1940 | 0.6341 | 0.3318 | 0.6341 | 0.7963 | | 0.0791 | 4.7598 | 1942 | 0.6109 | 0.3318 | 0.6109 | 0.7816 | | 0.0791 | 4.7647 | 1944 | 0.5942 | 0.3467 | 0.5942 | 0.7708 | | 0.0791 | 4.7696 | 1946 | 0.5860 | 0.3077 | 0.5860 | 0.7655 | | 0.0791 | 4.7745 | 1948 | 0.5697 | 0.3077 | 0.5697 | 0.7548 | | 0.0791 | 4.7794 | 1950 | 0.5595 | 0.3467 | 0.5595 | 0.7480 | | 0.0791 | 4.7843 | 1952 | 0.5565 | 0.3467 | 0.5565 | 0.7460 | | 0.0791 | 4.7892 | 1954 | 0.5562 | 0.3467 | 0.5562 | 0.7458 | | 0.0791 | 4.7941 | 1956 | 0.5550 | 0.3467 | 0.5550 | 0.7450 | | 0.0791 | 4.7990 | 1958 | 0.5606 | 0.3467 | 0.5606 | 0.7488 | | 0.0791 | 4.8039 | 1960 | 0.5687 | 0.3318 | 0.5687 | 0.7541 | | 0.0791 | 4.8088 | 1962 | 0.5724 | 0.1793 | 0.5724 | 0.7566 | | 0.0791 | 4.8137 | 1964 | 0.5674 | 0.3865 | 0.5674 | 0.7532 | | 0.0791 | 4.8186 | 1966 | 0.5649 | 0.3467 | 0.5649 | 0.7516 | | 0.0791 | 4.8235 | 1968 | 0.5609 | 0.2373 | 0.5609 | 0.7489 | | 0.0791 | 4.8284 | 1970 | 0.5599 | 0.1962 | 0.5599 | 0.7482 | | 0.0791 | 4.8333 | 1972 | 0.5604 | 0.4000 | 0.5604 | 0.7486 | | 0.0791 | 4.8382 | 1974 | 0.5587 | 0.3609 | 0.5587 | 0.7475 | | 0.0791 | 4.8431 | 1976 | 0.5640 | 0.4000 | 0.5640 | 0.7510 | | 0.0791 | 4.8480 | 1978 | 0.5731 | 0.3467 | 0.5731 | 0.7570 | | 0.0791 | 4.8529 | 1980 | 0.5777 | 0.3865 | 0.5777 | 0.7601 | | 0.0791 | 4.8578 | 1982 | 0.5758 | 0.3865 | 0.5758 | 0.7588 | | 0.0791 | 4.8627 | 1984 | 0.5747 | 0.2364 | 0.5747 | 0.7581 | | 0.0791 | 4.8676 | 1986 | 0.5802 | 0.2759 | 0.5802 | 0.7617 | | 0.0791 | 4.8725 | 1988 | 0.5758 | 0.2759 | 0.5758 | 0.7588 | | 0.0791 | 4.8775 | 1990 | 0.5640 | 0.2759 | 0.5640 | 0.7510 | | 0.0791 | 4.8824 | 1992 | 0.5500 | 0.2364 | 0.5500 | 0.7416 | | 0.0791 | 4.8873 | 1994 | 0.5369 | 0.3865 | 0.5369 | 0.7327 | | 0.0791 | 4.8922 | 1996 | 0.5348 | 0.3865 | 0.5348 | 0.7313 | | 0.0791 | 4.8971 | 1998 | 0.5365 | 0.3865 | 0.5365 | 0.7325 | | 0.0614 | 4.9020 | 2000 | 0.5377 | 0.3467 | 0.5377 | 0.7333 | | 0.0614 | 4.9069 | 2002 | 0.5395 | 0.4000 | 0.5395 | 0.7345 | | 0.0614 | 4.9118 | 2004 | 0.5483 | 0.3609 | 0.5483 | 0.7405 | | 0.0614 | 4.9167 | 2006 | 0.5586 | 0.3609 | 0.5586 | 0.7474 | | 0.0614 | 4.9216 | 2008 | 0.5619 | 0.3609 | 0.5619 | 0.7496 | | 0.0614 | 4.9265 | 2010 | 0.5632 | 0.3609 | 0.5632 | 0.7504 | | 0.0614 | 4.9314 | 2012 | 0.5689 | 0.4000 | 0.5689 | 0.7542 | | 0.0614 | 4.9363 | 2014 | 0.5818 | 0.2364 | 0.5818 | 0.7628 | | 0.0614 | 4.9412 | 2016 | 0.6181 | 0.3163 | 0.6181 | 0.7862 | | 0.0614 | 4.9461 | 2018 | 0.6791 | 0.3163 | 0.6791 | 0.8241 | | 0.0614 | 4.9510 | 2020 | 0.7314 | 0.4154 | 0.7314 | 0.8552 | | 0.0614 | 4.9559 | 2022 | 0.7389 | 0.3636 | 0.7389 | 0.8596 | | 0.0614 | 4.9608 | 2024 | 0.7179 | 0.2588 | 0.7179 | 0.8473 | | 0.0614 | 4.9657 | 2026 | 0.6848 | 0.2588 | 0.6848 | 0.8275 | | 0.0614 | 4.9706 | 2028 | 0.6596 | 0.2588 | 0.6596 | 0.8122 | | 0.0614 | 4.9755 | 2030 | 0.6251 | 0.3163 | 0.6251 | 0.7906 | | 0.0614 | 4.9804 | 2032 | 0.6034 | 0.3163 | 0.6034 | 0.7768 | | 0.0614 | 4.9853 | 2034 | 0.5772 | 0.3163 | 0.5772 | 0.7597 | | 0.0614 | 4.9902 | 2036 | 0.5650 | 0.3163 | 0.5650 | 0.7517 | | 0.0614 | 4.9951 | 2038 | 0.5640 | 0.2759 | 0.5640 | 0.7510 | | 0.0614 | 5.0 | 2040 | 0.5614 | 0.2759 | 0.5614 | 0.7493 | | 0.0614 | 5.0049 | 2042 | 0.5694 | 0.2759 | 0.5694 | 0.7546 | | 0.0614 | 5.0098 | 2044 | 0.5940 | 0.3163 | 0.5940 | 0.7707 | | 0.0614 | 5.0147 | 2046 | 0.6169 | 0.2588 | 0.6169 | 0.7854 | | 0.0614 | 5.0196 | 2048 | 0.6261 | 0.2588 | 0.6261 | 0.7913 | | 0.0614 | 5.0245 | 2050 | 0.6128 | 0.3163 | 0.6128 | 0.7828 | | 0.0614 | 5.0294 | 2052 | 0.6006 | 0.3163 | 0.6006 | 0.7750 | | 0.0614 | 5.0343 | 2054 | 0.5880 | 0.2759 | 0.5880 | 0.7668 | | 0.0614 | 5.0392 | 2056 | 0.5835 | 0.2759 | 0.5835 | 0.7639 | | 0.0614 | 5.0441 | 2058 | 0.5798 | 0.2759 | 0.5798 | 0.7614 | | 0.0614 | 5.0490 | 2060 | 0.5838 | 0.2759 | 0.5838 | 0.7640 | | 0.0614 | 5.0539 | 2062 | 0.5912 | 0.2759 | 0.5912 | 0.7689 | | 0.0614 | 5.0588 | 2064 | 0.5955 | 0.2759 | 0.5955 | 0.7717 | | 0.0614 | 5.0637 | 2066 | 0.6004 | 0.2759 | 0.6004 | 0.7749 | | 0.0614 | 5.0686 | 2068 | 0.5992 | 0.0916 | 0.5992 | 0.7741 | | 0.0614 | 5.0735 | 2070 | 0.5929 | 0.1558 | 0.5929 | 0.7700 | | 0.0614 | 5.0784 | 2072 | 0.5890 | 0.1558 | 0.5890 | 0.7675 | | 0.0614 | 5.0833 | 2074 | 0.5808 | 0.1141 | 0.5808 | 0.7621 | | 0.0614 | 5.0882 | 2076 | 0.5731 | 0.1141 | 0.5731 | 0.7570 | | 0.0614 | 5.0931 | 2078 | 0.5785 | 0.1141 | 0.5785 | 0.7606 | | 0.0614 | 5.0980 | 2080 | 0.5892 | 0.1141 | 0.5892 | 0.7676 | | 0.0614 | 5.1029 | 2082 | 0.5970 | 0.1141 | 0.5970 | 0.7727 | | 0.0614 | 5.1078 | 2084 | 0.6177 | 0.1793 | 0.6177 | 0.7859 | | 0.0614 | 5.1127 | 2086 | 0.6396 | 0.2186 | 0.6396 | 0.7998 | | 0.0614 | 5.1176 | 2088 | 0.6584 | 0.2186 | 0.6584 | 0.8114 | | 0.0614 | 5.1225 | 2090 | 0.6824 | 0.2186 | 0.6824 | 0.8261 | | 0.0614 | 5.1275 | 2092 | 0.6983 | 0.2186 | 0.6983 | 0.8357 | | 0.0614 | 5.1324 | 2094 | 0.7067 | 0.2186 | 0.7067 | 0.8407 | | 0.0614 | 5.1373 | 2096 | 0.7107 | 0.2186 | 0.7107 | 0.8430 | | 0.0614 | 5.1422 | 2098 | 0.6967 | 0.2186 | 0.6967 | 0.8347 | | 0.0614 | 5.1471 | 2100 | 0.6927 | 0.2186 | 0.6927 | 0.8323 | | 0.0614 | 5.1520 | 2102 | 0.6830 | 0.2186 | 0.6830 | 0.8265 | | 0.0614 | 5.1569 | 2104 | 0.6678 | 0.2186 | 0.6678 | 0.8172 | | 0.0614 | 5.1618 | 2106 | 0.6531 | 0.2186 | 0.6531 | 0.8081 | | 0.0614 | 5.1667 | 2108 | 0.6331 | 0.2186 | 0.6331 | 0.7957 | | 0.0614 | 5.1716 | 2110 | 0.6273 | 0.2186 | 0.6273 | 0.7920 | | 0.0614 | 5.1765 | 2112 | 0.6299 | 0.2186 | 0.6299 | 0.7936 | | 0.0614 | 5.1814 | 2114 | 0.6357 | 0.2186 | 0.6357 | 0.7973 | | 0.0614 | 5.1863 | 2116 | 0.6402 | 0.2186 | 0.6402 | 0.8001 | | 0.0614 | 5.1912 | 2118 | 0.6259 | 0.2186 | 0.6259 | 0.7912 | | 0.0614 | 5.1961 | 2120 | 0.6253 | 0.2186 | 0.6253 | 0.7908 | | 0.0614 | 5.2010 | 2122 | 0.6312 | 0.2588 | 0.6312 | 0.7945 | | 0.0614 | 5.2059 | 2124 | 0.6262 | 0.2588 | 0.6262 | 0.7913 | | 0.0614 | 5.2108 | 2126 | 0.6246 | 0.2588 | 0.6246 | 0.7903 | | 0.0614 | 5.2157 | 2128 | 0.6368 | 0.2588 | 0.6368 | 0.7980 | | 0.0614 | 5.2206 | 2130 | 0.6516 | 0.2588 | 0.6516 | 0.8072 | | 0.0614 | 5.2255 | 2132 | 0.6769 | 0.2588 | 0.6769 | 0.8227 | | 0.0614 | 5.2304 | 2134 | 0.6758 | 0.2588 | 0.6758 | 0.8221 | | 0.0614 | 5.2353 | 2136 | 0.6577 | 0.2588 | 0.6577 | 0.8110 | | 0.0614 | 5.2402 | 2138 | 0.6295 | 0.2186 | 0.6295 | 0.7934 | | 0.0614 | 5.2451 | 2140 | 0.6117 | 0.2186 | 0.6117 | 0.7821 | | 0.0614 | 5.25 | 2142 | 0.6120 | 0.2186 | 0.6120 | 0.7823 | | 0.0614 | 5.2549 | 2144 | 0.6369 | 0.2588 | 0.6369 | 0.7980 | | 0.0614 | 5.2598 | 2146 | 0.6788 | 0.2588 | 0.6788 | 0.8239 | | 0.0614 | 5.2647 | 2148 | 0.7103 | 0.3636 | 0.7103 | 0.8428 | | 0.0614 | 5.2696 | 2150 | 0.7214 | 0.3636 | 0.7214 | 0.8494 | | 0.0614 | 5.2745 | 2152 | 0.6943 | 0.3636 | 0.6943 | 0.8332 | | 0.0614 | 5.2794 | 2154 | 0.6540 | 0.2588 | 0.6540 | 0.8087 | | 0.0614 | 5.2843 | 2156 | 0.6182 | 0.2588 | 0.6182 | 0.7863 | | 0.0614 | 5.2892 | 2158 | 0.5937 | 0.2186 | 0.5937 | 0.7705 | | 0.0614 | 5.2941 | 2160 | 0.5796 | 0.2186 | 0.5796 | 0.7613 | | 0.0614 | 5.2990 | 2162 | 0.5847 | 0.2186 | 0.5847 | 0.7646 | | 0.0614 | 5.3039 | 2164 | 0.6082 | 0.2186 | 0.6082 | 0.7799 | | 0.0614 | 5.3088 | 2166 | 0.6444 | 0.2186 | 0.6444 | 0.8027 | | 0.0614 | 5.3137 | 2168 | 0.6782 | 0.3255 | 0.6782 | 0.8235 | | 0.0614 | 5.3186 | 2170 | 0.6920 | 0.3255 | 0.6920 | 0.8319 | | 0.0614 | 5.3235 | 2172 | 0.7042 | 0.3255 | 0.7042 | 0.8392 | | 0.0614 | 5.3284 | 2174 | 0.7288 | 0.3255 | 0.7288 | 0.8537 | | 0.0614 | 5.3333 | 2176 | 0.7344 | 0.3255 | 0.7344 | 0.8570 | | 0.0614 | 5.3382 | 2178 | 0.7464 | 0.3255 | 0.7464 | 0.8640 | | 0.0614 | 5.3431 | 2180 | 0.7523 | 0.3255 | 0.7523 | 0.8673 | | 0.0614 | 5.3480 | 2182 | 0.7357 | 0.3255 | 0.7357 | 0.8577 | | 0.0614 | 5.3529 | 2184 | 0.7328 | 0.3255 | 0.7328 | 0.8560 | | 0.0614 | 5.3578 | 2186 | 0.7272 | 0.2186 | 0.7272 | 0.8527 | | 0.0614 | 5.3627 | 2188 | 0.7104 | 0.2186 | 0.7104 | 0.8429 | | 0.0614 | 5.3676 | 2190 | 0.6987 | 0.1793 | 0.6987 | 0.8359 | | 0.0614 | 5.3725 | 2192 | 0.6896 | 0.1793 | 0.6896 | 0.8304 | | 0.0614 | 5.3775 | 2194 | 0.6858 | 0.1793 | 0.6858 | 0.8282 | | 0.0614 | 5.3824 | 2196 | 0.6823 | 0.1793 | 0.6823 | 0.8260 | | 0.0614 | 5.3873 | 2198 | 0.6700 | 0.1793 | 0.6700 | 0.8185 | | 0.0614 | 5.3922 | 2200 | 0.6680 | 0.1793 | 0.6680 | 0.8173 | | 0.0614 | 5.3971 | 2202 | 0.6586 | 0.1793 | 0.6586 | 0.8115 | | 0.0614 | 5.4020 | 2204 | 0.6395 | 0.1793 | 0.6395 | 0.7997 | | 0.0614 | 5.4069 | 2206 | 0.6233 | 0.1793 | 0.6233 | 0.7895 | | 0.0614 | 5.4118 | 2208 | 0.6176 | 0.1793 | 0.6176 | 0.7859 | | 0.0614 | 5.4167 | 2210 | 0.6240 | 0.1793 | 0.6240 | 0.7899 | | 0.0614 | 5.4216 | 2212 | 0.6289 | 0.1793 | 0.6289 | 0.7931 | | 0.0614 | 5.4265 | 2214 | 0.6382 | 0.2186 | 0.6382 | 0.7989 | | 0.0614 | 5.4314 | 2216 | 0.6462 | 0.2186 | 0.6462 | 0.8039 | | 0.0614 | 5.4363 | 2218 | 0.6551 | 0.2186 | 0.6551 | 0.8094 | | 0.0614 | 5.4412 | 2220 | 0.6627 | 0.2186 | 0.6627 | 0.8141 | | 0.0614 | 5.4461 | 2222 | 0.6976 | 0.2186 | 0.6976 | 0.8352 | | 0.0614 | 5.4510 | 2224 | 0.7623 | 0.3636 | 0.7623 | 0.8731 | | 0.0614 | 5.4559 | 2226 | 0.7952 | 0.3687 | 0.7952 | 0.8917 | | 0.0614 | 5.4608 | 2228 | 0.7802 | 0.3255 | 0.7802 | 0.8833 | | 0.0614 | 5.4657 | 2230 | 0.7324 | 0.2186 | 0.7324 | 0.8558 | | 0.0614 | 5.4706 | 2232 | 0.6900 | 0.2186 | 0.6900 | 0.8307 | | 0.0614 | 5.4755 | 2234 | 0.6697 | 0.2186 | 0.6697 | 0.8184 | | 0.0614 | 5.4804 | 2236 | 0.6738 | 0.2186 | 0.6738 | 0.8209 | | 0.0614 | 5.4853 | 2238 | 0.6660 | 0.2186 | 0.6660 | 0.8161 | | 0.0614 | 5.4902 | 2240 | 0.6609 | 0.2186 | 0.6609 | 0.8130 | | 0.0614 | 5.4951 | 2242 | 0.6573 | 0.2186 | 0.6573 | 0.8107 | | 0.0614 | 5.5 | 2244 | 0.6559 | 0.2186 | 0.6559 | 0.8099 | | 0.0614 | 5.5049 | 2246 | 0.6564 | 0.2186 | 0.6564 | 0.8102 | | 0.0614 | 5.5098 | 2248 | 0.6626 | 0.2186 | 0.6626 | 0.8140 | | 0.0614 | 5.5147 | 2250 | 0.6437 | 0.2186 | 0.6437 | 0.8023 | | 0.0614 | 5.5196 | 2252 | 0.6196 | 0.2186 | 0.6196 | 0.7871 | | 0.0614 | 5.5245 | 2254 | 0.6034 | 0.3724 | 0.6034 | 0.7768 | | 0.0614 | 5.5294 | 2256 | 0.6037 | 0.3724 | 0.6037 | 0.7770 | | 0.0614 | 5.5343 | 2258 | 0.6203 | 0.2186 | 0.6203 | 0.7876 | | 0.0614 | 5.5392 | 2260 | 0.6469 | 0.2186 | 0.6469 | 0.8043 | | 0.0614 | 5.5441 | 2262 | 0.6694 | 0.2186 | 0.6694 | 0.8182 | | 0.0614 | 5.5490 | 2264 | 0.6919 | 0.2186 | 0.6919 | 0.8318 | | 0.0614 | 5.5539 | 2266 | 0.6978 | 0.2186 | 0.6978 | 0.8354 | | 0.0614 | 5.5588 | 2268 | 0.6986 | 0.2186 | 0.6986 | 0.8358 | | 0.0614 | 5.5637 | 2270 | 0.6926 | 0.2186 | 0.6926 | 0.8323 | | 0.0614 | 5.5686 | 2272 | 0.6569 | 0.2186 | 0.6569 | 0.8105 | | 0.0614 | 5.5735 | 2274 | 0.6151 | 0.2186 | 0.6151 | 0.7843 | | 0.0614 | 5.5784 | 2276 | 0.5745 | 0.3318 | 0.5745 | 0.7580 | | 0.0614 | 5.5833 | 2278 | 0.5459 | 0.3467 | 0.5459 | 0.7388 | | 0.0614 | 5.5882 | 2280 | 0.5401 | 0.3467 | 0.5401 | 0.7349 | | 0.0614 | 5.5931 | 2282 | 0.5416 | 0.3467 | 0.5416 | 0.7359 | | 0.0614 | 5.5980 | 2284 | 0.5397 | 0.3467 | 0.5397 | 0.7347 | | 0.0614 | 5.6029 | 2286 | 0.5499 | 0.3865 | 0.5499 | 0.7416 | | 0.0614 | 5.6078 | 2288 | 0.5791 | 0.3724 | 0.5791 | 0.7610 | | 0.0614 | 5.6127 | 2290 | 0.6159 | 0.3724 | 0.6159 | 0.7848 | | 0.0614 | 5.6176 | 2292 | 0.6454 | 0.3255 | 0.6454 | 0.8033 | | 0.0614 | 5.6225 | 2294 | 0.6487 | 0.3255 | 0.6487 | 0.8054 | | 0.0614 | 5.6275 | 2296 | 0.6381 | 0.2186 | 0.6381 | 0.7988 | | 0.0614 | 5.6324 | 2298 | 0.6163 | 0.2186 | 0.6163 | 0.7851 | | 0.0614 | 5.6373 | 2300 | 0.5910 | 0.3724 | 0.5910 | 0.7688 | | 0.0614 | 5.6422 | 2302 | 0.5704 | 0.3724 | 0.5704 | 0.7552 | | 0.0614 | 5.6471 | 2304 | 0.5536 | 0.4273 | 0.5536 | 0.7440 | | 0.0614 | 5.6520 | 2306 | 0.5563 | 0.3865 | 0.5563 | 0.7458 | | 0.0614 | 5.6569 | 2308 | 0.5666 | 0.3865 | 0.5666 | 0.7527 | | 0.0614 | 5.6618 | 2310 | 0.5782 | 0.4273 | 0.5782 | 0.7604 | | 0.0614 | 5.6667 | 2312 | 0.5814 | 0.4273 | 0.5814 | 0.7625 | | 0.0614 | 5.6716 | 2314 | 0.5907 | 0.4273 | 0.5907 | 0.7686 | | 0.0614 | 5.6765 | 2316 | 0.5889 | 0.3865 | 0.5889 | 0.7674 | | 0.0614 | 5.6814 | 2318 | 0.5885 | 0.4273 | 0.5885 | 0.7672 | | 0.0614 | 5.6863 | 2320 | 0.5983 | 0.3724 | 0.5983 | 0.7735 | | 0.0614 | 5.6912 | 2322 | 0.6071 | 0.3724 | 0.6071 | 0.7791 | | 0.0614 | 5.6961 | 2324 | 0.6186 | 0.3724 | 0.6186 | 0.7865 | | 0.0614 | 5.7010 | 2326 | 0.6283 | 0.3724 | 0.6283 | 0.7927 | | 0.0614 | 5.7059 | 2328 | 0.6276 | 0.3724 | 0.6276 | 0.7922 | | 0.0614 | 5.7108 | 2330 | 0.6149 | 0.3724 | 0.6149 | 0.7842 | | 0.0614 | 5.7157 | 2332 | 0.6086 | 0.3724 | 0.6086 | 0.7801 | | 0.0614 | 5.7206 | 2334 | 0.6033 | 0.3724 | 0.6033 | 0.7767 | | 0.0614 | 5.7255 | 2336 | 0.5917 | 0.3724 | 0.5917 | 0.7692 | | 0.0614 | 5.7304 | 2338 | 0.5824 | 0.3318 | 0.5824 | 0.7632 | | 0.0614 | 5.7353 | 2340 | 0.5792 | 0.3318 | 0.5792 | 0.7610 | | 0.0614 | 5.7402 | 2342 | 0.5761 | 0.3318 | 0.5761 | 0.7590 | | 0.0614 | 5.7451 | 2344 | 0.5759 | 0.2533 | 0.5759 | 0.7589 | | 0.0614 | 5.75 | 2346 | 0.5735 | 0.3318 | 0.5735 | 0.7573 | | 0.0614 | 5.7549 | 2348 | 0.5756 | 0.3724 | 0.5756 | 0.7587 | | 0.0614 | 5.7598 | 2350 | 0.5897 | 0.3724 | 0.5897 | 0.7679 | | 0.0614 | 5.7647 | 2352 | 0.6112 | 0.3724 | 0.6112 | 0.7818 | | 0.0614 | 5.7696 | 2354 | 0.6272 | 0.3724 | 0.6272 | 0.7919 | | 0.0614 | 5.7745 | 2356 | 0.6303 | 0.3724 | 0.6303 | 0.7939 | | 0.0614 | 5.7794 | 2358 | 0.6222 | 0.3724 | 0.6222 | 0.7888 | | 0.0614 | 5.7843 | 2360 | 0.6133 | 0.3724 | 0.6133 | 0.7831 | | 0.0614 | 5.7892 | 2362 | 0.6069 | 0.3724 | 0.6069 | 0.7790 | | 0.0614 | 5.7941 | 2364 | 0.6120 | 0.3724 | 0.6120 | 0.7823 | | 0.0614 | 5.7990 | 2366 | 0.6179 | 0.3724 | 0.6179 | 0.7860 | | 0.0614 | 5.8039 | 2368 | 0.6327 | 0.4140 | 0.6327 | 0.7954 | | 0.0614 | 5.8088 | 2370 | 0.6337 | 0.2588 | 0.6337 | 0.7960 | | 0.0614 | 5.8137 | 2372 | 0.6242 | 0.4140 | 0.6242 | 0.7901 | | 0.0614 | 5.8186 | 2374 | 0.6180 | 0.4140 | 0.6180 | 0.7861 | | 0.0614 | 5.8235 | 2376 | 0.6003 | 0.3724 | 0.6003 | 0.7748 | | 0.0614 | 5.8284 | 2378 | 0.5881 | 0.3724 | 0.5881 | 0.7669 | | 0.0614 | 5.8333 | 2380 | 0.5781 | 0.3724 | 0.5781 | 0.7603 | | 0.0614 | 5.8382 | 2382 | 0.5773 | 0.3724 | 0.5773 | 0.7598 | | 0.0614 | 5.8431 | 2384 | 0.5765 | 0.3865 | 0.5765 | 0.7593 | | 0.0614 | 5.8480 | 2386 | 0.5628 | 0.3865 | 0.5628 | 0.7502 | | 0.0614 | 5.8529 | 2388 | 0.5582 | 0.3865 | 0.5582 | 0.7471 | | 0.0614 | 5.8578 | 2390 | 0.5647 | 0.3865 | 0.5647 | 0.7514 | | 0.0614 | 5.8627 | 2392 | 0.5633 | 0.3865 | 0.5633 | 0.7505 | | 0.0614 | 5.8676 | 2394 | 0.5591 | 0.4400 | 0.5591 | 0.7477 | | 0.0614 | 5.8725 | 2396 | 0.5588 | 0.4400 | 0.5588 | 0.7475 | | 0.0614 | 5.8775 | 2398 | 0.5587 | 0.4400 | 0.5587 | 0.7475 | | 0.0614 | 5.8824 | 2400 | 0.5598 | 0.3318 | 0.5598 | 0.7482 | | 0.0614 | 5.8873 | 2402 | 0.5681 | 0.3724 | 0.5681 | 0.7537 | | 0.0614 | 5.8922 | 2404 | 0.5759 | 0.3724 | 0.5759 | 0.7589 | | 0.0614 | 5.8971 | 2406 | 0.6007 | 0.4140 | 0.6007 | 0.7750 | | 0.0614 | 5.9020 | 2408 | 0.6132 | 0.2588 | 0.6132 | 0.7831 | | 0.0614 | 5.9069 | 2410 | 0.6169 | 0.3636 | 0.6169 | 0.7854 | | 0.0614 | 5.9118 | 2412 | 0.6200 | 0.3636 | 0.6200 | 0.7874 | | 0.0614 | 5.9167 | 2414 | 0.6188 | 0.4661 | 0.6188 | 0.7866 | | 0.0614 | 5.9216 | 2416 | 0.6097 | 0.4661 | 0.6097 | 0.7808 | | 0.0614 | 5.9265 | 2418 | 0.6117 | 0.4661 | 0.6117 | 0.7821 | | 0.0614 | 5.9314 | 2420 | 0.6331 | 0.4661 | 0.6331 | 0.7957 | | 0.0614 | 5.9363 | 2422 | 0.6584 | 0.4661 | 0.6584 | 0.8114 | | 0.0614 | 5.9412 | 2424 | 0.6692 | 0.3255 | 0.6692 | 0.8180 | | 0.0614 | 5.9461 | 2426 | 0.6618 | 0.3255 | 0.6618 | 0.8135 | | 0.0614 | 5.9510 | 2428 | 0.6336 | 0.4661 | 0.6336 | 0.7960 | | 0.0614 | 5.9559 | 2430 | 0.6206 | 0.4661 | 0.6206 | 0.7878 | | 0.0614 | 5.9608 | 2432 | 0.6073 | 0.4661 | 0.6073 | 0.7793 | | 0.0614 | 5.9657 | 2434 | 0.5952 | 0.4661 | 0.5952 | 0.7715 | | 0.0614 | 5.9706 | 2436 | 0.6036 | 0.3255 | 0.6036 | 0.7769 | | 0.0614 | 5.9755 | 2438 | 0.6183 | 0.3636 | 0.6183 | 0.7863 | | 0.0614 | 5.9804 | 2440 | 0.6499 | 0.3636 | 0.6499 | 0.8062 | | 0.0614 | 5.9853 | 2442 | 0.6780 | 0.3636 | 0.6780 | 0.8234 | | 0.0614 | 5.9902 | 2444 | 0.7000 | 0.3636 | 0.7000 | 0.8367 | | 0.0614 | 5.9951 | 2446 | 0.7083 | 0.3636 | 0.7083 | 0.8416 | | 0.0614 | 6.0 | 2448 | 0.7052 | 0.3636 | 0.7052 | 0.8397 | | 0.0614 | 6.0049 | 2450 | 0.7019 | 0.3636 | 0.7019 | 0.8378 | | 0.0614 | 6.0098 | 2452 | 0.6930 | 0.3636 | 0.6930 | 0.8324 | | 0.0614 | 6.0147 | 2454 | 0.6944 | 0.3636 | 0.6944 | 0.8333 | | 0.0614 | 6.0196 | 2456 | 0.7027 | 0.3636 | 0.7027 | 0.8383 | | 0.0614 | 6.0245 | 2458 | 0.6920 | 0.3636 | 0.6920 | 0.8319 | | 0.0614 | 6.0294 | 2460 | 0.6733 | 0.2588 | 0.6733 | 0.8205 | | 0.0614 | 6.0343 | 2462 | 0.6524 | 0.2588 | 0.6524 | 0.8077 | | 0.0614 | 6.0392 | 2464 | 0.6511 | 0.2588 | 0.6511 | 0.8069 | | 0.0614 | 6.0441 | 2466 | 0.6484 | 0.2588 | 0.6484 | 0.8052 | | 0.0614 | 6.0490 | 2468 | 0.6634 | 0.3636 | 0.6634 | 0.8145 | | 0.0614 | 6.0539 | 2470 | 0.6881 | 0.3636 | 0.6881 | 0.8295 | | 0.0614 | 6.0588 | 2472 | 0.6930 | 0.3636 | 0.6930 | 0.8325 | | 0.0614 | 6.0637 | 2474 | 0.6936 | 0.3636 | 0.6936 | 0.8328 | | 0.0614 | 6.0686 | 2476 | 0.6729 | 0.3636 | 0.6729 | 0.8203 | | 0.0614 | 6.0735 | 2478 | 0.6464 | 0.3636 | 0.6464 | 0.8040 | | 0.0614 | 6.0784 | 2480 | 0.6377 | 0.3636 | 0.6377 | 0.7986 | | 0.0614 | 6.0833 | 2482 | 0.6405 | 0.3636 | 0.6405 | 0.8003 | | 0.0614 | 6.0882 | 2484 | 0.6337 | 0.3255 | 0.6337 | 0.7961 | | 0.0614 | 6.0931 | 2486 | 0.6346 | 0.3255 | 0.6346 | 0.7966 | | 0.0614 | 6.0980 | 2488 | 0.6329 | 0.3255 | 0.6329 | 0.7955 | | 0.0614 | 6.1029 | 2490 | 0.6558 | 0.3255 | 0.6558 | 0.8098 | | 0.0614 | 6.1078 | 2492 | 0.6809 | 0.3636 | 0.6809 | 0.8252 | | 0.0614 | 6.1127 | 2494 | 0.7015 | 0.3636 | 0.7015 | 0.8376 | | 0.0614 | 6.1176 | 2496 | 0.6841 | 0.3636 | 0.6841 | 0.8271 | | 0.0614 | 6.1225 | 2498 | 0.6441 | 0.3255 | 0.6441 | 0.8026 | | 0.0484 | 6.1275 | 2500 | 0.6127 | 0.2186 | 0.6127 | 0.7828 | | 0.0484 | 6.1324 | 2502 | 0.6060 | 0.2186 | 0.6060 | 0.7785 | | 0.0484 | 6.1373 | 2504 | 0.6087 | 0.2186 | 0.6087 | 0.7802 | | 0.0484 | 6.1422 | 2506 | 0.6224 | 0.2588 | 0.6224 | 0.7889 | | 0.0484 | 6.1471 | 2508 | 0.6370 | 0.2588 | 0.6370 | 0.7981 | | 0.0484 | 6.1520 | 2510 | 0.6629 | 0.3636 | 0.6629 | 0.8142 | | 0.0484 | 6.1569 | 2512 | 0.6811 | 0.3636 | 0.6811 | 0.8253 | | 0.0484 | 6.1618 | 2514 | 0.7128 | 0.3636 | 0.7128 | 0.8442 | | 0.0484 | 6.1667 | 2516 | 0.7161 | 0.3636 | 0.7161 | 0.8462 | | 0.0484 | 6.1716 | 2518 | 0.6999 | 0.3636 | 0.6999 | 0.8366 | | 0.0484 | 6.1765 | 2520 | 0.6761 | 0.3636 | 0.6761 | 0.8223 | | 0.0484 | 6.1814 | 2522 | 0.6490 | 0.2588 | 0.6490 | 0.8056 | | 0.0484 | 6.1863 | 2524 | 0.6282 | 0.2588 | 0.6282 | 0.7926 | | 0.0484 | 6.1912 | 2526 | 0.6187 | 0.2588 | 0.6187 | 0.7866 | | 0.0484 | 6.1961 | 2528 | 0.6129 | 0.2588 | 0.6129 | 0.7829 | | 0.0484 | 6.2010 | 2530 | 0.6103 | 0.2588 | 0.6103 | 0.7812 | | 0.0484 | 6.2059 | 2532 | 0.6198 | 0.2588 | 0.6198 | 0.7873 | | 0.0484 | 6.2108 | 2534 | 0.6412 | 0.2588 | 0.6412 | 0.8008 | | 0.0484 | 6.2157 | 2536 | 0.6727 | 0.3636 | 0.6727 | 0.8202 | | 0.0484 | 6.2206 | 2538 | 0.6996 | 0.3636 | 0.6996 | 0.8364 | | 0.0484 | 6.2255 | 2540 | 0.7080 | 0.3636 | 0.7080 | 0.8414 | | 0.0484 | 6.2304 | 2542 | 0.7206 | 0.3636 | 0.7206 | 0.8489 | | 0.0484 | 6.2353 | 2544 | 0.7063 | 0.3636 | 0.7063 | 0.8404 | | 0.0484 | 6.2402 | 2546 | 0.6860 | 0.3636 | 0.6860 | 0.8283 | | 0.0484 | 6.2451 | 2548 | 0.6647 | 0.3255 | 0.6647 | 0.8153 | | 0.0484 | 6.25 | 2550 | 0.6601 | 0.3255 | 0.6601 | 0.8125 | | 0.0484 | 6.2549 | 2552 | 0.6519 | 0.3255 | 0.6519 | 0.8074 | | 0.0484 | 6.2598 | 2554 | 0.6432 | 0.3724 | 0.6432 | 0.8020 | | 0.0484 | 6.2647 | 2556 | 0.6465 | 0.3724 | 0.6465 | 0.8040 | | 0.0484 | 6.2696 | 2558 | 0.6502 | 0.3318 | 0.6502 | 0.8064 | | 0.0484 | 6.2745 | 2560 | 0.6516 | 0.2533 | 0.6516 | 0.8072 | | 0.0484 | 6.2794 | 2562 | 0.6555 | 0.2154 | 0.6555 | 0.8096 | | 0.0484 | 6.2843 | 2564 | 0.6617 | 0.2154 | 0.6617 | 0.8134 | | 0.0484 | 6.2892 | 2566 | 0.6590 | 0.2154 | 0.6590 | 0.8118 | | 0.0484 | 6.2941 | 2568 | 0.6497 | 0.2154 | 0.6497 | 0.8061 | | 0.0484 | 6.2990 | 2570 | 0.6417 | 0.2533 | 0.6417 | 0.8011 | | 0.0484 | 6.3039 | 2572 | 0.6340 | 0.3318 | 0.6340 | 0.7962 | | 0.0484 | 6.3088 | 2574 | 0.6232 | 0.3724 | 0.6232 | 0.7895 | | 0.0484 | 6.3137 | 2576 | 0.6147 | 0.3724 | 0.6147 | 0.7841 | | 0.0484 | 6.3186 | 2578 | 0.6080 | 0.3724 | 0.6080 | 0.7797 | | 0.0484 | 6.3235 | 2580 | 0.6087 | 0.3724 | 0.6087 | 0.7802 | | 0.0484 | 6.3284 | 2582 | 0.6127 | 0.3724 | 0.6127 | 0.7827 | | 0.0484 | 6.3333 | 2584 | 0.6291 | 0.2186 | 0.6291 | 0.7932 | | 0.0484 | 6.3382 | 2586 | 0.6663 | 0.3255 | 0.6663 | 0.8163 | | 0.0484 | 6.3431 | 2588 | 0.7074 | 0.3255 | 0.7074 | 0.8411 | | 0.0484 | 6.3480 | 2590 | 0.7460 | 0.3636 | 0.7460 | 0.8637 | | 0.0484 | 6.3529 | 2592 | 0.7678 | 0.3636 | 0.7678 | 0.8762 | | 0.0484 | 6.3578 | 2594 | 0.7978 | 0.2948 | 0.7978 | 0.8932 | | 0.0484 | 6.3627 | 2596 | 0.8107 | 0.2948 | 0.8107 | 0.9004 | | 0.0484 | 6.3676 | 2598 | 0.7841 | 0.2948 | 0.7841 | 0.8855 | | 0.0484 | 6.3725 | 2600 | 0.7430 | 0.3636 | 0.7430 | 0.8620 | | 0.0484 | 6.3775 | 2602 | 0.6871 | 0.3255 | 0.6871 | 0.8289 | | 0.0484 | 6.3824 | 2604 | 0.6371 | 0.2186 | 0.6371 | 0.7982 | | 0.0484 | 6.3873 | 2606 | 0.6100 | 0.2186 | 0.6100 | 0.7810 | | 0.0484 | 6.3922 | 2608 | 0.5906 | 0.3724 | 0.5906 | 0.7685 | | 0.0484 | 6.3971 | 2610 | 0.5793 | 0.2794 | 0.5793 | 0.7611 | | 0.0484 | 6.4020 | 2612 | 0.5736 | 0.2794 | 0.5736 | 0.7573 | | 0.0484 | 6.4069 | 2614 | 0.5737 | 0.2794 | 0.5737 | 0.7574 | | 0.0484 | 6.4118 | 2616 | 0.5758 | 0.2184 | 0.5758 | 0.7588 | | 0.0484 | 6.4167 | 2618 | 0.5824 | 0.0916 | 0.5824 | 0.7631 | | 0.0484 | 6.4216 | 2620 | 0.6002 | 0.2186 | 0.6002 | 0.7747 | | 0.0484 | 6.4265 | 2622 | 0.6291 | 0.2588 | 0.6291 | 0.7931 | | 0.0484 | 6.4314 | 2624 | 0.6543 | 0.2588 | 0.6543 | 0.8089 | | 0.0484 | 6.4363 | 2626 | 0.6780 | 0.3636 | 0.6780 | 0.8234 | | 0.0484 | 6.4412 | 2628 | 0.6863 | 0.3636 | 0.6863 | 0.8284 | | 0.0484 | 6.4461 | 2630 | 0.6714 | 0.3636 | 0.6714 | 0.8194 | | 0.0484 | 6.4510 | 2632 | 0.6420 | 0.2588 | 0.6420 | 0.8012 | | 0.0484 | 6.4559 | 2634 | 0.6160 | 0.2186 | 0.6160 | 0.7848 | | 0.0484 | 6.4608 | 2636 | 0.6051 | 0.2186 | 0.6051 | 0.7779 | | 0.0484 | 6.4657 | 2638 | 0.5968 | 0.2186 | 0.5968 | 0.7725 | | 0.0484 | 6.4706 | 2640 | 0.5931 | 0.2186 | 0.5931 | 0.7701 | | 0.0484 | 6.4755 | 2642 | 0.5914 | 0.1793 | 0.5914 | 0.7690 | | 0.0484 | 6.4804 | 2644 | 0.5892 | 0.1793 | 0.5892 | 0.7676 | | 0.0484 | 6.4853 | 2646 | 0.5866 | 0.1793 | 0.5866 | 0.7659 | | 0.0484 | 6.4902 | 2648 | 0.5893 | 0.1793 | 0.5893 | 0.7677 | | 0.0484 | 6.4951 | 2650 | 0.5929 | 0.1793 | 0.5929 | 0.7700 | | 0.0484 | 6.5 | 2652 | 0.5972 | 0.1793 | 0.5972 | 0.7728 | | 0.0484 | 6.5049 | 2654 | 0.6033 | 0.1793 | 0.6033 | 0.7768 | | 0.0484 | 6.5098 | 2656 | 0.6103 | 0.1793 | 0.6103 | 0.7812 | | 0.0484 | 6.5147 | 2658 | 0.6120 | 0.1793 | 0.6120 | 0.7823 | | 0.0484 | 6.5196 | 2660 | 0.6065 | 0.3318 | 0.6065 | 0.7788 | | 0.0484 | 6.5245 | 2662 | 0.6071 | 0.3865 | 0.6071 | 0.7792 | | 0.0484 | 6.5294 | 2664 | 0.6065 | 0.3865 | 0.6065 | 0.7788 | | 0.0484 | 6.5343 | 2666 | 0.6056 | 0.3865 | 0.6056 | 0.7782 | | 0.0484 | 6.5392 | 2668 | 0.6064 | 0.3865 | 0.6064 | 0.7787 | | 0.0484 | 6.5441 | 2670 | 0.6086 | 0.3865 | 0.6086 | 0.7801 | | 0.0484 | 6.5490 | 2672 | 0.6113 | 0.3865 | 0.6113 | 0.7819 | | 0.0484 | 6.5539 | 2674 | 0.6099 | 0.3318 | 0.6099 | 0.7809 | | 0.0484 | 6.5588 | 2676 | 0.6092 | 0.3318 | 0.6092 | 0.7805 | | 0.0484 | 6.5637 | 2678 | 0.6099 | 0.3318 | 0.6099 | 0.7810 | | 0.0484 | 6.5686 | 2680 | 0.6109 | 0.1793 | 0.6109 | 0.7816 | | 0.0484 | 6.5735 | 2682 | 0.6145 | 0.2186 | 0.6145 | 0.7839 | | 0.0484 | 6.5784 | 2684 | 0.6220 | 0.2588 | 0.6220 | 0.7887 | | 0.0484 | 6.5833 | 2686 | 0.6172 | 0.2588 | 0.6172 | 0.7856 | | 0.0484 | 6.5882 | 2688 | 0.6112 | 0.2588 | 0.6112 | 0.7818 | | 0.0484 | 6.5931 | 2690 | 0.6046 | 0.2588 | 0.6046 | 0.7776 | | 0.0484 | 6.5980 | 2692 | 0.5943 | 0.2588 | 0.5943 | 0.7709 | | 0.0484 | 6.6029 | 2694 | 0.5916 | 0.2588 | 0.5916 | 0.7692 | | 0.0484 | 6.6078 | 2696 | 0.5931 | 0.3636 | 0.5931 | 0.7701 | | 0.0484 | 6.6127 | 2698 | 0.5978 | 0.3255 | 0.5978 | 0.7732 | | 0.0484 | 6.6176 | 2700 | 0.6067 | 0.3255 | 0.6067 | 0.7789 | | 0.0484 | 6.6225 | 2702 | 0.6134 | 0.3255 | 0.6134 | 0.7832 | | 0.0484 | 6.6275 | 2704 | 0.6143 | 0.3255 | 0.6143 | 0.7838 | | 0.0484 | 6.6324 | 2706 | 0.6157 | 0.2186 | 0.6157 | 0.7847 | | 0.0484 | 6.6373 | 2708 | 0.6146 | 0.3724 | 0.6146 | 0.7840 | | 0.0484 | 6.6422 | 2710 | 0.6124 | 0.3724 | 0.6124 | 0.7826 | | 0.0484 | 6.6471 | 2712 | 0.6135 | 0.3724 | 0.6135 | 0.7832 | | 0.0484 | 6.6520 | 2714 | 0.6120 | 0.3724 | 0.6120 | 0.7823 | | 0.0484 | 6.6569 | 2716 | 0.6095 | 0.3318 | 0.6095 | 0.7807 | | 0.0484 | 6.6618 | 2718 | 0.6086 | 0.3865 | 0.6086 | 0.7801 | | 0.0484 | 6.6667 | 2720 | 0.6084 | 0.3077 | 0.6084 | 0.7800 | | 0.0484 | 6.6716 | 2722 | 0.6088 | 0.3077 | 0.6088 | 0.7802 | | 0.0484 | 6.6765 | 2724 | 0.6084 | 0.3077 | 0.6084 | 0.7800 | | 0.0484 | 6.6814 | 2726 | 0.6100 | 0.3865 | 0.6100 | 0.7810 | | 0.0484 | 6.6863 | 2728 | 0.6099 | 0.3865 | 0.6099 | 0.7810 | | 0.0484 | 6.6912 | 2730 | 0.6075 | 0.3865 | 0.6075 | 0.7795 | | 0.0484 | 6.6961 | 2732 | 0.6094 | 0.3865 | 0.6094 | 0.7806 | | 0.0484 | 6.7010 | 2734 | 0.6067 | 0.3865 | 0.6067 | 0.7789 | | 0.0484 | 6.7059 | 2736 | 0.6002 | 0.3865 | 0.6002 | 0.7747 | | 0.0484 | 6.7108 | 2738 | 0.5945 | 0.3865 | 0.5945 | 0.7711 | | 0.0484 | 6.7157 | 2740 | 0.5854 | 0.3865 | 0.5854 | 0.7651 | | 0.0484 | 6.7206 | 2742 | 0.5839 | 0.3724 | 0.5839 | 0.7641 | | 0.0484 | 6.7255 | 2744 | 0.5930 | 0.3724 | 0.5930 | 0.7701 | | 0.0484 | 6.7304 | 2746 | 0.6021 | 0.3724 | 0.6021 | 0.7760 | | 0.0484 | 6.7353 | 2748 | 0.6130 | 0.2186 | 0.6130 | 0.7830 | | 0.0484 | 6.7402 | 2750 | 0.6345 | 0.2588 | 0.6345 | 0.7966 | | 0.0484 | 6.7451 | 2752 | 0.6582 | 0.3636 | 0.6582 | 0.8113 | | 0.0484 | 6.75 | 2754 | 0.6611 | 0.3636 | 0.6611 | 0.8131 | | 0.0484 | 6.7549 | 2756 | 0.6475 | 0.3636 | 0.6475 | 0.8047 | | 0.0484 | 6.7598 | 2758 | 0.6347 | 0.2588 | 0.6347 | 0.7967 | | 0.0484 | 6.7647 | 2760 | 0.6102 | 0.3724 | 0.6102 | 0.7812 | | 0.0484 | 6.7696 | 2762 | 0.5882 | 0.3724 | 0.5882 | 0.7670 | | 0.0484 | 6.7745 | 2764 | 0.5803 | 0.3724 | 0.5803 | 0.7618 | | 0.0484 | 6.7794 | 2766 | 0.5746 | 0.3724 | 0.5746 | 0.7580 | | 0.0484 | 6.7843 | 2768 | 0.5763 | 0.3724 | 0.5763 | 0.7591 | | 0.0484 | 6.7892 | 2770 | 0.5820 | 0.3724 | 0.5820 | 0.7629 | | 0.0484 | 6.7941 | 2772 | 0.5859 | 0.3724 | 0.5859 | 0.7655 | | 0.0484 | 6.7990 | 2774 | 0.5917 | 0.3724 | 0.5917 | 0.7692 | | 0.0484 | 6.8039 | 2776 | 0.6052 | 0.3724 | 0.6052 | 0.7779 | | 0.0484 | 6.8088 | 2778 | 0.6158 | 0.3724 | 0.6158 | 0.7847 | | 0.0484 | 6.8137 | 2780 | 0.6289 | 0.3724 | 0.6289 | 0.7930 | | 0.0484 | 6.8186 | 2782 | 0.6449 | 0.3724 | 0.6449 | 0.8031 | | 0.0484 | 6.8235 | 2784 | 0.6509 | 0.4140 | 0.6509 | 0.8068 | | 0.0484 | 6.8284 | 2786 | 0.6423 | 0.4140 | 0.6423 | 0.8015 | | 0.0484 | 6.8333 | 2788 | 0.6323 | 0.4140 | 0.6323 | 0.7952 | | 0.0484 | 6.8382 | 2790 | 0.6192 | 0.3724 | 0.6192 | 0.7869 | | 0.0484 | 6.8431 | 2792 | 0.6140 | 0.3724 | 0.6140 | 0.7836 | | 0.0484 | 6.8480 | 2794 | 0.6046 | 0.3724 | 0.6046 | 0.7776 | | 0.0484 | 6.8529 | 2796 | 0.5935 | 0.4273 | 0.5935 | 0.7704 | | 0.0484 | 6.8578 | 2798 | 0.5860 | 0.3226 | 0.5860 | 0.7655 | | 0.0484 | 6.8627 | 2800 | 0.5835 | 0.4273 | 0.5835 | 0.7639 | | 0.0484 | 6.8676 | 2802 | 0.5858 | 0.3724 | 0.5858 | 0.7654 | | 0.0484 | 6.8725 | 2804 | 0.5940 | 0.3724 | 0.5940 | 0.7707 | | 0.0484 | 6.8775 | 2806 | 0.6006 | 0.4140 | 0.6006 | 0.7750 | | 0.0484 | 6.8824 | 2808 | 0.5999 | 0.3724 | 0.5999 | 0.7746 | | 0.0484 | 6.8873 | 2810 | 0.6014 | 0.3724 | 0.6014 | 0.7755 | | 0.0484 | 6.8922 | 2812 | 0.6036 | 0.3724 | 0.6036 | 0.7769 | | 0.0484 | 6.8971 | 2814 | 0.6012 | 0.3724 | 0.6012 | 0.7754 | | 0.0484 | 6.9020 | 2816 | 0.5993 | 0.3724 | 0.5993 | 0.7742 | | 0.0484 | 6.9069 | 2818 | 0.6011 | 0.3724 | 0.6011 | 0.7753 | | 0.0484 | 6.9118 | 2820 | 0.6006 | 0.3724 | 0.6006 | 0.7750 | | 0.0484 | 6.9167 | 2822 | 0.5976 | 0.3318 | 0.5976 | 0.7731 | | 0.0484 | 6.9216 | 2824 | 0.5950 | 0.2921 | 0.5950 | 0.7713 | | 0.0484 | 6.9265 | 2826 | 0.5923 | 0.3077 | 0.5923 | 0.7696 | | 0.0484 | 6.9314 | 2828 | 0.5876 | 0.1962 | 0.5876 | 0.7666 | | 0.0484 | 6.9363 | 2830 | 0.5836 | 0.2373 | 0.5836 | 0.7640 | | 0.0484 | 6.9412 | 2832 | 0.5781 | 0.2373 | 0.5781 | 0.7604 | | 0.0484 | 6.9461 | 2834 | 0.5741 | 0.2794 | 0.5741 | 0.7577 | | 0.0484 | 6.9510 | 2836 | 0.5697 | 0.3226 | 0.5697 | 0.7548 | | 0.0484 | 6.9559 | 2838 | 0.5672 | 0.2613 | 0.5672 | 0.7531 | | 0.0484 | 6.9608 | 2840 | 0.5661 | 0.2613 | 0.5661 | 0.7524 | | 0.0484 | 6.9657 | 2842 | 0.5678 | 0.3724 | 0.5678 | 0.7535 | | 0.0484 | 6.9706 | 2844 | 0.5757 | 0.3724 | 0.5757 | 0.7587 | | 0.0484 | 6.9755 | 2846 | 0.5816 | 0.3724 | 0.5816 | 0.7626 | | 0.0484 | 6.9804 | 2848 | 0.5889 | 0.3724 | 0.5889 | 0.7674 | | 0.0484 | 6.9853 | 2850 | 0.5996 | 0.3724 | 0.5996 | 0.7744 | | 0.0484 | 6.9902 | 2852 | 0.6045 | 0.3724 | 0.6045 | 0.7775 | | 0.0484 | 6.9951 | 2854 | 0.6024 | 0.3724 | 0.6024 | 0.7762 | | 0.0484 | 7.0 | 2856 | 0.5987 | 0.3724 | 0.5987 | 0.7737 | | 0.0484 | 7.0049 | 2858 | 0.5955 | 0.3724 | 0.5955 | 0.7717 | | 0.0484 | 7.0098 | 2860 | 0.5973 | 0.3724 | 0.5973 | 0.7729 | | 0.0484 | 7.0147 | 2862 | 0.6018 | 0.3724 | 0.6018 | 0.7758 | | 0.0484 | 7.0196 | 2864 | 0.6024 | 0.3724 | 0.6024 | 0.7761 | | 0.0484 | 7.0245 | 2866 | 0.5999 | 0.3724 | 0.5999 | 0.7745 | | 0.0484 | 7.0294 | 2868 | 0.5949 | 0.3724 | 0.5949 | 0.7713 | | 0.0484 | 7.0343 | 2870 | 0.5896 | 0.3724 | 0.5896 | 0.7678 | | 0.0484 | 7.0392 | 2872 | 0.5883 | 0.3724 | 0.5883 | 0.7670 | | 0.0484 | 7.0441 | 2874 | 0.5895 | 0.3724 | 0.5895 | 0.7678 | | 0.0484 | 7.0490 | 2876 | 0.5927 | 0.3724 | 0.5927 | 0.7699 | | 0.0484 | 7.0539 | 2878 | 0.5880 | 0.2186 | 0.5880 | 0.7668 | | 0.0484 | 7.0588 | 2880 | 0.5826 | 0.2186 | 0.5826 | 0.7633 | | 0.0484 | 7.0637 | 2882 | 0.5830 | 0.2588 | 0.5830 | 0.7636 | | 0.0484 | 7.0686 | 2884 | 0.5818 | 0.2588 | 0.5818 | 0.7627 | | 0.0484 | 7.0735 | 2886 | 0.5761 | 0.2588 | 0.5761 | 0.7590 | | 0.0484 | 7.0784 | 2888 | 0.5695 | 0.2588 | 0.5695 | 0.7546 | | 0.0484 | 7.0833 | 2890 | 0.5596 | 0.2588 | 0.5596 | 0.7480 | | 0.0484 | 7.0882 | 2892 | 0.5524 | 0.2186 | 0.5524 | 0.7433 | | 0.0484 | 7.0931 | 2894 | 0.5499 | 0.2186 | 0.5499 | 0.7416 | | 0.0484 | 7.0980 | 2896 | 0.5533 | 0.2186 | 0.5533 | 0.7438 | | 0.0484 | 7.1029 | 2898 | 0.5597 | 0.2186 | 0.5597 | 0.7481 | | 0.0484 | 7.1078 | 2900 | 0.5655 | 0.2186 | 0.5655 | 0.7520 | | 0.0484 | 7.1127 | 2902 | 0.5726 | 0.2186 | 0.5726 | 0.7567 | | 0.0484 | 7.1176 | 2904 | 0.5815 | 0.2186 | 0.5815 | 0.7625 | | 0.0484 | 7.1225 | 2906 | 0.5797 | 0.2186 | 0.5797 | 0.7614 | | 0.0484 | 7.1275 | 2908 | 0.5757 | 0.3724 | 0.5757 | 0.7587 | | 0.0484 | 7.1324 | 2910 | 0.5769 | 0.3724 | 0.5769 | 0.7595 | | 0.0484 | 7.1373 | 2912 | 0.5821 | 0.3724 | 0.5821 | 0.7630 | | 0.0484 | 7.1422 | 2914 | 0.5868 | 0.2186 | 0.5868 | 0.7660 | | 0.0484 | 7.1471 | 2916 | 0.5949 | 0.2588 | 0.5949 | 0.7713 | | 0.0484 | 7.1520 | 2918 | 0.6055 | 0.2588 | 0.6055 | 0.7782 | | 0.0484 | 7.1569 | 2920 | 0.6056 | 0.2588 | 0.6056 | 0.7782 | | 0.0484 | 7.1618 | 2922 | 0.5956 | 0.2186 | 0.5956 | 0.7718 | | 0.0484 | 7.1667 | 2924 | 0.5841 | 0.2186 | 0.5841 | 0.7643 | | 0.0484 | 7.1716 | 2926 | 0.5740 | 0.3724 | 0.5740 | 0.7576 | | 0.0484 | 7.1765 | 2928 | 0.5638 | 0.4273 | 0.5638 | 0.7509 | | 0.0484 | 7.1814 | 2930 | 0.5598 | 0.4273 | 0.5598 | 0.7482 | | 0.0484 | 7.1863 | 2932 | 0.5587 | 0.4273 | 0.5587 | 0.7475 | | 0.0484 | 7.1912 | 2934 | 0.5577 | 0.4273 | 0.5577 | 0.7468 | | 0.0484 | 7.1961 | 2936 | 0.5575 | 0.4273 | 0.5575 | 0.7466 | | 0.0484 | 7.2010 | 2938 | 0.5605 | 0.4273 | 0.5605 | 0.7486 | | 0.0484 | 7.2059 | 2940 | 0.5650 | 0.4273 | 0.5650 | 0.7516 | | 0.0484 | 7.2108 | 2942 | 0.5784 | 0.4273 | 0.5784 | 0.7605 | | 0.0484 | 7.2157 | 2944 | 0.5920 | 0.3724 | 0.5920 | 0.7694 | | 0.0484 | 7.2206 | 2946 | 0.5985 | 0.2186 | 0.5985 | 0.7736 | | 0.0484 | 7.2255 | 2948 | 0.6026 | 0.2588 | 0.6026 | 0.7762 | | 0.0484 | 7.2304 | 2950 | 0.6018 | 0.3724 | 0.6018 | 0.7758 | | 0.0484 | 7.2353 | 2952 | 0.5978 | 0.3724 | 0.5978 | 0.7732 | | 0.0484 | 7.2402 | 2954 | 0.6014 | 0.3724 | 0.6014 | 0.7755 | | 0.0484 | 7.2451 | 2956 | 0.5995 | 0.4273 | 0.5995 | 0.7743 | | 0.0484 | 7.25 | 2958 | 0.5931 | 0.4273 | 0.5931 | 0.7701 | | 0.0484 | 7.2549 | 2960 | 0.5918 | 0.4273 | 0.5918 | 0.7693 | | 0.0484 | 7.2598 | 2962 | 0.5924 | 0.4273 | 0.5924 | 0.7697 | | 0.0484 | 7.2647 | 2964 | 0.5894 | 0.4273 | 0.5894 | 0.7677 | | 0.0484 | 7.2696 | 2966 | 0.5884 | 0.3865 | 0.5884 | 0.7671 | | 0.0484 | 7.2745 | 2968 | 0.5891 | 0.3077 | 0.5891 | 0.7675 | | 0.0484 | 7.2794 | 2970 | 0.5899 | 0.3077 | 0.5899 | 0.7680 | | 0.0484 | 7.2843 | 2972 | 0.5917 | 0.1962 | 0.5917 | 0.7693 | | 0.0484 | 7.2892 | 2974 | 0.5945 | 0.1962 | 0.5945 | 0.7711 | | 0.0484 | 7.2941 | 2976 | 0.5986 | 0.1962 | 0.5986 | 0.7737 | | 0.0484 | 7.2990 | 2978 | 0.5996 | 0.1962 | 0.5996 | 0.7743 | | 0.0484 | 7.3039 | 2980 | 0.5938 | 0.1962 | 0.5938 | 0.7706 | | 0.0484 | 7.3088 | 2982 | 0.5896 | 0.1962 | 0.5896 | 0.7679 | | 0.0484 | 7.3137 | 2984 | 0.5903 | 0.4273 | 0.5903 | 0.7683 | | 0.0484 | 7.3186 | 2986 | 0.6007 | 0.4273 | 0.6007 | 0.7750 | | 0.0484 | 7.3235 | 2988 | 0.6197 | 0.2588 | 0.6197 | 0.7872 | | 0.0484 | 7.3284 | 2990 | 0.6438 | 0.2588 | 0.6438 | 0.8024 | | 0.0484 | 7.3333 | 2992 | 0.6531 | 0.2588 | 0.6531 | 0.8081 | | 0.0484 | 7.3382 | 2994 | 0.6506 | 0.2588 | 0.6506 | 0.8066 | | 0.0484 | 7.3431 | 2996 | 0.6399 | 0.2588 | 0.6399 | 0.8000 | | 0.0484 | 7.3480 | 2998 | 0.6246 | 0.2588 | 0.6246 | 0.7903 | | 0.0411 | 7.3529 | 3000 | 0.6190 | 0.2588 | 0.6190 | 0.7868 | | 0.0411 | 7.3578 | 3002 | 0.6147 | 0.2588 | 0.6147 | 0.7840 | | 0.0411 | 7.3627 | 3004 | 0.6192 | 0.2588 | 0.6192 | 0.7869 | | 0.0411 | 7.3676 | 3006 | 0.6184 | 0.2588 | 0.6184 | 0.7864 | | 0.0411 | 7.3725 | 3008 | 0.6144 | 0.2588 | 0.6144 | 0.7838 | | 0.0411 | 7.3775 | 3010 | 0.6032 | 0.2588 | 0.6032 | 0.7767 | | 0.0411 | 7.3824 | 3012 | 0.5871 | 0.3724 | 0.5871 | 0.7662 | | 0.0411 | 7.3873 | 3014 | 0.5713 | 0.4273 | 0.5713 | 0.7558 | | 0.0411 | 7.3922 | 3016 | 0.5613 | 0.4273 | 0.5613 | 0.7492 | | 0.0411 | 7.3971 | 3018 | 0.5541 | 0.4273 | 0.5541 | 0.7443 | | 0.0411 | 7.4020 | 3020 | 0.5492 | 0.3467 | 0.5492 | 0.7411 | | 0.0411 | 7.4069 | 3022 | 0.5470 | 0.3077 | 0.5470 | 0.7396 | | 0.0411 | 7.4118 | 3024 | 0.5447 | 0.3467 | 0.5447 | 0.7380 | | 0.0411 | 7.4167 | 3026 | 0.5460 | 0.4273 | 0.5460 | 0.7389 | | 0.0411 | 7.4216 | 3028 | 0.5570 | 0.4273 | 0.5570 | 0.7463 | | 0.0411 | 7.4265 | 3030 | 0.5654 | 0.3724 | 0.5654 | 0.7519 | | 0.0411 | 7.4314 | 3032 | 0.5718 | 0.4661 | 0.5718 | 0.7562 | | 0.0411 | 7.4363 | 3034 | 0.5704 | 0.4661 | 0.5704 | 0.7552 | | 0.0411 | 7.4412 | 3036 | 0.5678 | 0.4661 | 0.5678 | 0.7536 | | 0.0411 | 7.4461 | 3038 | 0.5751 | 0.4661 | 0.5751 | 0.7583 | | 0.0411 | 7.4510 | 3040 | 0.5945 | 0.4661 | 0.5945 | 0.7710 | | 0.0411 | 7.4559 | 3042 | 0.6046 | 0.4661 | 0.6046 | 0.7776 | | 0.0411 | 7.4608 | 3044 | 0.6135 | 0.3255 | 0.6135 | 0.7833 | | 0.0411 | 7.4657 | 3046 | 0.6191 | 0.3255 | 0.6191 | 0.7869 | | 0.0411 | 7.4706 | 3048 | 0.6098 | 0.3255 | 0.6098 | 0.7809 | | 0.0411 | 7.4755 | 3050 | 0.5983 | 0.3255 | 0.5983 | 0.7735 | | 0.0411 | 7.4804 | 3052 | 0.5858 | 0.3255 | 0.5858 | 0.7654 | | 0.0411 | 7.4853 | 3054 | 0.5704 | 0.2186 | 0.5704 | 0.7552 | | 0.0411 | 7.4902 | 3056 | 0.5561 | 0.3724 | 0.5561 | 0.7458 | | 0.0411 | 7.4951 | 3058 | 0.5418 | 0.3724 | 0.5418 | 0.7361 | | 0.0411 | 7.5 | 3060 | 0.5366 | 0.4273 | 0.5366 | 0.7325 | | 0.0411 | 7.5049 | 3062 | 0.5382 | 0.3724 | 0.5382 | 0.7336 | | 0.0411 | 7.5098 | 3064 | 0.5390 | 0.3724 | 0.5390 | 0.7341 | | 0.0411 | 7.5147 | 3066 | 0.5369 | 0.3724 | 0.5369 | 0.7327 | | 0.0411 | 7.5196 | 3068 | 0.5412 | 0.3724 | 0.5412 | 0.7357 | | 0.0411 | 7.5245 | 3070 | 0.5541 | 0.3724 | 0.5541 | 0.7444 | | 0.0411 | 7.5294 | 3072 | 0.5643 | 0.4661 | 0.5643 | 0.7512 | | 0.0411 | 7.5343 | 3074 | 0.5738 | 0.4661 | 0.5738 | 0.7575 | | 0.0411 | 7.5392 | 3076 | 0.5723 | 0.4661 | 0.5723 | 0.7565 | | 0.0411 | 7.5441 | 3078 | 0.5720 | 0.4661 | 0.5720 | 0.7563 | | 0.0411 | 7.5490 | 3080 | 0.5743 | 0.4661 | 0.5743 | 0.7578 | | 0.0411 | 7.5539 | 3082 | 0.5861 | 0.4661 | 0.5861 | 0.7656 | | 0.0411 | 7.5588 | 3084 | 0.6017 | 0.4661 | 0.6017 | 0.7757 | | 0.0411 | 7.5637 | 3086 | 0.6255 | 0.3255 | 0.6255 | 0.7909 | | 0.0411 | 7.5686 | 3088 | 0.6465 | 0.3255 | 0.6465 | 0.8041 | | 0.0411 | 7.5735 | 3090 | 0.6455 | 0.3255 | 0.6455 | 0.8034 | | 0.0411 | 7.5784 | 3092 | 0.6340 | 0.3255 | 0.6340 | 0.7962 | | 0.0411 | 7.5833 | 3094 | 0.6106 | 0.3255 | 0.6106 | 0.7814 | | 0.0411 | 7.5882 | 3096 | 0.5799 | 0.4661 | 0.5799 | 0.7615 | | 0.0411 | 7.5931 | 3098 | 0.5602 | 0.4661 | 0.5602 | 0.7484 | | 0.0411 | 7.5980 | 3100 | 0.5480 | 0.3724 | 0.5480 | 0.7402 | | 0.0411 | 7.6029 | 3102 | 0.5363 | 0.4273 | 0.5363 | 0.7324 | | 0.0411 | 7.6078 | 3104 | 0.5275 | 0.4273 | 0.5275 | 0.7263 | | 0.0411 | 7.6127 | 3106 | 0.5241 | 0.4273 | 0.5241 | 0.7240 | | 0.0411 | 7.6176 | 3108 | 0.5264 | 0.4273 | 0.5264 | 0.7255 | | 0.0411 | 7.6225 | 3110 | 0.5288 | 0.4273 | 0.5288 | 0.7272 | | 0.0411 | 7.6275 | 3112 | 0.5320 | 0.4273 | 0.5320 | 0.7294 | | 0.0411 | 7.6324 | 3114 | 0.5335 | 0.3865 | 0.5335 | 0.7304 | | 0.0411 | 7.6373 | 3116 | 0.5385 | 0.3865 | 0.5385 | 0.7338 | | 0.0411 | 7.6422 | 3118 | 0.5465 | 0.3467 | 0.5465 | 0.7393 | | 0.0411 | 7.6471 | 3120 | 0.5531 | 0.3467 | 0.5531 | 0.7437 | | 0.0411 | 7.6520 | 3122 | 0.5552 | 0.3467 | 0.5552 | 0.7451 | | 0.0411 | 7.6569 | 3124 | 0.5537 | 0.3865 | 0.5537 | 0.7441 | | 0.0411 | 7.6618 | 3126 | 0.5555 | 0.3865 | 0.5555 | 0.7453 | | 0.0411 | 7.6667 | 3128 | 0.5601 | 0.3865 | 0.5601 | 0.7484 | | 0.0411 | 7.6716 | 3130 | 0.5727 | 0.4273 | 0.5727 | 0.7568 | | 0.0411 | 7.6765 | 3132 | 0.5882 | 0.3724 | 0.5882 | 0.7669 | | 0.0411 | 7.6814 | 3134 | 0.6032 | 0.3724 | 0.6032 | 0.7767 | | 0.0411 | 7.6863 | 3136 | 0.6175 | 0.2186 | 0.6175 | 0.7858 | | 0.0411 | 7.6912 | 3138 | 0.6188 | 0.2186 | 0.6188 | 0.7867 | | 0.0411 | 7.6961 | 3140 | 0.6089 | 0.2186 | 0.6089 | 0.7803 | | 0.0411 | 7.7010 | 3142 | 0.6012 | 0.2186 | 0.6012 | 0.7754 | | 0.0411 | 7.7059 | 3144 | 0.5923 | 0.2186 | 0.5923 | 0.7696 | | 0.0411 | 7.7108 | 3146 | 0.5804 | 0.3724 | 0.5804 | 0.7618 | | 0.0411 | 7.7157 | 3148 | 0.5662 | 0.4273 | 0.5662 | 0.7525 | | 0.0411 | 7.7206 | 3150 | 0.5564 | 0.4273 | 0.5564 | 0.7459 | | 0.0411 | 7.7255 | 3152 | 0.5497 | 0.4273 | 0.5497 | 0.7414 | | 0.0411 | 7.7304 | 3154 | 0.5447 | 0.4273 | 0.5447 | 0.7380 | | 0.0411 | 7.7353 | 3156 | 0.5450 | 0.4273 | 0.5450 | 0.7382 | | 0.0411 | 7.7402 | 3158 | 0.5508 | 0.4273 | 0.5508 | 0.7422 | | 0.0411 | 7.7451 | 3160 | 0.5564 | 0.4273 | 0.5564 | 0.7459 | | 0.0411 | 7.75 | 3162 | 0.5608 | 0.4273 | 0.5608 | 0.7489 | | 0.0411 | 7.7549 | 3164 | 0.5661 | 0.3724 | 0.5662 | 0.7524 | | 0.0411 | 7.7598 | 3166 | 0.5762 | 0.2186 | 0.5762 | 0.7591 | | 0.0411 | 7.7647 | 3168 | 0.5785 | 0.2186 | 0.5785 | 0.7606 | | 0.0411 | 7.7696 | 3170 | 0.5750 | 0.2186 | 0.5750 | 0.7583 | | 0.0411 | 7.7745 | 3172 | 0.5703 | 0.2186 | 0.5703 | 0.7552 | | 0.0411 | 7.7794 | 3174 | 0.5662 | 0.2186 | 0.5662 | 0.7525 | | 0.0411 | 7.7843 | 3176 | 0.5640 | 0.2186 | 0.5640 | 0.7510 | | 0.0411 | 7.7892 | 3178 | 0.5623 | 0.2186 | 0.5623 | 0.7498 | | 0.0411 | 7.7941 | 3180 | 0.5666 | 0.2186 | 0.5666 | 0.7527 | | 0.0411 | 7.7990 | 3182 | 0.5736 | 0.2186 | 0.5736 | 0.7574 | | 0.0411 | 7.8039 | 3184 | 0.5749 | 0.2186 | 0.5749 | 0.7582 | | 0.0411 | 7.8088 | 3186 | 0.5747 | 0.3255 | 0.5747 | 0.7581 | | 0.0411 | 7.8137 | 3188 | 0.5698 | 0.3255 | 0.5698 | 0.7549 | | 0.0411 | 7.8186 | 3190 | 0.5756 | 0.3255 | 0.5756 | 0.7587 | | 0.0411 | 7.8235 | 3192 | 0.5875 | 0.3636 | 0.5875 | 0.7665 | | 0.0411 | 7.8284 | 3194 | 0.5931 | 0.3636 | 0.5931 | 0.7701 | | 0.0411 | 7.8333 | 3196 | 0.5926 | 0.3636 | 0.5926 | 0.7698 | | 0.0411 | 7.8382 | 3198 | 0.5871 | 0.3636 | 0.5871 | 0.7662 | | 0.0411 | 7.8431 | 3200 | 0.5834 | 0.3636 | 0.5834 | 0.7638 | | 0.0411 | 7.8480 | 3202 | 0.5857 | 0.3636 | 0.5857 | 0.7653 | | 0.0411 | 7.8529 | 3204 | 0.5867 | 0.3255 | 0.5867 | 0.7659 | | 0.0411 | 7.8578 | 3206 | 0.5769 | 0.2186 | 0.5769 | 0.7596 | | 0.0411 | 7.8627 | 3208 | 0.5661 | 0.2186 | 0.5661 | 0.7524 | | 0.0411 | 7.8676 | 3210 | 0.5590 | 0.2186 | 0.5590 | 0.7476 | | 0.0411 | 7.8725 | 3212 | 0.5509 | 0.3724 | 0.5509 | 0.7422 | | 0.0411 | 7.8775 | 3214 | 0.5431 | 0.3724 | 0.5431 | 0.7369 | | 0.0411 | 7.8824 | 3216 | 0.5374 | 0.3724 | 0.5374 | 0.7331 | | 0.0411 | 7.8873 | 3218 | 0.5318 | 0.3724 | 0.5318 | 0.7292 | | 0.0411 | 7.8922 | 3220 | 0.5291 | 0.3724 | 0.5291 | 0.7274 | | 0.0411 | 7.8971 | 3222 | 0.5220 | 0.3724 | 0.5220 | 0.7225 | | 0.0411 | 7.9020 | 3224 | 0.5191 | 0.3724 | 0.5191 | 0.7205 | | 0.0411 | 7.9069 | 3226 | 0.5246 | 0.3724 | 0.5246 | 0.7243 | | 0.0411 | 7.9118 | 3228 | 0.5357 | 0.3724 | 0.5357 | 0.7319 | | 0.0411 | 7.9167 | 3230 | 0.5448 | 0.3724 | 0.5448 | 0.7381 | | 0.0411 | 7.9216 | 3232 | 0.5568 | 0.2186 | 0.5568 | 0.7462 | | 0.0411 | 7.9265 | 3234 | 0.5743 | 0.3255 | 0.5743 | 0.7579 | | 0.0411 | 7.9314 | 3236 | 0.5857 | 0.3255 | 0.5857 | 0.7653 | | 0.0411 | 7.9363 | 3238 | 0.5902 | 0.3255 | 0.5902 | 0.7682 | | 0.0411 | 7.9412 | 3240 | 0.5941 | 0.3255 | 0.5941 | 0.7708 | | 0.0411 | 7.9461 | 3242 | 0.5940 | 0.3255 | 0.5940 | 0.7707 | | 0.0411 | 7.9510 | 3244 | 0.5792 | 0.3255 | 0.5792 | 0.7611 | | 0.0411 | 7.9559 | 3246 | 0.5686 | 0.2186 | 0.5686 | 0.7541 | | 0.0411 | 7.9608 | 3248 | 0.5643 | 0.2186 | 0.5643 | 0.7512 | | 0.0411 | 7.9657 | 3250 | 0.5577 | 0.3724 | 0.5577 | 0.7468 | | 0.0411 | 7.9706 | 3252 | 0.5513 | 0.3724 | 0.5513 | 0.7425 | | 0.0411 | 7.9755 | 3254 | 0.5494 | 0.3724 | 0.5494 | 0.7412 | | 0.0411 | 7.9804 | 3256 | 0.5517 | 0.3724 | 0.5517 | 0.7428 | | 0.0411 | 7.9853 | 3258 | 0.5462 | 0.3724 | 0.5462 | 0.7391 | | 0.0411 | 7.9902 | 3260 | 0.5383 | 0.4273 | 0.5383 | 0.7337 | | 0.0411 | 7.9951 | 3262 | 0.5321 | 0.4273 | 0.5321 | 0.7295 | | 0.0411 | 8.0 | 3264 | 0.5273 | 0.4273 | 0.5273 | 0.7262 | | 0.0411 | 8.0049 | 3266 | 0.5219 | 0.4273 | 0.5219 | 0.7224 | | 0.0411 | 8.0098 | 3268 | 0.5199 | 0.4273 | 0.5199 | 0.7211 | | 0.0411 | 8.0147 | 3270 | 0.5266 | 0.4273 | 0.5266 | 0.7257 | | 0.0411 | 8.0196 | 3272 | 0.5405 | 0.3724 | 0.5405 | 0.7352 | | 0.0411 | 8.0245 | 3274 | 0.5558 | 0.3724 | 0.5558 | 0.7455 | | 0.0411 | 8.0294 | 3276 | 0.5620 | 0.3724 | 0.5620 | 0.7497 | | 0.0411 | 8.0343 | 3278 | 0.5611 | 0.3724 | 0.5611 | 0.7490 | | 0.0411 | 8.0392 | 3280 | 0.5626 | 0.3724 | 0.5626 | 0.7501 | | 0.0411 | 8.0441 | 3282 | 0.5697 | 0.3724 | 0.5697 | 0.7548 | | 0.0411 | 8.0490 | 3284 | 0.5774 | 0.3724 | 0.5774 | 0.7599 | | 0.0411 | 8.0539 | 3286 | 0.5862 | 0.3724 | 0.5862 | 0.7656 | | 0.0411 | 8.0588 | 3288 | 0.5873 | 0.3724 | 0.5873 | 0.7663 | | 0.0411 | 8.0637 | 3290 | 0.5762 | 0.3724 | 0.5762 | 0.7591 | | 0.0411 | 8.0686 | 3292 | 0.5645 | 0.3724 | 0.5645 | 0.7513 | | 0.0411 | 8.0735 | 3294 | 0.5631 | 0.3724 | 0.5631 | 0.7504 | | 0.0411 | 8.0784 | 3296 | 0.5640 | 0.3724 | 0.5640 | 0.7510 | | 0.0411 | 8.0833 | 3298 | 0.5624 | 0.3724 | 0.5624 | 0.7499 | | 0.0411 | 8.0882 | 3300 | 0.5652 | 0.3724 | 0.5652 | 0.7518 | | 0.0411 | 8.0931 | 3302 | 0.5702 | 0.3724 | 0.5702 | 0.7551 | | 0.0411 | 8.0980 | 3304 | 0.5709 | 0.3724 | 0.5709 | 0.7556 | | 0.0411 | 8.1029 | 3306 | 0.5677 | 0.3724 | 0.5677 | 0.7534 | | 0.0411 | 8.1078 | 3308 | 0.5623 | 0.3724 | 0.5623 | 0.7499 | | 0.0411 | 8.1127 | 3310 | 0.5543 | 0.3724 | 0.5543 | 0.7445 | | 0.0411 | 8.1176 | 3312 | 0.5523 | 0.3724 | 0.5523 | 0.7432 | | 0.0411 | 8.1225 | 3314 | 0.5503 | 0.3724 | 0.5503 | 0.7418 | | 0.0411 | 8.1275 | 3316 | 0.5503 | 0.3724 | 0.5503 | 0.7418 | | 0.0411 | 8.1324 | 3318 | 0.5545 | 0.3724 | 0.5545 | 0.7447 | | 0.0411 | 8.1373 | 3320 | 0.5562 | 0.3724 | 0.5562 | 0.7458 | | 0.0411 | 8.1422 | 3322 | 0.5553 | 0.3724 | 0.5553 | 0.7452 | | 0.0411 | 8.1471 | 3324 | 0.5520 | 0.3724 | 0.5520 | 0.7430 | | 0.0411 | 8.1520 | 3326 | 0.5528 | 0.3724 | 0.5528 | 0.7435 | | 0.0411 | 8.1569 | 3328 | 0.5582 | 0.3724 | 0.5582 | 0.7471 | | 0.0411 | 8.1618 | 3330 | 0.5704 | 0.4661 | 0.5704 | 0.7552 | | 0.0411 | 8.1667 | 3332 | 0.5821 | 0.4661 | 0.5821 | 0.7630 | | 0.0411 | 8.1716 | 3334 | 0.6006 | 0.4661 | 0.6006 | 0.7750 | | 0.0411 | 8.1765 | 3336 | 0.6148 | 0.4661 | 0.6148 | 0.7841 | | 0.0411 | 8.1814 | 3338 | 0.6344 | 0.4661 | 0.6344 | 0.7965 | | 0.0411 | 8.1863 | 3340 | 0.6494 | 0.3255 | 0.6494 | 0.8059 | | 0.0411 | 8.1912 | 3342 | 0.6600 | 0.3255 | 0.6600 | 0.8124 | | 0.0411 | 8.1961 | 3344 | 0.6578 | 0.3255 | 0.6578 | 0.8110 | | 0.0411 | 8.2010 | 3346 | 0.6555 | 0.3255 | 0.6555 | 0.8097 | | 0.0411 | 8.2059 | 3348 | 0.6544 | 0.3255 | 0.6544 | 0.8090 | | 0.0411 | 8.2108 | 3350 | 0.6493 | 0.3255 | 0.6493 | 0.8058 | | 0.0411 | 8.2157 | 3352 | 0.6386 | 0.4661 | 0.6386 | 0.7991 | | 0.0411 | 8.2206 | 3354 | 0.6250 | 0.4661 | 0.6250 | 0.7906 | | 0.0411 | 8.2255 | 3356 | 0.6029 | 0.4661 | 0.6029 | 0.7765 | | 0.0411 | 8.2304 | 3358 | 0.5803 | 0.4661 | 0.5803 | 0.7618 | | 0.0411 | 8.2353 | 3360 | 0.5643 | 0.4661 | 0.5643 | 0.7512 | | 0.0411 | 8.2402 | 3362 | 0.5510 | 0.4273 | 0.5510 | 0.7423 | | 0.0411 | 8.2451 | 3364 | 0.5470 | 0.4273 | 0.5470 | 0.7396 | | 0.0411 | 8.25 | 3366 | 0.5462 | 0.4273 | 0.5462 | 0.7390 | | 0.0411 | 8.2549 | 3368 | 0.5461 | 0.4273 | 0.5461 | 0.7390 | | 0.0411 | 8.2598 | 3370 | 0.5462 | 0.4273 | 0.5462 | 0.7390 | | 0.0411 | 8.2647 | 3372 | 0.5451 | 0.4273 | 0.5451 | 0.7383 | | 0.0411 | 8.2696 | 3374 | 0.5457 | 0.4273 | 0.5457 | 0.7387 | | 0.0411 | 8.2745 | 3376 | 0.5488 | 0.4273 | 0.5488 | 0.7408 | | 0.0411 | 8.2794 | 3378 | 0.5547 | 0.4273 | 0.5547 | 0.7448 | | 0.0411 | 8.2843 | 3380 | 0.5624 | 0.4273 | 0.5624 | 0.7499 | | 0.0411 | 8.2892 | 3382 | 0.5724 | 0.4273 | 0.5724 | 0.7566 | | 0.0411 | 8.2941 | 3384 | 0.5800 | 0.4273 | 0.5800 | 0.7616 | | 0.0411 | 8.2990 | 3386 | 0.5879 | 0.4661 | 0.5879 | 0.7667 | | 0.0411 | 8.3039 | 3388 | 0.5921 | 0.4661 | 0.5921 | 0.7695 | | 0.0411 | 8.3088 | 3390 | 0.5915 | 0.4661 | 0.5915 | 0.7691 | | 0.0411 | 8.3137 | 3392 | 0.5945 | 0.4661 | 0.5945 | 0.7710 | | 0.0411 | 8.3186 | 3394 | 0.5942 | 0.4661 | 0.5942 | 0.7708 | | 0.0411 | 8.3235 | 3396 | 0.5920 | 0.4661 | 0.5920 | 0.7694 | | 0.0411 | 8.3284 | 3398 | 0.5883 | 0.4661 | 0.5883 | 0.7670 | | 0.0411 | 8.3333 | 3400 | 0.5803 | 0.5157 | 0.5803 | 0.7618 | | 0.0411 | 8.3382 | 3402 | 0.5729 | 0.4273 | 0.5729 | 0.7569 | | 0.0411 | 8.3431 | 3404 | 0.5660 | 0.4273 | 0.5660 | 0.7523 | | 0.0411 | 8.3480 | 3406 | 0.5620 | 0.4273 | 0.5620 | 0.7497 | | 0.0411 | 8.3529 | 3408 | 0.5580 | 0.4273 | 0.5580 | 0.7470 | | 0.0411 | 8.3578 | 3410 | 0.5587 | 0.4273 | 0.5587 | 0.7475 | | 0.0411 | 8.3627 | 3412 | 0.5578 | 0.4273 | 0.5578 | 0.7469 | | 0.0411 | 8.3676 | 3414 | 0.5577 | 0.4273 | 0.5577 | 0.7468 | | 0.0411 | 8.3725 | 3416 | 0.5602 | 0.4273 | 0.5602 | 0.7485 | | 0.0411 | 8.3775 | 3418 | 0.5601 | 0.4273 | 0.5601 | 0.7484 | | 0.0411 | 8.3824 | 3420 | 0.5637 | 0.4273 | 0.5637 | 0.7508 | | 0.0411 | 8.3873 | 3422 | 0.5684 | 0.4273 | 0.5684 | 0.7539 | | 0.0411 | 8.3922 | 3424 | 0.5722 | 0.4273 | 0.5722 | 0.7565 | | 0.0411 | 8.3971 | 3426 | 0.5776 | 0.3724 | 0.5776 | 0.7600 | | 0.0411 | 8.4020 | 3428 | 0.5828 | 0.3724 | 0.5828 | 0.7634 | | 0.0411 | 8.4069 | 3430 | 0.5846 | 0.3724 | 0.5846 | 0.7646 | | 0.0411 | 8.4118 | 3432 | 0.5913 | 0.3724 | 0.5913 | 0.7690 | | 0.0411 | 8.4167 | 3434 | 0.6020 | 0.4661 | 0.6020 | 0.7759 | | 0.0411 | 8.4216 | 3436 | 0.6085 | 0.3255 | 0.6085 | 0.7801 | | 0.0411 | 8.4265 | 3438 | 0.6069 | 0.3255 | 0.6069 | 0.7790 | | 0.0411 | 8.4314 | 3440 | 0.6069 | 0.3255 | 0.6069 | 0.7791 | | 0.0411 | 8.4363 | 3442 | 0.6114 | 0.3255 | 0.6114 | 0.7819 | | 0.0411 | 8.4412 | 3444 | 0.6174 | 0.3255 | 0.6174 | 0.7858 | | 0.0411 | 8.4461 | 3446 | 0.6170 | 0.3255 | 0.6170 | 0.7855 | | 0.0411 | 8.4510 | 3448 | 0.6171 | 0.3255 | 0.6171 | 0.7855 | | 0.0411 | 8.4559 | 3450 | 0.6143 | 0.3255 | 0.6143 | 0.7838 | | 0.0411 | 8.4608 | 3452 | 0.6091 | 0.3255 | 0.6091 | 0.7804 | | 0.0411 | 8.4657 | 3454 | 0.6011 | 0.3255 | 0.6011 | 0.7753 | | 0.0411 | 8.4706 | 3456 | 0.5933 | 0.4661 | 0.5933 | 0.7703 | | 0.0411 | 8.4755 | 3458 | 0.5810 | 0.4661 | 0.5810 | 0.7622 | | 0.0411 | 8.4804 | 3460 | 0.5703 | 0.4661 | 0.5703 | 0.7552 | | 0.0411 | 8.4853 | 3462 | 0.5600 | 0.3724 | 0.5600 | 0.7483 | | 0.0411 | 8.4902 | 3464 | 0.5534 | 0.4273 | 0.5534 | 0.7439 | | 0.0411 | 8.4951 | 3466 | 0.5496 | 0.4273 | 0.5496 | 0.7414 | | 0.0411 | 8.5 | 3468 | 0.5488 | 0.4273 | 0.5488 | 0.7408 | | 0.0411 | 8.5049 | 3470 | 0.5537 | 0.4273 | 0.5537 | 0.7441 | | 0.0411 | 8.5098 | 3472 | 0.5573 | 0.5157 | 0.5573 | 0.7465 | | 0.0411 | 8.5147 | 3474 | 0.5669 | 0.4661 | 0.5669 | 0.7529 | | 0.0411 | 8.5196 | 3476 | 0.5744 | 0.4661 | 0.5744 | 0.7579 | | 0.0411 | 8.5245 | 3478 | 0.5785 | 0.4661 | 0.5785 | 0.7606 | | 0.0411 | 8.5294 | 3480 | 0.5774 | 0.4661 | 0.5774 | 0.7599 | | 0.0411 | 8.5343 | 3482 | 0.5785 | 0.4661 | 0.5785 | 0.7606 | | 0.0411 | 8.5392 | 3484 | 0.5789 | 0.4661 | 0.5789 | 0.7609 | | 0.0411 | 8.5441 | 3486 | 0.5788 | 0.4661 | 0.5788 | 0.7608 | | 0.0411 | 8.5490 | 3488 | 0.5818 | 0.4661 | 0.5818 | 0.7628 | | 0.0411 | 8.5539 | 3490 | 0.5864 | 0.4661 | 0.5864 | 0.7658 | | 0.0411 | 8.5588 | 3492 | 0.5885 | 0.4661 | 0.5885 | 0.7671 | | 0.0411 | 8.5637 | 3494 | 0.5878 | 0.4661 | 0.5878 | 0.7667 | | 0.0411 | 8.5686 | 3496 | 0.5867 | 0.4661 | 0.5867 | 0.7660 | | 0.0411 | 8.5735 | 3498 | 0.5905 | 0.4661 | 0.5905 | 0.7684 | | 0.0395 | 8.5784 | 3500 | 0.5949 | 0.4661 | 0.5949 | 0.7713 | | 0.0395 | 8.5833 | 3502 | 0.6033 | 0.4661 | 0.6033 | 0.7767 | | 0.0395 | 8.5882 | 3504 | 0.6088 | 0.4661 | 0.6088 | 0.7803 | | 0.0395 | 8.5931 | 3506 | 0.6136 | 0.3255 | 0.6136 | 0.7834 | | 0.0395 | 8.5980 | 3508 | 0.6195 | 0.3255 | 0.6195 | 0.7871 | | 0.0395 | 8.6029 | 3510 | 0.6209 | 0.3255 | 0.6209 | 0.7880 | | 0.0395 | 8.6078 | 3512 | 0.6172 | 0.3255 | 0.6172 | 0.7856 | | 0.0395 | 8.6127 | 3514 | 0.6131 | 0.3255 | 0.6131 | 0.7830 | | 0.0395 | 8.6176 | 3516 | 0.6108 | 0.3255 | 0.6108 | 0.7815 | | 0.0395 | 8.6225 | 3518 | 0.6101 | 0.3255 | 0.6101 | 0.7811 | | 0.0395 | 8.6275 | 3520 | 0.6114 | 0.3255 | 0.6114 | 0.7819 | | 0.0395 | 8.6324 | 3522 | 0.6152 | 0.3255 | 0.6152 | 0.7843 | | 0.0395 | 8.6373 | 3524 | 0.6157 | 0.3255 | 0.6157 | 0.7847 | | 0.0395 | 8.6422 | 3526 | 0.6097 | 0.3255 | 0.6097 | 0.7809 | | 0.0395 | 8.6471 | 3528 | 0.5981 | 0.3255 | 0.5981 | 0.7734 | | 0.0395 | 8.6520 | 3530 | 0.5852 | 0.3255 | 0.5852 | 0.7650 | | 0.0395 | 8.6569 | 3532 | 0.5769 | 0.3255 | 0.5769 | 0.7595 | | 0.0395 | 8.6618 | 3534 | 0.5712 | 0.2186 | 0.5712 | 0.7558 | | 0.0395 | 8.6667 | 3536 | 0.5646 | 0.3724 | 0.5646 | 0.7514 | | 0.0395 | 8.6716 | 3538 | 0.5561 | 0.3724 | 0.5561 | 0.7457 | | 0.0395 | 8.6765 | 3540 | 0.5544 | 0.3724 | 0.5544 | 0.7446 | | 0.0395 | 8.6814 | 3542 | 0.5542 | 0.3724 | 0.5542 | 0.7445 | | 0.0395 | 8.6863 | 3544 | 0.5583 | 0.3724 | 0.5583 | 0.7472 | | 0.0395 | 8.6912 | 3546 | 0.5577 | 0.3724 | 0.5577 | 0.7468 | | 0.0395 | 8.6961 | 3548 | 0.5577 | 0.3724 | 0.5577 | 0.7468 | | 0.0395 | 8.7010 | 3550 | 0.5605 | 0.3724 | 0.5605 | 0.7486 | | 0.0395 | 8.7059 | 3552 | 0.5652 | 0.3724 | 0.5652 | 0.7518 | | 0.0395 | 8.7108 | 3554 | 0.5696 | 0.3724 | 0.5696 | 0.7547 | | 0.0395 | 8.7157 | 3556 | 0.5758 | 0.3724 | 0.5758 | 0.7588 | | 0.0395 | 8.7206 | 3558 | 0.5789 | 0.3724 | 0.5789 | 0.7609 | | 0.0395 | 8.7255 | 3560 | 0.5809 | 0.3724 | 0.5809 | 0.7622 | | 0.0395 | 8.7304 | 3562 | 0.5822 | 0.3724 | 0.5822 | 0.7630 | | 0.0395 | 8.7353 | 3564 | 0.5844 | 0.3724 | 0.5844 | 0.7645 | | 0.0395 | 8.7402 | 3566 | 0.5884 | 0.3724 | 0.5884 | 0.7671 | | 0.0395 | 8.7451 | 3568 | 0.5900 | 0.3724 | 0.5900 | 0.7681 | | 0.0395 | 8.75 | 3570 | 0.5911 | 0.3724 | 0.5911 | 0.7688 | | 0.0395 | 8.7549 | 3572 | 0.5946 | 0.3724 | 0.5946 | 0.7711 | | 0.0395 | 8.7598 | 3574 | 0.5956 | 0.3724 | 0.5956 | 0.7718 | | 0.0395 | 8.7647 | 3576 | 0.5976 | 0.3724 | 0.5976 | 0.7731 | | 0.0395 | 8.7696 | 3578 | 0.5984 | 0.3724 | 0.5984 | 0.7736 | | 0.0395 | 8.7745 | 3580 | 0.5987 | 0.3724 | 0.5987 | 0.7738 | | 0.0395 | 8.7794 | 3582 | 0.5977 | 0.3724 | 0.5977 | 0.7731 | | 0.0395 | 8.7843 | 3584 | 0.5946 | 0.3724 | 0.5946 | 0.7711 | | 0.0395 | 8.7892 | 3586 | 0.5934 | 0.3724 | 0.5934 | 0.7703 | | 0.0395 | 8.7941 | 3588 | 0.5942 | 0.3724 | 0.5942 | 0.7709 | | 0.0395 | 8.7990 | 3590 | 0.5933 | 0.3724 | 0.5933 | 0.7702 | | 0.0395 | 8.8039 | 3592 | 0.5892 | 0.3724 | 0.5892 | 0.7676 | | 0.0395 | 8.8088 | 3594 | 0.5852 | 0.3724 | 0.5852 | 0.7650 | | 0.0395 | 8.8137 | 3596 | 0.5821 | 0.3724 | 0.5821 | 0.7629 | | 0.0395 | 8.8186 | 3598 | 0.5784 | 0.3724 | 0.5784 | 0.7605 | | 0.0395 | 8.8235 | 3600 | 0.5757 | 0.3724 | 0.5757 | 0.7588 | | 0.0395 | 8.8284 | 3602 | 0.5732 | 0.3724 | 0.5732 | 0.7571 | | 0.0395 | 8.8333 | 3604 | 0.5686 | 0.3724 | 0.5686 | 0.7541 | | 0.0395 | 8.8382 | 3606 | 0.5621 | 0.4273 | 0.5621 | 0.7497 | | 0.0395 | 8.8431 | 3608 | 0.5570 | 0.4273 | 0.5570 | 0.7463 | | 0.0395 | 8.8480 | 3610 | 0.5540 | 0.4273 | 0.5540 | 0.7443 | | 0.0395 | 8.8529 | 3612 | 0.5528 | 0.3865 | 0.5528 | 0.7435 | | 0.0395 | 8.8578 | 3614 | 0.5531 | 0.4273 | 0.5531 | 0.7437 | | 0.0395 | 8.8627 | 3616 | 0.5538 | 0.4273 | 0.5538 | 0.7442 | | 0.0395 | 8.8676 | 3618 | 0.5534 | 0.4273 | 0.5534 | 0.7439 | | 0.0395 | 8.8725 | 3620 | 0.5550 | 0.4273 | 0.5550 | 0.7450 | | 0.0395 | 8.8775 | 3622 | 0.5559 | 0.4273 | 0.5559 | 0.7456 | | 0.0395 | 8.8824 | 3624 | 0.5563 | 0.4273 | 0.5563 | 0.7458 | | 0.0395 | 8.8873 | 3626 | 0.5566 | 0.4273 | 0.5566 | 0.7461 | | 0.0395 | 8.8922 | 3628 | 0.5598 | 0.4273 | 0.5598 | 0.7482 | | 0.0395 | 8.8971 | 3630 | 0.5633 | 0.4273 | 0.5633 | 0.7506 | | 0.0395 | 8.9020 | 3632 | 0.5721 | 0.4273 | 0.5721 | 0.7563 | | 0.0395 | 8.9069 | 3634 | 0.5803 | 0.2186 | 0.5803 | 0.7618 | | 0.0395 | 8.9118 | 3636 | 0.5836 | 0.2186 | 0.5836 | 0.7640 | | 0.0395 | 8.9167 | 3638 | 0.5866 | 0.2186 | 0.5866 | 0.7659 | | 0.0395 | 8.9216 | 3640 | 0.5892 | 0.2186 | 0.5892 | 0.7676 | | 0.0395 | 8.9265 | 3642 | 0.5961 | 0.2186 | 0.5961 | 0.7721 | | 0.0395 | 8.9314 | 3644 | 0.6027 | 0.2186 | 0.6027 | 0.7764 | | 0.0395 | 8.9363 | 3646 | 0.6056 | 0.3255 | 0.6056 | 0.7782 | | 0.0395 | 8.9412 | 3648 | 0.6084 | 0.3636 | 0.6084 | 0.7800 | | 0.0395 | 8.9461 | 3650 | 0.6100 | 0.3636 | 0.6100 | 0.7810 | | 0.0395 | 8.9510 | 3652 | 0.6083 | 0.3636 | 0.6083 | 0.7799 | | 0.0395 | 8.9559 | 3654 | 0.6041 | 0.3636 | 0.6041 | 0.7772 | | 0.0395 | 8.9608 | 3656 | 0.5978 | 0.3636 | 0.5978 | 0.7732 | | 0.0395 | 8.9657 | 3658 | 0.5888 | 0.2588 | 0.5888 | 0.7673 | | 0.0395 | 8.9706 | 3660 | 0.5776 | 0.2186 | 0.5776 | 0.7600 | | 0.0395 | 8.9755 | 3662 | 0.5683 | 0.2186 | 0.5683 | 0.7539 | | 0.0395 | 8.9804 | 3664 | 0.5618 | 0.2186 | 0.5618 | 0.7495 | | 0.0395 | 8.9853 | 3666 | 0.5585 | 0.3724 | 0.5585 | 0.7473 | | 0.0395 | 8.9902 | 3668 | 0.5572 | 0.3724 | 0.5572 | 0.7465 | | 0.0395 | 8.9951 | 3670 | 0.5576 | 0.3724 | 0.5576 | 0.7467 | | 0.0395 | 9.0 | 3672 | 0.5591 | 0.2186 | 0.5591 | 0.7477 | | 0.0395 | 9.0049 | 3674 | 0.5627 | 0.2186 | 0.5627 | 0.7501 | | 0.0395 | 9.0098 | 3676 | 0.5669 | 0.2186 | 0.5669 | 0.7529 | | 0.0395 | 9.0147 | 3678 | 0.5710 | 0.2186 | 0.5710 | 0.7556 | | 0.0395 | 9.0196 | 3680 | 0.5723 | 0.2186 | 0.5723 | 0.7565 | | 0.0395 | 9.0245 | 3682 | 0.5713 | 0.2186 | 0.5713 | 0.7559 | | 0.0395 | 9.0294 | 3684 | 0.5690 | 0.2186 | 0.5690 | 0.7543 | | 0.0395 | 9.0343 | 3686 | 0.5685 | 0.2186 | 0.5685 | 0.7540 | | 0.0395 | 9.0392 | 3688 | 0.5687 | 0.2186 | 0.5687 | 0.7541 | | 0.0395 | 9.0441 | 3690 | 0.5692 | 0.2186 | 0.5692 | 0.7544 | | 0.0395 | 9.0490 | 3692 | 0.5677 | 0.2186 | 0.5677 | 0.7535 | | 0.0395 | 9.0539 | 3694 | 0.5653 | 0.2186 | 0.5653 | 0.7519 | | 0.0395 | 9.0588 | 3696 | 0.5648 | 0.2186 | 0.5648 | 0.7516 | | 0.0395 | 9.0637 | 3698 | 0.5657 | 0.2186 | 0.5657 | 0.7521 | | 0.0395 | 9.0686 | 3700 | 0.5678 | 0.2186 | 0.5678 | 0.7536 | | 0.0395 | 9.0735 | 3702 | 0.5700 | 0.2186 | 0.5700 | 0.7550 | | 0.0395 | 9.0784 | 3704 | 0.5745 | 0.2186 | 0.5745 | 0.7579 | | 0.0395 | 9.0833 | 3706 | 0.5772 | 0.2186 | 0.5772 | 0.7597 | | 0.0395 | 9.0882 | 3708 | 0.5789 | 0.2186 | 0.5789 | 0.7609 | | 0.0395 | 9.0931 | 3710 | 0.5789 | 0.2186 | 0.5789 | 0.7609 | | 0.0395 | 9.0980 | 3712 | 0.5778 | 0.2759 | 0.5778 | 0.7602 | | 0.0395 | 9.1029 | 3714 | 0.5768 | 0.4273 | 0.5768 | 0.7595 | | 0.0395 | 9.1078 | 3716 | 0.5758 | 0.4273 | 0.5758 | 0.7588 | | 0.0395 | 9.1127 | 3718 | 0.5743 | 0.4273 | 0.5743 | 0.7579 | | 0.0395 | 9.1176 | 3720 | 0.5733 | 0.4273 | 0.5733 | 0.7571 | | 0.0395 | 9.1225 | 3722 | 0.5704 | 0.4273 | 0.5704 | 0.7553 | | 0.0395 | 9.1275 | 3724 | 0.5683 | 0.4273 | 0.5683 | 0.7539 | | 0.0395 | 9.1324 | 3726 | 0.5648 | 0.4273 | 0.5648 | 0.7515 | | 0.0395 | 9.1373 | 3728 | 0.5603 | 0.4273 | 0.5603 | 0.7485 | | 0.0395 | 9.1422 | 3730 | 0.5565 | 0.4273 | 0.5565 | 0.7460 | | 0.0395 | 9.1471 | 3732 | 0.5521 | 0.3865 | 0.5521 | 0.7431 | | 0.0395 | 9.1520 | 3734 | 0.5487 | 0.3865 | 0.5487 | 0.7407 | | 0.0395 | 9.1569 | 3736 | 0.5448 | 0.3865 | 0.5448 | 0.7381 | | 0.0395 | 9.1618 | 3738 | 0.5402 | 0.3865 | 0.5402 | 0.7350 | | 0.0395 | 9.1667 | 3740 | 0.5361 | 0.3865 | 0.5361 | 0.7322 | | 0.0395 | 9.1716 | 3742 | 0.5336 | 0.3865 | 0.5336 | 0.7305 | | 0.0395 | 9.1765 | 3744 | 0.5327 | 0.3865 | 0.5327 | 0.7299 | | 0.0395 | 9.1814 | 3746 | 0.5325 | 0.3865 | 0.5325 | 0.7297 | | 0.0395 | 9.1863 | 3748 | 0.5332 | 0.3865 | 0.5332 | 0.7302 | | 0.0395 | 9.1912 | 3750 | 0.5334 | 0.3865 | 0.5334 | 0.7304 | | 0.0395 | 9.1961 | 3752 | 0.5338 | 0.3865 | 0.5338 | 0.7306 | | 0.0395 | 9.2010 | 3754 | 0.5335 | 0.3865 | 0.5335 | 0.7304 | | 0.0395 | 9.2059 | 3756 | 0.5345 | 0.3865 | 0.5345 | 0.7311 | | 0.0395 | 9.2108 | 3758 | 0.5365 | 0.3865 | 0.5365 | 0.7325 | | 0.0395 | 9.2157 | 3760 | 0.5391 | 0.3865 | 0.5391 | 0.7342 | | 0.0395 | 9.2206 | 3762 | 0.5403 | 0.4273 | 0.5403 | 0.7350 | | 0.0395 | 9.2255 | 3764 | 0.5400 | 0.4273 | 0.5400 | 0.7349 | | 0.0395 | 9.2304 | 3766 | 0.5395 | 0.4273 | 0.5395 | 0.7345 | | 0.0395 | 9.2353 | 3768 | 0.5383 | 0.3865 | 0.5383 | 0.7337 | | 0.0395 | 9.2402 | 3770 | 0.5370 | 0.3865 | 0.5370 | 0.7328 | | 0.0395 | 9.2451 | 3772 | 0.5368 | 0.3865 | 0.5368 | 0.7327 | | 0.0395 | 9.25 | 3774 | 0.5364 | 0.4273 | 0.5364 | 0.7324 | | 0.0395 | 9.2549 | 3776 | 0.5382 | 0.4273 | 0.5382 | 0.7336 | | 0.0395 | 9.2598 | 3778 | 0.5408 | 0.4273 | 0.5408 | 0.7354 | | 0.0395 | 9.2647 | 3780 | 0.5430 | 0.4273 | 0.5430 | 0.7369 | | 0.0395 | 9.2696 | 3782 | 0.5467 | 0.4273 | 0.5467 | 0.7394 | | 0.0395 | 9.2745 | 3784 | 0.5521 | 0.4273 | 0.5521 | 0.7430 | | 0.0395 | 9.2794 | 3786 | 0.5571 | 0.3724 | 0.5571 | 0.7464 | | 0.0395 | 9.2843 | 3788 | 0.5607 | 0.2186 | 0.5607 | 0.7488 | | 0.0395 | 9.2892 | 3790 | 0.5620 | 0.2186 | 0.5620 | 0.7496 | | 0.0395 | 9.2941 | 3792 | 0.5625 | 0.3724 | 0.5625 | 0.7500 | | 0.0395 | 9.2990 | 3794 | 0.5618 | 0.3724 | 0.5618 | 0.7496 | | 0.0395 | 9.3039 | 3796 | 0.5623 | 0.3724 | 0.5623 | 0.7499 | | 0.0395 | 9.3088 | 3798 | 0.5630 | 0.3724 | 0.5630 | 0.7503 | | 0.0395 | 9.3137 | 3800 | 0.5649 | 0.3724 | 0.5649 | 0.7516 | | 0.0395 | 9.3186 | 3802 | 0.5654 | 0.3724 | 0.5654 | 0.7519 | | 0.0395 | 9.3235 | 3804 | 0.5646 | 0.3724 | 0.5646 | 0.7514 | | 0.0395 | 9.3284 | 3806 | 0.5633 | 0.3724 | 0.5633 | 0.7506 | | 0.0395 | 9.3333 | 3808 | 0.5630 | 0.3724 | 0.5630 | 0.7503 | | 0.0395 | 9.3382 | 3810 | 0.5640 | 0.3724 | 0.5640 | 0.7510 | | 0.0395 | 9.3431 | 3812 | 0.5662 | 0.3724 | 0.5662 | 0.7525 | | 0.0395 | 9.3480 | 3814 | 0.5707 | 0.4661 | 0.5707 | 0.7555 | | 0.0395 | 9.3529 | 3816 | 0.5756 | 0.4661 | 0.5756 | 0.7587 | | 0.0395 | 9.3578 | 3818 | 0.5784 | 0.4661 | 0.5784 | 0.7605 | | 0.0395 | 9.3627 | 3820 | 0.5805 | 0.4661 | 0.5805 | 0.7619 | | 0.0395 | 9.3676 | 3822 | 0.5831 | 0.4661 | 0.5831 | 0.7636 | | 0.0395 | 9.3725 | 3824 | 0.5853 | 0.4661 | 0.5853 | 0.7651 | | 0.0395 | 9.3775 | 3826 | 0.5859 | 0.3255 | 0.5859 | 0.7654 | | 0.0395 | 9.3824 | 3828 | 0.5855 | 0.3255 | 0.5855 | 0.7652 | | 0.0395 | 9.3873 | 3830 | 0.5837 | 0.4661 | 0.5837 | 0.7640 | | 0.0395 | 9.3922 | 3832 | 0.5815 | 0.4661 | 0.5815 | 0.7626 | | 0.0395 | 9.3971 | 3834 | 0.5779 | 0.4661 | 0.5779 | 0.7602 | | 0.0395 | 9.4020 | 3836 | 0.5757 | 0.4661 | 0.5757 | 0.7587 | | 0.0395 | 9.4069 | 3838 | 0.5724 | 0.4661 | 0.5724 | 0.7566 | | 0.0395 | 9.4118 | 3840 | 0.5706 | 0.4661 | 0.5706 | 0.7554 | | 0.0395 | 9.4167 | 3842 | 0.5696 | 0.4661 | 0.5696 | 0.7547 | | 0.0395 | 9.4216 | 3844 | 0.5677 | 0.4661 | 0.5677 | 0.7535 | | 0.0395 | 9.4265 | 3846 | 0.5645 | 0.3724 | 0.5645 | 0.7514 | | 0.0395 | 9.4314 | 3848 | 0.5597 | 0.3724 | 0.5597 | 0.7481 | | 0.0395 | 9.4363 | 3850 | 0.5552 | 0.4273 | 0.5552 | 0.7451 | | 0.0395 | 9.4412 | 3852 | 0.5515 | 0.4273 | 0.5515 | 0.7426 | | 0.0395 | 9.4461 | 3854 | 0.5495 | 0.4273 | 0.5495 | 0.7413 | | 0.0395 | 9.4510 | 3856 | 0.5481 | 0.4273 | 0.5481 | 0.7403 | | 0.0395 | 9.4559 | 3858 | 0.5491 | 0.4273 | 0.5491 | 0.7410 | | 0.0395 | 9.4608 | 3860 | 0.5512 | 0.4273 | 0.5512 | 0.7424 | | 0.0395 | 9.4657 | 3862 | 0.5539 | 0.4273 | 0.5539 | 0.7443 | | 0.0395 | 9.4706 | 3864 | 0.5568 | 0.4273 | 0.5568 | 0.7462 | | 0.0395 | 9.4755 | 3866 | 0.5586 | 0.4273 | 0.5586 | 0.7474 | | 0.0395 | 9.4804 | 3868 | 0.5591 | 0.4273 | 0.5591 | 0.7477 | | 0.0395 | 9.4853 | 3870 | 0.5600 | 0.3724 | 0.5600 | 0.7483 | | 0.0395 | 9.4902 | 3872 | 0.5613 | 0.3724 | 0.5613 | 0.7492 | | 0.0395 | 9.4951 | 3874 | 0.5638 | 0.3724 | 0.5638 | 0.7509 | | 0.0395 | 9.5 | 3876 | 0.5661 | 0.3724 | 0.5661 | 0.7524 | | 0.0395 | 9.5049 | 3878 | 0.5688 | 0.4661 | 0.5688 | 0.7542 | | 0.0395 | 9.5098 | 3880 | 0.5699 | 0.4661 | 0.5699 | 0.7549 | | 0.0395 | 9.5147 | 3882 | 0.5689 | 0.4661 | 0.5689 | 0.7543 | | 0.0395 | 9.5196 | 3884 | 0.5698 | 0.4661 | 0.5698 | 0.7549 | | 0.0395 | 9.5245 | 3886 | 0.5715 | 0.4661 | 0.5715 | 0.7560 | | 0.0395 | 9.5294 | 3888 | 0.5728 | 0.4661 | 0.5728 | 0.7569 | | 0.0395 | 9.5343 | 3890 | 0.5726 | 0.4661 | 0.5726 | 0.7567 | | 0.0395 | 9.5392 | 3892 | 0.5726 | 0.4661 | 0.5726 | 0.7567 | | 0.0395 | 9.5441 | 3894 | 0.5726 | 0.4661 | 0.5726 | 0.7567 | | 0.0395 | 9.5490 | 3896 | 0.5733 | 0.4661 | 0.5733 | 0.7572 | | 0.0395 | 9.5539 | 3898 | 0.5744 | 0.4661 | 0.5744 | 0.7579 | | 0.0395 | 9.5588 | 3900 | 0.5754 | 0.4661 | 0.5754 | 0.7585 | | 0.0395 | 9.5637 | 3902 | 0.5769 | 0.4661 | 0.5769 | 0.7595 | | 0.0395 | 9.5686 | 3904 | 0.5777 | 0.4661 | 0.5777 | 0.7601 | | 0.0395 | 9.5735 | 3906 | 0.5774 | 0.4661 | 0.5774 | 0.7599 | | 0.0395 | 9.5784 | 3908 | 0.5773 | 0.4661 | 0.5773 | 0.7598 | | 0.0395 | 9.5833 | 3910 | 0.5783 | 0.4661 | 0.5783 | 0.7604 | | 0.0395 | 9.5882 | 3912 | 0.5782 | 0.4661 | 0.5782 | 0.7604 | | 0.0395 | 9.5931 | 3914 | 0.5770 | 0.4661 | 0.5770 | 0.7596 | | 0.0395 | 9.5980 | 3916 | 0.5759 | 0.4661 | 0.5759 | 0.7589 | | 0.0395 | 9.6029 | 3918 | 0.5747 | 0.4661 | 0.5747 | 0.7581 | | 0.0395 | 9.6078 | 3920 | 0.5728 | 0.4661 | 0.5728 | 0.7568 | | 0.0395 | 9.6127 | 3922 | 0.5706 | 0.3724 | 0.5706 | 0.7554 | | 0.0395 | 9.6176 | 3924 | 0.5685 | 0.3724 | 0.5685 | 0.7540 | | 0.0395 | 9.6225 | 3926 | 0.5657 | 0.3724 | 0.5657 | 0.7521 | | 0.0395 | 9.6275 | 3928 | 0.5639 | 0.3724 | 0.5639 | 0.7509 | | 0.0395 | 9.6324 | 3930 | 0.5626 | 0.3724 | 0.5626 | 0.7501 | | 0.0395 | 9.6373 | 3932 | 0.5616 | 0.3724 | 0.5616 | 0.7494 | | 0.0395 | 9.6422 | 3934 | 0.5607 | 0.3724 | 0.5607 | 0.7488 | | 0.0395 | 9.6471 | 3936 | 0.5582 | 0.3724 | 0.5582 | 0.7471 | | 0.0395 | 9.6520 | 3938 | 0.5567 | 0.3724 | 0.5567 | 0.7461 | | 0.0395 | 9.6569 | 3940 | 0.5553 | 0.3724 | 0.5553 | 0.7452 | | 0.0395 | 9.6618 | 3942 | 0.5543 | 0.3724 | 0.5543 | 0.7445 | | 0.0395 | 9.6667 | 3944 | 0.5538 | 0.3724 | 0.5538 | 0.7441 | | 0.0395 | 9.6716 | 3946 | 0.5537 | 0.3724 | 0.5537 | 0.7441 | | 0.0395 | 9.6765 | 3948 | 0.5541 | 0.3724 | 0.5541 | 0.7444 | | 0.0395 | 9.6814 | 3950 | 0.5545 | 0.3724 | 0.5545 | 0.7447 | | 0.0395 | 9.6863 | 3952 | 0.5553 | 0.3724 | 0.5553 | 0.7452 | | 0.0395 | 9.6912 | 3954 | 0.5557 | 0.3724 | 0.5557 | 0.7455 | | 0.0395 | 9.6961 | 3956 | 0.5560 | 0.3724 | 0.5560 | 0.7456 | | 0.0395 | 9.7010 | 3958 | 0.5553 | 0.3724 | 0.5553 | 0.7452 | | 0.0395 | 9.7059 | 3960 | 0.5555 | 0.3724 | 0.5555 | 0.7453 | | 0.0395 | 9.7108 | 3962 | 0.5563 | 0.3724 | 0.5563 | 0.7459 | | 0.0395 | 9.7157 | 3964 | 0.5571 | 0.3724 | 0.5571 | 0.7464 | | 0.0395 | 9.7206 | 3966 | 0.5581 | 0.3724 | 0.5581 | 0.7471 | | 0.0395 | 9.7255 | 3968 | 0.5588 | 0.3724 | 0.5588 | 0.7475 | | 0.0395 | 9.7304 | 3970 | 0.5593 | 0.3724 | 0.5593 | 0.7478 | | 0.0395 | 9.7353 | 3972 | 0.5601 | 0.3724 | 0.5601 | 0.7484 | | 0.0395 | 9.7402 | 3974 | 0.5604 | 0.3724 | 0.5604 | 0.7486 | | 0.0395 | 9.7451 | 3976 | 0.5609 | 0.3724 | 0.5609 | 0.7490 | | 0.0395 | 9.75 | 3978 | 0.5611 | 0.3724 | 0.5611 | 0.7490 | | 0.0395 | 9.7549 | 3980 | 0.5616 | 0.4661 | 0.5616 | 0.7494 | | 0.0395 | 9.7598 | 3982 | 0.5622 | 0.4661 | 0.5622 | 0.7498 | | 0.0395 | 9.7647 | 3984 | 0.5627 | 0.4661 | 0.5627 | 0.7501 | | 0.0395 | 9.7696 | 3986 | 0.5633 | 0.4661 | 0.5633 | 0.7505 | | 0.0395 | 9.7745 | 3988 | 0.5643 | 0.4661 | 0.5643 | 0.7512 | | 0.0395 | 9.7794 | 3990 | 0.5650 | 0.4661 | 0.5650 | 0.7517 | | 0.0395 | 9.7843 | 3992 | 0.5657 | 0.4661 | 0.5657 | 0.7522 | | 0.0395 | 9.7892 | 3994 | 0.5667 | 0.3255 | 0.5667 | 0.7528 | | 0.0395 | 9.7941 | 3996 | 0.5677 | 0.3255 | 0.5677 | 0.7535 | | 0.0395 | 9.7990 | 3998 | 0.5690 | 0.3255 | 0.5690 | 0.7543 | | 0.0358 | 9.8039 | 4000 | 0.5703 | 0.3255 | 0.5703 | 0.7552 | | 0.0358 | 9.8088 | 4002 | 0.5722 | 0.3255 | 0.5722 | 0.7564 | | 0.0358 | 9.8137 | 4004 | 0.5739 | 0.3255 | 0.5739 | 0.7576 | | 0.0358 | 9.8186 | 4006 | 0.5745 | 0.3255 | 0.5745 | 0.7580 | | 0.0358 | 9.8235 | 4008 | 0.5747 | 0.3255 | 0.5747 | 0.7581 | | 0.0358 | 9.8284 | 4010 | 0.5741 | 0.3255 | 0.5741 | 0.7577 | | 0.0358 | 9.8333 | 4012 | 0.5736 | 0.3255 | 0.5736 | 0.7574 | | 0.0358 | 9.8382 | 4014 | 0.5741 | 0.3255 | 0.5741 | 0.7577 | | 0.0358 | 9.8431 | 4016 | 0.5744 | 0.3255 | 0.5744 | 0.7579 | | 0.0358 | 9.8480 | 4018 | 0.5741 | 0.3255 | 0.5741 | 0.7577 | | 0.0358 | 9.8529 | 4020 | 0.5739 | 0.3255 | 0.5739 | 0.7576 | | 0.0358 | 9.8578 | 4022 | 0.5734 | 0.3255 | 0.5734 | 0.7572 | | 0.0358 | 9.8627 | 4024 | 0.5731 | 0.3255 | 0.5731 | 0.7570 | | 0.0358 | 9.8676 | 4026 | 0.5723 | 0.3255 | 0.5723 | 0.7565 | | 0.0358 | 9.8725 | 4028 | 0.5719 | 0.3255 | 0.5719 | 0.7562 | | 0.0358 | 9.8775 | 4030 | 0.5717 | 0.3255 | 0.5717 | 0.7561 | | 0.0358 | 9.8824 | 4032 | 0.5713 | 0.3255 | 0.5713 | 0.7558 | | 0.0358 | 9.8873 | 4034 | 0.5709 | 0.3255 | 0.5709 | 0.7556 | | 0.0358 | 9.8922 | 4036 | 0.5706 | 0.3255 | 0.5706 | 0.7554 | | 0.0358 | 9.8971 | 4038 | 0.5701 | 0.3255 | 0.5701 | 0.7550 | | 0.0358 | 9.9020 | 4040 | 0.5696 | 0.3255 | 0.5696 | 0.7547 | | 0.0358 | 9.9069 | 4042 | 0.5691 | 0.3255 | 0.5691 | 0.7544 | | 0.0358 | 9.9118 | 4044 | 0.5685 | 0.3255 | 0.5685 | 0.7540 | | 0.0358 | 9.9167 | 4046 | 0.5679 | 0.3255 | 0.5679 | 0.7536 | | 0.0358 | 9.9216 | 4048 | 0.5674 | 0.3255 | 0.5674 | 0.7533 | | 0.0358 | 9.9265 | 4050 | 0.5670 | 0.3255 | 0.5670 | 0.7530 | | 0.0358 | 9.9314 | 4052 | 0.5666 | 0.3255 | 0.5666 | 0.7527 | | 0.0358 | 9.9363 | 4054 | 0.5663 | 0.4661 | 0.5663 | 0.7525 | | 0.0358 | 9.9412 | 4056 | 0.5662 | 0.4661 | 0.5662 | 0.7524 | | 0.0358 | 9.9461 | 4058 | 0.5659 | 0.4661 | 0.5659 | 0.7523 | | 0.0358 | 9.9510 | 4060 | 0.5657 | 0.4661 | 0.5657 | 0.7521 | | 0.0358 | 9.9559 | 4062 | 0.5656 | 0.4661 | 0.5656 | 0.7521 | | 0.0358 | 9.9608 | 4064 | 0.5656 | 0.4661 | 0.5656 | 0.7521 | | 0.0358 | 9.9657 | 4066 | 0.5656 | 0.4661 | 0.5656 | 0.7520 | | 0.0358 | 9.9706 | 4068 | 0.5655 | 0.4661 | 0.5655 | 0.7520 | | 0.0358 | 9.9755 | 4070 | 0.5654 | 0.4661 | 0.5654 | 0.7519 | | 0.0358 | 9.9804 | 4072 | 0.5654 | 0.4661 | 0.5654 | 0.7519 | | 0.0358 | 9.9853 | 4074 | 0.5654 | 0.4661 | 0.5654 | 0.7519 | | 0.0358 | 9.9902 | 4076 | 0.5654 | 0.4661 | 0.5654 | 0.7519 | | 0.0358 | 9.9951 | 4078 | 0.5654 | 0.4661 | 0.5654 | 0.7519 | | 0.0358 | 10.0 | 4080 | 0.5654 | 0.4661 | 0.5654 | 0.7519 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

SeppeV/ernie2p0_ft_pref_10pc

SeppeV

2024-11-25T09:09:00Z

89

0

transformers

[ "transformers", "safetensors", "ernie", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-23T15:34:09Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

allknowingroger/Marco-01-slerp7-7B

allknowingroger

2024-11-25T09:08:36Z

10

1

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "base_model:AIDC-AI/Marco-o1", "base_model:merge:AIDC-AI/Marco-o1", "base_model:ZeroXClem/Qwen2.5-7B-HomerCreative-Mix", "base_model:merge:ZeroXClem/Qwen2.5-7B-HomerCreative-Mix", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T08:54:00Z

--- base_model: - ZeroXClem/Qwen2.5-7B-HomerCreative-Mix - AIDC-AI/Marco-o1 library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [ZeroXClem/Qwen2.5-7B-HomerCreative-Mix](https://huggingface.co/ZeroXClem/Qwen2.5-7B-HomerCreative-Mix) * [AIDC-AI/Marco-o1](https://huggingface.co/AIDC-AI/Marco-o1) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: AIDC-AI/Marco-o1 - model: ZeroXClem/Qwen2.5-7B-HomerCreative-Mix merge_method: slerp base_model: AIDC-AI/Marco-o1 dtype: bfloat16 parameters: t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers ```

jebish7/cde-small-v1_MNR_3

jebish7

2024-11-25T09:06:55Z

6

0

sentence-transformers

[ "sentence-transformers", "safetensors", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:29545", "loss:MultipleNegativesRankingLoss", "custom_code", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:jxm/cde-small-v1", "base_model:finetune:jxm/cde-small-v1", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-11-25T09:06:28Z

--- base_model: jxm/cde-small-v1 library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:29545 - loss:MultipleNegativesRankingLoss widget: - source_sentence: In terms of audited accounts submission for an Applicant, could you clarify the scenarios in which the Regulator might agree that a reviewed pro forma statement of financial position is not needed, and what factors would be considered in making that determination? sentences: - "DocumentID: 1 | PassageID: 4.2.1.(3) | Passage: Where the regulator in another\ \ jurisdiction does not permit the implementation of policies, procedures, systems\ \ and controls consistent with these Rules, the Relevant Person must:\n(a)\tinform\ \ the Regulator in writing immediately; and\n(b)\tapply appropriate additional\ \ measures to manage the money laundering risks posed by the relevant branch or\ \ subsidiary." - "DocumentID: 11 | PassageID: 2.3.15.(4) | Passage: The Applicant must submit to\ \ the Regulator the following records, as applicable:\n(a)\tAudited accounts,\ \ for the purposes of this Rule and Rule 2.3.2(1), for the last three full financial\ \ years, noting that:\n(i)\tif the Applicant applies for admission less than ninety\ \ days after the end of its last financial year, unless the Applicant has audited\ \ accounts for its latest full financial year, the accounts may be for the three\ \ years to the end of the previous financial year, but must also include audited\ \ or reviewed accounts for its most recent semi-annual financial reporting period;\ \ and\n(ii)\tif the Applicant applies for admission more than six months and seventy-five\ \ days after the end of its last financial year, audited or reviewed accounts\ \ for its most recent semi-annual financial reporting period (or longer period\ \ if available).\n(b)\tUnless the Regulator agrees it is not needed, a reviewed\ \ pro forma statement of financial position. The review must be conducted by an\ \ accredited professional auditor of the company or an independent accountant." - 'DocumentID: 36 | PassageID: D.1.3. | Passage: Principle 1 – Oversight and responsibility of climate-related financial risk exposures.Certain functions related to the management of climate-related financial risks may be delegated, but, as with other risks, the board is ultimately responsible and accountable for monitoring, managing and overseeing climate-related risks for the financial firm. ' - source_sentence: A financial institution is interested in multiple designations, including the ADGM Green Fund and ADGM Green Bond. For each application, what fee will the institution incur? sentences: - 'DocumentID: 31 | PassageID: 63) | Passage: INITIAL DISCLOSURE OF MATERIAL ESTIMATES. Disclosure of material estimates of Contingent Resources Section 2.3 of the PRMS Guidelines states that Contingent Resources may be assigned for Petroleum Projects that are dependent on ‘technology under development’, and further recommended that a number of guidelines are followed in order to distinguish these estimates from those that should be classified as Unrecoverable Petroleum. By way of Rule 12.10.1(3), the FSRA fully supports and requires compliance with what is set out in the PRMS Guidelines. ' - 'DocumentID: 19 | PassageID: 40) | Passage: REGULATORY REQUIREMENTS FOR AUTHORISED PERSONS ENGAGED IN REGULATED ACTIVITIES IN RELATION TO VIRTUAL ASSETS Anti-Money Laundering and Countering Financing of Terrorism On 21 June 2019, FATF released a revised Guidance for a Risk-Based Approach (RBA) for VAs and VASPs, as well as an Interpretative Note for Recommendation 15. This built upon previous FATF statements by clarifying a RBA for Anti-Money Laundering and Countering the Financing of Terrorism (“AML/CFT”) purposes. The basic principle underlying the FATF Guidelines is that VASPs are expected to “identify, assess, and take effective action to mitigate their ML/TF risks” with respect to VAs. ' - "DocumentID: 4 | PassageID: 10.1.1 | Passage: A Person applying to the Regulator\ \ for any of the following designations:\n(a)\tADGM Green Fund;\n(b)\tADGM Climate\ \ Transition Fund;\n(c)\tADGM Green Portfolio;\n(d)\tADGM Climate Transition Portfolio;\n\ (e)\tADGM Green Bond; or\n(f)\tADGM Sustainability Linked Bond\nmust pay to the\ \ Regulator an application fee of $2,000." - source_sentence: How does the ADGM expect Authorised Persons to incorporate the eligibility of collateral types into their overall risk management framework, particularly concerning Islamic finance principles? sentences: - 'DocumentID: 17 | PassageID: Schedule 1.Part 2.Chapter 5.42.(2) | Passage: In determining for the purposes of sub-paragraph ‎(1)‎(b) whether Deposits are accepted only on particular occasions, regard is to be had to the frequency of those occasions and to any characteristics distinguishing them from each other.' - "DocumentID: 9 | PassageID: 6.8.5 | Passage: \n(a)\tA Fund Manager of an Islamic\ \ REIT may obtain financing either directly or through its Special Purpose Vehicle\ \ up to 65% of the total gross asset value of the Fund provided that such financing\ \ is provided in a Shari'a-compliant manner.\n(b)\tUpon becoming aware that the\ \ borrowing limit set out in 6.8.5(a) has been exceeded, the Fund Manager shall:\n\ (c)\timmediately inform Unitholders and the Regulator of the details of the breach\ \ and the proposed remedial action;\n(d)\tuse its best endeavours to reduce the\ \ excess borrowings;\n(e)\tnot permit the Fund to engage in additional borrowing;\ \ and\n(f)\tinform Unitholders and the Regulator on a regular basis as to the\ \ progress of the remedial action." - 'DocumentID: 9 | PassageID: 5.1.1.Guidance.(ii) | Passage: The prudential Category for Islamic Financial Institutions and other Authorised Persons (acting through an Islamic Window) undertaking the Regulated Activity of Managing PSIAs (which may be either a Restricted PSIA or an Unrestricted PSIA) is determined in accordance with PRU Rule 1.3. An Authorised Person which Manages PSIAs (whether as an Islamic Financial Institution or through an Islamic Window) must comply with the requirements in PRU in relation to specific prudential requirements relating to Trading Book and Non-Trading Book activities, including Credit Risk, Market Risk, Liquidity Risk and Group Risk.' - source_sentence: Can you please detail the specific Anti-Money Laundering (AML) and Countering Financing of Terrorism (CFT) measures and controls that our firm must have in place when dealing with Spot Commodities as per the FSRA's requirements? sentences: - 'DocumentID: 34 | PassageID: 65) | Passage: REGULATORY REQUIREMENTS - SPOT COMMODITY ACTIVITIES Sanctions Pursuant to AML Rule 11.2.1(1), an Authorised Person must have arrangements in place to ensure that only Spot Commodities that are not subject to sanctions or associated with an entity in the supply chain that is itself subject to a sanction, are used as part of its Regulated Activities, or utilised as part of a delivery and/or storage facility operated by itself (or by any third parties it uses). In demonstrating compliance with the Rule, an Authorised Person must have powers to resolve any breach in a timely fashion, such as taking emergency action itself or by compelling the delivery and/or storage facility to take appropriate action. The FSRA expects this to include the Authorised Person having the ability to sanction a Member, market participant or the delivery and/or storage facility for acts or omissions that compromise compliance with applicable sanctions. ' - "DocumentID: 18 | PassageID: 3.2 | Passage: Financial Services Permissions. VC\ \ Managers operating in ADGM require a Financial Services Permission (“FSP”) to\ \ undertake any Regulated Activity pertaining to VC Funds and/or co-investments\ \ by third parties in VC Funds. The Regulated Activities covered by the FSP will\ \ be dependent on the VC Managers’ investment strategy and business model.\n(a)\t\ Managing a Collective Investment Fund: this includes carrying out fund management\ \ activities in respect of a VC Fund.\n(b)\tAdvising on Investments or Credit\ \ : for VC Managers these activities will be restricted to activities related\ \ to co-investment alongside a VC Fund which the VC Manager manages, such as recommending\ \ that a client invest in an investee company alongside the VC Fund and on the\ \ strategy and structure required to make the investment.\n(c)\tArranging Deals\ \ in Investments: VC Managers may also wish to make arrangements to facilitate\ \ co-investments in the investee company.\nAuthorisation fees and supervision\ \ fees for a VC Manager are capped at USD 10,000 regardless of whether one or\ \ both of the additional Regulated Activities in b) and c) above in relation to\ \ co-investments are included in its FSP. The FSP will include restrictions appropriate\ \ to the business model of a VC Manager." - 'DocumentID: 24 | PassageID: 3.9 | Passage: Principle 2 – High Standards for Authorisation. This discerning approach is shown by the FSRA’s power to only permit VAs that it deems ‘acceptable’, as determined by risk factors such as security and traceability, in order to prevent the build-up of risk from illiquid or immature assets. Additionally, we do not permit stablecoins based on the algorithmic model of valuation to the underlying fiat currency.' - source_sentence: What are the common scenarios or instances where assets and liabilities are not covered by the bases of accounting in Rule 5.3.2, and how should an Insurer address these in their reporting? sentences: - 'DocumentID: 1 | PassageID: 14.4.1.Guidance.1. | Passage: Relevant Persons are reminded that in accordance with Federal AML Legislation, Relevant Persons or any of their Employees must not tip off any Person, that is, inform any Person that he is being scrutinised, or investigated by any other competent authority, for possible involvement in suspicious Transactions or activity related to money laundering or terrorist financing.' - "DocumentID: 12 | PassageID: 5.3.1.Guidance | Passage: \nThe exceptions provided\ \ in this Chapter relate to the following:\na.\tspecific Rules in respect of certain\ \ assets and liabilities, intended to achieve a regulatory objective not achieved\ \ by application of either or both of the bases of accounting set out in Rule\ \ ‎5.3.2;\nb.\tassets and liabilities that are not dealt with in either or both\ \ of the bases of accounting set out in Rule ‎5.3.2; and\nc.\tthe overriding power\ \ of the Regulator, set out in Rule ‎5.1.6, to require an Insurer to adopt a particular\ \ measurement for a specific asset or liability." - 'DocumentID: 1 | PassageID: 6.2.1.Guidance.2. | Passage: The risk assessment under Rule ‎6.2.1(c) should identify actions to mitigate risks associated with undertaking NFTF business generally, and the use of eKYC specifically. This is because distinct risks are often likely to arise where business is conducted entirely in an NFTF manner, compared to when the business relationship includes a mix of face-to-face and NFTF interactions. The assessment should make reference to risk mitigation measures recommended by the Regulator, a competent authority of the U.A.E., FATF, and other relevant bodies. ' --- # SentenceTransformer based on jxm/cde-small-v1 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [jxm/cde-small-v1](https://huggingface.co/jxm/cde-small-v1) on the csv dataset. It maps sentences & paragraphs to a None-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [jxm/cde-small-v1](https://huggingface.co/jxm/cde-small-v1)  - **Maximum Sequence Length:** None tokens - **Output Dimensionality:** None tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - csv   ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({}) with Transformer model: DatasetTransformer ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("jebish7/cde-small-v1_MNR_3") # Run inference sentences = [ 'What are the common scenarios or instances where assets and liabilities are not covered by the bases of accounting in Rule 5.3.2, and how should an Insurer address these in their reporting?', 'DocumentID: 12 | PassageID: 5.3.1.Guidance | Passage: \nThe exceptions provided in this Chapter relate to the following:\na.\tspecific Rules in respect of certain assets and liabilities, intended to achieve a regulatory objective not achieved by application of either or both of the bases of accounting set out in Rule \u200e5.3.2;\nb.\tassets and liabilities that are not dealt with in either or both of the bases of accounting set out in Rule \u200e5.3.2; and\nc.\tthe overriding power of the Regulator, set out in Rule \u200e5.1.6, to require an Insurer to adopt a particular measurement for a specific asset or liability.', 'DocumentID: 1 | PassageID: 14.4.1.Guidance.1. | Passage: Relevant Persons are reminded that in accordance with Federal AML Legislation, Relevant Persons or any of their Employees must not tip off any Person, that is, inform any Person that he is being scrutinised, or investigated by any other competent authority, for possible involvement in suspicious Transactions or activity related to money laundering or terrorist financing.', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 1024] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```      ## Training Details ### Training Dataset #### csv * Dataset: csv * Size: 29,545 training samples * Columns: <code>anchor</code> and <code>positive</code> * Approximate statistics based on the first 1000 samples: | | anchor | positive | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 16 tokens</li><li>mean: 34.95 tokens</li><li>max: 68 tokens</li></ul> | <ul><li>min: 35 tokens</li><li>mean: 132.0 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | anchor | positive | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>If a financial institution offers Money Remittance as one of its services, under what circumstances is it deemed to be holding Relevant Money and therefore subject to regulatory compliance (a)?</code> | <code>DocumentID: 13 | PassageID: 3.7.1.Guidance.1. | Passage: An Authorised Person is considered to be holding Relevant Money and subject to (a) where it offers Payment Services alongside currency exchange or Money Remittance.<br></code> | | <code>What are the consequences for a Recognised Body or Authorised Person if they fail to comply with ADGM's requirements regarding severance payments?</code> | <code>DocumentID: 7 | PassageID: APP1.A1.2.Guidance.9. | Passage: Severance payments. Where an Authorised Person or Recognised Body provides discretionary payouts on termination of employment ("severance payments", also called "golden parachutes"), such payment should generally be subject to appropriate limits or shareholder approval. In any case, such payouts should be aligned with the Authorised Person or Recognised Body's overall financial condition and performance over an appropriate time horizon and should not be payable in the case of failure or threatened failure of the Authorised Person or Recognised Body, particularly to an individual whose actions may have contributed to the failure or potential failure of the Authorised Person or Recognised Body.<br></code> | | <code>If a Public Fund is structured as an Investment Trust, to whom should the Fund Manager report the review findings regarding delegated Regulated Activities or outsourced functions?</code> | <code>DocumentID: 6 | PassageID: PART 5.12.12.8.(1) | Passage: A Fund Manager or the Trustee of a Public Fund, which has delegated any Regulated Activities or outsourced any functions, must conduct a review of the carrying out of the relevant activities or functions by the Service Provider and present the findings of the review to either:<br>(a) the Fund's Governing Body every 6 months at the Fund's board meeting; or<br>(b) in the case of a Fund structured as an Investment Trust, to the Trustee.</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 16 - `learning_rate`: 2e-05 - `warmup_ratio`: 0.1 - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: no - `prediction_loss_only`: True - `per_device_train_batch_size`: 16 - `per_device_eval_batch_size`: 8 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | |:------:|:----:|:-------------:| | 0.1082 | 100 | 1.9962 | | 0.2165 | 200 | 1.1626 | | 0.3247 | 300 | 0.9907 | | 0.4329 | 400 | 0.8196 | | 0.5411 | 500 | 0.8082 | | 0.6494 | 600 | 0.6944 | | 0.7576 | 700 | 0.6559 | | 0.8658 | 800 | 0.6242 | | 0.9740 | 900 | 0.6299 | | 1.0823 | 1000 | 0.6051 | | 1.1905 | 1100 | 0.567 | | 1.2987 | 1200 | 0.4679 | | 1.4069 | 1300 | 0.3443 | | 1.5152 | 1400 | 0.3356 | | 1.6234 | 1500 | 0.2958 | | 1.7316 | 1600 | 0.254 | | 1.8398 | 1700 | 0.2694 | | 1.9481 | 1800 | 0.2497 | | 2.0563 | 1900 | 0.2671 | | 2.1645 | 2000 | 0.2558 | | 2.2727 | 2100 | 0.1943 | | 2.3810 | 2200 | 0.1242 | | 2.4892 | 2300 | 0.116 | | 2.5974 | 2400 | 0.1081 | | 2.7056 | 2500 | 0.1056 | | 2.8139 | 2600 | 0.107 | | 2.9221 | 2700 | 0.1154 | ### Framework Versions - Python: 3.10.14 - Sentence Transformers: 3.1.1 - Transformers: 4.45.2 - PyTorch: 2.4.0 - Accelerate: 0.34.2 - Datasets: 3.0.1 - Tokenizers: 0.20.0 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

nuyyep81/results

nuyyep81

2024-11-25T09:04:27Z

176

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-base", "base_model:finetune:google-t5/t5-base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-25T08:38:06Z

--- library_name: transformers license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer model-index: - name: results results: [] ---  # results This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4484 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 352 | 1.4879 | | 1.8923 | 2.0 | 704 | 1.4566 | | 1.5369 | 3.0 | 1056 | 1.4484 | ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

mradermacher/BioinspiredLLM-GGUF

mradermacher

2024-11-25T09:00:52Z

138

1

transformers

[ "transformers", "gguf", "biology", "materials science", "code", "scientific AI", "biological materials", "bioinspiration", "machine learning", "generative", "en", "base_model:lamm-mit/BioinspiredLLM", "base_model:quantized:lamm-mit/BioinspiredLLM", "endpoints_compatible", "region:us" ]

null

2024-11-22T23:58:59Z

--- base_model: lamm-mit/BioinspiredLLM language: - en library_name: transformers quantized_by: mradermacher tags: - biology - materials science - code - scientific AI - biological materials - bioinspiration - machine learning - generative --- ## About      static quants of https://huggingface.co/lamm-mit/BioinspiredLLM  weighted/imatrix quants are available at https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-GGUF/resolve/main/BioinspiredLLM.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF

mradermacher

2024-11-25T09:00:29Z

9

1

transformers

[ "transformers", "gguf", "chocolatine", "fr", "en", "dataset:jpacifico/french-orca-dpo-pairs-revised", "base_model:jpacifico/Chocolatine-32B-Instruct-DPO-v1.2", "base_model:quantized:jpacifico/Chocolatine-32B-Instruct-DPO-v1.2", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T00:20:45Z

--- base_model: jpacifico/Chocolatine-32B-Instruct-DPO-v1.2 datasets: - jpacifico/french-orca-dpo-pairs-revised language: - fr - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - chocolatine --- ## About      weighted/imatrix quants of https://huggingface.co/jpacifico/Chocolatine-32B-Instruct-DPO-v1.2  static quants are available at https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 | | | [GGUF](https://huggingface.co/mradermacher/Chocolatine-32B-Instruct-DPO-v1.2-i1-GGUF/resolve/main/Chocolatine-32B-Instruct-DPO-v1.2.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/ECE-TW3-JRGL-V5-GGUF

mradermacher

2024-11-25T09:00:23Z

28

1

transformers

[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "davidkim205/Rhea-72b-v0.5", "abacusai/Smaug-72B-v0.1", "en", "base_model:MatthieuJ/ECE-TW3-JRGL-V5", "base_model:quantized:MatthieuJ/ECE-TW3-JRGL-V5", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-11-23T01:16:48Z

--- base_model: MatthieuJ/ECE-TW3-JRGL-V5 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - davidkim205/Rhea-72b-v0.5 - abacusai/Smaug-72B-v0.1 --- ## About      static quants of https://huggingface.co/MatthieuJ/ECE-TW3-JRGL-V5  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q2_K.gguf) | Q2_K | 27.2 | | | [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q3_K_S.gguf) | Q3_K_S | 31.7 | | | [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q3_K_M.gguf) | Q3_K_M | 35.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q3_K_L.gguf) | Q3_K_L | 38.6 | | | [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.IQ4_XS.gguf) | IQ4_XS | 39.2 | | | [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q4_K_S.gguf) | Q4_K_S | 41.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q4_K_M.gguf) | Q4_K_M | 43.9 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q5_K_S.gguf) | Q5_K_S | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q5_K_M.gguf.part2of2) | Q5_K_M | 51.4 | | | [PART 1](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q6_K.gguf.part2of2) | Q6_K | 59.4 | very good quality | | [PART 1](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/ECE-TW3-JRGL-V5-GGUF/resolve/main/ECE-TW3-JRGL-V5.Q8_0.gguf.part2of2) | Q8_0 | 76.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF

mradermacher

2024-11-25T09:00:17Z

38

1

transformers

[ "transformers", "gguf", "en", "base_model:Deev124/hermes-llama3-roleplay-3500-v1", "base_model:quantized:Deev124/hermes-llama3-roleplay-3500-v1", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T01:28:41Z

--- base_model: Deev124/hermes-llama3-roleplay-3500-v1 language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About      weighted/imatrix quants of https://huggingface.co/Deev124/hermes-llama3-roleplay-3500-v1  static quants are available at https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/hermes-llama3-roleplay-3500-v1-i1-GGUF/resolve/main/hermes-llama3-roleplay-3500-v1.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/blossom-v5.1-34b-i1-GGUF

mradermacher

2024-11-25T08:59:58Z

347

1

transformers

[ "transformers", "gguf", "zh", "en", "dataset:Azure99/blossom-chat-v3", "dataset:Azure99/blossom-math-v4", "dataset:Azure99/blossom-wizard-v3", "dataset:Azure99/blossom-orca-v3", "base_model:Azure99/blossom-v5.1-34b", "base_model:quantized:Azure99/blossom-v5.1-34b", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T03:42:15Z

--- base_model: Azure99/blossom-v5.1-34b datasets: - Azure99/blossom-chat-v3 - Azure99/blossom-math-v4 - Azure99/blossom-wizard-v3 - Azure99/blossom-orca-v3 language: - zh - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/Azure99/blossom-v5.1-34b  static quants are available at https://huggingface.co/mradermacher/blossom-v5.1-34b-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/blossom-v5.1-34b-i1-GGUF/resolve/main/blossom-v5.1-34b.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/Ice0.41-22.11-RP-GGUF

mradermacher

2024-11-25T08:59:30Z

24

2

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:icefog72/Ice0.41-22.11-RP", "base_model:quantized:icefog72/Ice0.41-22.11-RP", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T04:28:09Z

--- base_model: icefog72/Ice0.41-22.11-RP language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/icefog72/Ice0.41-22.11-RP  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Ice0.41-22.11-RP-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Ice0.41-22.11-RP-GGUF/resolve/main/Ice0.41-22.11-RP.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF

mradermacher

2024-11-25T08:59:05Z

13

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:win10/EVA-Meissa-Coder-14B-Instruct", "base_model:quantized:win10/EVA-Meissa-Coder-14B-Instruct", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T05:10:49Z

--- base_model: win10/EVA-Meissa-Coder-14B-Instruct language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/win10/EVA-Meissa-Coder-14B-Instruct  weighted/imatrix quants are available at https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q2_K.gguf) | Q2_K | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q3_K_S.gguf) | Q3_K_S | 6.8 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q3_K_L.gguf) | Q3_K_L | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.IQ4_XS.gguf) | IQ4_XS | 8.3 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q4_0_4_4.gguf) | Q4_0_4_4 | 8.6 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q5_K_S.gguf) | Q5_K_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q5_K_M.gguf) | Q5_K_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q6_K.gguf) | Q6_K | 12.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

MakiAi/Llama-3-2-3B-Instruct-bnb-4bit-OKU-v1-1epochs_GGUF

MakiAi

2024-11-25T08:58:44Z

13

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-25T08:58:01Z

--- base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** MakiAi - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF

mradermacher

2024-11-25T08:58:36Z

13

1

transformers

[ "transformers", "gguf", "generated_from_trainer", "en", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:Nopm/Opus_WritingStruct", "dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned", "dataset:Gryphe/Sonnet3.5-Charcard-Roleplay", "dataset:Gryphe/ChatGPT-4o-Writing-Prompts", "dataset:Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned", "dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned", "dataset:nothingiisreal/Reddit-Dirty-And-WritingPrompts", "dataset:allura-org/Celeste-1.x-data-mixture", "dataset:cognitivecomputations/dolphin-2.9.3", "base_model:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "base_model:quantized:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "license:other", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T07:28:40Z

--- base_model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 datasets: - anthracite-org/kalo-opus-instruct-22k-no-refusal - Nopm/Opus_WritingStruct - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - Gryphe/Sonnet3.5-Charcard-Roleplay - Gryphe/ChatGPT-4o-Writing-Prompts - Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - nothingiisreal/Reddit-Dirty-And-WritingPrompts - allura-org/Celeste-1.x-data-mixture - cognitivecomputations/dolphin-2.9.3 language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE license_name: qwen quantized_by: mradermacher tags: - generated_from_trainer --- ## About      static quants of https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2  weighted/imatrix quants are available at https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q2_K.gguf) | Q2_K | 29.9 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q3_K_S.gguf) | Q3_K_S | 34.6 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q3_K_M.gguf) | Q3_K_M | 37.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q3_K_L.gguf) | Q3_K_L | 39.6 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.IQ4_XS.gguf) | IQ4_XS | 40.3 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q4_K_S.gguf) | Q4_K_S | 44.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q4_K_M.gguf) | Q4_K_M | 47.5 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q5_K_S.gguf.part2of2) | Q5_K_S | 51.5 | | | [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q5_K_M.gguf.part2of2) | Q5_K_M | 54.5 | | | [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q6_K.gguf.part2of2) | Q6_K | 64.4 | very good quality | | [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.Q8_0.gguf.part2of2) | Q8_0 | 77.4 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF

mradermacher

2024-11-25T08:58:23Z

42

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:win10/EVA-Meissa-big-pro-v2", "base_model:quantized:win10/EVA-Meissa-big-pro-v2", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T08:16:24Z

--- base_model: win10/EVA-Meissa-big-pro-v2 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/win10/EVA-Meissa-big-pro-v2  static quants are available at https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 5.2 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 5.6 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.3 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 6.9 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 7.3 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q2_K.gguf) | i1-Q2_K | 8.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 8.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 9.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 9.9 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 10.2 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 10.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 11.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.0 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q4_0.gguf) | i1-Q4_0 | 12.7 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 12.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 13.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 15.3 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-big-pro-v2-i1-GGUF/resolve/main/EVA-Meissa-big-pro-v2.i1-Q6_K.gguf) | i1-Q6_K | 18.1 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/BioinspiredLLM-i1-GGUF

mradermacher

2024-11-25T08:57:49Z

202

1

transformers

[ "transformers", "gguf", "biology", "materials science", "code", "scientific AI", "biological materials", "bioinspiration", "machine learning", "generative", "en", "base_model:lamm-mit/BioinspiredLLM", "base_model:quantized:lamm-mit/BioinspiredLLM", "endpoints_compatible", "region:us", "imatrix" ]

null

2024-11-23T08:42:52Z

--- base_model: lamm-mit/BioinspiredLLM language: - en library_name: transformers quantized_by: mradermacher tags: - biology - materials science - code - scientific AI - biological materials - bioinspiration - machine learning - generative --- ## About      weighted/imatrix quants of https://huggingface.co/lamm-mit/BioinspiredLLM  static quants are available at https://huggingface.co/mradermacher/BioinspiredLLM-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ1_M.gguf) | i1-IQ1_M | 3.2 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ2_S.gguf) | i1-IQ2_S | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ2_M.gguf) | i1-IQ2_M | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q2_K.gguf) | i1-Q2_K | 5.0 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ3_S.gguf) | i1-IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ3_M.gguf) | i1-IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.5 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.5 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_0.gguf) | i1-Q4_0 | 7.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.5 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/BioinspiredLLM-i1-GGUF/resolve/main/BioinspiredLLM.i1-Q6_K.gguf) | i1-Q6_K | 10.8 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF

mradermacher

2024-11-25T08:57:45Z

29

1

transformers

[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "ChaoticNeutrals/Eris_Remix_7B", "Virt-io/Erebus-Holodeck-7B", "jeiku/Eros_Prodigadigm_7B", "Epiculous/Mika-7B", "en", "base_model:weezywitasneezy/OxytocinErosEngineering_v0-4x7B-passthrough", "base_model:quantized:weezywitasneezy/OxytocinErosEngineering_v0-4x7B-passthrough", "endpoints_compatible", "region:us" ]

null

2024-11-23T08:43:07Z

--- base_model: weezywitasneezy/OxytocinErosEngineering_v0-4x7B-passthrough language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - ChaoticNeutrals/Eris_Remix_7B - Virt-io/Erebus-Holodeck-7B - jeiku/Eros_Prodigadigm_7B - Epiculous/Mika-7B --- ## About      static quants of https://huggingface.co/weezywitasneezy/OxytocinErosEngineering_v0-4x7B-passthrough  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q2_K.gguf) | Q2_K | 6.7 | | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q3_K_S.gguf) | Q3_K_S | 7.9 | | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q3_K_M.gguf) | Q3_K_M | 8.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q3_K_L.gguf) | Q3_K_L | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.IQ4_XS.gguf) | IQ4_XS | 9.7 | | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q4_K_S.gguf) | Q4_K_S | 10.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q4_K_M.gguf) | Q4_K_M | 10.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q5_K_S.gguf) | Q5_K_S | 12.3 | | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q5_K_M.gguf) | Q5_K_M | 12.6 | | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q6_K.gguf) | Q6_K | 14.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineering_v0-4x7B-passthrough-GGUF/resolve/main/OxytocinErosEngineering_v0-4x7B-passthrough.Q8_0.gguf) | Q8_0 | 18.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF

mradermacher

2024-11-25T08:57:36Z

67

1

transformers

[ "transformers", "gguf", "ko", "en", "base_model:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T08:43:54Z

--- base_model: gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0  weighted/imatrix quants are available at https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q2_K.gguf) | Q2_K | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q3_K_S.gguf) | Q3_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q3_K_L.gguf) | Q3_K_L | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.IQ4_XS.gguf) | IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q4_0_4_4.gguf) | Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q5_K_S.gguf) | Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q5_K_M.gguf) | Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q6_K.gguf) | Q6_K | 8.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.f16.gguf) | f16 | 21.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF

mradermacher

2024-11-25T08:57:30Z

123

2

transformers

[ "transformers", "gguf", "generated_from_trainer", "en", "dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal", "dataset:Nopm/Opus_WritingStruct", "dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned", "dataset:Gryphe/Sonnet3.5-Charcard-Roleplay", "dataset:Gryphe/ChatGPT-4o-Writing-Prompts", "dataset:Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned", "dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned", "dataset:nothingiisreal/Reddit-Dirty-And-WritingPrompts", "dataset:allura-org/Celeste-1.x-data-mixture", "dataset:cognitivecomputations/dolphin-2.9.3", "base_model:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "base_model:quantized:EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2", "license:other", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T09:13:55Z

--- base_model: EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2 datasets: - anthracite-org/kalo-opus-instruct-22k-no-refusal - Nopm/Opus_WritingStruct - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned - Gryphe/Sonnet3.5-Charcard-Roleplay - Gryphe/ChatGPT-4o-Writing-Prompts - Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned - Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned - nothingiisreal/Reddit-Dirty-And-WritingPrompts - allura-org/Celeste-1.x-data-mixture - cognitivecomputations/dolphin-2.9.3 language: - en library_name: transformers license: other license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE license_name: qwen quantized_by: mradermacher tags: - generated_from_trainer --- ## About      weighted/imatrix quants of https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-72B-v0.2  static quants are available at https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ1_S.gguf) | i1-IQ1_S | 22.8 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ1_M.gguf) | i1-IQ1_M | 23.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 25.6 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 27.2 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ2_S.gguf) | i1-IQ2_S | 28.0 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ2_M.gguf) | i1-IQ2_M | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q2_K.gguf) | i1-Q2_K | 29.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 31.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 32.9 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ3_S.gguf) | i1-IQ3_S | 34.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 34.6 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ3_M.gguf) | i1-IQ3_M | 35.6 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 37.8 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 39.6 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 39.8 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q4_0.gguf) | i1-Q4_0 | 41.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 44.0 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 47.5 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 51.5 | | | [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 54.5 | | | [PART 1](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/EVA-Qwen2.5-72B-v0.2-i1-GGUF/resolve/main/EVA-Qwen2.5-72B-v0.2.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 64.4 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/mixtral-4x7b_slerp-GGUF

mradermacher

2024-11-25T08:57:19Z

14

1

transformers

[ "transformers", "gguf", "en", "base_model:isemmanuelolowe/mixtral-4x7b_slerp", "base_model:quantized:isemmanuelolowe/mixtral-4x7b_slerp", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-11-23T10:34:25Z

--- base_model: isemmanuelolowe/mixtral-4x7b_slerp language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/isemmanuelolowe/mixtral-4x7b_slerp  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q2_K.gguf) | Q2_K | 8.9 | | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q3_K_S.gguf) | Q3_K_S | 10.5 | | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q3_K_M.gguf) | Q3_K_M | 11.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q3_K_L.gguf) | Q3_K_L | 12.6 | | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.IQ4_XS.gguf) | IQ4_XS | 13.1 | | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q4_K_S.gguf) | Q4_K_S | 13.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q4_K_M.gguf) | Q4_K_M | 14.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q5_K_S.gguf) | Q5_K_S | 16.7 | | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q5_K_M.gguf) | Q5_K_M | 17.2 | | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q6_K.gguf) | Q6_K | 19.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/mixtral-4x7b_slerp-GGUF/resolve/main/mixtral-4x7b_slerp.Q8_0.gguf) | Q8_0 | 25.8 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/llama-2-13b-cf-GGUF

mradermacher

2024-11-25T08:56:53Z

5

1

transformers

[ "transformers", "gguf", "en", "base_model:iestynmullinor/llama-2-13b-cf", "base_model:quantized:iestynmullinor/llama-2-13b-cf", "endpoints_compatible", "region:us" ]

null

2024-11-23T12:50:07Z

--- base_model: iestynmullinor/llama-2-13b-cf language: - en library_name: transformers quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/iestynmullinor/llama-2-13b-cf  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-GGUF/resolve/main/llama-2-13b-cf.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF

mradermacher

2024-11-25T08:56:46Z

24

1

transformers

[ "transformers", "gguf", "ko", "en", "base_model:KBNIT/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0", "base_model:quantized:KBNIT/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T13:25:35Z

--- base_model: KBNIT/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/KBNIT/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0  static quants are available at https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ3_M.gguf) | i1-IQ3_M | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 6.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 6.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_0.gguf) | i1-Q4_0 | 6.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.3 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.6 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.8 | | | [GGUF](https://huggingface.co/mradermacher/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0-i1-GGUF/resolve/main/KoSOLAR-10.7B-QLoRA-NEFTune-kolon-v2.0.i1-Q6_K.gguf) | i1-Q6_K | 9.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/llama-2-13b-cf-ds-GGUF

mradermacher

2024-11-25T08:56:41Z

8

1

transformers

[ "transformers", "gguf", "en", "base_model:iestynmullinor/llama-2-13b-cf-ds", "base_model:quantized:iestynmullinor/llama-2-13b-cf-ds", "endpoints_compatible", "region:us" ]

null

2024-11-23T13:56:00Z

--- base_model: iestynmullinor/llama-2-13b-cf-ds language: - en library_name: transformers quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/iestynmullinor/llama-2-13b-cf-ds  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-ds-GGUF/resolve/main/llama-2-13b-cf-ds.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF

mradermacher

2024-11-25T08:56:31Z

11

1

transformers

[ "transformers", "gguf", "ko", "en", "base_model:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T15:12:29Z

--- base_model: gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-5-v3.0  static quants are available at https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ3_M.gguf) | i1-IQ3_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 6.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 6.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_0.gguf) | i1-Q4_0 | 6.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-5-v3.0-i1-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-5-v3.0.i1-Q6_K.gguf) | i1-Q6_K | 8.9 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF

mradermacher

2024-11-25T08:56:27Z

15

1

transformers

[ "transformers", "gguf", "ko", "base_model:juengsi/DT-EQ-SOLAR-10.7B-v0.1", "base_model:quantized:juengsi/DT-EQ-SOLAR-10.7B-v0.1", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

null

2024-11-23T16:30:10Z

--- base_model: juengsi/DT-EQ-SOLAR-10.7B-v0.1 language: - ko library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/juengsi/DT-EQ-SOLAR-10.7B-v0.1  weighted/imatrix quants are available at https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q2_K.gguf) | Q2_K | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q3_K_S.gguf) | Q3_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q3_K_L.gguf) | Q3_K_L | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.IQ4_XS.gguf) | IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q5_K_S.gguf) | Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q5_K_M.gguf) | Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q6_K.gguf) | Q6_K | 8.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/DT-EQ-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-EQ-SOLAR-10.7B-v0.1.f16.gguf) | f16 | 21.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/occiglot-10b-de-en-instruct-GGUF

mradermacher

2024-11-25T08:56:23Z

7

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:mayflowergmbh/occiglot-10b-de-en-instruct", "base_model:quantized:mayflowergmbh/occiglot-10b-de-en-instruct", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T16:57:11Z

--- base_model: mayflowergmbh/occiglot-10b-de-en-instruct language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/mayflowergmbh/occiglot-10b-de-en-instruct  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q2_K.gguf) | Q2_K | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q3_K_S.gguf) | Q3_K_S | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q3_K_L.gguf) | Q3_K_L | 5.3 | | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.IQ4_XS.gguf) | IQ4_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q4_0_4_4.gguf) | Q4_0_4_4 | 5.7 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q4_K_S.gguf) | Q4_K_S | 5.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q4_K_M.gguf) | Q4_K_M | 6.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q5_K_S.gguf) | Q5_K_S | 6.9 | | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q5_K_M.gguf) | Q5_K_M | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q6_K.gguf) | Q6_K | 8.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.Q8_0.gguf) | Q8_0 | 10.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/occiglot-10b-de-en-instruct-GGUF/resolve/main/occiglot-10b-de-en-instruct.f16.gguf) | f16 | 19.8 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF

mradermacher

2024-11-25T08:56:19Z

42

1

transformers

[ "transformers", "gguf", "llama-2", "code", "base_model:opencsg/opencsg-CodeLlama-34b-v0.1", "base_model:quantized:opencsg/opencsg-CodeLlama-34b-v0.1", "license:llama2", "endpoints_compatible", "region:us" ]

null

2024-11-23T17:27:45Z

--- base_model: opencsg/opencsg-CodeLlama-34b-v0.1 language: - code library_name: transformers license: llama2 quantized_by: mradermacher tags: - llama-2 --- ## About      static quants of https://huggingface.co/opencsg/opencsg-CodeLlama-34b-v0.1  weighted/imatrix quants are available at https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q2_K.gguf) | Q2_K | 12.6 | | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q3_K_S.gguf) | Q3_K_S | 14.7 | | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q3_K_M.gguf) | Q3_K_M | 16.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q3_K_L.gguf) | Q3_K_L | 17.9 | | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.IQ4_XS.gguf) | IQ4_XS | 18.3 | | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q4_K_S.gguf) | Q4_K_S | 19.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q4_K_M.gguf) | Q4_K_M | 20.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q5_K_S.gguf) | Q5_K_S | 23.3 | | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q5_K_M.gguf) | Q5_K_M | 23.9 | | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q6_K.gguf) | Q6_K | 27.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/opencsg-CodeLlama-34b-v0.1-GGUF/resolve/main/opencsg-CodeLlama-34b-v0.1.Q8_0.gguf) | Q8_0 | 36.0 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF

mradermacher

2024-11-25T08:56:15Z

55

1

transformers

[ "transformers", "gguf", "merge", "wizardlm", "mique", "en", "base_model:MaziyarPanahi/WizardLM-Math-70B-v0.1", "base_model:quantized:MaziyarPanahi/WizardLM-Math-70B-v0.1", "license:agpl-3.0", "endpoints_compatible", "region:us", "imatrix" ]

null

2024-11-23T17:31:48Z

--- base_model: MaziyarPanahi/WizardLM-Math-70B-v0.1 language: - en library_name: transformers license: agpl-3.0 quantized_by: mradermacher tags: - merge - wizardlm - mique --- ## About      weighted/imatrix quants of https://huggingface.co/MaziyarPanahi/WizardLM-Math-70B-v0.1  static quants are available at https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | | | [GGUF](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | | | [PART 1](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WizardLM-Math-70B-v0.1-i1-GGUF/resolve/main/WizardLM-Math-70B-v0.1.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/free-solar-slerp-v0.3-i1-GGUF

mradermacher

2024-11-25T08:56:07Z

31

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:freewheelin/free-solar-slerp-v0.3", "base_model:quantized:freewheelin/free-solar-slerp-v0.3", "license:mit", "endpoints_compatible", "region:us", "imatrix" ]

null

2024-11-23T17:55:36Z

--- base_model: freewheelin/free-solar-slerp-v0.3 language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/freewheelin/free-solar-slerp-v0.3  static quants are available at https://huggingface.co/mradermacher/free-solar-slerp-v0.3-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.4 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ2_S.gguf) | i1-IQ2_S | 3.6 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ2_M.gguf) | i1-IQ2_M | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q2_K.gguf) | i1-Q2_K | 4.2 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ3_S.gguf) | i1-IQ3_S | 4.9 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ3_M.gguf) | i1-IQ3_M | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 6.3 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 6.3 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 6.3 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_0.gguf) | i1-Q4_0 | 6.3 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.3 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.6 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.8 | | | [GGUF](https://huggingface.co/mradermacher/free-solar-slerp-v0.3-i1-GGUF/resolve/main/free-solar-slerp-v0.3.i1-Q6_K.gguf) | i1-Q6_K | 9.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF

mradermacher

2024-11-25T08:55:42Z

5

1

transformers

[ "transformers", "gguf", "ko", "base_model:juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1", "base_model:quantized:juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

null

2024-11-23T18:34:56Z

--- base_model: juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1 language: - ko library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1  weighted/imatrix quants are available at https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q2_K.gguf) | Q2_K | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q3_K_S.gguf) | Q3_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q3_K_L.gguf) | Q3_K_L | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.IQ4_XS.gguf) | IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q4_0_4_4.gguf) | Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q5_K_S.gguf) | Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q5_K_M.gguf) | Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q6_K.gguf) | Q6_K | 8.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.f16.gguf) | f16 | 21.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF

mradermacher

2024-11-25T08:55:36Z

15

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:win10/EVA-Meissa-Coder-14B-Instruct", "base_model:quantized:win10/EVA-Meissa-Coder-14B-Instruct", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T19:00:49Z

--- base_model: win10/EVA-Meissa-Coder-14B-Instruct language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      weighted/imatrix quants of https://huggingface.co/win10/EVA-Meissa-Coder-14B-Instruct  static quants are available at https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 4.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 8.6 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 8.6 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 8.6 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | | | [GGUF](https://huggingface.co/mradermacher/EVA-Meissa-Coder-14B-Instruct-i1-GGUF/resolve/main/EVA-Meissa-Coder-14B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/Fusion-7B-Quintessence-GGUF

mradermacher

2024-11-25T08:55:32Z

6

1

transformers

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:ilevytate/Fusion-7B-Quintessence", "base_model:quantized:ilevytate/Fusion-7B-Quintessence", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T19:03:24Z

--- base_model: ilevytate/Fusion-7B-Quintessence language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About      static quants of https://huggingface.co/ilevytate/Fusion-7B-Quintessence  weighted/imatrix quants are available at https://huggingface.co/mradermacher/Fusion-7B-Quintessence-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q2_K.gguf) | Q2_K | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q3_K_S.gguf) | Q3_K_S | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q3_K_M.gguf) | Q3_K_M | 10.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q3_K_L.gguf) | Q3_K_L | 11.2 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.IQ4_XS.gguf) | IQ4_XS | 11.6 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q4_K_S.gguf) | Q4_K_S | 12.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q4_K_M.gguf) | Q4_K_M | 12.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q5_K_S.gguf) | Q5_K_S | 14.7 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q5_K_M.gguf) | Q5_K_M | 15.1 | | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q6_K.gguf) | Q6_K | 17.5 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Fusion-7B-Quintessence-GGUF/resolve/main/Fusion-7B-Quintessence.Q8_0.gguf) | Q8_0 | 22.6 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/llm4decompile-6.7b-nsp-GGUF

mradermacher

2024-11-25T08:55:28Z

37

1

transformers

[ "transformers", "gguf", "decompile", "binary", "en", "base_model:arise-sustech/llm4decompile-6.7b-nsp", "base_model:quantized:arise-sustech/llm4decompile-6.7b-nsp", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-11-23T19:31:46Z

--- base_model: arise-sustech/llm4decompile-6.7b-nsp language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - decompile - binary --- ## About      static quants of https://huggingface.co/arise-sustech/llm4decompile-6.7b-nsp  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q2_K.gguf) | Q2_K | 2.6 | | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q3_K_S.gguf) | Q3_K_S | 3.1 | | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q3_K_L.gguf) | Q3_K_L | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.IQ4_XS.gguf) | IQ4_XS | 3.7 | | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q4_0_4_4.gguf) | Q4_0_4_4 | 3.9 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q5_K_S.gguf) | Q5_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q5_K_M.gguf) | Q5_K_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q6_K.gguf) | Q6_K | 5.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/llm4decompile-6.7b-nsp-GGUF/resolve/main/llm4decompile-6.7b-nsp.f16.gguf) | f16 | 13.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF

mradermacher

2024-11-25T08:55:04Z

340

1

transformers

[ "transformers", "gguf", "T3Q-ko-solar-sft-v3.0", "kyujinpy/KoCommercial-NoSSL", "en", "dataset:davidkim205/ko_common_gen", "base_model:chlee10/T3Q-ko-solar-sft-v3.0", "base_model:quantized:chlee10/T3Q-ko-solar-sft-v3.0", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix", "conversational" ]

null

2024-11-23T20:21:26Z

--- base_model: chlee10/T3Q-ko-solar-sft-v3.0 datasets: - davidkim205/ko_common_gen language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - T3Q-ko-solar-sft-v3.0 - kyujinpy/KoCommercial-NoSSL --- ## About      weighted/imatrix quants of https://huggingface.co/chlee10/T3Q-ko-solar-sft-v3.0  static quants are available at https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ3_M.gguf) | i1-IQ3_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 6.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 6.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_0.gguf) | i1-Q4_0 | 6.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/T3Q-ko-solar-sft-v3.0-i1-GGUF/resolve/main/T3Q-ko-solar-sft-v3.0.i1-Q6_K.gguf) | i1-Q6_K | 8.9 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF

mradermacher

2024-11-25T08:55:00Z

34

1

transformers

[ "transformers", "gguf", "ko", "base_model:juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1", "base_model:quantized:juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1", "license:cc-by-4.0", "endpoints_compatible", "region:us", "imatrix" ]

null

2024-11-23T20:36:50Z

--- base_model: juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1 language: - ko library_name: transformers license: cc-by-4.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/juengsi/DT-SL-MLP-SOLAR-10.7B-v0.1  static quants are available at https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ1_M.gguf) | i1-IQ1_M | 2.7 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.0 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ2_S.gguf) | i1-IQ2_S | 3.5 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ2_M.gguf) | i1-IQ2_M | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q2_K.gguf) | i1-Q2_K | 4.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.5 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.8 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ3_S.gguf) | i1-IQ3_S | 4.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ3_M.gguf) | i1-IQ3_M | 4.9 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 5.3 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.8 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 6.2 | fast on arm+i8mm, low quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 6.2 | fast on arm+sve, low quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_0.gguf) | i1-Q4_0 | 6.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 6.2 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/DT-SL-MLP-SOLAR-10.7B-v0.1-i1-GGUF/resolve/main/DT-SL-MLP-SOLAR-10.7B-v0.1.i1-Q6_K.gguf) | i1-Q6_K | 8.9 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/BioMistral-7B-finetuned-GGUF

mradermacher

2024-11-25T08:54:51Z

10

1

transformers

[ "transformers", "gguf", "en", "dataset:camel-ai/biology", "base_model:mridul3301/BioMistral-7B-finetuned", "base_model:quantized:mridul3301/BioMistral-7B-finetuned", "license:mit", "endpoints_compatible", "region:us" ]

null

2024-11-23T20:42:34Z

--- base_model: mridul3301/BioMistral-7B-finetuned datasets: - camel-ai/biology language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/mridul3301/BioMistral-7B-finetuned  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/BioMistral-7B-finetuned-GGUF/resolve/main/BioMistral-7B-finetuned.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/llama-2-13b-cf-of-GGUF

mradermacher

2024-11-25T08:54:31Z

5

1

transformers

[ "transformers", "gguf", "en", "base_model:iestynmullinor/llama-2-13b-cf-of", "base_model:quantized:iestynmullinor/llama-2-13b-cf-of", "endpoints_compatible", "region:us" ]

null

2024-11-23T21:21:58Z

--- base_model: iestynmullinor/llama-2-13b-cf-of language: - en library_name: transformers quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/iestynmullinor/llama-2-13b-cf-of  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q4_0_4_4.gguf) | Q4_0_4_4 | 7.5 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/llama-2-13b-cf-of-GGUF/resolve/main/llama-2-13b-cf-of.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/NEBULA-XB-v1.0-GGUF

mradermacher

2024-11-25T08:54:09Z

8

0

transformers

[ "transformers", "gguf", "en", "dataset:Open-Orca/SlimOrca", "base_model:TeeZee/NEBULA-XB-v1.0", "base_model:quantized:TeeZee/NEBULA-XB-v1.0", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-25T04:52:10Z

--- base_model: TeeZee/NEBULA-XB-v1.0 datasets: - Open-Orca/SlimOrca language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/TeeZee/NEBULA-XB-v1.0  weighted/imatrix quants are available at https://huggingface.co/mradermacher/NEBULA-XB-v1.0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q2_K.gguf) | Q2_K | 8.9 | | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q3_K_S.gguf) | Q3_K_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q3_K_L.gguf) | Q3_K_L | 12.6 | | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.IQ4_XS.gguf) | IQ4_XS | 13.0 | | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q5_K_S.gguf) | Q5_K_S | 16.5 | | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q5_K_M.gguf) | Q5_K_M | 16.9 | | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q6_K.gguf) | Q6_K | 19.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/NEBULA-XB-v1.0-GGUF/resolve/main/NEBULA-XB-v1.0.Q8_0.gguf) | Q8_0 | 25.4 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF

mradermacher

2024-11-25T08:54:03Z

35

2

transformers

[ "transformers", "gguf", "en", "base_model:EmilMarian/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned", "base_model:quantized:EmilMarian/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T22:24:58Z

--- base_model: EmilMarian/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/EmilMarian/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned-GGUF/resolve/main/OpenHermes-2.5-Mistral-7B-BOLA-Karate-Fine-Tuned.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF

mradermacher

2024-11-25T08:53:46Z

14

1

transformers

[ "transformers", "gguf", "ko", "en", "base_model:gwonny/nox-solar-10.7b-v4-kolon-all-10", "base_model:quantized:gwonny/nox-solar-10.7b-v4-kolon-all-10", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-23T23:34:17Z

--- base_model: gwonny/nox-solar-10.7b-v4-kolon-all-10 language: - ko - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/gwonny/nox-solar-10.7b-v4-kolon-all-10  weighted/imatrix quants are available at https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q2_K.gguf) | Q2_K | 4.1 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q3_K_S.gguf) | Q3_K_S | 4.8 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q3_K_L.gguf) | Q3_K_L | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.IQ4_XS.gguf) | IQ4_XS | 5.9 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q4_0_4_4.gguf) | Q4_0_4_4 | 6.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q5_K_S.gguf) | Q5_K_S | 7.5 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q5_K_M.gguf) | Q5_K_M | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q6_K.gguf) | Q6_K | 8.9 | very good quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.Q8_0.gguf) | Q8_0 | 11.5 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/nox-solar-10.7b-v4-kolon-all-10-GGUF/resolve/main/nox-solar-10.7b-v4-kolon-all-10.f16.gguf) | f16 | 21.6 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

mradermacher/Hermes-2-Pro-11B-GGUF

mradermacher

2024-11-25T08:53:39Z

81

1

transformers

[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "NousResearch/Hermes-2-Pro-Mistral-7B", "en", "base_model:mattshumer/Hermes-2-Pro-11B", "base_model:quantized:mattshumer/Hermes-2-Pro-11B", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-24T01:01:56Z

--- base_model: mattshumer/Hermes-2-Pro-11B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - NousResearch/Hermes-2-Pro-Mistral-7B --- ## About      static quants of https://huggingface.co/mattshumer/Hermes-2-Pro-11B  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q2_K.gguf) | Q2_K | 4.3 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q3_K_S.gguf) | Q3_K_S | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q3_K_M.gguf) | Q3_K_M | 5.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q3_K_L.gguf) | Q3_K_L | 6.0 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.IQ4_XS.gguf) | IQ4_XS | 6.2 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q4_0_4_4.gguf) | Q4_0_4_4 | 6.4 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q4_K_S.gguf) | Q4_K_S | 6.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q4_K_M.gguf) | Q4_K_M | 6.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q5_K_S.gguf) | Q5_K_S | 7.8 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q5_K_M.gguf) | Q5_K_M | 8.0 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q6_K.gguf) | Q6_K | 9.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Pro-11B-GGUF/resolve/main/Hermes-2-Pro-11B.Q8_0.gguf) | Q8_0 | 12.0 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF

mradermacher

2024-11-25T08:48:40Z

9

1

transformers

[ "transformers", "gguf", "en", "base_model:migtissera/Tess-2.0-Mixtral-8x7B-v0.2", "base_model:quantized:migtissera/Tess-2.0-Mixtral-8x7B-v0.2", "license:apache-2.0", "endpoints_compatible", "region:us", "imatrix" ]

null

2024-11-24T19:49:30Z

--- base_model: migtissera/Tess-2.0-Mixtral-8x7B-v0.2 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About      weighted/imatrix quants of https://huggingface.co/migtissera/Tess-2.0-Mixtral-8x7B-v0.2  static quants are available at https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ1_S.gguf) | i1-IQ1_S | 9.9 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ1_M.gguf) | i1-IQ1_M | 10.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.7 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 14.0 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ2_S.gguf) | i1-IQ2_S | 14.2 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ2_M.gguf) | i1-IQ2_M | 15.6 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q2_K.gguf) | i1-Q2_K | 17.4 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 19.5 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ3_S.gguf) | i1-IQ3_S | 20.5 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 20.5 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ3_M.gguf) | i1-IQ3_M | 21.5 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 22.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.3 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.2 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q4_0.gguf) | i1-Q4_0 | 26.7 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 26.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 28.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.3 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.3 | | | [GGUF](https://huggingface.co/mradermacher/Tess-2.0-Mixtral-8x7B-v0.2-i1-GGUF/resolve/main/Tess-2.0-Mixtral-8x7B-v0.2.i1-Q6_K.gguf) | i1-Q6_K | 38.5 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

ibrahimchristopher/whisper-small-dv

ibrahimchristopher

2024-11-25T08:44:29Z

78

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ha", "dataset:mozilla-foundation/common_voice_13_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-25T06:15:00Z

--- library_name: transformers language: - ha license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_13_0 metrics: - wer model-index: - name: Whisper Small Ha - Ibrahim Ibrahim results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13 type: mozilla-foundation/common_voice_13_0 metrics: - name: Wer type: wer value: 45.91914569031274 ---  # Whisper Small Ha - Ibrahim Ibrahim This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. It achieves the following results on the evaluation set: - Loss: 0.6920 - Wer Ortho: 48.8189 - Wer: 45.9191 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:------:|:----:|:---------------:|:---------:|:-------:| | 0.0754 | 3.1847 | 500 | 0.6920 | 48.8189 | 45.9191 | ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Tokenizers 0.20.3

nonhmello/whisper_medium_nonhmello

nonhmello

2024-11-25T08:37:56Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "asr", "speech-recognition", "thai", "custom-model", "generated_from_trainer", "th", "base_model:biodatlab/whisper-th-medium-combined", "base_model:finetune:biodatlab/whisper-th-medium-combined", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-11-25T02:25:57Z

--- library_name: transformers language: - th license: apache-2.0 base_model: biodatlab/whisper-th-medium-combined tags: - asr - speech-recognition - thai - custom-model - generated_from_trainer metrics: - wer model-index: - name: Whisper Medium TH - Nonhmello results: [] ---  # Whisper Medium TH - Nonhmello This model is a fine-tuned version of [biodatlab/whisper-th-medium-combined](https://huggingface.co/biodatlab/whisper-th-medium-combined) on the Custom dataset on local machine dataset. It achieves the following results on the evaluation set: - Loss: 0.3068 - Wer: 77.4194 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:--------:|:----:|:---------------:|:-------:| | 0.0092 | 83.3333 | 500 | 0.2586 | 90.3226 | | 0.0006 | 166.6667 | 1000 | 0.3068 | 77.4194 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

Cylingo/Xinyuan-VL-2B

Cylingo

2024-11-25T08:24:55Z

134

7

transformers

[ "transformers", "safetensors", "qwen2_vl", "image-text-to-text", "multimodal", "visual-question-answering", "en", "zh", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

visual-question-answering

2024-09-24T12:45:20Z

--- license: apache-2.0 language: - en - zh pipeline_tag: visual-question-answering tags: - multimodal library_name: transformers --- <div align=center><img src ="https://cdn-uploads.huggingface.co/production/uploads/6299c90ef1f2a097fcaa1293/XEfp5nnJOixkGAOyF8UtN.png"/></div> ## Introduction **Xinyuan-VL-2B** is a high-performance multimodal large model for the end-side from the Cylingo Group, which is fine-tuned with `Qwen/Qwen2-VL-2B-Instruct`, and uses more than 5M of multimodal data as well as a small amount of plain text data. It performs well on several authoritative Benchmarks. ## How to use In order to rely on the thriving ecology of the open source community, we chose to fine-tune [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) to form our `Cylingo/Xinyuan-VL- 2B`. Thus, using `Cylingo/Xinyuan-VL-2B` is consistent with using `Qwen/Qwen2-VL-2B-Instruct`: ```Python from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor from qwen_vl_utils import process_vision_info # default: Load the model on the available device(s) model = Qwen2VLForConditionalGeneration.from_pretrained( "Cylingo/Xinyuan-VL-2B", torch_dtype="auto", device_map="auto" ) # default processer processor = AutoProcessor.from_pretrained("Cylingo/Xinyuan-VL-2B") messages = [ { "role": "user", "content": [ { "type": "image", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", }, {"type": "text", "text": "Describe this image."}, ], } ] # Preparation for inference text = processor.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) image_inputs, video_inputs = process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt", ) inputs = inputs.to("cuda") # Inference: Generation of the output generated_ids = model.generate(**inputs, max_new_tokens=128) generated_ids_trimmed = [ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False ) print(output_text) ``` ## Evaluation We evaluated **[XinYuan-VL-2B](https://huggingface.co/thomas-yanxin/XinYuan-VL-2B)** using the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) toolkit across the following benchmarks and found that **XinYuan-VL-2B** **outperformed** [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) released by Alibaba Cloud, as well as other models of comparable parameter scale that have significant influence in the open-source community. <p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6299c90ef1f2a097fcaa1293/7ThTCYfd_lDzsvaFLlUv2.png"> </p> You can see the results in [opencompass/open_vlm_leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard): | Benchamrk | MiniCPM-2B | InternVL-2B | Qwen2-VL-2B | **XinYuan-VL-2B** | | :---: | :---: | :---: | :---: | :---: | | MMB-CN-V11-Test | 64.5 | 68.9 | 71.2 | **74.3** | | MMB-EN-V11-Test | 65.8 | 70.2 | 73.2 | **76.5** | | MMB-EN | 69.1 | 74.4 | 74.3 | **78.9** | | MMB-CN | 66.5 | 71.2 | 73.8 | **76.12** | | CCBench | 45.3 | 74.7 | 53.7 | 55.5 | | MMT-Bench | 53.5 | 50.8 | 54.5 | **55.2** | | RealWorld | 55.8 | 57.3 | 62.9 | **63.9** | | SEEDBench\_IMG | 67.1 | 70.9 | 72.86 | **73.4** | | AI2D | 56.3 | 74.1 | **74.7** | 74.2 | | MMMU | 38.2 | 36.3 | **41.1** | 40.9 | | HallusionBench | 36.2 | 36.2 | 42.4 | **55.00** | | POPE | 86.3 | 86.3 | 86.82 | **89.42** | | MME | 1808.6 | **1876.8** | 1872.0 | 1854.9 | | MMStar | 39.1 | 49.8 | 47.5 | **51.87** | | SEEDBench2\_Plus | 51.9 | 59.9 | 62.23 | **62.98** | | BLINK | 41.2 | 42.8 | **43.92** | 42.98 | | OCRBench | 605 | 781 | **794** | 782 | | TextVQA | 74.1 | 73.4 | **79.7** | 77.6 |

Miyoki/cve-mistral-7b-instruct-v0.3-bnb-4bit-02

Miyoki

2024-11-25T08:21:37Z

12

0

transformers

[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-25T08:18:38Z

--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** Miyoki - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

tl81092/my-drug-model_2

tl81092

2024-11-25T08:14:46Z

105

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T08:14:27Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

JNolet/Qwen2.5-Coder-14B_v11.25.24.0_CodeInstruct

JNolet

2024-11-25T08:13:51Z

18

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "mergekit", "merge", "conversational", "arxiv:2306.01708", "base_model:Qwen/Qwen2.5-Coder-14B", "base_model:merge:Qwen/Qwen2.5-Coder-14B", "base_model:Qwen/Qwen2.5-Coder-14B-Instruct", "base_model:merge:Qwen/Qwen2.5-Coder-14B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T08:05:29Z

--- base_model: - Qwen/Qwen2.5-Coder-14B - Qwen/Qwen2.5-Coder-14B-Instruct library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [Qwen/Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) as a base. ### Models Merged The following models were included in the merge: * [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Qwen/Qwen2.5-Coder-14B-Instruct parameters: weight: 1 density: 1 merge_method: ties base_model: Qwen/Qwen2.5-Coder-14B parameters: weight: 1 density: 1 normalise: true int8_mask: true dtype: bfloat16 ```

Kasobi/distilbert-base-uncased-finetuned-emotion

Kasobi

2024-11-25T08:09:49Z

105

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-23T12:39:59Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: [] ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2250 - Accuracy: 0.9245 - F1: 0.9245 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 250 | 0.3164 | 0.901 | 0.8999 | | No log | 2.0 | 500 | 0.2250 | 0.9245 | 0.9245 | ### Framework versions - Transformers 4.46.3 - Pytorch 2.5.1+cpu - Datasets 3.1.0 - Tokenizers 0.20.3

Shinyaaa/Face-travel-05-v1

Shinyaaa

2024-11-25T08:05:00Z

103

0

transformers

[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

feature-extraction

2024-11-25T08:04:33Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

msr2903/mrm8488-distilroberta-fine-tuned-financial-sentiment

msr2903

2024-11-25T07:58:40Z

134

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "dataset:NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed", "base_model:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "base_model:finetune:mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T07:35:07Z

--- library_name: transformers datasets: - NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed base_model: - mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis --- ### Model Description  This model is a fine-tuned version of [mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis](https://huggingface.co/mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis) on the [NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed](https://huggingface.co/datasets/NickyNicky/finance-financialmodelingprep-stock-news-sentiments-rss-feed) dataset. It achieves the following results on the evaluation set: - Loss: 0.4090 - Accuracy: 0.9171 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - num_epochs: 5 ### Training results | Training Loss | Epoch | Validation Loss | |:-------------:|:-----:|:---------------:| | 0.318500 | 1.0 | 0.294045 | | 0.281700 | 2.0 | 0.298364 | | 0.250100 | 3.0 | 0.302255 | | 0.186400 | 4.0 | 0.380530 | | 0.179100 | 5.0 | 0.409072 |

SMARTICT/paraphrase-multilingual-MiniLM-L12-v2-ft-tr-rag-v1

SMARTICT

2024-11-25T07:48:31Z

44

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "generated_from_trainer", "dataset_size:8970", "loss:MultipleNegativesRankingLoss", "en", "arxiv:1908.10084", "arxiv:1705.00652", "base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", "license:apache-2.0", "model-index", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2024-11-22T13:26:33Z

--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:8970 - loss:MultipleNegativesRankingLoss base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 widget: - source_sentence: 'Seri konum efekti tarafından oluşturulan şeklindeki seri konum eğrisini gösteren grafik. ''''''Seri konum etkisi'''''', bir kişinin, bir serideki ilk ve son ögeleri en iyi; ortanca ögeleri en kötü hatırlama eğilimidir. Bu terim, Hermann Ebbinghaus tarafından kendi üzerine yaptığı çalışmalar ile ve bu terim, hatırlama doğruluğunun, bir ögenin bir çalışma listesindeki konumunun bir fonksiyonu olarak değiştiği bulgusuna değinmektedir. Sırası fark etmeksizin (serbest hatırlama) listedeki ögelerin hatırlanması istenildiğinde, insanlar listenin sonundaki ögeleri hatırlamaya başlama eğilimindedir ve bu ögeleri en iyi şekilde hatırlarlar (''''''sonluk etkisi''''''). Daha önceki liste ögeleri arasında, ilk birkaç öge, orta ögelerden daha sık hatırlanır (''''''ilklik etkisi''''''). İlklik etkisi için önerilen bir neden, sunulan ilk ögelerin kendilerine ayrılmış daha fazla miktarda işlem nedeniyle en etkin şekilde hareketsiz bellekte depolanmasıdır. (İlk liste ögesi kendi başına prova edilebilir; ikincisi, birincisi ile birlikte prova edilmek zorundadır, üçüncü, birincisi ve ikincisi ile birlikte, ve böyle devam eder.) Ögeler hızlı bir şekilde sunulduğunda ilklik etkisi azalır ve yavaş sunulduğunda artar (her bir ögenin işlenmesini ve böylece kalıcı depolanmasını azaltan ve arttıran faktörler). Daha uzun sunum listelerinin ilklik etkisini azalttığı bulunmuştur. Sonluk etkisi için teorileşmiş bir neden, bu ögelerin geri hatırlanması talep edildiğinde hala aktif hafızada bulunmasıdır. Hiçbirinden yararlanmayan ögeler (ortanca ögeler) en kötü şekilde geri çağrılır. Sonluk etkisi için ek bir açıklama zamansal bağlamla ilgilidir: Mevcut zamansal bağlam, daha yeni ögelerin, farklı bir zamansal bağlamda (listenin başlarında) incelenen ögelere göre daha yüksek geri hatırlama olasılığına sahip olacağını haber veren bir geri hatırlama işareti olarak kullanılabilir. Araya giren bir görev verildiğinde sonluk etkisi azalır. Araya giren görevler, çalışan belleği kullanır, ve dikkat dağıtıcı aktivite süresi 15 ila 30 saniyeyi aşarsa, sonluk etkisini bozabilir. Ek olarak, geri hatırlama testten hemen sonra gelirse, sonluk etkisi çalışılan listenin uzunluğuna, veya sunum hızına bakılmaksızın istikrarlıdır. Kalıcı uzun süreli hafıza oluşturma kabiliyeti zayıf olan amnezyaklar ilklik etkisi göstermezler, ancak hatırlama çalışmadan hemen sonra gelirse bir sonluk etkisi gösterirler. Alzheimer hastalığı olan kişiler daha düşük bir ilklik etkisi sergiler, ancak hatırlamada bir sonluk etkisi göstermezler. İlklik etkisi İlklik etkisi, psikolojide ve sosyolojide, kişinin ilk verilen bilgiyi daha sonra verilen bilgiden daha iyi hatırlamasına neden olan bir bilişsel önyargıdır. Örneğin, yeterince uzun bir kelime listesini okuyan bir kişinin, listenin başındaki kelimeleri hatırlaması listenin ortasındakileri hatırlamasından daha yüksek ihtimallidir. Birçok araştırmacı bu olguyu serbest hatırlama null testler yoluyla açıklamaya çalışmıştır. Coluccia, Gamboz ve Brandimonte (2011), serbest hatırlamayı katılımcıların herhangi bir telkin olmaksızın bilgileri hatırlamaya çalışması olarak açıklamaktadır. 20. yüzyılın sonlarındaki bazı deneylerde, kendilerine sunulan bir listede test edileceklerini bilen katılımcıların ögeleri prova edeceği kaydedildi: Ögeler sunulduğunda katılımcılar bu ögeleri kendilerine tekrar edecek ve yeni ögeler sunuldukça katılımcılar daha yeni maddelerle birlikte önceki ögeleri prova etmeye devam edeceklerdi. İlklik etkisinin ögelerin sunumu arasında daha fazla zaman olduğunda hatırlama üzerinde daha büyük bir etkisi olduğu, böylece katılımcıların önceki (asal) ögeleri prova etme şansının daha yüksek olacağı gösterilmiştir. Açık prova katılımcıların prova örüntülerini test etmek için kullanılan bir teknikti. Bu tekniğin kullanıldığı bir deneyde, katılımcılardan akla gelen ögeleri yüksek sesle söylemeleri istendi. Bu şekilde deneyci, katılımcıların listenin başındaki ögeleri listenin ortasındaki ögelerden daha çok böylece onları daha sık prova yapacağını ve daha sonra listenin ortasındaki ögelerden daha iyi hatırlayacağını görebildi. Brodie ve Murdock tarafından yapılan başka bir deneyde, sonluk etkisinin ilklik etkisinden kısmen sorumlu olduğu bulunmuştur. Deneylerinde, aynı zamanda açık prova tekniğini kullandılar ve katılımcıların daha önceki ögeleri daha fazla prova yapmasının yanı sıra, listenin başındaki kelimeleri provada daha sonra söylediklerini keşfettiler. Bu şekilde, daha önceki ögeler prova yolu sayesinde test sonuna daha yakındı ve kısmen sonluk etkisi ile 2013 yılında yapılan bir araştırma, ilklik etkisinin, edimsel koşullama olarak da bilinen bir öğrenme süreci olan tekrarlanan seçim deneyime dayalı karar verme sürecinde de önemli olduğunu göstermiştir. Yazarlar, takip eden davranışın ilk ödülünün değerine verilen önemi göstermiş ve bu olguyu sonuç önceliği olarak ifade etmişlerdir. Başka bir çalışmada, katılımcılar iki cümleden birini aldı. Örneğin, cümlelerin biri "Steve akıllı, çalışkan, eleştirel, fevri ve kıskançtır."; diğeri ise "Steve kıskanç, fevri, eleştirel, çalışkan ve akıllıdır." olabilir. Bu iki cümle aynı bilgileri içerir. Birincisi başlangıçta pozitif özellikleri gösterirken, ikincisi olumsuz özelliklere sahiptir. Araştırmacılar, katılımcıların Steve''i ilk cümle verildiğinde ikincisine kıyasla daha olumlu buldular. Sonluk etkisi İki geleneksel teori sınıfı sonluk etkisini açıklar. Çift depo modelleri Bu modeller, en son listelenen çalışma ögelerinin oldukça erişilebilir kısa süreli ara bellekten, yani insan hafızasındaki kısa süreli depodan (KSD) alındığını varsayar. Bu, daha sonra incelenen ögelerin, daha önce incelenen ögelere göre bir avantaja sahip olmasını sağlar, çünkü daha önceki çalışma ögelerinin uzun süreli bellek deposundan (USD) geriye getirilmesi için daha fazla çaba harcanması gerekir. Bu tür modellerin önemli bir tahmini, alıkoyma döneminde (liste sunumu ile test arasındaki süre) 10-30 saniye aritmetik problemleri çözme gibi dikkat dağıtıcı bir sunumun yenilik etkisini azaltmasıdır. KSD sınırlı kapasiteye sahip olduğundan, dikkat dağınıklığı daha sonraki çalışma listesi ögelerini KSD''den değiştirir, böylece testte bu ögeler sadece USD''den alınabilir ve kısa süreli ara bellekten daha kolay alınabilme avantajlarını yitirebilir. Bu nedenle, çift depolu modeller, hem anlık hatırlama görevlerindeki sonluk etkisini hem de gecikmeli serbest geri hatırlama görevinde böyle bir etkinin zayıflamasını başarılı bir şekilde açıklar. Bununla birlikte, bu modelle ilgili büyük bir sorun, uyarıcılar arası zaman aralığı (aralıksız çeldirici görev) sırasında her çalışma maddesi arasında bir dikkat dağılması olduğunda, gecikmeli hatırlamada gözlemlenen uzun süreli etkisini tahmin edememesidir. Dikkatin dağılması, son çalışma maddesinden sonra hala mevcut olduğundan, çalışma maddesini KSD''den, sonluk etkisi azaltılacak şekilde Bu uzun vadeli sonluk etkisinin varlığı, anlık ve uzun süreli sonluk etkilerinin ortak bir mekanizmayı paylaşması olasılığını arttırmaktadır. Tek depo modelleri Tek depo teorilerine göre, dizisel konum etkilerinden tek bir mekanizma sorumludur. İlk model türü, her bir liste ögesinin incelenmesi ile test arasındaki sürenin, bir ögenin alınırken bellek izinin göreceli rekabetçiliğini belirlediği göreceli zamansal farklılığa dayanmaktadır. Bu modelde, liste sonu ögelerinin daha belirgin ve dolayısıyla daha kolay alınabileceği Başka bir model türü, ögelerin bellekten geri alınmasının yalnızca kişinin çalışma ögesinin kendisini değil, aynı zamanda çalışma bağlamını zihinsel temsiline bağlı olduğunu öne süren bağlamsal değişkenliğe dayanmaktadır. Bağlam zamanla değiştiğinden ve gittikçe değiştiğinden, bellek ögelerini geri almak için yarıştığında, anlık serbest hatırlama testinde, daha yakın zamanda incelenen ögelerin test bağlamıyla daha benzer kodlama bağlamları olacaktır ve geriye getirme olasılığı daha yüksektir. Anlık serbest hatırlama dışında, bu modeller gecikmeli serbest hatırlama ve sürekli çeldirici serbest hatırlama koşullarında sonluk etkisinin varlığını veya yokluğunu da tahmin edebilir. Gecikmeli hatırlama koşulları altında, test bağlamı artan tutma aralığıyla uzaklaşarak zayıflamış bir sonluk etkisi yaratır. Sürekli çeldirici hatırlama koşullarında, artan yorumlama aralıkları çalışma bağlamı ve test bağlamı arasındaki benzerlikleri azaltırken, maddeler arasındaki göreli benzerlikler değişmeden kalmaktadır. Hatırlama işlemi rekabetçi olduğu sürece, son ögeler kazanacaktır, bu nedenle bir sonluk etkisi gözlenir. Oran kuralı Genel olarak, sonluk etkisi ile ilgili önemli bir ampirik gözlem, mutlak tutma aralıkları (çalışma sonu ile test süresi arasındaki süre) veya sunumlar arası aralıklar (farklı çalışma ögeleri arasındaki süre) olmamasıdır. Bunun yerine, sonluk miktarı ile belirlenen oran; mutlak tutma aralıkları ve sunumlar arası aralıklar oranı (oran kuralı). Sonuç olarak, bu oran sabit kaldığı sürece, aralıkların mutlak değerlerinden bağımsız olarak yenilik gözlenecektir, böylece ''''''zaman ölçeği değişmezliği'''''' olarak bilinen bir fenomen olan tüm zaman ölçeklerinde yenilik gözlenebilir. Bu, yeniliğin KSD''nin büyüklüğüne ve KSD''deki ögelerin yer değiştirmesini yöneten kurala bağlı olduğunu varsayan çift depo modelleri ile çelişmektedir. Olası açıklamalar daha sonra tek, aynı bir mekanizma yoluyla ortaya çıkan sonluk etkisini açıklar ya da anlık ve uzun süreli sonluk etkileri için iki farklı mekanizmayı öngörebilen farklı bir modelle yeniden açıklar. Böyle bir açıklama Davelaar ve ark. (2005), tek bileşenli bir bellek modeli tarafından açıklanamayan anlık ve uzun süreli sonluk fenomenleri arasında ayrışmalar olduğunu, anlık ve sonluk açıklayan bir KSD''nin varlığını savunan ve bir saniye uzun süreli sonluğu açıklayan bağlamsal kaymaya dayanan mekanizmadır. İlgili etkiler 1977''de William Crano özellikle birbirinin zıttı olduğu söylenen ilklik ve sonluk etkileri başta olmak üzere sıra etkilerinin doğasını belirten bir çalışma hazırlamaya karar verdi. Crano tarafından test edilen özellikler: Anlam değişimi hipotezi Bir listenin başındaki ögeler, katılımcıların listenin geri kalanının da uymasını beklediği bir tema oluşturur. Katılımcı, listedeki bazı kelimelerin anlamlarını belirlediği beklentiye uyacak şekilde değiştirir. Watkins ve Peynircioğlu (1984), katılımcıların kelimelerin anlamlarını değiştirerek belirlenen temadan uzaklaşarak da olsa sunulan bilgideki sapmayı azalttığını açıklamıştır. Tutarsızlık durumda saymama Katılımcılar, kendilerine sunulan önceki maddelerle tutarlı olmayan bilgileri dikkate almazlar. Başka bir deyişle, tutarsızlık durumda saymama, sunulan diğer bilgilerle tutarsız olan bilgileri tutarlı olanlardan daha az önemli görmeyi içerir (Devine ve Ostrom, 1985). Dikkat azaltma hipotezi Önce sunulan bilgilerin katılımcılar üzerinde daha sonra sunulan bilgilerden daha fazla etkisi vardır ve bu bilgiler tutarlı olsa bile öncelikli bir etkinin ortaya çıkmasına neden olur. Steiner ve Rain (1989) insanların başlangıçta sunulan bilgilere daha fazla dikkat ettiklerini, ancak kendilerine sonradan sunulan bilgilere giderek daha az dikkat ettiklerini açıklamaktadır. İlklik etkisi, katılımcıların başlangıç bilgilerine dikkat etmeleri ve daha sonra sunulan bilgileri görmezden gelmeleri nedeniyle oluşur. Öte yandan, katılımcılar sürekli olarak bilgiye dikkat etmek zorunda oldukları bir durumdaysa, sonluk etkisi oluşabilir. ''''''Süreklilik etkisi'''''' veya gecikme etkisi, başarılı bir geri çağırma sonra, bir sonraki geri çağrılan ögenin, yakın bir seri konumdan ziyade, uzak bir seri konumdan gelme olasılığının düşük olduğunu tahmin eder (Kahana, Howard, Zaromb ve Wingfiend, 2002). İki ögenin seri konumu arasındaki fark seri konum gecikmesi olarak adlandırılır. Koşullu yanıt olasılığı olarak adlandırılan bir başka faktör, belirli bir seri konum gecikmesini hatırlama olasılığıdır. Ayrıca bakınız Anchoring Clive Wearing Serbest Hatırlama Henry Molaison İknada İlklik Yasası Öğrenme Eğrisi Hafıza Eğilimleri Listesi Bilişsel Eğilimler Listesi Sonucun İlkliği Öğrenme İlkeleri Tepe-Uç Kuralı Anımsama Yumrusu Kaynakça ;Atıflar ;Basılı eserler Konuyla ilgili yayınlar Liebermann, David A. L''''earning and memory: An integrative approach.'''' Belmont, CA: Thomson Wadsworth, 2004, Kategori:Bellek süreçleri eğilimler' sentences: - Sultan Bey'in hayatının ikinci kısmını oluşturan önemli olay nedir? - Aslanbaba hangi ilçeye bağlı bir mahalledir? - Seri konum eğrisinin şeklini hangi etmenlerin belirlediği anlatıyor musunuz? - source_sentence: (doğum adı '''David Gordon Kirkpatrick''' 13 Haziran 1927 19 Eylül 2003), Avustralyalı country müzik şarkıcısı ve söz yazarıydı. Avustralya için bir kültür ikonuydu ve ülkenin en çok ödül alan yıldızlarından biriydi. Haziran 1927'de Nulla Nulla Creek'te bir çiftçinin oğlu olarak doğan Dusty, ilk şarkısı "The Way the Cowboy Dies"ı 1937'de yazdı ve 1938'de 11 yaşındayken "Slim Dusty" sahne adını aldı. Yetmiş yıla yakın kariyerinde çok sayıda kayıt yaptı. Yüzden fazla albüm çıkardı, yedi milyondan fazla kayıt sattı ve 70'in üzerinde altın ve platin albüm sertifikası kazandı". Sidney 2000 Olimpiyat Oyunlarının kapanış töreninde Avustralya'da çok ünlü bir şarkı olan "Waltzing Matilda"yı seslendirdi. 1951'de Dusty, şarkıcı-söz yazarı Joy McKean ile evlendi ve onun desteğiyle Avustralya'da büyük başarılar elde etti. Çiftin, şarkıcı-söz yazarı olan Anne Kirkpatrick ve David Kirkpatrick adlı iki çocukları oldu. Akciğer ve böbrek kanseri ile uzun bir mücadelenin ardından 19 Eylül 2003'te 76 yaşında Yeni Güney Galler'deki evinde öldü. Kaynakça Hristiyanlar erkek şarkıcı-şarkı yazarları Şeref Nişanı sahipleri erkek gitaristler kanserinden ölenler Kategori:Böbrek kanserinden ölenler Kategori:Yeni Güney Galler'de kanserden ölenler asıllı Avustralyalılar gitaristler country şarkıcıları Kategori:ARIA Hall of Fame üyeleri Kategori:ARIA Ödülü sahipleri Kategori:APRA Ödülü sahipleri gitaristler Kategori:21. yüzyıl gitaristleri Kategori:20. yüzyıl gitaristleri Kategori:2003 yılında ölenler Kategori:1927 doğumlular sentences: - Bu Hollandalı aktrisin adı nedir? - Kimdi Slim Dusty? - Dusty Springfield'in müzik kariyeri ne kadar sürmüştür? - source_sentence: 14 Aralık 1929 tarihli Milliyet gazetesinde İstanbul'da Kır Koşusu Eski logosu '''Türkiye Atletizm Federasyonu''' ('''TAF'''), atletizm sporunun Türkiye'deki yönetim teşkilatı olan spor federasyonu. 1922'de Türkiye İdman Cemiyetleri İttifakı (TİCİ) bünyesinde kurulan Türkiye Atletizm Federasyonu, aynı yıl Uluslararası Atletizm Federasyonları Birliği (IAAF) üyeliğine kabul edildi. Görev yapmış başkanlar Türkiye Atletizm Federasyonu'nun kronolojik sırayla başkanları; Ali Seyfi Beyti Ahmet Fetgeri Burhan Felek Vildan Aşir Savaşır Saffet Gürol Adnan Hün İrfan Şahinbaş İsmail Hakkı Güngör Ali Naili Moran Refik Tagay Sadun Özdede Nejat Kök Behçet Beylem Erol Zorlu Kurthan Fişek Jerfi Fıratlı Nuri Turan Abdullah Kökpınar Cüneyt Koryürek Yılmaz Sazak İlker Çetin Hüseyin Manioğlu Ali Ergenç Muharrem Dalkılıç Aşkın Tuna Fikret Çetinkaya Semra Aksu Hüseyin Yıldırım Mehmet Yurdadön Mehmet Terzi Hüseyin Yıldırım Fatih Çintimar Kaynakça Dış bağlantılar Federasyonun resmi sitesi Atletizm Federasyon Kategori:Avrupa Atletizm Birliği üyesi federasyonlar Kategori:Ankara merkezli kuruluşlar Osmanlı kurulan oluşumlar kurulan spor kuruluşları sentences: - Leandro Pereira kimdir? - Türkiye Atletizm Federasyonu ne zaman kuruldu? - P.E.N. nedir? - source_sentence: '''''İlkbaharda Dağ Yolunda Yürümek'''' ''''''Ma Yuan'''''' (; 1160''lar-1225), Güney Song Hanedanı döneminde yaşamış Çinli bir ressamdı. Çalışmaları, Xia Gui''ninkiyle birlikte, sözde Ma-Xia resim okulunun temelini oluşturdu ve dönemin en iyileri arasında kabul edilmektedir. Eserleri hem Zhe okulunun Çinli sanatçılarına hem de ilk Japon ressamlar Shūbun ve Sesshū''ye ilham verdi. Kaynakça Dunlop, Ronald Ossory. 1954. ''''Landscape Painting: Ma Yüan to Picasso''''. London: Seeley, Service Co. Little, Stephen. '''' Taoism and the Arts of China,'''' p. 160. Chicago: Art Institute of Chicago. Dış bağlantılar Ma Yuan Painting Gallery at China Online Museum Sung and Yuan paintings an exhibition catalog from The Metropolitan Museum of Art Libraries (fully available online as PDF), which contains material on Ma Yuan (see list of paintings) doğanlar doğumlular Kategori:1225 yılında ölenler Kategori:Çinli ressamlar Kategori:Song Hanedanı kişileri Kategori:12. yüzyıl ressamları Kategori:13. yüzyıl ressamları' sentences: - Denon hangi sanatsal hareketle ilişkilendirilir? - Hammâd bin Süleyman'ın hocası kimdir? - Ma Yuan hangi okulun ressamıydı? - source_sentence: 'veya ''''''Afrika insansıları'''''', ilk kez John Edward Gray tarafından 1825 yılında tanımlanmış bir Hominidae alt familyasıdır. Açıklama (insansı) aile ağacı sol Mevcut (5 tür) ve soyu tükenmiş türleriyle birlikte iki oymak içerir: ''''''Hominini'''''' oymağı ve ''''''Gorillini'''''' oymağı. Kimi yazarlar ise, ''''Pan'''' cinsinin bazen kendi üçüncü oymağı Panini''ye ait olduğunu düşünür. Homininae, orangutanların (Ponginae alt familyası) hominid soyundan ayrılmasından (yaklaşık 16 myö) sonra ortaya çıkan, insanlarla orangutanlara göre daha yakın akraba olan tüm hominidleri içerir. Bu alt familyadaki canlılar, ''''hominine'''' veya ''''hominineler'''' olarak tanımlanır. Evrim Homininae alt familyasının yaşı son ortak atası) tahminlere göre 14 ila 12.5 milyon yıldır Gorillini ve Hominini oymaklarına ayrılmasının ("goril insan son ortak atası", GHLCA) geç Miyosen''de, nakayamai''''nin yaşadığı döneme yakın bir zamanda, ila 10 milyon yıl önce gerçekleştiği tahmin edilmiştir (TGHLCA). ''''Pan-Homo'''' bölünmesine kadar (5-7 myö) gorillerin ve ''''Pan-Homo'''' atalarının melezlendiğine dair kanıtlar vardır. Filogeni Parins-Fukuchi ''''ve 2019''daki çalışmasına göre oluşturulmuş, soyu tükenmiş homininleri içeren bir Homininae kladogramı: Ayrıca bakınız son ortak ata Ponginae Notlar Kaynakça Dış bağlantılar Kategori:John Edward Gray tarafından adlandırılmış taksonlar tanımlanan taksonlar' sentences: - Homininae alt familyası ilk kez ne zaman ve kim tarafından tanımlandı? - Amr Hassan Zaki hangi takımlarda forma giymiştir? - KKTC spor kulübü hangi şehirde kurulmuştur? pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: MiniLM-L12-TR results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.559679037111334 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.6720160481444333 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.7141424272818455 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.7542627883650953 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.559679037111334 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.22400534938147776 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.1428284854563691 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.07542627883650951 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.559679037111334 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.6720160481444333 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.7141424272818455 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.7542627883650953 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.6573432687197566 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.6262999315406539 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.6317830440458849 name: Cosine Map@100 --- # MiniLM-L12-TR This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the json dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2)  - **Maximum Sequence Length:** 128 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("SMARTICT/paraphrase-multilingual-MiniLM-L12-v2-ft-tr-rag-v1") # Run inference sentences = [ 'veya \'\'\'Afrika insansıları\'\'\', ilk kez John Edward Gray tarafından 1825 yılında tanımlanmış bir Hominidae alt familyasıdır. Açıklama (insansı) aile ağacı sol Mevcut (5 tür) ve soyu tükenmiş türleriyle birlikte iki oymak içerir: \'\'\'Hominini\'\'\' oymağı ve \'\'\'Gorillini\'\'\' oymağı. Kimi yazarlar ise, \'\'Pan\'\' cinsinin bazen kendi üçüncü oymağı Panini\'ye ait olduğunu düşünür. Homininae, orangutanların (Ponginae alt familyası) hominid soyundan ayrılmasından (yaklaşık 16 myö) sonra ortaya çıkan, insanlarla orangutanlara göre daha yakın akraba olan tüm hominidleri içerir. Bu alt familyadaki canlılar, \'\'hominine\'\' veya \'\'hominineler\'\' olarak tanımlanır. Evrim Homininae alt familyasının yaşı son ortak atası) tahminlere göre 14 ila 12.5 milyon yıldır Gorillini ve Hominini oymaklarına ayrılmasının ("goril insan son ortak atası", GHLCA) geç Miyosen\'de, nakayamai\'\'nin yaşadığı döneme yakın bir zamanda, ila 10 milyon yıl önce gerçekleştiği tahmin edilmiştir (TGHLCA). \'\'Pan-Homo\'\' bölünmesine kadar (5-7 myö) gorillerin ve \'\'Pan-Homo\'\' atalarının melezlendiğine dair kanıtlar vardır. Filogeni Parins-Fukuchi \'\'ve 2019\'daki çalışmasına göre oluşturulmuş, soyu tükenmiş homininleri içeren bir Homininae kladogramı: Ayrıca bakınız son ortak ata Ponginae Notlar Kaynakça Dış bağlantılar Kategori:John Edward Gray tarafından adlandırılmış taksonlar tanımlanan taksonlar', 'Homininae alt familyası ilk kez ne zaman ve kim tarafından tanımlandı?', 'Amr Hassan Zaki hangi takımlarda forma giymiştir?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ```    ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_384` * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.5597 | | cosine_accuracy@3 | 0.672 | | cosine_accuracy@5 | 0.7141 | | cosine_accuracy@10 | 0.7543 | | cosine_precision@1 | 0.5597 | | cosine_precision@3 | 0.224 | | cosine_precision@5 | 0.1428 | | cosine_precision@10 | 0.0754 | | cosine_recall@1 | 0.5597 | | cosine_recall@3 | 0.672 | | cosine_recall@5 | 0.7141 | | cosine_recall@10 | 0.7543 | | **cosine_ndcg@10** | **0.6573** | | cosine_mrr@10 | 0.6263 | | cosine_map@100 | 0.6318 |   ## Training Details ### Training Dataset #### json * Dataset: json * Size: 8,970 training samples * Columns: <code>positive</code> and <code>anchor</code> * Approximate statistics based on the first 1000 samples: | | positive | anchor | |:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 68 tokens</li><li>mean: 124.21 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 14.35 tokens</li><li>max: 35 tokens</li></ul> | * Samples: | positive | anchor | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------| | <code>Diyarbakır ilinin Bismil ilçesine bağlı bir mahalledir. Tarihçe Mahallenin adı, 1928 yılı kayıtlarında olarak geçmektedir. Coğrafya Diyarbakır il merkezine 57 km, Bismil ilçe merkezine 22 km uzaklıktadır. Nüfus Yıllara göre mahalle nüfus verileri 2007 2000 185 1997 165 Kaynakça Dış bağlantılar Yerelnet mahalleleri</code> | <code>Mahallenin adı ne zaman kaydedilmiştir?</code> | | <code>'''karmaşık neden''', '''nedensel aşırı '''nedensel veya '''indirgeme safsatası''', bir sonucun birkaç nedenden kaynaklanması mümkünken; bir tek nedeni olduğu varsayıldığında ortaya çıkan kuşkulu neden safsatasıdır. Mantıksal olarak şu şekilde açıklanabilir: "X, Y'ye neden oldu; bu nedenle, X, Y'nin tek nedeniydi" Nedensel aşırı basitleştirme, birleşik olasılıkların göz ardı edildiği belirli bir tür yanlış ikilemdir. Diğer bir deyişle, "A ve ve C" veya "A ve ama değil" şeklindeki öncüller dikkate alınmadığında olası nedenlerin "A veya veya C" olduğu varsayılır. Kaynakça</code> | <code>Karmaşık neden safsatası nedir ve nasıl oluşur?</code> | | <code>Akyazı Sakarya ili ilçesi Akyazı, Adıyaman Adıyaman ili merkez ilçesine bağlı köy Akyazı, Besni Adıyaman ili Besni ilçesine bağlı köy Akyazı, Amasya Amasya ili merkez ilçesine bağlı köy Akyazı, Adilcevaz Bitlis ili Adilcevaz ilçesine bağlı köy Akyazı, Düzce Düzce ili merkez ilçesine bağlı köy Akyazı, Çorum Çorum ili merkez ilçesine bağlı köy Akyazı, Aziziye Erzurum ili Aziziye ilçesine bağlı mahalle Akyazı, Kızıltepe Mardin ili Kızıltepe ilçesine bağlı mahalle Akyazı, Asarcık Samsun ili Asarcık ilçesine bağlı mahalle Akyazı, Ortahisar Trabzon ili Ortahisar ilçesine bağlı mahalle</code> | <code>Akyazı adında kaç köy vardır?</code> | * Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 16 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `tf32`: False - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 32 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 16 - `eval_accumulation_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional </details> ### Training Logs | Epoch | Step | Training Loss | dim_384_cosine_ndcg@10 | |:----------:|:------:|:-------------:|:----------------------:| | 0.5694 | 10 | 0.8456 | - | | 0.9680 | 17 | - | 0.5968 | | 1.1388 | 20 | 0.4964 | - | | 1.7082 | 30 | 0.393 | - | | 1.9929 | 35 | - | 0.6429 | | 2.2776 | 40 | 0.3235 | - | | 2.8470 | 50 | 0.2816 | - | | 2.9609 | 52 | - | 0.6532 | | 3.4164 | 60 | 0.2653 | - | | **3.9858** | **70** | **0.2408** | **0.6576** | | 4.5552 | 80 | 0.2379 | - | | 4.8399 | 85 | - | 0.6573 | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.12.7 - Sentence Transformers: 3.3.1 - Transformers: 4.41.2 - PyTorch: 2.5.1+cu124 - Accelerate: 1.1.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### MultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

MakiAi/Llama-3-2-3B-Instruct-bnb-4bit-OKU-v1-10epochs

MakiAi

2024-11-25T07:43:32Z

104

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T07:41:41Z

--- base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** MakiAi - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

MakiAi/Llama-3-2-3B-Instruct-bnb-4bit-OKU-v1-10epochs-adapter

MakiAi

2024-11-25T07:39:20Z

107

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "base_model:finetune:unsloth/Llama-3.2-3B-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T07:37:13Z

--- base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** MakiAi - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold1

MayBashendy

2024-11-25T07:35:14Z

162

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T07:19:13Z

--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold1 results: [] ---  # Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold1 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5329 - Qwk: 0.5 - Mse: 0.5329 - Rmse: 0.7300 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.0073 | 2 | 4.3121 | -0.0167 | 4.3121 | 2.0766 | | No log | 0.0147 | 4 | 2.4805 | -0.0610 | 2.4805 | 1.5749 | | No log | 0.0220 | 6 | 2.3076 | 0.1290 | 2.3076 | 1.5191 | | No log | 0.0293 | 8 | 2.6382 | 0.0656 | 2.6382 | 1.6242 | | No log | 0.0366 | 10 | 2.2411 | 0.1529 | 2.2411 | 1.4970 | | No log | 0.0440 | 12 | 1.5364 | 0.1322 | 1.5364 | 1.2395 | | No log | 0.0513 | 14 | 1.5650 | 0.0556 | 1.5650 | 1.2510 | | No log | 0.0586 | 16 | 1.4212 | 0.0556 | 1.4212 | 1.1921 | | No log | 0.0659 | 18 | 1.5321 | 0.0870 | 1.5321 | 1.2378 | | No log | 0.0733 | 20 | 1.1517 | 0.1266 | 1.1517 | 1.0732 | | No log | 0.0806 | 22 | 0.7274 | 0.1905 | 0.7274 | 0.8529 | | No log | 0.0879 | 24 | 0.6328 | 0.1639 | 0.6328 | 0.7955 | | No log | 0.0952 | 26 | 0.7007 | 0.1290 | 0.7007 | 0.8371 | | No log | 0.1026 | 28 | 0.8374 | 0.0 | 0.8374 | 0.9151 | | No log | 0.1099 | 30 | 1.4702 | 0.1395 | 1.4702 | 1.2125 | | No log | 0.1172 | 32 | 1.7090 | 0.1818 | 1.7090 | 1.3073 | | No log | 0.1245 | 34 | 1.4305 | 0.1111 | 1.4305 | 1.1960 | | No log | 0.1319 | 36 | 1.0387 | 0.0308 | 1.0387 | 1.0192 | | No log | 0.1392 | 38 | 0.8915 | 0.0308 | 0.8915 | 0.9442 | | No log | 0.1465 | 40 | 0.7929 | 0.1563 | 0.7929 | 0.8905 | | No log | 0.1538 | 42 | 1.0850 | 0.1266 | 1.0850 | 1.0416 | | No log | 0.1612 | 44 | 1.2941 | 0.1869 | 1.2941 | 1.1376 | | No log | 0.1685 | 46 | 2.0278 | 0.0559 | 2.0278 | 1.4240 | | No log | 0.1758 | 48 | 1.8639 | 0.0930 | 1.8639 | 1.3652 | | No log | 0.1832 | 50 | 1.0180 | 0.1127 | 1.0180 | 1.0090 | | No log | 0.1905 | 52 | 0.5459 | 0.4407 | 0.5459 | 0.7389 | | No log | 0.1978 | 54 | 0.5375 | 0.2105 | 0.5375 | 0.7331 | | No log | 0.2051 | 56 | 0.6592 | 0.2623 | 0.6592 | 0.8119 | | No log | 0.2125 | 58 | 0.9970 | 0.0 | 0.9970 | 0.9985 | | No log | 0.2198 | 60 | 1.2622 | 0.0 | 1.2622 | 1.1235 | | No log | 0.2271 | 62 | 1.2415 | 0.0 | 1.2415 | 1.1142 | | No log | 0.2344 | 64 | 0.9522 | 0.0 | 0.9522 | 0.9758 | | No log | 0.2418 | 66 | 0.6901 | 0.1905 | 0.6901 | 0.8307 | | No log | 0.2491 | 68 | 0.5747 | 0.0339 | 0.5747 | 0.7581 | | No log | 0.2564 | 70 | 0.5642 | 0.0339 | 0.5642 | 0.7512 | | No log | 0.2637 | 72 | 0.6176 | 0.2000 | 0.6176 | 0.7859 | | No log | 0.2711 | 74 | 0.6859 | 0.2258 | 0.6859 | 0.8282 | | No log | 0.2784 | 76 | 0.8934 | 0.1563 | 0.8934 | 0.9452 | | No log | 0.2857 | 78 | 0.9462 | 0.1563 | 0.9462 | 0.9727 | | No log | 0.2930 | 80 | 0.8605 | 0.1905 | 0.8605 | 0.9276 | | No log | 0.3004 | 82 | 0.7688 | 0.1905 | 0.7688 | 0.8768 | | No log | 0.3077 | 84 | 0.6366 | 0.0339 | 0.6366 | 0.7979 | | No log | 0.3150 | 86 | 0.6321 | 0.0339 | 0.6321 | 0.7951 | | No log | 0.3223 | 88 | 0.7504 | 0.2000 | 0.7504 | 0.8663 | | No log | 0.3297 | 90 | 0.8476 | 0.2258 | 0.8476 | 0.9206 | | No log | 0.3370 | 92 | 0.7770 | 0.2258 | 0.7770 | 0.8815 | | No log | 0.3443 | 94 | 0.6783 | 0.2000 | 0.6783 | 0.8236 | | No log | 0.3516 | 96 | 0.5899 | 0.2105 | 0.5899 | 0.7680 | | No log | 0.3590 | 98 | 0.5677 | 0.1429 | 0.5677 | 0.7535 | | No log | 0.3663 | 100 | 0.5985 | 0.2105 | 0.5985 | 0.7737 | | No log | 0.3736 | 102 | 0.6789 | 0.3390 | 0.6789 | 0.8239 | | No log | 0.3810 | 104 | 0.7268 | 0.2258 | 0.7268 | 0.8525 | | No log | 0.3883 | 106 | 0.8349 | 0.1563 | 0.8349 | 0.9137 | | No log | 0.3956 | 108 | 0.7980 | 0.1563 | 0.7980 | 0.8933 | | No log | 0.4029 | 110 | 0.6952 | 0.1563 | 0.6952 | 0.8338 | | No log | 0.4103 | 112 | 0.6014 | 0.2258 | 0.6014 | 0.7755 | | No log | 0.4176 | 114 | 0.4932 | 0.3793 | 0.4932 | 0.7023 | | No log | 0.4249 | 116 | 0.4988 | 0.3333 | 0.4988 | 0.7062 | | No log | 0.4322 | 118 | 0.5628 | 0.1818 | 0.5628 | 0.7502 | | No log | 0.4396 | 120 | 0.6661 | -0.0755 | 0.6661 | 0.8161 | | No log | 0.4469 | 122 | 0.7210 | -0.0408 | 0.7210 | 0.8491 | | No log | 0.4542 | 124 | 0.7354 | 0.0400 | 0.7354 | 0.8576 | | No log | 0.4615 | 126 | 0.6108 | 0.1818 | 0.6108 | 0.7816 | | No log | 0.4689 | 128 | 0.6323 | 0.2373 | 0.6323 | 0.7952 | | No log | 0.4762 | 130 | 0.7069 | 0.1724 | 0.7069 | 0.8408 | | No log | 0.4835 | 132 | 0.7427 | 0.2105 | 0.7427 | 0.8618 | | No log | 0.4908 | 134 | 0.7664 | -0.0755 | 0.7664 | 0.8754 | | No log | 0.4982 | 136 | 0.7678 | 0.0 | 0.7678 | 0.8762 | | No log | 0.5055 | 138 | 0.7011 | 0.1509 | 0.7011 | 0.8373 | | No log | 0.5128 | 140 | 0.6116 | 0.2105 | 0.6116 | 0.7820 | | No log | 0.5201 | 142 | 0.6154 | 0.2105 | 0.6154 | 0.7845 | | No log | 0.5275 | 144 | 0.8091 | 0.3200 | 0.8091 | 0.8995 | | No log | 0.5348 | 146 | 0.9449 | 0.2895 | 0.9449 | 0.9720 | | No log | 0.5421 | 148 | 0.9432 | 0.2895 | 0.9432 | 0.9712 | | No log | 0.5495 | 150 | 0.7335 | 0.2258 | 0.7335 | 0.8564 | | No log | 0.5568 | 152 | 0.5818 | 0.4407 | 0.5818 | 0.7627 | | No log | 0.5641 | 154 | 0.5265 | 0.3226 | 0.5265 | 0.7256 | | No log | 0.5714 | 156 | 0.5171 | 0.3284 | 0.5171 | 0.7191 | | No log | 0.5788 | 158 | 0.5730 | 0.3836 | 0.5730 | 0.7569 | | No log | 0.5861 | 160 | 0.6338 | 0.3836 | 0.6338 | 0.7961 | | No log | 0.5934 | 162 | 0.7125 | 0.3836 | 0.7125 | 0.8441 | | No log | 0.6007 | 164 | 0.9304 | 0.3762 | 0.9304 | 0.9646 | | No log | 0.6081 | 166 | 0.8147 | 0.2921 | 0.8147 | 0.9026 | | No log | 0.6154 | 168 | 0.7102 | 0.2258 | 0.7102 | 0.8428 | | No log | 0.6227 | 170 | 0.7472 | 0.3143 | 0.7472 | 0.8644 | | No log | 0.6300 | 172 | 0.8547 | 0.2857 | 0.8547 | 0.9245 | | No log | 0.6374 | 174 | 0.9096 | 0.2826 | 0.9096 | 0.9537 | | No log | 0.6447 | 176 | 0.9356 | 0.3077 | 0.9356 | 0.9673 | | No log | 0.6520 | 178 | 0.8476 | 0.1972 | 0.8476 | 0.9207 | | No log | 0.6593 | 180 | 0.8232 | 0.0727 | 0.8232 | 0.9073 | | No log | 0.6667 | 182 | 0.8438 | 0.0727 | 0.8438 | 0.9186 | | No log | 0.6740 | 184 | 0.9355 | 0.0282 | 0.9355 | 0.9672 | | No log | 0.6813 | 186 | 0.9849 | 0.2308 | 0.9849 | 0.9924 | | No log | 0.6886 | 188 | 0.9396 | 0.2308 | 0.9396 | 0.9693 | | No log | 0.6960 | 190 | 0.8917 | 0.1600 | 0.8917 | 0.9443 | | No log | 0.7033 | 192 | 0.9917 | -0.1000 | 0.9917 | 0.9958 | | No log | 0.7106 | 194 | 0.9400 | 0.0625 | 0.9400 | 0.9695 | | No log | 0.7179 | 196 | 0.8076 | 0.2192 | 0.8076 | 0.8986 | | No log | 0.7253 | 198 | 0.8555 | 0.3544 | 0.8555 | 0.9249 | | No log | 0.7326 | 200 | 0.8250 | 0.2059 | 0.8250 | 0.9083 | | No log | 0.7399 | 202 | 0.8208 | -0.1887 | 0.8208 | 0.9060 | | No log | 0.7473 | 204 | 0.8430 | -0.0800 | 0.8430 | 0.9181 | | No log | 0.7546 | 206 | 0.7761 | 0.0 | 0.7761 | 0.8809 | | No log | 0.7619 | 208 | 0.7385 | 0.0 | 0.7385 | 0.8594 | | No log | 0.7692 | 210 | 0.7824 | 0.2105 | 0.7824 | 0.8846 | | No log | 0.7766 | 212 | 0.7792 | 0.0 | 0.7792 | 0.8827 | | No log | 0.7839 | 214 | 0.7983 | 0.0 | 0.7983 | 0.8935 | | No log | 0.7912 | 216 | 0.8124 | 0.0 | 0.8124 | 0.9013 | | No log | 0.7985 | 218 | 0.8442 | 0.0690 | 0.8442 | 0.9188 | | No log | 0.8059 | 220 | 0.8922 | 0.0690 | 0.8922 | 0.9446 | | No log | 0.8132 | 222 | 0.8998 | 0.1231 | 0.8998 | 0.9486 | | No log | 0.8205 | 224 | 0.8578 | 0.0 | 0.8578 | 0.9262 | | No log | 0.8278 | 226 | 0.8195 | 0.1639 | 0.8195 | 0.9053 | | No log | 0.8352 | 228 | 0.8399 | 0.2623 | 0.8399 | 0.9165 | | No log | 0.8425 | 230 | 0.9020 | 0.2247 | 0.9020 | 0.9497 | | No log | 0.8498 | 232 | 0.9211 | 0.2069 | 0.9211 | 0.9598 | | No log | 0.8571 | 234 | 0.8606 | 0.4000 | 0.8606 | 0.9277 | | No log | 0.8645 | 236 | 0.8381 | 0.4396 | 0.8381 | 0.9155 | | No log | 0.8718 | 238 | 0.7499 | 0.2895 | 0.7499 | 0.8659 | | No log | 0.8791 | 240 | 0.7132 | 0.3284 | 0.7132 | 0.8445 | | No log | 0.8864 | 242 | 0.7819 | 0.3143 | 0.7819 | 0.8843 | | No log | 0.8938 | 244 | 0.7760 | 0.2154 | 0.7760 | 0.8809 | | No log | 0.9011 | 246 | 0.7711 | 0.2154 | 0.7711 | 0.8781 | | No log | 0.9084 | 248 | 0.8401 | 0.25 | 0.8401 | 0.9166 | | No log | 0.9158 | 250 | 0.9757 | 0.2041 | 0.9757 | 0.9878 | | No log | 0.9231 | 252 | 0.9501 | 0.1758 | 0.9501 | 0.9747 | | No log | 0.9304 | 254 | 0.8671 | 0.3415 | 0.8671 | 0.9312 | | No log | 0.9377 | 256 | 0.8438 | 0.3250 | 0.8438 | 0.9186 | | No log | 0.9451 | 258 | 0.8097 | 0.3377 | 0.8097 | 0.8998 | | No log | 0.9524 | 260 | 0.7259 | 0.4935 | 0.7259 | 0.8520 | | No log | 0.9597 | 262 | 0.6525 | 0.3014 | 0.6525 | 0.8078 | | No log | 0.9670 | 264 | 0.6580 | 0.4935 | 0.6580 | 0.8112 | | No log | 0.9744 | 266 | 0.8068 | 0.3544 | 0.8068 | 0.8982 | | No log | 0.9817 | 268 | 0.8286 | 0.24 | 0.8286 | 0.9103 | | No log | 0.9890 | 270 | 0.7183 | 0.3836 | 0.7183 | 0.8475 | | No log | 0.9963 | 272 | 0.5954 | 0.4706 | 0.5954 | 0.7716 | | No log | 1.0037 | 274 | 0.5651 | 0.2759 | 0.5651 | 0.7517 | | No log | 1.0110 | 276 | 0.5733 | 0.3390 | 0.5733 | 0.7572 | | No log | 1.0183 | 278 | 0.6954 | 0.3836 | 0.6954 | 0.8339 | | No log | 1.0256 | 280 | 0.8501 | 0.24 | 0.8501 | 0.9220 | | No log | 1.0330 | 282 | 0.7854 | 0.2703 | 0.7854 | 0.8862 | | No log | 1.0403 | 284 | 0.6793 | 0.3014 | 0.6793 | 0.8242 | | No log | 1.0476 | 286 | 0.6577 | 0.3077 | 0.6577 | 0.8110 | | No log | 1.0549 | 288 | 0.6799 | 0.1905 | 0.6799 | 0.8246 | | No log | 1.0623 | 290 | 0.7062 | 0.1356 | 0.7062 | 0.8403 | | No log | 1.0696 | 292 | 0.8099 | 0.0870 | 0.8099 | 0.8999 | | No log | 1.0769 | 294 | 0.8894 | 0.0571 | 0.8894 | 0.9431 | | No log | 1.0842 | 296 | 0.9339 | 0.2222 | 0.9339 | 0.9664 | | No log | 1.0916 | 298 | 0.8429 | 0.0 | 0.8429 | 0.9181 | | No log | 1.0989 | 300 | 0.7654 | 0.1290 | 0.7654 | 0.8749 | | No log | 1.1062 | 302 | 0.7428 | 0.1290 | 0.7428 | 0.8619 | | No log | 1.1136 | 304 | 0.7310 | 0.1290 | 0.7310 | 0.8550 | | No log | 1.1209 | 306 | 0.7282 | 0.2623 | 0.7282 | 0.8533 | | No log | 1.1282 | 308 | 0.7923 | 0.1818 | 0.7923 | 0.8901 | | No log | 1.1355 | 310 | 0.7819 | 0.1739 | 0.7819 | 0.8843 | | No log | 1.1429 | 312 | 0.7345 | 0.2059 | 0.7345 | 0.8570 | | No log | 1.1502 | 314 | 0.7329 | 0.2059 | 0.7329 | 0.8561 | | No log | 1.1575 | 316 | 0.7583 | 0.2059 | 0.7583 | 0.8708 | | No log | 1.1648 | 318 | 0.7983 | 0.0833 | 0.7983 | 0.8935 | | No log | 1.1722 | 320 | 0.7779 | 0.2192 | 0.7779 | 0.8820 | | No log | 1.1795 | 322 | 0.6693 | 0.2623 | 0.6693 | 0.8181 | | No log | 1.1868 | 324 | 0.6334 | 0.2000 | 0.6334 | 0.7958 | | No log | 1.1941 | 326 | 0.6238 | 0.2759 | 0.6238 | 0.7898 | | No log | 1.2015 | 328 | 0.6233 | 0.2353 | 0.6233 | 0.7895 | | No log | 1.2088 | 330 | 0.6391 | 0.4 | 0.6391 | 0.7994 | | No log | 1.2161 | 332 | 0.6363 | 0.4 | 0.6363 | 0.7977 | | No log | 1.2234 | 334 | 0.6430 | 0.3810 | 0.6430 | 0.8018 | | No log | 1.2308 | 336 | 0.6525 | 0.3077 | 0.6525 | 0.8078 | | No log | 1.2381 | 338 | 0.6687 | 0.3636 | 0.6687 | 0.8178 | | No log | 1.2454 | 340 | 0.6741 | 0.3636 | 0.6741 | 0.8210 | | No log | 1.2527 | 342 | 0.6576 | 0.2500 | 0.6576 | 0.8109 | | No log | 1.2601 | 344 | 0.6509 | 0.4 | 0.6509 | 0.8068 | | No log | 1.2674 | 346 | 0.6404 | 0.3571 | 0.6404 | 0.8002 | | No log | 1.2747 | 348 | 0.6342 | 0.2759 | 0.6342 | 0.7964 | | No log | 1.2821 | 350 | 0.6277 | 0.2222 | 0.6277 | 0.7922 | | No log | 1.2894 | 352 | 0.6099 | 0.3571 | 0.6099 | 0.7810 | | No log | 1.2967 | 354 | 0.6032 | 0.3390 | 0.6032 | 0.7767 | | No log | 1.3040 | 356 | 0.6133 | 0.3390 | 0.6133 | 0.7832 | | No log | 1.3114 | 358 | 0.6409 | 0.2941 | 0.6409 | 0.8005 | | No log | 1.3187 | 360 | 0.6656 | 0.1429 | 0.6656 | 0.8158 | | No log | 1.3260 | 362 | 0.6355 | 0.3636 | 0.6355 | 0.7972 | | No log | 1.3333 | 364 | 0.6714 | 0.3662 | 0.6714 | 0.8194 | | No log | 1.3407 | 366 | 0.7443 | 0.3478 | 0.7443 | 0.8627 | | No log | 1.3480 | 368 | 0.6946 | 0.3377 | 0.6946 | 0.8335 | | No log | 1.3553 | 370 | 0.6518 | 0.3284 | 0.6518 | 0.8074 | | No log | 1.3626 | 372 | 0.6539 | 0.3284 | 0.6539 | 0.8086 | | No log | 1.3700 | 374 | 0.6596 | 0.3284 | 0.6596 | 0.8122 | | No log | 1.3773 | 376 | 0.6561 | 0.2059 | 0.6561 | 0.8100 | | No log | 1.3846 | 378 | 0.6469 | 0.2059 | 0.6469 | 0.8043 | | No log | 1.3919 | 380 | 0.6530 | 0.3636 | 0.6530 | 0.8081 | | No log | 1.3993 | 382 | 0.6661 | 0.2609 | 0.6661 | 0.8162 | | No log | 1.4066 | 384 | 0.6523 | 0.3636 | 0.6523 | 0.8077 | | No log | 1.4139 | 386 | 0.6462 | 0.2941 | 0.6462 | 0.8039 | | No log | 1.4212 | 388 | 0.6491 | 0.3143 | 0.6491 | 0.8057 | | No log | 1.4286 | 390 | 0.6460 | 0.3143 | 0.6460 | 0.8037 | | No log | 1.4359 | 392 | 0.6472 | 0.3143 | 0.6472 | 0.8045 | | No log | 1.4432 | 394 | 0.6201 | 0.3284 | 0.6201 | 0.7875 | | No log | 1.4505 | 396 | 0.6237 | 0.5135 | 0.6237 | 0.7898 | | No log | 1.4579 | 398 | 0.6301 | 0.5135 | 0.6301 | 0.7938 | | No log | 1.4652 | 400 | 0.6250 | 0.3662 | 0.6250 | 0.7906 | | No log | 1.4725 | 402 | 0.6350 | 0.3824 | 0.6350 | 0.7968 | | No log | 1.4799 | 404 | 0.6210 | 0.2623 | 0.6210 | 0.7880 | | No log | 1.4872 | 406 | 0.6001 | 0.4407 | 0.6001 | 0.7746 | | No log | 1.4945 | 408 | 0.5934 | 0.4407 | 0.5934 | 0.7703 | | No log | 1.5018 | 410 | 0.5976 | 0.2623 | 0.5976 | 0.7730 | | No log | 1.5092 | 412 | 0.6302 | 0.2500 | 0.6302 | 0.7939 | | No log | 1.5165 | 414 | 0.6600 | 0.1231 | 0.6600 | 0.8124 | | No log | 1.5238 | 416 | 0.6476 | 0.2500 | 0.6476 | 0.8047 | | No log | 1.5311 | 418 | 0.5943 | 0.2258 | 0.5943 | 0.7709 | | No log | 1.5385 | 420 | 0.5791 | 0.3793 | 0.5791 | 0.7610 | | No log | 1.5458 | 422 | 0.5757 | 0.3793 | 0.5757 | 0.7587 | | No log | 1.5531 | 424 | 0.5579 | 0.2800 | 0.5579 | 0.7469 | | No log | 1.5604 | 426 | 0.5364 | 0.4231 | 0.5364 | 0.7324 | | No log | 1.5678 | 428 | 0.5809 | 0.2727 | 0.5809 | 0.7621 | | No log | 1.5751 | 430 | 0.7448 | 0.2941 | 0.7448 | 0.8630 | | No log | 1.5824 | 432 | 0.7859 | 0.24 | 0.7859 | 0.8865 | | No log | 1.5897 | 434 | 0.6662 | 0.2941 | 0.6662 | 0.8162 | | No log | 1.5971 | 436 | 0.5368 | 0.2500 | 0.5368 | 0.7327 | | No log | 1.6044 | 438 | 0.5174 | 0.4375 | 0.5174 | 0.7193 | | No log | 1.6117 | 440 | 0.5398 | 0.4194 | 0.5398 | 0.7347 | | No log | 1.6190 | 442 | 0.5335 | 0.4762 | 0.5335 | 0.7304 | | No log | 1.6264 | 444 | 0.5624 | 0.2941 | 0.5624 | 0.7499 | | No log | 1.6337 | 446 | 0.5967 | 0.4156 | 0.5967 | 0.7724 | | No log | 1.6410 | 448 | 0.5637 | 0.3284 | 0.5637 | 0.7508 | | No log | 1.6484 | 450 | 0.5546 | 0.4923 | 0.5546 | 0.7447 | | No log | 1.6557 | 452 | 0.5538 | 0.3810 | 0.5538 | 0.7442 | | No log | 1.6630 | 454 | 0.5660 | 0.3158 | 0.5660 | 0.7523 | | No log | 1.6703 | 456 | 0.5565 | 0.3390 | 0.5565 | 0.7460 | | No log | 1.6777 | 458 | 0.5662 | 0.2623 | 0.5662 | 0.7525 | | No log | 1.6850 | 460 | 0.5690 | 0.4 | 0.5690 | 0.7543 | | No log | 1.6923 | 462 | 0.5852 | 0.2623 | 0.5852 | 0.7650 | | No log | 1.6996 | 464 | 0.6280 | 0.2500 | 0.6280 | 0.7925 | | No log | 1.7070 | 466 | 0.5984 | 0.2258 | 0.5984 | 0.7736 | | No log | 1.7143 | 468 | 0.5443 | 0.2623 | 0.5443 | 0.7378 | | No log | 1.7216 | 470 | 0.5330 | 0.4375 | 0.5330 | 0.7301 | | No log | 1.7289 | 472 | 0.5178 | 0.4375 | 0.5178 | 0.7196 | | No log | 1.7363 | 474 | 0.5149 | 0.3077 | 0.5149 | 0.7176 | | No log | 1.7436 | 476 | 0.5277 | 0.3077 | 0.5277 | 0.7265 | | No log | 1.7509 | 478 | 0.5569 | 0.3662 | 0.5569 | 0.7462 | | No log | 1.7582 | 480 | 0.6063 | 0.3662 | 0.6063 | 0.7787 | | No log | 1.7656 | 482 | 0.5898 | 0.3662 | 0.5898 | 0.7680 | | No log | 1.7729 | 484 | 0.5742 | 0.3662 | 0.5742 | 0.7577 | | No log | 1.7802 | 486 | 0.5415 | 0.3662 | 0.5415 | 0.7358 | | No log | 1.7875 | 488 | 0.5302 | 0.3143 | 0.5302 | 0.7281 | | No log | 1.7949 | 490 | 0.5378 | 0.3143 | 0.5378 | 0.7333 | | No log | 1.8022 | 492 | 0.5824 | 0.3077 | 0.5824 | 0.7631 | | No log | 1.8095 | 494 | 0.6343 | 0.3684 | 0.6343 | 0.7965 | | No log | 1.8168 | 496 | 0.6774 | 0.3544 | 0.6774 | 0.8231 | | No log | 1.8242 | 498 | 0.6618 | 0.1972 | 0.6618 | 0.8135 | | 0.4394 | 1.8315 | 500 | 0.6764 | 0.3333 | 0.6764 | 0.8224 | | 0.4394 | 1.8388 | 502 | 0.5942 | 0.3077 | 0.5942 | 0.7708 | | 0.4394 | 1.8462 | 504 | 0.5660 | 0.4375 | 0.5660 | 0.7524 | | 0.4394 | 1.8535 | 506 | 0.5729 | 0.3810 | 0.5729 | 0.7569 | | 0.4394 | 1.8608 | 508 | 0.5689 | 0.4375 | 0.5689 | 0.7542 | | 0.4394 | 1.8681 | 510 | 0.6016 | 0.3077 | 0.6016 | 0.7756 | | 0.4394 | 1.8755 | 512 | 0.6428 | 0.1739 | 0.6428 | 0.8017 | | 0.4394 | 1.8828 | 514 | 0.6807 | 0.1429 | 0.6807 | 0.8251 | | 0.4394 | 1.8901 | 516 | 0.7161 | 0.1039 | 0.7161 | 0.8462 | | 0.4394 | 1.8974 | 518 | 0.7035 | 0.1039 | 0.7035 | 0.8387 | | 0.4394 | 1.9048 | 520 | 0.6607 | 0.2727 | 0.6607 | 0.8128 | | 0.4394 | 1.9121 | 522 | 0.6605 | 0.4348 | 0.6605 | 0.8127 | | 0.4394 | 1.9194 | 524 | 0.6348 | 0.4762 | 0.6348 | 0.7967 | | 0.4394 | 1.9267 | 526 | 0.6357 | 0.0870 | 0.6357 | 0.7973 | | 0.4394 | 1.9341 | 528 | 0.6515 | 0.1972 | 0.6515 | 0.8071 | | 0.4394 | 1.9414 | 530 | 0.6058 | 0.0909 | 0.6058 | 0.7783 | | 0.4394 | 1.9487 | 532 | 0.5824 | 0.0339 | 0.5824 | 0.7631 | | 0.4394 | 1.9560 | 534 | 0.5448 | 0.1000 | 0.5448 | 0.7381 | | 0.4394 | 1.9634 | 536 | 0.5489 | 0.0 | 0.5489 | 0.7409 | | 0.4394 | 1.9707 | 538 | 0.5613 | 0.2623 | 0.5613 | 0.7492 | | 0.4394 | 1.9780 | 540 | 0.5872 | 0.3478 | 0.5872 | 0.7663 | | 0.4394 | 1.9853 | 542 | 0.6412 | 0.0909 | 0.6412 | 0.8007 | | 0.4394 | 1.9927 | 544 | 0.6194 | 0.3478 | 0.6194 | 0.7870 | | 0.4394 | 2.0 | 546 | 0.5844 | 0.3478 | 0.5844 | 0.7644 | | 0.4394 | 2.0073 | 548 | 0.5885 | 0.3226 | 0.5885 | 0.7672 | | 0.4394 | 2.0147 | 550 | 0.6054 | 0.3662 | 0.6054 | 0.7780 | | 0.4394 | 2.0220 | 552 | 0.5808 | 0.3607 | 0.5808 | 0.7621 | | 0.4394 | 2.0293 | 554 | 0.5551 | 0.3284 | 0.5551 | 0.7450 | | 0.4394 | 2.0366 | 556 | 0.5516 | 0.2941 | 0.5516 | 0.7427 | | 0.4394 | 2.0440 | 558 | 0.5470 | 0.4407 | 0.5470 | 0.7396 | | 0.4394 | 2.0513 | 560 | 0.5433 | 0.4828 | 0.5433 | 0.7371 | | 0.4394 | 2.0586 | 562 | 0.5563 | 0.3284 | 0.5563 | 0.7458 | | 0.4394 | 2.0659 | 564 | 0.5637 | 0.3284 | 0.5637 | 0.7508 | | 0.4394 | 2.0733 | 566 | 0.5684 | 0.3284 | 0.5684 | 0.7539 | | 0.4394 | 2.0806 | 568 | 0.5737 | 0.4348 | 0.5737 | 0.7574 | | 0.4394 | 2.0879 | 570 | 0.5828 | 0.3836 | 0.5828 | 0.7634 | | 0.4394 | 2.0952 | 572 | 0.5724 | 0.4923 | 0.5724 | 0.7566 | | 0.4394 | 2.1026 | 574 | 0.6167 | 0.4 | 0.6167 | 0.7853 | | 0.4394 | 2.1099 | 576 | 0.6451 | 0.5 | 0.6451 | 0.8032 | | 0.4394 | 2.1172 | 578 | 0.6168 | 0.5 | 0.6168 | 0.7854 | | 0.4394 | 2.1245 | 580 | 0.5485 | 0.3284 | 0.5485 | 0.7406 | | 0.4394 | 2.1319 | 582 | 0.5082 | 0.4375 | 0.5082 | 0.7129 | | 0.4394 | 2.1392 | 584 | 0.5101 | 0.4375 | 0.5101 | 0.7142 | | 0.4394 | 2.1465 | 586 | 0.5350 | 0.3636 | 0.5350 | 0.7314 | | 0.4394 | 2.1538 | 588 | 0.6042 | 0.5352 | 0.6042 | 0.7773 | | 0.4394 | 2.1612 | 590 | 0.6711 | 0.4179 | 0.6711 | 0.8192 | | 0.4394 | 2.1685 | 592 | 0.6731 | 0.4179 | 0.6731 | 0.8204 | | 0.4394 | 2.1758 | 594 | 0.5895 | 0.5352 | 0.5895 | 0.7678 | | 0.4394 | 2.1832 | 596 | 0.5354 | 0.4348 | 0.5354 | 0.7317 | | 0.4394 | 2.1905 | 598 | 0.5138 | 0.3390 | 0.5138 | 0.7168 | | 0.4394 | 2.1978 | 600 | 0.5322 | 0.4 | 0.5322 | 0.7295 | | 0.4394 | 2.2051 | 602 | 0.5366 | 0.4 | 0.5366 | 0.7325 | | 0.4394 | 2.2125 | 604 | 0.5474 | 0.3636 | 0.5474 | 0.7398 | | 0.4394 | 2.2198 | 606 | 0.5727 | 0.3810 | 0.5727 | 0.7568 | | 0.4394 | 2.2271 | 608 | 0.5590 | 0.2623 | 0.5590 | 0.7476 | | 0.4394 | 2.2344 | 610 | 0.5206 | 0.3390 | 0.5206 | 0.7215 | | 0.4394 | 2.2418 | 612 | 0.5047 | 0.2041 | 0.5047 | 0.7104 | | 0.4394 | 2.2491 | 614 | 0.5069 | 0.2041 | 0.5069 | 0.7120 | | 0.4394 | 2.2564 | 616 | 0.5034 | 0.2041 | 0.5034 | 0.7095 | | 0.4394 | 2.2637 | 618 | 0.5097 | 0.3774 | 0.5097 | 0.7139 | | 0.4394 | 2.2711 | 620 | 0.5589 | 0.2857 | 0.5589 | 0.7476 | | 0.4394 | 2.2784 | 622 | 0.5975 | 0.3077 | 0.5975 | 0.7730 | | 0.4394 | 2.2857 | 624 | 0.6019 | 0.4179 | 0.6019 | 0.7759 | | 0.4394 | 2.2930 | 626 | 0.5930 | 0.4179 | 0.5930 | 0.7701 | | 0.4394 | 2.3004 | 628 | 0.5941 | 0.3824 | 0.5941 | 0.7708 | | 0.4394 | 2.3077 | 630 | 0.5769 | 0.3636 | 0.5769 | 0.7596 | | 0.4394 | 2.3150 | 632 | 0.5728 | 0.4857 | 0.5728 | 0.7568 | | 0.4394 | 2.3223 | 634 | 0.5773 | 0.4857 | 0.5773 | 0.7598 | | 0.4394 | 2.3297 | 636 | 0.5714 | 0.4375 | 0.5714 | 0.7559 | | 0.4394 | 2.3370 | 638 | 0.5587 | 0.3636 | 0.5587 | 0.7475 | | 0.4394 | 2.3443 | 640 | 0.5817 | 0.2857 | 0.5817 | 0.7627 | | 0.4394 | 2.3516 | 642 | 0.6151 | 0.3077 | 0.6151 | 0.7843 | | 0.4394 | 2.3590 | 644 | 0.6204 | 0.3077 | 0.6204 | 0.7876 | | 0.4394 | 2.3663 | 646 | 0.5875 | 0.4179 | 0.5875 | 0.7665 | | 0.4394 | 2.3736 | 648 | 0.5832 | 0.4179 | 0.5832 | 0.7637 | | 0.4394 | 2.3810 | 650 | 0.5847 | 0.4179 | 0.5847 | 0.7647 | | 0.4394 | 2.3883 | 652 | 0.5908 | 0.4179 | 0.5908 | 0.7686 | | 0.4394 | 2.3956 | 654 | 0.6069 | 0.4348 | 0.6069 | 0.7791 | | 0.4394 | 2.4029 | 656 | 0.6180 | 0.4 | 0.6180 | 0.7861 | | 0.4394 | 2.4103 | 658 | 0.6022 | 0.4 | 0.6022 | 0.7760 | | 0.4394 | 2.4176 | 660 | 0.5792 | 0.3824 | 0.5792 | 0.7610 | | 0.4394 | 2.4249 | 662 | 0.5708 | 0.4167 | 0.5708 | 0.7555 | | 0.4394 | 2.4322 | 664 | 0.5678 | 0.3662 | 0.5678 | 0.7535 | | 0.4394 | 2.4396 | 666 | 0.5786 | 0.2500 | 0.5786 | 0.7607 | | 0.4394 | 2.4469 | 668 | 0.5853 | 0.2500 | 0.5853 | 0.7650 | | 0.4394 | 2.4542 | 670 | 0.5670 | 0.2500 | 0.5670 | 0.7530 | | 0.4394 | 2.4615 | 672 | 0.5476 | 0.3226 | 0.5476 | 0.7400 | | 0.4394 | 2.4689 | 674 | 0.5228 | 0.3607 | 0.5228 | 0.7230 | | 0.4394 | 2.4762 | 676 | 0.5152 | 0.4762 | 0.5152 | 0.7178 | | 0.4394 | 2.4835 | 678 | 0.5273 | 0.4706 | 0.5273 | 0.7261 | | 0.4394 | 2.4908 | 680 | 0.5398 | 0.4167 | 0.5398 | 0.7347 | | 0.4394 | 2.4982 | 682 | 0.5394 | 0.3684 | 0.5394 | 0.7344 | | 0.4394 | 2.5055 | 684 | 0.5716 | 0.4304 | 0.5716 | 0.7560 | | 0.4394 | 2.5128 | 686 | 0.5776 | 0.3846 | 0.5776 | 0.7600 | | 0.4394 | 2.5201 | 688 | 0.5735 | 0.3704 | 0.5735 | 0.7573 | | 0.4394 | 2.5275 | 690 | 0.5887 | 0.4 | 0.5887 | 0.7673 | | 0.4394 | 2.5348 | 692 | 0.5916 | 0.4 | 0.5916 | 0.7691 | | 0.4394 | 2.5421 | 694 | 0.5472 | 0.4 | 0.5472 | 0.7397 | | 0.4394 | 2.5495 | 696 | 0.5257 | 0.2222 | 0.5257 | 0.7251 | | 0.4394 | 2.5568 | 698 | 0.5270 | 0.2222 | 0.5270 | 0.7259 | | 0.4394 | 2.5641 | 700 | 0.5273 | 0.2222 | 0.5273 | 0.7262 | | 0.4394 | 2.5714 | 702 | 0.5283 | 0.2222 | 0.5283 | 0.7268 | | 0.4394 | 2.5788 | 704 | 0.5297 | 0.3158 | 0.5297 | 0.7278 | | 0.4394 | 2.5861 | 706 | 0.5331 | 0.4194 | 0.5331 | 0.7301 | | 0.4394 | 2.5934 | 708 | 0.5773 | 0.5312 | 0.5773 | 0.7598 | | 0.4394 | 2.6007 | 710 | 0.6296 | 0.5 | 0.6296 | 0.7935 | | 0.4394 | 2.6081 | 712 | 0.5837 | 0.4 | 0.5837 | 0.7640 | | 0.4394 | 2.6154 | 714 | 0.5268 | 0.4590 | 0.5268 | 0.7258 | | 0.4394 | 2.6227 | 716 | 0.5174 | 0.3438 | 0.5174 | 0.7193 | | 0.4394 | 2.6300 | 718 | 0.5159 | 0.4590 | 0.5159 | 0.7183 | | 0.4394 | 2.6374 | 720 | 0.5134 | 0.4706 | 0.5134 | 0.7165 | | 0.4394 | 2.6447 | 722 | 0.5149 | 0.3636 | 0.5149 | 0.7175 | | 0.4394 | 2.6520 | 724 | 0.5306 | 0.4194 | 0.5306 | 0.7284 | | 0.4394 | 2.6593 | 726 | 0.5574 | 0.3836 | 0.5574 | 0.7466 | | 0.4394 | 2.6667 | 728 | 0.5736 | 0.3333 | 0.5736 | 0.7574 | | 0.4394 | 2.6740 | 730 | 0.5960 | 0.3333 | 0.5960 | 0.7720 | | 0.4394 | 2.6813 | 732 | 0.5995 | 0.3377 | 0.5995 | 0.7742 | | 0.4394 | 2.6886 | 734 | 0.6189 | 0.2941 | 0.6189 | 0.7867 | | 0.4394 | 2.6960 | 736 | 0.5905 | 0.3478 | 0.5905 | 0.7685 | | 0.4394 | 2.7033 | 738 | 0.5434 | 0.3077 | 0.5434 | 0.7372 | | 0.4394 | 2.7106 | 740 | 0.5324 | 0.4857 | 0.5324 | 0.7297 | | 0.4394 | 2.7179 | 742 | 0.5279 | 0.4857 | 0.5279 | 0.7265 | | 0.4394 | 2.7253 | 744 | 0.5274 | 0.4857 | 0.5274 | 0.7262 | | 0.4394 | 2.7326 | 746 | 0.5309 | 0.3824 | 0.5309 | 0.7286 | | 0.4394 | 2.7399 | 748 | 0.5575 | 0.3438 | 0.5575 | 0.7467 | | 0.4394 | 2.7473 | 750 | 0.5721 | 0.2941 | 0.5721 | 0.7564 | | 0.4394 | 2.7546 | 752 | 0.5613 | 0.2857 | 0.5613 | 0.7492 | | 0.4394 | 2.7619 | 754 | 0.5312 | 0.4 | 0.5312 | 0.7288 | | 0.4394 | 2.7692 | 756 | 0.5035 | 0.4590 | 0.5035 | 0.7096 | | 0.4394 | 2.7766 | 758 | 0.5060 | 0.3284 | 0.5060 | 0.7113 | | 0.4394 | 2.7839 | 760 | 0.5022 | 0.4348 | 0.5022 | 0.7087 | | 0.4394 | 2.7912 | 762 | 0.4806 | 0.3077 | 0.4806 | 0.6933 | | 0.4394 | 2.7985 | 764 | 0.4960 | 0.4545 | 0.4960 | 0.7043 | | 0.4394 | 2.8059 | 766 | 0.5268 | 0.4 | 0.5268 | 0.7258 | | 0.4394 | 2.8132 | 768 | 0.5510 | 0.3478 | 0.5510 | 0.7423 | | 0.4394 | 2.8205 | 770 | 0.5621 | 0.2941 | 0.5621 | 0.7497 | | 0.4394 | 2.8278 | 772 | 0.5218 | 0.4 | 0.5218 | 0.7223 | | 0.4394 | 2.8352 | 774 | 0.5103 | 0.3824 | 0.5103 | 0.7144 | | 0.4394 | 2.8425 | 776 | 0.5223 | 0.4179 | 0.5223 | 0.7227 | | 0.4394 | 2.8498 | 778 | 0.5300 | 0.4324 | 0.5300 | 0.7280 | | 0.4394 | 2.8571 | 780 | 0.5428 | 0.3636 | 0.5428 | 0.7368 | | 0.4394 | 2.8645 | 782 | 0.5500 | 0.3077 | 0.5500 | 0.7416 | | 0.4394 | 2.8718 | 784 | 0.5484 | 0.3077 | 0.5484 | 0.7406 | | 0.4394 | 2.8791 | 786 | 0.5431 | 0.3000 | 0.5431 | 0.7370 | | 0.4394 | 2.8864 | 788 | 0.5381 | 0.3000 | 0.5381 | 0.7336 | | 0.4394 | 2.8938 | 790 | 0.5317 | 0.3390 | 0.5317 | 0.7292 | | 0.4394 | 2.9011 | 792 | 0.5342 | 0.3390 | 0.5342 | 0.7309 | | 0.4394 | 2.9084 | 794 | 0.5367 | 0.3390 | 0.5367 | 0.7326 | | 0.4394 | 2.9158 | 796 | 0.5334 | 0.4 | 0.5334 | 0.7303 | | 0.4394 | 2.9231 | 798 | 0.5481 | 0.3077 | 0.5481 | 0.7403 | | 0.4394 | 2.9304 | 800 | 0.5912 | 0.3636 | 0.5912 | 0.7689 | | 0.4394 | 2.9377 | 802 | 0.6102 | 0.3514 | 0.6102 | 0.7812 | | 0.4394 | 2.9451 | 804 | 0.5929 | 0.3636 | 0.5929 | 0.7700 | | 0.4394 | 2.9524 | 806 | 0.5741 | 0.3143 | 0.5741 | 0.7577 | | 0.4394 | 2.9597 | 808 | 0.5686 | 0.3143 | 0.5686 | 0.7541 | | 0.4394 | 2.9670 | 810 | 0.5581 | 0.3077 | 0.5581 | 0.7471 | | 0.4394 | 2.9744 | 812 | 0.5456 | 0.3077 | 0.5456 | 0.7386 | | 0.4394 | 2.9817 | 814 | 0.5521 | 0.4348 | 0.5521 | 0.7430 | | 0.4394 | 2.9890 | 816 | 0.5743 | 0.4658 | 0.5743 | 0.7578 | | 0.4394 | 2.9963 | 818 | 0.5881 | 0.3200 | 0.5881 | 0.7669 | | 0.4394 | 3.0037 | 820 | 0.6378 | 0.3571 | 0.6378 | 0.7986 | | 0.4394 | 3.0110 | 822 | 0.6884 | 0.3571 | 0.6884 | 0.8297 | | 0.4394 | 3.0183 | 824 | 0.7122 | 0.3077 | 0.7122 | 0.8439 | | 0.4394 | 3.0256 | 826 | 0.7344 | 0.3077 | 0.7344 | 0.8570 | | 0.4394 | 3.0330 | 828 | 0.7099 | 0.3077 | 0.7099 | 0.8425 | | 0.4394 | 3.0403 | 830 | 0.6956 | 0.3077 | 0.6956 | 0.8340 | | 0.4394 | 3.0476 | 832 | 0.6589 | 0.2921 | 0.6589 | 0.8117 | | 0.4394 | 3.0549 | 834 | 0.6135 | 0.3377 | 0.6135 | 0.7833 | | 0.4394 | 3.0623 | 836 | 0.5683 | 0.3284 | 0.5683 | 0.7538 | | 0.4394 | 3.0696 | 838 | 0.5688 | 0.3077 | 0.5688 | 0.7542 | | 0.4394 | 3.0769 | 840 | 0.6306 | 0.4179 | 0.6306 | 0.7941 | | 0.4394 | 3.0842 | 842 | 0.6851 | 0.4179 | 0.6851 | 0.8277 | | 0.4394 | 3.0916 | 844 | 0.8455 | 0.2105 | 0.8455 | 0.9195 | | 0.4394 | 3.0989 | 846 | 0.9481 | 0.2105 | 0.9481 | 0.9737 | | 0.4394 | 3.1062 | 848 | 0.9267 | 0.2105 | 0.9267 | 0.9626 | | 0.4394 | 3.1136 | 850 | 0.7810 | 0.2105 | 0.7810 | 0.8837 | | 0.4394 | 3.1209 | 852 | 0.6059 | 0.3077 | 0.6059 | 0.7784 | | 0.4394 | 3.1282 | 854 | 0.5549 | 0.3077 | 0.5549 | 0.7449 | | 0.4394 | 3.1355 | 856 | 0.5495 | 0.2500 | 0.5495 | 0.7413 | | 0.4394 | 3.1429 | 858 | 0.5506 | 0.3636 | 0.5506 | 0.7420 | | 0.4394 | 3.1502 | 860 | 0.5643 | 0.3636 | 0.5643 | 0.7512 | | 0.4394 | 3.1575 | 862 | 0.5817 | 0.2623 | 0.5817 | 0.7627 | | 0.4394 | 3.1648 | 864 | 0.5854 | 0.2623 | 0.5854 | 0.7651 | | 0.4394 | 3.1722 | 866 | 0.5982 | 0.2623 | 0.5982 | 0.7734 | | 0.4394 | 3.1795 | 868 | 0.6201 | 0.3226 | 0.6201 | 0.7875 | | 0.4394 | 3.1868 | 870 | 0.6246 | 0.3226 | 0.6246 | 0.7903 | | 0.4394 | 3.1941 | 872 | 0.6608 | 0.1972 | 0.6608 | 0.8129 | | 0.4394 | 3.2015 | 874 | 0.6514 | 0.1972 | 0.6514 | 0.8071 | | 0.4394 | 3.2088 | 876 | 0.6241 | 0.3226 | 0.6241 | 0.7900 | | 0.4394 | 3.2161 | 878 | 0.5893 | 0.2623 | 0.5893 | 0.7677 | | 0.4394 | 3.2234 | 880 | 0.5654 | 0.4211 | 0.5654 | 0.7519 | | 0.4394 | 3.2308 | 882 | 0.5684 | 0.3571 | 0.5684 | 0.7540 | | 0.4394 | 3.2381 | 884 | 0.5743 | 0.3607 | 0.5743 | 0.7579 | | 0.4394 | 3.2454 | 886 | 0.5726 | 0.4211 | 0.5726 | 0.7567 | | 0.4394 | 3.2527 | 888 | 0.5852 | 0.3390 | 0.5852 | 0.7650 | | 0.4394 | 3.2601 | 890 | 0.6132 | 0.3607 | 0.6132 | 0.7831 | | 0.4394 | 3.2674 | 892 | 0.6081 | 0.3607 | 0.6081 | 0.7798 | | 0.4394 | 3.2747 | 894 | 0.5994 | 0.4179 | 0.5994 | 0.7742 | | 0.4394 | 3.2821 | 896 | 0.5885 | 0.4923 | 0.5885 | 0.7672 | | 0.4394 | 3.2894 | 898 | 0.5921 | 0.4 | 0.5921 | 0.7695 | | 0.4394 | 3.2967 | 900 | 0.5924 | 0.4 | 0.5924 | 0.7697 | | 0.4394 | 3.3040 | 902 | 0.5828 | 0.4 | 0.5828 | 0.7634 | | 0.4394 | 3.3114 | 904 | 0.5967 | 0.4 | 0.5967 | 0.7725 | | 0.4394 | 3.3187 | 906 | 0.6072 | 0.4 | 0.6072 | 0.7792 | | 0.4394 | 3.3260 | 908 | 0.6175 | 0.2759 | 0.6175 | 0.7858 | | 0.4394 | 3.3333 | 910 | 0.6268 | 0.3390 | 0.6268 | 0.7917 | | 0.4394 | 3.3407 | 912 | 0.6329 | 0.4 | 0.6329 | 0.7955 | | 0.4394 | 3.3480 | 914 | 0.6434 | 0.2623 | 0.6434 | 0.8021 | | 0.4394 | 3.3553 | 916 | 0.6510 | 0.2623 | 0.6510 | 0.8068 | | 0.4394 | 3.3626 | 918 | 0.6418 | 0.4 | 0.6418 | 0.8011 | | 0.4394 | 3.3700 | 920 | 0.6444 | 0.4 | 0.6444 | 0.8028 | | 0.4394 | 3.3773 | 922 | 0.6565 | 0.2258 | 0.6565 | 0.8103 | | 0.4394 | 3.3846 | 924 | 0.6755 | 0.1739 | 0.6755 | 0.8219 | | 0.4394 | 3.3919 | 926 | 0.7127 | 0.25 | 0.7127 | 0.8442 | | 0.4394 | 3.3993 | 928 | 0.8145 | 0.2192 | 0.8145 | 0.9025 | | 0.4394 | 3.4066 | 930 | 0.8950 | 0.2410 | 0.8950 | 0.9461 | | 0.4394 | 3.4139 | 932 | 0.8864 | 0.2410 | 0.8864 | 0.9415 | | 0.4394 | 3.4212 | 934 | 0.7805 | 0.3077 | 0.7805 | 0.8835 | | 0.4394 | 3.4286 | 936 | 0.7266 | 0.2597 | 0.7266 | 0.8524 | | 0.4394 | 3.4359 | 938 | 0.6929 | 0.3294 | 0.6929 | 0.8324 | | 0.4394 | 3.4432 | 940 | 0.6470 | 0.3704 | 0.6470 | 0.8044 | | 0.4394 | 3.4505 | 942 | 0.6227 | 0.475 | 0.6227 | 0.7891 | | 0.4394 | 3.4579 | 944 | 0.6089 | 0.475 | 0.6089 | 0.7803 | | 0.4394 | 3.4652 | 946 | 0.5860 | 0.4923 | 0.5860 | 0.7655 | | 0.4394 | 3.4725 | 948 | 0.6036 | 0.2857 | 0.6036 | 0.7769 | | 0.4394 | 3.4799 | 950 | 0.6775 | 0.2192 | 0.6775 | 0.8231 | | 0.4394 | 3.4872 | 952 | 0.7340 | 0.2192 | 0.7340 | 0.8567 | | 0.4394 | 3.4945 | 954 | 0.7522 | 0.2192 | 0.7522 | 0.8673 | | 0.4394 | 3.5018 | 956 | 0.7190 | 0.2192 | 0.7190 | 0.8480 | | 0.4394 | 3.5092 | 958 | 0.6386 | 0.2817 | 0.6386 | 0.7991 | | 0.4394 | 3.5165 | 960 | 0.6002 | 0.2857 | 0.6002 | 0.7747 | | 0.4394 | 3.5238 | 962 | 0.6018 | 0.2857 | 0.6018 | 0.7758 | | 0.4394 | 3.5311 | 964 | 0.6261 | 0.2857 | 0.6261 | 0.7913 | | 0.4394 | 3.5385 | 966 | 0.6825 | 0.2192 | 0.6825 | 0.8261 | | 0.4394 | 3.5458 | 968 | 0.7351 | 0.2192 | 0.7351 | 0.8574 | | 0.4394 | 3.5531 | 970 | 0.7172 | 0.2192 | 0.7172 | 0.8469 | | 0.4394 | 3.5604 | 972 | 0.6545 | 0.25 | 0.6545 | 0.8090 | | 0.4394 | 3.5678 | 974 | 0.6189 | 0.2857 | 0.6189 | 0.7867 | | 0.4394 | 3.5751 | 976 | 0.6039 | 0.3824 | 0.6039 | 0.7771 | | 0.4394 | 3.5824 | 978 | 0.6051 | 0.3836 | 0.6051 | 0.7779 | | 0.4394 | 3.5897 | 980 | 0.6115 | 0.3836 | 0.6115 | 0.7820 | | 0.4394 | 3.5971 | 982 | 0.6087 | 0.3824 | 0.6087 | 0.7802 | | 0.4394 | 3.6044 | 984 | 0.5944 | 0.4167 | 0.5944 | 0.7709 | | 0.4394 | 3.6117 | 986 | 0.5826 | 0.4857 | 0.5826 | 0.7633 | | 0.4394 | 3.6190 | 988 | 0.5780 | 0.475 | 0.5780 | 0.7602 | | 0.4394 | 3.6264 | 990 | 0.5735 | 0.4167 | 0.5735 | 0.7573 | | 0.4394 | 3.6337 | 992 | 0.5578 | 0.4167 | 0.5578 | 0.7469 | | 0.4394 | 3.6410 | 994 | 0.5438 | 0.4706 | 0.5438 | 0.7374 | | 0.4394 | 3.6484 | 996 | 0.5378 | 0.3438 | 0.5378 | 0.7333 | | 0.4394 | 3.6557 | 998 | 0.5408 | 0.4 | 0.5408 | 0.7354 | | 0.1143 | 3.6630 | 1000 | 0.5414 | 0.4000 | 0.5414 | 0.7358 | | 0.1143 | 3.6703 | 1002 | 0.5372 | 0.3438 | 0.5372 | 0.7330 | | 0.1143 | 3.6777 | 1004 | 0.5406 | 0.3478 | 0.5406 | 0.7353 | | 0.1143 | 3.6850 | 1006 | 0.5488 | 0.4179 | 0.5488 | 0.7408 | | 0.1143 | 3.6923 | 1008 | 0.5570 | 0.4545 | 0.5570 | 0.7463 | | 0.1143 | 3.6996 | 1010 | 0.5454 | 0.4545 | 0.5454 | 0.7385 | | 0.1143 | 3.7070 | 1012 | 0.5398 | 0.2857 | 0.5398 | 0.7347 | | 0.1143 | 3.7143 | 1014 | 0.5517 | 0.3662 | 0.5517 | 0.7427 | | 0.1143 | 3.7216 | 1016 | 0.5589 | 0.3662 | 0.5589 | 0.7476 | | 0.1143 | 3.7289 | 1018 | 0.5665 | 0.3836 | 0.5665 | 0.7526 | | 0.1143 | 3.7363 | 1020 | 0.5563 | 0.3333 | 0.5563 | 0.7458 | | 0.1143 | 3.7436 | 1022 | 0.5465 | 0.3478 | 0.5465 | 0.7393 | | 0.1143 | 3.7509 | 1024 | 0.5525 | 0.4 | 0.5525 | 0.7433 | | 0.1143 | 3.7582 | 1026 | 0.5444 | 0.4 | 0.5444 | 0.7378 | | 0.1143 | 3.7656 | 1028 | 0.5281 | 0.4194 | 0.5281 | 0.7267 | | 0.1143 | 3.7729 | 1030 | 0.5320 | 0.3662 | 0.5320 | 0.7294 | | 0.1143 | 3.7802 | 1032 | 0.5687 | 0.2857 | 0.5687 | 0.7541 | | 0.1143 | 3.7875 | 1034 | 0.5829 | 0.3438 | 0.5829 | 0.7635 | | 0.1143 | 3.7949 | 1036 | 0.5588 | 0.2857 | 0.5588 | 0.7475 | | 0.1143 | 3.8022 | 1038 | 0.5383 | 0.3662 | 0.5383 | 0.7337 | | 0.1143 | 3.8095 | 1040 | 0.5492 | 0.4324 | 0.5492 | 0.7411 | | 0.1143 | 3.8168 | 1042 | 0.5737 | 0.3143 | 0.5737 | 0.7575 | | 0.1143 | 3.8242 | 1044 | 0.5792 | 0.3846 | 0.5792 | 0.7611 | | 0.1143 | 3.8315 | 1046 | 0.5800 | 0.4304 | 0.5800 | 0.7616 | | 0.1143 | 3.8388 | 1048 | 0.5746 | 0.4304 | 0.5746 | 0.7580 | | 0.1143 | 3.8462 | 1050 | 0.5622 | 0.3684 | 0.5622 | 0.7498 | | 0.1143 | 3.8535 | 1052 | 0.5480 | 0.3684 | 0.5480 | 0.7402 | | 0.1143 | 3.8608 | 1054 | 0.5426 | 0.3333 | 0.5426 | 0.7366 | | 0.1143 | 3.8681 | 1056 | 0.5311 | 0.4762 | 0.5311 | 0.7288 | | 0.1143 | 3.8755 | 1058 | 0.5236 | 0.4407 | 0.5236 | 0.7236 | | 0.1143 | 3.8828 | 1060 | 0.5318 | 0.2500 | 0.5318 | 0.7292 | | 0.1143 | 3.8901 | 1062 | 0.5379 | 0.2500 | 0.5379 | 0.7334 | | 0.1143 | 3.8974 | 1064 | 0.5222 | 0.3607 | 0.5222 | 0.7226 | | 0.1143 | 3.9048 | 1066 | 0.5056 | 0.4407 | 0.5056 | 0.7110 | | 0.1143 | 3.9121 | 1068 | 0.5038 | 0.4658 | 0.5038 | 0.7098 | | 0.1143 | 3.9194 | 1070 | 0.5007 | 0.4658 | 0.5007 | 0.7076 | | 0.1143 | 3.9267 | 1072 | 0.4909 | 0.4857 | 0.4909 | 0.7006 | | 0.1143 | 3.9341 | 1074 | 0.5105 | 0.5714 | 0.5105 | 0.7145 | | 0.1143 | 3.9414 | 1076 | 0.5745 | 0.4167 | 0.5745 | 0.7579 | | 0.1143 | 3.9487 | 1078 | 0.5981 | 0.4167 | 0.5981 | 0.7734 | | 0.1143 | 3.9560 | 1080 | 0.5752 | 0.4167 | 0.5752 | 0.7584 | | 0.1143 | 3.9634 | 1082 | 0.5215 | 0.5312 | 0.5215 | 0.7221 | | 0.1143 | 3.9707 | 1084 | 0.4851 | 0.3607 | 0.4851 | 0.6965 | | 0.1143 | 3.9780 | 1086 | 0.4790 | 0.4706 | 0.4790 | 0.6921 | | 0.1143 | 3.9853 | 1088 | 0.5138 | 0.3662 | 0.5138 | 0.7168 | | 0.1143 | 3.9927 | 1090 | 0.5353 | 0.3662 | 0.5353 | 0.7316 | | 0.1143 | 4.0 | 1092 | 0.5272 | 0.3662 | 0.5272 | 0.7261 | | 0.1143 | 4.0073 | 1094 | 0.5186 | 0.4762 | 0.5186 | 0.7201 | | 0.1143 | 4.0147 | 1096 | 0.5170 | 0.3774 | 0.5170 | 0.7190 | | 0.1143 | 4.0220 | 1098 | 0.5283 | 0.3774 | 0.5283 | 0.7269 | | 0.1143 | 4.0293 | 1100 | 0.5382 | 0.3774 | 0.5382 | 0.7336 | | 0.1143 | 4.0366 | 1102 | 0.5581 | 0.4706 | 0.5581 | 0.7471 | | 0.1143 | 4.0440 | 1104 | 0.5907 | 0.3636 | 0.5907 | 0.7686 | | 0.1143 | 4.0513 | 1106 | 0.6598 | 0.2941 | 0.6598 | 0.8123 | | 0.1143 | 4.0586 | 1108 | 0.7703 | 0.2941 | 0.7703 | 0.8777 | | 0.1143 | 4.0659 | 1110 | 0.8313 | 0.3014 | 0.8313 | 0.9118 | | 0.1143 | 4.0733 | 1112 | 0.8157 | 0.3014 | 0.8157 | 0.9032 | | 0.1143 | 4.0806 | 1114 | 0.7357 | 0.2941 | 0.7357 | 0.8577 | | 0.1143 | 4.0879 | 1116 | 0.6680 | 0.3143 | 0.6680 | 0.8173 | | 0.1143 | 4.0952 | 1118 | 0.6461 | 0.3143 | 0.6461 | 0.8038 | | 0.1143 | 4.1026 | 1120 | 0.6621 | 0.3143 | 0.6621 | 0.8137 | | 0.1143 | 4.1099 | 1122 | 0.6931 | 0.2941 | 0.6931 | 0.8325 | | 0.1143 | 4.1172 | 1124 | 0.7210 | 0.2941 | 0.7210 | 0.8491 | | 0.1143 | 4.1245 | 1126 | 0.7188 | 0.2941 | 0.7188 | 0.8478 | | 0.1143 | 4.1319 | 1128 | 0.7036 | 0.1905 | 0.7036 | 0.8388 | | 0.1143 | 4.1392 | 1130 | 0.6642 | 0.1724 | 0.6642 | 0.8150 | | 0.1143 | 4.1465 | 1132 | 0.6442 | 0.2000 | 0.6442 | 0.8026 | | 0.1143 | 4.1538 | 1134 | 0.6241 | 0.2623 | 0.6241 | 0.7900 | | 0.1143 | 4.1612 | 1136 | 0.6157 | 0.2857 | 0.6157 | 0.7846 | | 0.1143 | 4.1685 | 1138 | 0.5988 | 0.3478 | 0.5988 | 0.7738 | | 0.1143 | 4.1758 | 1140 | 0.5894 | 0.2623 | 0.5894 | 0.7677 | | 0.1143 | 4.1832 | 1142 | 0.5887 | 0.3636 | 0.5887 | 0.7672 | | 0.1143 | 4.1905 | 1144 | 0.5813 | 0.3636 | 0.5813 | 0.7624 | | 0.1143 | 4.1978 | 1146 | 0.5722 | 0.4167 | 0.5722 | 0.7565 | | 0.1143 | 4.2051 | 1148 | 0.5623 | 0.3478 | 0.5623 | 0.7499 | | 0.1143 | 4.2125 | 1150 | 0.5490 | 0.3478 | 0.5490 | 0.7409 | | 0.1143 | 4.2198 | 1152 | 0.5421 | 0.3478 | 0.5421 | 0.7363 | | 0.1143 | 4.2271 | 1154 | 0.5436 | 0.4179 | 0.5436 | 0.7373 | | 0.1143 | 4.2344 | 1156 | 0.5422 | 0.4179 | 0.5422 | 0.7364 | | 0.1143 | 4.2418 | 1158 | 0.5436 | 0.4179 | 0.5436 | 0.7373 | | 0.1143 | 4.2491 | 1160 | 0.5430 | 0.4179 | 0.5430 | 0.7369 | | 0.1143 | 4.2564 | 1162 | 0.5419 | 0.4179 | 0.5419 | 0.7361 | | 0.1143 | 4.2637 | 1164 | 0.5488 | 0.4706 | 0.5488 | 0.7408 | | 0.1143 | 4.2711 | 1166 | 0.5601 | 0.4179 | 0.5601 | 0.7484 | | 0.1143 | 4.2784 | 1168 | 0.5667 | 0.4179 | 0.5667 | 0.7528 | | 0.1143 | 4.2857 | 1170 | 0.5588 | 0.4706 | 0.5588 | 0.7475 | | 0.1143 | 4.2930 | 1172 | 0.5596 | 0.3077 | 0.5596 | 0.7480 | | 0.1143 | 4.3004 | 1174 | 0.5684 | 0.3077 | 0.5684 | 0.7539 | | 0.1143 | 4.3077 | 1176 | 0.5721 | 0.3077 | 0.5721 | 0.7563 | | 0.1143 | 4.3150 | 1178 | 0.5731 | 0.4348 | 0.5731 | 0.7570 | | 0.1143 | 4.3223 | 1180 | 0.5817 | 0.4658 | 0.5817 | 0.7627 | | 0.1143 | 4.3297 | 1182 | 0.5898 | 0.4658 | 0.5898 | 0.7680 | | 0.1143 | 4.3370 | 1184 | 0.5876 | 0.4658 | 0.5876 | 0.7666 | | 0.1143 | 4.3443 | 1186 | 0.5899 | 0.4658 | 0.5899 | 0.7680 | | 0.1143 | 4.3516 | 1188 | 0.5864 | 0.4706 | 0.5864 | 0.7658 | | 0.1143 | 4.3590 | 1190 | 0.5837 | 0.4706 | 0.5837 | 0.7640 | | 0.1143 | 4.3663 | 1192 | 0.5813 | 0.4179 | 0.5813 | 0.7624 | | 0.1143 | 4.3736 | 1194 | 0.5738 | 0.4194 | 0.5738 | 0.7575 | | 0.1143 | 4.3810 | 1196 | 0.5648 | 0.4194 | 0.5648 | 0.7515 | | 0.1143 | 4.3883 | 1198 | 0.5529 | 0.4762 | 0.5529 | 0.7436 | | 0.1143 | 4.3956 | 1200 | 0.5436 | 0.4375 | 0.5436 | 0.7373 | | 0.1143 | 4.4029 | 1202 | 0.5540 | 0.4923 | 0.5540 | 0.7443 | | 0.1143 | 4.4103 | 1204 | 0.5701 | 0.3824 | 0.5701 | 0.7551 | | 0.1143 | 4.4176 | 1206 | 0.5621 | 0.3824 | 0.5621 | 0.7497 | | 0.1143 | 4.4249 | 1208 | 0.5417 | 0.4375 | 0.5417 | 0.7360 | | 0.1143 | 4.4322 | 1210 | 0.5344 | 0.4762 | 0.5344 | 0.7310 | | 0.1143 | 4.4396 | 1212 | 0.5432 | 0.4179 | 0.5432 | 0.7370 | | 0.1143 | 4.4469 | 1214 | 0.5499 | 0.4179 | 0.5499 | 0.7415 | | 0.1143 | 4.4542 | 1216 | 0.5482 | 0.4179 | 0.5482 | 0.7404 | | 0.1143 | 4.4615 | 1218 | 0.5493 | 0.4179 | 0.5493 | 0.7412 | | 0.1143 | 4.4689 | 1220 | 0.5286 | 0.4179 | 0.5286 | 0.7271 | | 0.1143 | 4.4762 | 1222 | 0.5128 | 0.4407 | 0.5128 | 0.7161 | | 0.1143 | 4.4835 | 1224 | 0.5087 | 0.4407 | 0.5087 | 0.7133 | | 0.1143 | 4.4908 | 1226 | 0.5101 | 0.4407 | 0.5101 | 0.7142 | | 0.1143 | 4.4982 | 1228 | 0.5108 | 0.4407 | 0.5108 | 0.7147 | | 0.1143 | 4.5055 | 1230 | 0.5117 | 0.3793 | 0.5117 | 0.7153 | | 0.1143 | 4.5128 | 1232 | 0.5148 | 0.4211 | 0.5148 | 0.7175 | | 0.1143 | 4.5201 | 1234 | 0.5074 | 0.3793 | 0.5074 | 0.7124 | | 0.1143 | 4.5275 | 1236 | 0.5035 | 0.4407 | 0.5035 | 0.7095 | | 0.1143 | 4.5348 | 1238 | 0.5034 | 0.4407 | 0.5034 | 0.7095 | | 0.1143 | 4.5421 | 1240 | 0.5095 | 0.1818 | 0.5095 | 0.7138 | | 0.1143 | 4.5495 | 1242 | 0.5172 | 0.2500 | 0.5172 | 0.7191 | | 0.1143 | 4.5568 | 1244 | 0.5273 | 0.4828 | 0.5273 | 0.7261 | | 0.1143 | 4.5641 | 1246 | 0.5454 | 0.4828 | 0.5454 | 0.7385 | | 0.1143 | 4.5714 | 1248 | 0.5448 | 0.4828 | 0.5448 | 0.7381 | | 0.1143 | 4.5788 | 1250 | 0.5454 | 0.4000 | 0.5454 | 0.7385 | | 0.1143 | 4.5861 | 1252 | 0.5368 | 0.2500 | 0.5368 | 0.7327 | | 0.1143 | 4.5934 | 1254 | 0.5316 | 0.1818 | 0.5316 | 0.7291 | | 0.1143 | 4.6007 | 1256 | 0.5241 | 0.3333 | 0.5241 | 0.7239 | | 0.1143 | 4.6081 | 1258 | 0.5251 | 0.3333 | 0.5251 | 0.7247 | | 0.1143 | 4.6154 | 1260 | 0.5255 | 0.3333 | 0.5255 | 0.7249 | | 0.1143 | 4.6227 | 1262 | 0.5295 | 0.4407 | 0.5295 | 0.7276 | | 0.1143 | 4.6300 | 1264 | 0.5422 | 0.4590 | 0.5422 | 0.7363 | | 0.1143 | 4.6374 | 1266 | 0.5419 | 0.4658 | 0.5419 | 0.7362 | | 0.1143 | 4.6447 | 1268 | 0.5345 | 0.4545 | 0.5345 | 0.7311 | | 0.1143 | 4.6520 | 1270 | 0.5347 | 0.3077 | 0.5347 | 0.7312 | | 0.1143 | 4.6593 | 1272 | 0.5360 | 0.4348 | 0.5360 | 0.7321 | | 0.1143 | 4.6667 | 1274 | 0.5461 | 0.3377 | 0.5461 | 0.7390 | | 0.1143 | 4.6740 | 1276 | 0.5432 | 0.4444 | 0.5432 | 0.7370 | | 0.1143 | 4.6813 | 1278 | 0.5521 | 0.4578 | 0.5521 | 0.7430 | | 0.1143 | 4.6886 | 1280 | 0.5509 | 0.4578 | 0.5509 | 0.7422 | | 0.1143 | 4.6960 | 1282 | 0.5790 | 0.4615 | 0.5790 | 0.7609 | | 0.1143 | 4.7033 | 1284 | 0.5741 | 0.4615 | 0.5741 | 0.7577 | | 0.1143 | 4.7106 | 1286 | 0.5505 | 0.4615 | 0.5505 | 0.7420 | | 0.1143 | 4.7179 | 1288 | 0.5444 | 0.4407 | 0.5444 | 0.7378 | | 0.1143 | 4.7253 | 1290 | 0.5377 | 0.4407 | 0.5377 | 0.7333 | | 0.1143 | 4.7326 | 1292 | 0.5183 | 0.3810 | 0.5183 | 0.7199 | | 0.1143 | 4.7399 | 1294 | 0.5145 | 0.3810 | 0.5145 | 0.7173 | | 0.1143 | 4.7473 | 1296 | 0.5093 | 0.3810 | 0.5093 | 0.7136 | | 0.1143 | 4.7546 | 1298 | 0.5178 | 0.5312 | 0.5178 | 0.7196 | | 0.1143 | 4.7619 | 1300 | 0.5614 | 0.4507 | 0.5614 | 0.7493 | | 0.1143 | 4.7692 | 1302 | 0.6205 | 0.4304 | 0.6205 | 0.7877 | | 0.1143 | 4.7766 | 1304 | 0.6633 | 0.4304 | 0.6633 | 0.8144 | | 0.1143 | 4.7839 | 1306 | 0.6658 | 0.4304 | 0.6658 | 0.8160 | | 0.1143 | 4.7912 | 1308 | 0.6320 | 0.4615 | 0.6320 | 0.7950 | | 0.1143 | 4.7985 | 1310 | 0.5866 | 0.4615 | 0.5866 | 0.7659 | | 0.1143 | 4.8059 | 1312 | 0.5547 | 0.4935 | 0.5547 | 0.7448 | | 0.1143 | 4.8132 | 1314 | 0.5454 | 0.3014 | 0.5454 | 0.7385 | | 0.1143 | 4.8205 | 1316 | 0.5463 | 0.3077 | 0.5463 | 0.7391 | | 0.1143 | 4.8278 | 1318 | 0.5439 | 0.3000 | 0.5439 | 0.7375 | | 0.1143 | 4.8352 | 1320 | 0.5485 | 0.3000 | 0.5485 | 0.7406 | | 0.1143 | 4.8425 | 1322 | 0.5573 | 0.4474 | 0.5573 | 0.7465 | | 0.1143 | 4.8498 | 1324 | 0.5751 | 0.4474 | 0.5751 | 0.7583 | | 0.1143 | 4.8571 | 1326 | 0.5801 | 0.4474 | 0.5801 | 0.7616 | | 0.1143 | 4.8645 | 1328 | 0.5763 | 0.2703 | 0.5763 | 0.7591 | | 0.1143 | 4.8718 | 1330 | 0.5680 | 0.2703 | 0.5680 | 0.7537 | | 0.1143 | 4.8791 | 1332 | 0.5531 | 0.2373 | 0.5531 | 0.7437 | | 0.1143 | 4.8864 | 1334 | 0.5443 | 0.2373 | 0.5443 | 0.7378 | | 0.1143 | 4.8938 | 1336 | 0.5449 | 0.2373 | 0.5449 | 0.7382 | | 0.1143 | 4.9011 | 1338 | 0.5440 | 0.3000 | 0.5440 | 0.7376 | | 0.1143 | 4.9084 | 1340 | 0.5456 | 0.3000 | 0.5456 | 0.7386 | | 0.1143 | 4.9158 | 1342 | 0.5576 | 0.1818 | 0.5576 | 0.7467 | | 0.1143 | 4.9231 | 1344 | 0.5666 | 0.2500 | 0.5666 | 0.7528 | | 0.1143 | 4.9304 | 1346 | 0.5575 | 0.1818 | 0.5575 | 0.7467 | | 0.1143 | 4.9377 | 1348 | 0.5477 | 0.2373 | 0.5477 | 0.7401 | | 0.1143 | 4.9451 | 1350 | 0.5585 | 0.2909 | 0.5585 | 0.7473 | | 0.1143 | 4.9524 | 1352 | 0.5740 | 0.2373 | 0.5740 | 0.7576 | | 0.1143 | 4.9597 | 1354 | 0.5771 | 0.3000 | 0.5771 | 0.7597 | | 0.1143 | 4.9670 | 1356 | 0.5632 | 0.3607 | 0.5632 | 0.7505 | | 0.1143 | 4.9744 | 1358 | 0.5554 | 0.2759 | 0.5554 | 0.7453 | | 0.1143 | 4.9817 | 1360 | 0.5923 | 0.3684 | 0.5923 | 0.7696 | | 0.1143 | 4.9890 | 1362 | 0.6620 | 0.3836 | 0.6620 | 0.8136 | | 0.1143 | 4.9963 | 1364 | 0.6963 | 0.3836 | 0.6963 | 0.8344 | | 0.1143 | 5.0037 | 1366 | 0.6720 | 0.3836 | 0.6720 | 0.8197 | | 0.1143 | 5.0110 | 1368 | 0.6244 | 0.3836 | 0.6244 | 0.7902 | | 0.1143 | 5.0183 | 1370 | 0.5813 | 0.3077 | 0.5813 | 0.7624 | | 0.1143 | 5.0256 | 1372 | 0.5446 | 0.2500 | 0.5446 | 0.7380 | | 0.1143 | 5.0330 | 1374 | 0.5357 | 0.4211 | 0.5357 | 0.7319 | | 0.1143 | 5.0403 | 1376 | 0.5512 | 0.3571 | 0.5512 | 0.7424 | | 0.1143 | 5.0476 | 1378 | 0.5718 | 0.3571 | 0.5718 | 0.7562 | | 0.1143 | 5.0549 | 1380 | 0.5826 | 0.3636 | 0.5826 | 0.7633 | | 0.1143 | 5.0623 | 1382 | 0.5946 | 0.3636 | 0.5946 | 0.7711 | | 0.1143 | 5.0696 | 1384 | 0.6004 | 0.3836 | 0.6004 | 0.7749 | | 0.1143 | 5.0769 | 1386 | 0.6060 | 0.3836 | 0.6060 | 0.7784 | | 0.1143 | 5.0842 | 1388 | 0.6213 | 0.4 | 0.6213 | 0.7882 | | 0.1143 | 5.0916 | 1390 | 0.6363 | 0.4507 | 0.6363 | 0.7977 | | 0.1143 | 5.0989 | 1392 | 0.6506 | 0.3333 | 0.6506 | 0.8066 | | 0.1143 | 5.1062 | 1394 | 0.6585 | 0.2817 | 0.6585 | 0.8115 | | 0.1143 | 5.1136 | 1396 | 0.6567 | 0.4 | 0.6567 | 0.8103 | | 0.1143 | 5.1209 | 1398 | 0.6670 | 0.3077 | 0.6670 | 0.8167 | | 0.1143 | 5.1282 | 1400 | 0.6849 | 0.3077 | 0.6849 | 0.8276 | | 0.1143 | 5.1355 | 1402 | 0.6744 | 0.3077 | 0.6744 | 0.8212 | | 0.1143 | 5.1429 | 1404 | 0.6510 | 0.3077 | 0.6510 | 0.8068 | | 0.1143 | 5.1502 | 1406 | 0.6246 | 0.4179 | 0.6246 | 0.7903 | | 0.1143 | 5.1575 | 1408 | 0.5984 | 0.3000 | 0.5984 | 0.7736 | | 0.1143 | 5.1648 | 1410 | 0.5763 | 0.3000 | 0.5763 | 0.7591 | | 0.1143 | 5.1722 | 1412 | 0.5652 | 0.3607 | 0.5652 | 0.7518 | | 0.1143 | 5.1795 | 1414 | 0.5632 | 0.4348 | 0.5632 | 0.7504 | | 0.1143 | 5.1868 | 1416 | 0.5569 | 0.4348 | 0.5569 | 0.7463 | | 0.1143 | 5.1941 | 1418 | 0.5537 | 0.5714 | 0.5537 | 0.7441 | | 0.1143 | 5.2015 | 1420 | 0.5302 | 0.5714 | 0.5302 | 0.7281 | | 0.1143 | 5.2088 | 1422 | 0.5109 | 0.5714 | 0.5109 | 0.7148 | | 0.1143 | 5.2161 | 1424 | 0.4934 | 0.5714 | 0.4934 | 0.7024 | | 0.1143 | 5.2234 | 1426 | 0.4786 | 0.5714 | 0.4786 | 0.6918 | | 0.1143 | 5.2308 | 1428 | 0.4825 | 0.5714 | 0.4825 | 0.6946 | | 0.1143 | 5.2381 | 1430 | 0.4879 | 0.5714 | 0.4879 | 0.6985 | | 0.1143 | 5.2454 | 1432 | 0.5100 | 0.5312 | 0.5100 | 0.7141 | | 0.1143 | 5.2527 | 1434 | 0.5228 | 0.5312 | 0.5228 | 0.7231 | | 0.1143 | 5.2601 | 1436 | 0.5326 | 0.4507 | 0.5326 | 0.7298 | | 0.1143 | 5.2674 | 1438 | 0.5174 | 0.5312 | 0.5174 | 0.7193 | | 0.1143 | 5.2747 | 1440 | 0.5059 | 0.5588 | 0.5059 | 0.7113 | | 0.1143 | 5.2821 | 1442 | 0.4966 | 0.4179 | 0.4966 | 0.7047 | | 0.1143 | 5.2894 | 1444 | 0.4972 | 0.3636 | 0.4972 | 0.7051 | | 0.1143 | 5.2967 | 1446 | 0.5015 | 0.4179 | 0.5015 | 0.7081 | | 0.1143 | 5.3040 | 1448 | 0.5136 | 0.4179 | 0.5136 | 0.7166 | | 0.1143 | 5.3114 | 1450 | 0.5291 | 0.5714 | 0.5291 | 0.7274 | | 0.1143 | 5.3187 | 1452 | 0.5439 | 0.5714 | 0.5439 | 0.7375 | | 0.1143 | 5.3260 | 1454 | 0.5473 | 0.5714 | 0.5473 | 0.7398 | | 0.1143 | 5.3333 | 1456 | 0.5538 | 0.4857 | 0.5538 | 0.7442 | | 0.1143 | 5.3407 | 1458 | 0.5484 | 0.4179 | 0.5484 | 0.7405 | | 0.1143 | 5.3480 | 1460 | 0.5461 | 0.3662 | 0.5461 | 0.7390 | | 0.1143 | 5.3553 | 1462 | 0.5479 | 0.3143 | 0.5479 | 0.7402 | | 0.1143 | 5.3626 | 1464 | 0.5568 | 0.3143 | 0.5568 | 0.7462 | | 0.1143 | 5.3700 | 1466 | 0.5584 | 0.3143 | 0.5584 | 0.7472 | | 0.1143 | 5.3773 | 1468 | 0.5606 | 0.3143 | 0.5606 | 0.7487 | | 0.1143 | 5.3846 | 1470 | 0.5728 | 0.3662 | 0.5728 | 0.7568 | | 0.1143 | 5.3919 | 1472 | 0.5775 | 0.4167 | 0.5775 | 0.7600 | | 0.1143 | 5.3993 | 1474 | 0.5806 | 0.4167 | 0.5806 | 0.7620 | | 0.1143 | 5.4066 | 1476 | 0.5719 | 0.4179 | 0.5719 | 0.7562 | | 0.1143 | 5.4139 | 1478 | 0.5519 | 0.3000 | 0.5519 | 0.7429 | | 0.1143 | 5.4212 | 1480 | 0.5372 | 0.4407 | 0.5372 | 0.7329 | | 0.1143 | 5.4286 | 1482 | 0.5357 | 0.4194 | 0.5357 | 0.7319 | | 0.1143 | 5.4359 | 1484 | 0.5434 | 0.4179 | 0.5434 | 0.7372 | | 0.1143 | 5.4432 | 1486 | 0.5581 | 0.4 | 0.5581 | 0.7471 | | 0.1143 | 5.4505 | 1488 | 0.5640 | 0.4 | 0.5640 | 0.7510 | | 0.1143 | 5.4579 | 1490 | 0.5585 | 0.3636 | 0.5585 | 0.7474 | | 0.1143 | 5.4652 | 1492 | 0.5518 | 0.4179 | 0.5518 | 0.7429 | | 0.1143 | 5.4725 | 1494 | 0.5509 | 0.4706 | 0.5509 | 0.7423 | | 0.1143 | 5.4799 | 1496 | 0.5699 | 0.3077 | 0.5699 | 0.7549 | | 0.1143 | 5.4872 | 1498 | 0.6039 | 0.4324 | 0.6039 | 0.7771 | | 0.0785 | 5.4945 | 1500 | 0.6255 | 0.4 | 0.6255 | 0.7909 | | 0.0785 | 5.5018 | 1502 | 0.6286 | 0.4 | 0.6286 | 0.7928 | | 0.0785 | 5.5092 | 1504 | 0.6112 | 0.4 | 0.6112 | 0.7818 | | 0.0785 | 5.5165 | 1506 | 0.5890 | 0.3836 | 0.5890 | 0.7675 | | 0.0785 | 5.5238 | 1508 | 0.5915 | 0.3636 | 0.5915 | 0.7691 | | 0.0785 | 5.5311 | 1510 | 0.6011 | 0.4 | 0.6011 | 0.7753 | | 0.0785 | 5.5385 | 1512 | 0.6219 | 0.3377 | 0.6219 | 0.7886 | | 0.0785 | 5.5458 | 1514 | 0.6203 | 0.3377 | 0.6203 | 0.7876 | | 0.0785 | 5.5531 | 1516 | 0.5927 | 0.3377 | 0.5927 | 0.7699 | | 0.0785 | 5.5604 | 1518 | 0.5717 | 0.3226 | 0.5717 | 0.7561 | | 0.0785 | 5.5678 | 1520 | 0.5418 | 0.3636 | 0.5418 | 0.7361 | | 0.0785 | 5.5751 | 1522 | 0.5330 | 0.4762 | 0.5330 | 0.7301 | | 0.0785 | 5.5824 | 1524 | 0.5321 | 0.4762 | 0.5321 | 0.7295 | | 0.0785 | 5.5897 | 1526 | 0.5302 | 0.4762 | 0.5302 | 0.7281 | | 0.0785 | 5.5971 | 1528 | 0.5304 | 0.4828 | 0.5304 | 0.7283 | | 0.0785 | 5.6044 | 1530 | 0.5411 | 0.3607 | 0.5411 | 0.7356 | | 0.0785 | 5.6117 | 1532 | 0.5745 | 0.3478 | 0.5745 | 0.7580 | | 0.0785 | 5.6190 | 1534 | 0.6291 | 0.3836 | 0.6291 | 0.7931 | | 0.0785 | 5.6264 | 1536 | 0.6675 | 0.3836 | 0.6675 | 0.8170 | | 0.0785 | 5.6337 | 1538 | 0.6625 | 0.3836 | 0.6625 | 0.8139 | | 0.0785 | 5.6410 | 1540 | 0.6231 | 0.4615 | 0.6231 | 0.7894 | | 0.0785 | 5.6484 | 1542 | 0.5886 | 0.4 | 0.5886 | 0.7672 | | 0.0785 | 5.6557 | 1544 | 0.5588 | 0.3607 | 0.5588 | 0.7475 | | 0.0785 | 5.6630 | 1546 | 0.5505 | 0.3636 | 0.5505 | 0.7419 | | 0.0785 | 5.6703 | 1548 | 0.5476 | 0.3662 | 0.5476 | 0.7400 | | 0.0785 | 5.6777 | 1550 | 0.5485 | 0.4 | 0.5485 | 0.7406 | | 0.0785 | 5.6850 | 1552 | 0.5457 | 0.3662 | 0.5457 | 0.7387 | | 0.0785 | 5.6923 | 1554 | 0.5478 | 0.3607 | 0.5478 | 0.7401 | | 0.0785 | 5.6996 | 1556 | 0.5622 | 0.3607 | 0.5622 | 0.7498 | | 0.0785 | 5.7070 | 1558 | 0.5911 | 0.25 | 0.5911 | 0.7688 | | 0.0785 | 5.7143 | 1560 | 0.6215 | 0.3836 | 0.6215 | 0.7883 | | 0.0785 | 5.7216 | 1562 | 0.6482 | 0.3836 | 0.6482 | 0.8051 | | 0.0785 | 5.7289 | 1564 | 0.6569 | 0.3836 | 0.6569 | 0.8105 | | 0.0785 | 5.7363 | 1566 | 0.6452 | 0.3836 | 0.6452 | 0.8032 | | 0.0785 | 5.7436 | 1568 | 0.6124 | 0.3836 | 0.6124 | 0.7825 | | 0.0785 | 5.7509 | 1570 | 0.5850 | 0.25 | 0.5850 | 0.7649 | | 0.0785 | 5.7582 | 1572 | 0.5645 | 0.2154 | 0.5645 | 0.7514 | | 0.0785 | 5.7656 | 1574 | 0.5427 | 0.2500 | 0.5427 | 0.7367 | | 0.0785 | 5.7729 | 1576 | 0.5345 | 0.2500 | 0.5345 | 0.7311 | | 0.0785 | 5.7802 | 1578 | 0.5340 | 0.2500 | 0.5340 | 0.7308 | | 0.0785 | 5.7875 | 1580 | 0.5423 | 0.2759 | 0.5423 | 0.7364 | | 0.0785 | 5.7949 | 1582 | 0.5488 | 0.2759 | 0.5488 | 0.7408 | | 0.0785 | 5.8022 | 1584 | 0.5647 | 0.2154 | 0.5647 | 0.7515 | | 0.0785 | 5.8095 | 1586 | 0.5873 | 0.2154 | 0.5873 | 0.7663 | | 0.0785 | 5.8168 | 1588 | 0.5979 | 0.2154 | 0.5979 | 0.7732 | | 0.0785 | 5.8242 | 1590 | 0.5858 | 0.2154 | 0.5858 | 0.7653 | | 0.0785 | 5.8315 | 1592 | 0.5605 | 0.2154 | 0.5605 | 0.7487 | | 0.0785 | 5.8388 | 1594 | 0.5355 | 0.2500 | 0.5355 | 0.7318 | | 0.0785 | 5.8462 | 1596 | 0.5271 | 0.3607 | 0.5271 | 0.7260 | | 0.0785 | 5.8535 | 1598 | 0.5243 | 0.3607 | 0.5243 | 0.7241 | | 0.0785 | 5.8608 | 1600 | 0.5297 | 0.3607 | 0.5297 | 0.7278 | | 0.0785 | 5.8681 | 1602 | 0.5378 | 0.3226 | 0.5378 | 0.7333 | | 0.0785 | 5.8755 | 1604 | 0.5375 | 0.3226 | 0.5375 | 0.7332 | | 0.0785 | 5.8828 | 1606 | 0.5447 | 0.3810 | 0.5447 | 0.7380 | | 0.0785 | 5.8901 | 1608 | 0.5549 | 0.3810 | 0.5549 | 0.7449 | | 0.0785 | 5.8974 | 1610 | 0.5440 | 0.3810 | 0.5440 | 0.7375 | | 0.0785 | 5.9048 | 1612 | 0.5320 | 0.3607 | 0.5320 | 0.7294 | | 0.0785 | 5.9121 | 1614 | 0.5329 | 0.3607 | 0.5329 | 0.7300 | | 0.0785 | 5.9194 | 1616 | 0.5427 | 0.3607 | 0.5427 | 0.7367 | | 0.0785 | 5.9267 | 1618 | 0.5505 | 0.3000 | 0.5505 | 0.7420 | | 0.0785 | 5.9341 | 1620 | 0.5538 | 0.4407 | 0.5538 | 0.7441 | | 0.0785 | 5.9414 | 1622 | 0.5582 | 0.4706 | 0.5582 | 0.7471 | | 0.0785 | 5.9487 | 1624 | 0.5661 | 0.4179 | 0.5661 | 0.7524 | | 0.0785 | 5.9560 | 1626 | 0.5699 | 0.3636 | 0.5699 | 0.7549 | | 0.0785 | 5.9634 | 1628 | 0.5730 | 0.3636 | 0.5730 | 0.7570 | | 0.0785 | 5.9707 | 1630 | 0.5679 | 0.3636 | 0.5679 | 0.7536 | | 0.0785 | 5.9780 | 1632 | 0.5599 | 0.4211 | 0.5599 | 0.7482 | | 0.0785 | 5.9853 | 1634 | 0.5522 | 0.4211 | 0.5522 | 0.7431 | | 0.0785 | 5.9927 | 1636 | 0.5549 | 0.3333 | 0.5549 | 0.7449 | | 0.0785 | 6.0 | 1638 | 0.5564 | 0.3333 | 0.5564 | 0.7459 | | 0.0785 | 6.0073 | 1640 | 0.5510 | 0.3333 | 0.5510 | 0.7423 | | 0.0785 | 6.0147 | 1642 | 0.5467 | 0.3333 | 0.5467 | 0.7394 | | 0.0785 | 6.0220 | 1644 | 0.5392 | 0.4407 | 0.5392 | 0.7343 | | 0.0785 | 6.0293 | 1646 | 0.5342 | 0.4828 | 0.5342 | 0.7309 | | 0.0785 | 6.0366 | 1648 | 0.5362 | 0.4211 | 0.5362 | 0.7323 | | 0.0785 | 6.0440 | 1650 | 0.5381 | 0.4211 | 0.5381 | 0.7336 | | 0.0785 | 6.0513 | 1652 | 0.5411 | 0.4828 | 0.5411 | 0.7356 | | 0.0785 | 6.0586 | 1654 | 0.5436 | 0.4828 | 0.5436 | 0.7373 | | 0.0785 | 6.0659 | 1656 | 0.5494 | 0.4407 | 0.5494 | 0.7412 | | 0.0785 | 6.0733 | 1658 | 0.5486 | 0.4407 | 0.5486 | 0.7407 | | 0.0785 | 6.0806 | 1660 | 0.5475 | 0.4375 | 0.5475 | 0.7399 | | 0.0785 | 6.0879 | 1662 | 0.5544 | 0.4407 | 0.5544 | 0.7446 | | 0.0785 | 6.0952 | 1664 | 0.5639 | 0.4407 | 0.5639 | 0.7509 | | 0.0785 | 6.1026 | 1666 | 0.5616 | 0.4375 | 0.5616 | 0.7494 | | 0.0785 | 6.1099 | 1668 | 0.5488 | 0.4375 | 0.5488 | 0.7408 | | 0.0785 | 6.1172 | 1670 | 0.5373 | 0.4375 | 0.5373 | 0.7330 | | 0.0785 | 6.1245 | 1672 | 0.5328 | 0.4375 | 0.5328 | 0.7300 | | 0.0785 | 6.1319 | 1674 | 0.5309 | 0.4762 | 0.5309 | 0.7286 | | 0.0785 | 6.1392 | 1676 | 0.5343 | 0.4194 | 0.5343 | 0.7310 | | 0.0785 | 6.1465 | 1678 | 0.5386 | 0.4194 | 0.5386 | 0.7339 | | 0.0785 | 6.1538 | 1680 | 0.5375 | 0.4194 | 0.5375 | 0.7332 | | 0.0785 | 6.1612 | 1682 | 0.5348 | 0.4828 | 0.5348 | 0.7313 | | 0.0785 | 6.1685 | 1684 | 0.5329 | 0.4407 | 0.5329 | 0.7300 | | 0.0785 | 6.1758 | 1686 | 0.5310 | 0.4828 | 0.5310 | 0.7287 | | 0.0785 | 6.1832 | 1688 | 0.5338 | 0.4828 | 0.5338 | 0.7306 | | 0.0785 | 6.1905 | 1690 | 0.5376 | 0.4828 | 0.5376 | 0.7332 | | 0.0785 | 6.1978 | 1692 | 0.5447 | 0.4762 | 0.5447 | 0.7380 | | 0.0785 | 6.2051 | 1694 | 0.5505 | 0.4762 | 0.5505 | 0.7420 | | 0.0785 | 6.2125 | 1696 | 0.5531 | 0.4762 | 0.5531 | 0.7437 | | 0.0785 | 6.2198 | 1698 | 0.5579 | 0.4375 | 0.5579 | 0.7470 | | 0.0785 | 6.2271 | 1700 | 0.5778 | 0.3000 | 0.5778 | 0.7601 | | 0.0785 | 6.2344 | 1702 | 0.6168 | 0.3810 | 0.6168 | 0.7854 | | 0.0785 | 6.2418 | 1704 | 0.6542 | 0.4 | 0.6542 | 0.8088 | | 0.0785 | 6.2491 | 1706 | 0.6772 | 0.4 | 0.6772 | 0.8229 | | 0.0785 | 6.2564 | 1708 | 0.6775 | 0.3810 | 0.6775 | 0.8231 | | 0.0785 | 6.2637 | 1710 | 0.6582 | 0.3810 | 0.6582 | 0.8113 | | 0.0785 | 6.2711 | 1712 | 0.6226 | 0.3824 | 0.6226 | 0.7890 | | 0.0785 | 6.2784 | 1714 | 0.6071 | 0.4324 | 0.6071 | 0.7792 | | 0.0785 | 6.2857 | 1716 | 0.6268 | 0.4304 | 0.6268 | 0.7917 | | 0.0785 | 6.2930 | 1718 | 0.6376 | 0.4304 | 0.6376 | 0.7985 | | 0.0785 | 6.3004 | 1720 | 0.6409 | 0.4304 | 0.6409 | 0.8006 | | 0.0785 | 6.3077 | 1722 | 0.6393 | 0.4304 | 0.6393 | 0.7996 | | 0.0785 | 6.3150 | 1724 | 0.6354 | 0.4304 | 0.6354 | 0.7971 | | 0.0785 | 6.3223 | 1726 | 0.6314 | 0.3333 | 0.6314 | 0.7946 | | 0.0785 | 6.3297 | 1728 | 0.6413 | 0.3544 | 0.6413 | 0.8008 | | 0.0785 | 6.3370 | 1730 | 0.6527 | 0.4 | 0.6527 | 0.8079 | | 0.0785 | 6.3443 | 1732 | 0.6517 | 0.4 | 0.6517 | 0.8073 | | 0.0785 | 6.3516 | 1734 | 0.6385 | 0.4 | 0.6385 | 0.7991 | | 0.0785 | 6.3590 | 1736 | 0.6126 | 0.2759 | 0.6126 | 0.7827 | | 0.0785 | 6.3663 | 1738 | 0.5898 | 0.3571 | 0.5898 | 0.7680 | | 0.0785 | 6.3736 | 1740 | 0.5878 | 0.3571 | 0.5878 | 0.7667 | | 0.0785 | 6.3810 | 1742 | 0.5816 | 0.3571 | 0.5816 | 0.7626 | | 0.0785 | 6.3883 | 1744 | 0.5882 | 0.3571 | 0.5882 | 0.7670 | | 0.0785 | 6.3956 | 1746 | 0.6022 | 0.4375 | 0.6022 | 0.7760 | | 0.0785 | 6.4029 | 1748 | 0.6130 | 0.4375 | 0.6130 | 0.7829 | | 0.0785 | 6.4103 | 1750 | 0.6340 | 0.3077 | 0.6340 | 0.7963 | | 0.0785 | 6.4176 | 1752 | 0.6440 | 0.3077 | 0.6440 | 0.8025 | | 0.0785 | 6.4249 | 1754 | 0.6436 | 0.3077 | 0.6436 | 0.8022 | | 0.0785 | 6.4322 | 1756 | 0.6396 | 0.3077 | 0.6396 | 0.7997 | | 0.0785 | 6.4396 | 1758 | 0.6209 | 0.4 | 0.6209 | 0.7879 | | 0.0785 | 6.4469 | 1760 | 0.6296 | 0.4 | 0.6296 | 0.7935 | | 0.0785 | 6.4542 | 1762 | 0.6375 | 0.4 | 0.6375 | 0.7984 | | 0.0785 | 6.4615 | 1764 | 0.6117 | 0.3514 | 0.6117 | 0.7821 | | 0.0785 | 6.4689 | 1766 | 0.5947 | 0.4658 | 0.5947 | 0.7712 | | 0.0785 | 6.4762 | 1768 | 0.5827 | 0.4545 | 0.5827 | 0.7633 | | 0.0785 | 6.4835 | 1770 | 0.5708 | 0.4545 | 0.5708 | 0.7555 | | 0.0785 | 6.4908 | 1772 | 0.5596 | 0.4545 | 0.5596 | 0.7481 | | 0.0785 | 6.4982 | 1774 | 0.5544 | 0.4590 | 0.5544 | 0.7446 | | 0.0785 | 6.5055 | 1776 | 0.5494 | 0.4590 | 0.5494 | 0.7412 | | 0.0785 | 6.5128 | 1778 | 0.5529 | 0.4590 | 0.5529 | 0.7436 | | 0.0785 | 6.5201 | 1780 | 0.5577 | 0.4590 | 0.5577 | 0.7468 | | 0.0785 | 6.5275 | 1782 | 0.5497 | 0.3571 | 0.5497 | 0.7415 | | 0.0785 | 6.5348 | 1784 | 0.5359 | 0.4407 | 0.5359 | 0.7321 | | 0.0785 | 6.5421 | 1786 | 0.5334 | 0.4211 | 0.5334 | 0.7303 | | 0.0785 | 6.5495 | 1788 | 0.5440 | 0.3333 | 0.5440 | 0.7376 | | 0.0785 | 6.5568 | 1790 | 0.5522 | 0.3333 | 0.5522 | 0.7431 | | 0.0785 | 6.5641 | 1792 | 0.5509 | 0.3333 | 0.5509 | 0.7422 | | 0.0785 | 6.5714 | 1794 | 0.5431 | 0.3571 | 0.5431 | 0.7369 | | 0.0785 | 6.5788 | 1796 | 0.5425 | 0.4407 | 0.5425 | 0.7366 | | 0.0785 | 6.5861 | 1798 | 0.5610 | 0.4 | 0.5610 | 0.7490 | | 0.0785 | 6.5934 | 1800 | 0.5857 | 0.2105 | 0.5857 | 0.7653 | | 0.0785 | 6.6007 | 1802 | 0.6020 | 0.2500 | 0.6020 | 0.7759 | | 0.0785 | 6.6081 | 1804 | 0.5989 | 0.3478 | 0.5989 | 0.7739 | | 0.0785 | 6.6154 | 1806 | 0.5875 | 0.4706 | 0.5875 | 0.7665 | | 0.0785 | 6.6227 | 1808 | 0.5905 | 0.4706 | 0.5905 | 0.7684 | | 0.0785 | 6.6300 | 1810 | 0.5894 | 0.4706 | 0.5894 | 0.7677 | | 0.0785 | 6.6374 | 1812 | 0.5904 | 0.4706 | 0.5904 | 0.7684 | | 0.0785 | 6.6447 | 1814 | 0.5963 | 0.4706 | 0.5963 | 0.7722 | | 0.0785 | 6.6520 | 1816 | 0.5869 | 0.4545 | 0.5869 | 0.7661 | | 0.0785 | 6.6593 | 1818 | 0.5739 | 0.4545 | 0.5739 | 0.7576 | | 0.0785 | 6.6667 | 1820 | 0.5618 | 0.4407 | 0.5618 | 0.7496 | | 0.0785 | 6.6740 | 1822 | 0.5597 | 0.4828 | 0.5597 | 0.7481 | | 0.0785 | 6.6813 | 1824 | 0.5619 | 0.4828 | 0.5619 | 0.7496 | | 0.0785 | 6.6886 | 1826 | 0.5629 | 0.4407 | 0.5629 | 0.7503 | | 0.0785 | 6.6960 | 1828 | 0.5663 | 0.4407 | 0.5663 | 0.7525 | | 0.0785 | 6.7033 | 1830 | 0.5694 | 0.4407 | 0.5694 | 0.7546 | | 0.0785 | 6.7106 | 1832 | 0.5728 | 0.3607 | 0.5728 | 0.7568 | | 0.0785 | 6.7179 | 1834 | 0.5708 | 0.3607 | 0.5708 | 0.7555 | | 0.0785 | 6.7253 | 1836 | 0.5598 | 0.3607 | 0.5598 | 0.7482 | | 0.0785 | 6.7326 | 1838 | 0.5501 | 0.4407 | 0.5501 | 0.7417 | | 0.0785 | 6.7399 | 1840 | 0.5444 | 0.4407 | 0.5444 | 0.7378 | | 0.0785 | 6.7473 | 1842 | 0.5413 | 0.4407 | 0.5413 | 0.7357 | | 0.0785 | 6.7546 | 1844 | 0.5408 | 0.4828 | 0.5408 | 0.7354 | | 0.0785 | 6.7619 | 1846 | 0.5459 | 0.4211 | 0.5459 | 0.7388 | | 0.0785 | 6.7692 | 1848 | 0.5445 | 0.4211 | 0.5445 | 0.7379 | | 0.0785 | 6.7766 | 1850 | 0.5371 | 0.4211 | 0.5371 | 0.7329 | | 0.0785 | 6.7839 | 1852 | 0.5333 | 0.4211 | 0.5333 | 0.7303 | | 0.0785 | 6.7912 | 1854 | 0.5341 | 0.4407 | 0.5341 | 0.7308 | | 0.0785 | 6.7985 | 1856 | 0.5402 | 0.4407 | 0.5402 | 0.7350 | | 0.0785 | 6.8059 | 1858 | 0.5510 | 0.4 | 0.5510 | 0.7423 | | 0.0785 | 6.8132 | 1860 | 0.5658 | 0.3571 | 0.5658 | 0.7522 | | 0.0785 | 6.8205 | 1862 | 0.5707 | 0.4706 | 0.5707 | 0.7555 | | 0.0785 | 6.8278 | 1864 | 0.5679 | 0.4179 | 0.5679 | 0.7536 | | 0.0785 | 6.8352 | 1866 | 0.5624 | 0.4545 | 0.5624 | 0.7499 | | 0.0785 | 6.8425 | 1868 | 0.5571 | 0.4828 | 0.5571 | 0.7464 | | 0.0785 | 6.8498 | 1870 | 0.5582 | 0.4211 | 0.5582 | 0.7472 | | 0.0785 | 6.8571 | 1872 | 0.5629 | 0.4211 | 0.5629 | 0.7503 | | 0.0785 | 6.8645 | 1874 | 0.5692 | 0.3571 | 0.5692 | 0.7545 | | 0.0785 | 6.8718 | 1876 | 0.5711 | 0.3571 | 0.5711 | 0.7557 | | 0.0785 | 6.8791 | 1878 | 0.5676 | 0.4211 | 0.5676 | 0.7534 | | 0.0785 | 6.8864 | 1880 | 0.5624 | 0.4211 | 0.5624 | 0.7500 | | 0.0785 | 6.8938 | 1882 | 0.5502 | 0.4828 | 0.5502 | 0.7418 | | 0.0785 | 6.9011 | 1884 | 0.5424 | 0.4828 | 0.5424 | 0.7365 | | 0.0785 | 6.9084 | 1886 | 0.5386 | 0.4407 | 0.5386 | 0.7339 | | 0.0785 | 6.9158 | 1888 | 0.5338 | 0.4407 | 0.5338 | 0.7306 | | 0.0785 | 6.9231 | 1890 | 0.5356 | 0.4 | 0.5356 | 0.7319 | | 0.0785 | 6.9304 | 1892 | 0.5434 | 0.4590 | 0.5434 | 0.7371 | | 0.0785 | 6.9377 | 1894 | 0.5461 | 0.4590 | 0.5461 | 0.7390 | | 0.0785 | 6.9451 | 1896 | 0.5444 | 0.4590 | 0.5444 | 0.7379 | | 0.0785 | 6.9524 | 1898 | 0.5455 | 0.4 | 0.5455 | 0.7386 | | 0.0785 | 6.9597 | 1900 | 0.5497 | 0.4179 | 0.5497 | 0.7414 | | 0.0785 | 6.9670 | 1902 | 0.5565 | 0.4179 | 0.5565 | 0.7460 | | 0.0785 | 6.9744 | 1904 | 0.5577 | 0.3478 | 0.5577 | 0.7468 | | 0.0785 | 6.9817 | 1906 | 0.5550 | 0.3478 | 0.5550 | 0.7450 | | 0.0785 | 6.9890 | 1908 | 0.5493 | 0.3226 | 0.5493 | 0.7412 | | 0.0785 | 6.9963 | 1910 | 0.5453 | 0.4590 | 0.5453 | 0.7384 | | 0.0785 | 7.0037 | 1912 | 0.5432 | 0.4590 | 0.5432 | 0.7370 | | 0.0785 | 7.0110 | 1914 | 0.5445 | 0.3226 | 0.5445 | 0.7379 | | 0.0785 | 7.0183 | 1916 | 0.5450 | 0.3226 | 0.5450 | 0.7383 | | 0.0785 | 7.0256 | 1918 | 0.5438 | 0.3226 | 0.5438 | 0.7374 | | 0.0785 | 7.0330 | 1920 | 0.5457 | 0.3226 | 0.5457 | 0.7387 | | 0.0785 | 7.0403 | 1922 | 0.5393 | 0.3226 | 0.5393 | 0.7344 | | 0.0785 | 7.0476 | 1924 | 0.5270 | 0.4590 | 0.5270 | 0.7259 | | 0.0785 | 7.0549 | 1926 | 0.5209 | 0.4407 | 0.5209 | 0.7218 | | 0.0785 | 7.0623 | 1928 | 0.5182 | 0.4407 | 0.5182 | 0.7199 | | 0.0785 | 7.0696 | 1930 | 0.5177 | 0.4407 | 0.5177 | 0.7195 | | 0.0785 | 7.0769 | 1932 | 0.5186 | 0.4407 | 0.5186 | 0.7201 | | 0.0785 | 7.0842 | 1934 | 0.5213 | 0.4590 | 0.5213 | 0.7220 | | 0.0785 | 7.0916 | 1936 | 0.5270 | 0.3226 | 0.5270 | 0.7260 | | 0.0785 | 7.0989 | 1938 | 0.5305 | 0.3226 | 0.5305 | 0.7284 | | 0.0785 | 7.1062 | 1940 | 0.5364 | 0.3226 | 0.5364 | 0.7324 | | 0.0785 | 7.1136 | 1942 | 0.5438 | 0.3226 | 0.5438 | 0.7374 | | 0.0785 | 7.1209 | 1944 | 0.5539 | 0.3478 | 0.5539 | 0.7442 | | 0.0785 | 7.1282 | 1946 | 0.5597 | 0.3478 | 0.5597 | 0.7482 | | 0.0785 | 7.1355 | 1948 | 0.5635 | 0.3478 | 0.5635 | 0.7507 | | 0.0785 | 7.1429 | 1950 | 0.5595 | 0.3824 | 0.5595 | 0.7480 | | 0.0785 | 7.1502 | 1952 | 0.5540 | 0.4407 | 0.5540 | 0.7443 | | 0.0785 | 7.1575 | 1954 | 0.5507 | 0.4375 | 0.5507 | 0.7421 | | 0.0785 | 7.1648 | 1956 | 0.5478 | 0.4348 | 0.5478 | 0.7401 | | 0.0785 | 7.1722 | 1958 | 0.5478 | 0.3824 | 0.5478 | 0.7401 | | 0.0785 | 7.1795 | 1960 | 0.5443 | 0.3824 | 0.5443 | 0.7378 | | 0.0785 | 7.1868 | 1962 | 0.5416 | 0.3793 | 0.5416 | 0.7359 | | 0.0785 | 7.1941 | 1964 | 0.5451 | 0.4407 | 0.5451 | 0.7383 | | 0.0785 | 7.2015 | 1966 | 0.5532 | 0.5 | 0.5532 | 0.7438 | | 0.0785 | 7.2088 | 1968 | 0.5682 | 0.3478 | 0.5682 | 0.7538 | | 0.0785 | 7.2161 | 1970 | 0.5856 | 0.3478 | 0.5856 | 0.7652 | | 0.0785 | 7.2234 | 1972 | 0.5935 | 0.3478 | 0.5935 | 0.7704 | | 0.0785 | 7.2308 | 1974 | 0.5892 | 0.3478 | 0.5892 | 0.7676 | | 0.0785 | 7.2381 | 1976 | 0.5806 | 0.3478 | 0.5806 | 0.7620 | | 0.0785 | 7.2454 | 1978 | 0.5707 | 0.3478 | 0.5707 | 0.7555 | | 0.0785 | 7.2527 | 1980 | 0.5673 | 0.3478 | 0.5673 | 0.7532 | | 0.0785 | 7.2601 | 1982 | 0.5657 | 0.3478 | 0.5657 | 0.7521 | | 0.0785 | 7.2674 | 1984 | 0.5613 | 0.3478 | 0.5613 | 0.7492 | | 0.0785 | 7.2747 | 1986 | 0.5587 | 0.3478 | 0.5587 | 0.7475 | | 0.0785 | 7.2821 | 1988 | 0.5552 | 0.3478 | 0.5552 | 0.7451 | | 0.0785 | 7.2894 | 1990 | 0.5536 | 0.3478 | 0.5536 | 0.7440 | | 0.0785 | 7.2967 | 1992 | 0.5563 | 0.3478 | 0.5563 | 0.7458 | | 0.0785 | 7.3040 | 1994 | 0.5599 | 0.3478 | 0.5599 | 0.7483 | | 0.0785 | 7.3114 | 1996 | 0.5662 | 0.3478 | 0.5662 | 0.7524 | | 0.0785 | 7.3187 | 1998 | 0.5687 | 0.3478 | 0.5687 | 0.7541 | | 0.0555 | 7.3260 | 2000 | 0.5800 | 0.3478 | 0.5800 | 0.7616 | | 0.0555 | 7.3333 | 2002 | 0.5922 | 0.3478 | 0.5922 | 0.7696 | | 0.0555 | 7.3407 | 2004 | 0.6046 | 0.3478 | 0.6046 | 0.7776 | | 0.0555 | 7.3480 | 2006 | 0.6190 | 0.4 | 0.6190 | 0.7868 | | 0.0555 | 7.3553 | 2008 | 0.6313 | 0.3077 | 0.6313 | 0.7945 | | 0.0555 | 7.3626 | 2010 | 0.6427 | 0.25 | 0.6427 | 0.8017 | | 0.0555 | 7.3700 | 2012 | 0.6403 | 0.25 | 0.6403 | 0.8002 | | 0.0555 | 7.3773 | 2014 | 0.6257 | 0.3077 | 0.6257 | 0.7910 | | 0.0555 | 7.3846 | 2016 | 0.6071 | 0.4 | 0.6071 | 0.7792 | | 0.0555 | 7.3919 | 2018 | 0.5883 | 0.3478 | 0.5883 | 0.7670 | | 0.0555 | 7.3993 | 2020 | 0.5755 | 0.3478 | 0.5755 | 0.7586 | | 0.0555 | 7.4066 | 2022 | 0.5697 | 0.3824 | 0.5697 | 0.7548 | | 0.0555 | 7.4139 | 2024 | 0.5716 | 0.3824 | 0.5716 | 0.7560 | | 0.0555 | 7.4212 | 2026 | 0.5698 | 0.3824 | 0.5698 | 0.7549 | | 0.0555 | 7.4286 | 2028 | 0.5649 | 0.4545 | 0.5649 | 0.7516 | | 0.0555 | 7.4359 | 2030 | 0.5636 | 0.4545 | 0.5636 | 0.7508 | | 0.0555 | 7.4432 | 2032 | 0.5669 | 0.4545 | 0.5669 | 0.7529 | | 0.0555 | 7.4505 | 2034 | 0.5736 | 0.4545 | 0.5736 | 0.7574 | | 0.0555 | 7.4579 | 2036 | 0.5813 | 0.4545 | 0.5813 | 0.7624 | | 0.0555 | 7.4652 | 2038 | 0.5913 | 0.3824 | 0.5913 | 0.7690 | | 0.0555 | 7.4725 | 2040 | 0.5968 | 0.3824 | 0.5968 | 0.7725 | | 0.0555 | 7.4799 | 2042 | 0.6034 | 0.3824 | 0.6034 | 0.7768 | | 0.0555 | 7.4872 | 2044 | 0.6114 | 0.3478 | 0.6114 | 0.7819 | | 0.0555 | 7.4945 | 2046 | 0.6161 | 0.3478 | 0.6161 | 0.7849 | | 0.0555 | 7.5018 | 2048 | 0.6128 | 0.3478 | 0.6128 | 0.7828 | | 0.0555 | 7.5092 | 2050 | 0.6013 | 0.3478 | 0.6013 | 0.7755 | | 0.0555 | 7.5165 | 2052 | 0.5807 | 0.3478 | 0.5807 | 0.7620 | | 0.0555 | 7.5238 | 2054 | 0.5669 | 0.3824 | 0.5669 | 0.7529 | | 0.0555 | 7.5311 | 2056 | 0.5605 | 0.3824 | 0.5605 | 0.7486 | | 0.0555 | 7.5385 | 2058 | 0.5567 | 0.3478 | 0.5567 | 0.7461 | | 0.0555 | 7.5458 | 2060 | 0.5571 | 0.3478 | 0.5571 | 0.7464 | | 0.0555 | 7.5531 | 2062 | 0.5519 | 0.3478 | 0.5519 | 0.7429 | | 0.0555 | 7.5604 | 2064 | 0.5492 | 0.2500 | 0.5492 | 0.7411 | | 0.0555 | 7.5678 | 2066 | 0.5532 | 0.2500 | 0.5532 | 0.7438 | | 0.0555 | 7.5751 | 2068 | 0.5684 | 0.3077 | 0.5684 | 0.7539 | | 0.0555 | 7.5824 | 2070 | 0.5736 | 0.3077 | 0.5736 | 0.7574 | | 0.0555 | 7.5897 | 2072 | 0.5827 | 0.3077 | 0.5827 | 0.7633 | | 0.0555 | 7.5971 | 2074 | 0.5899 | 0.3077 | 0.5899 | 0.7680 | | 0.0555 | 7.6044 | 2076 | 0.5898 | 0.3077 | 0.5898 | 0.7680 | | 0.0555 | 7.6117 | 2078 | 0.5746 | 0.3077 | 0.5746 | 0.7580 | | 0.0555 | 7.6190 | 2080 | 0.5513 | 0.3478 | 0.5513 | 0.7425 | | 0.0555 | 7.6264 | 2082 | 0.5376 | 0.4706 | 0.5376 | 0.7332 | | 0.0555 | 7.6337 | 2084 | 0.5317 | 0.4545 | 0.5317 | 0.7292 | | 0.0555 | 7.6410 | 2086 | 0.5350 | 0.4923 | 0.5350 | 0.7314 | | 0.0555 | 7.6484 | 2088 | 0.5422 | 0.48 | 0.5422 | 0.7363 | | 0.0555 | 7.6557 | 2090 | 0.5505 | 0.48 | 0.5505 | 0.7420 | | 0.0555 | 7.6630 | 2092 | 0.5577 | 0.4507 | 0.5577 | 0.7468 | | 0.0555 | 7.6703 | 2094 | 0.5662 | 0.5075 | 0.5662 | 0.7524 | | 0.0555 | 7.6777 | 2096 | 0.5695 | 0.5075 | 0.5695 | 0.7547 | | 0.0555 | 7.6850 | 2098 | 0.5703 | 0.5075 | 0.5703 | 0.7552 | | 0.0555 | 7.6923 | 2100 | 0.5664 | 0.5075 | 0.5664 | 0.7526 | | 0.0555 | 7.6996 | 2102 | 0.5653 | 0.5075 | 0.5653 | 0.7518 | | 0.0555 | 7.7070 | 2104 | 0.5644 | 0.4545 | 0.5644 | 0.7513 | | 0.0555 | 7.7143 | 2106 | 0.5670 | 0.4545 | 0.5670 | 0.7530 | | 0.0555 | 7.7216 | 2108 | 0.5719 | 0.5075 | 0.5719 | 0.7562 | | 0.0555 | 7.7289 | 2110 | 0.5746 | 0.5075 | 0.5746 | 0.7580 | | 0.0555 | 7.7363 | 2112 | 0.5802 | 0.3478 | 0.5802 | 0.7617 | | 0.0555 | 7.7436 | 2114 | 0.5827 | 0.3478 | 0.5827 | 0.7633 | | 0.0555 | 7.7509 | 2116 | 0.5757 | 0.3478 | 0.5757 | 0.7588 | | 0.0555 | 7.7582 | 2118 | 0.5691 | 0.3478 | 0.5691 | 0.7544 | | 0.0555 | 7.7656 | 2120 | 0.5626 | 0.3478 | 0.5626 | 0.7501 | | 0.0555 | 7.7729 | 2122 | 0.5588 | 0.3824 | 0.5588 | 0.7475 | | 0.0555 | 7.7802 | 2124 | 0.5517 | 0.5075 | 0.5517 | 0.7428 | | 0.0555 | 7.7875 | 2126 | 0.5434 | 0.5075 | 0.5434 | 0.7372 | | 0.0555 | 7.7949 | 2128 | 0.5372 | 0.4375 | 0.5372 | 0.7330 | | 0.0555 | 7.8022 | 2130 | 0.5356 | 0.4375 | 0.5356 | 0.7319 | | 0.0555 | 7.8095 | 2132 | 0.5345 | 0.4375 | 0.5345 | 0.7311 | | 0.0555 | 7.8168 | 2134 | 0.5349 | 0.4407 | 0.5349 | 0.7313 | | 0.0555 | 7.8242 | 2136 | 0.5366 | 0.4407 | 0.5366 | 0.7325 | | 0.0555 | 7.8315 | 2138 | 0.5416 | 0.4545 | 0.5416 | 0.7359 | | 0.0555 | 7.8388 | 2140 | 0.5494 | 0.5075 | 0.5494 | 0.7412 | | 0.0555 | 7.8462 | 2142 | 0.5597 | 0.3478 | 0.5597 | 0.7481 | | 0.0555 | 7.8535 | 2144 | 0.5673 | 0.3478 | 0.5673 | 0.7532 | | 0.0555 | 7.8608 | 2146 | 0.5786 | 0.3478 | 0.5786 | 0.7606 | | 0.0555 | 7.8681 | 2148 | 0.5814 | 0.3478 | 0.5814 | 0.7625 | | 0.0555 | 7.8755 | 2150 | 0.5745 | 0.3478 | 0.5745 | 0.7579 | | 0.0555 | 7.8828 | 2152 | 0.5703 | 0.3478 | 0.5703 | 0.7552 | | 0.0555 | 7.8901 | 2154 | 0.5699 | 0.3478 | 0.5699 | 0.7549 | | 0.0555 | 7.8974 | 2156 | 0.5678 | 0.3478 | 0.5678 | 0.7536 | | 0.0555 | 7.9048 | 2158 | 0.5636 | 0.5075 | 0.5636 | 0.7507 | | 0.0555 | 7.9121 | 2160 | 0.5581 | 0.5075 | 0.5581 | 0.7470 | | 0.0555 | 7.9194 | 2162 | 0.5490 | 0.4545 | 0.5490 | 0.7409 | | 0.0555 | 7.9267 | 2164 | 0.5494 | 0.4545 | 0.5494 | 0.7412 | | 0.0555 | 7.9341 | 2166 | 0.5558 | 0.5075 | 0.5558 | 0.7455 | | 0.0555 | 7.9414 | 2168 | 0.5637 | 0.5075 | 0.5637 | 0.7508 | | 0.0555 | 7.9487 | 2170 | 0.5699 | 0.3478 | 0.5699 | 0.7549 | | 0.0555 | 7.9560 | 2172 | 0.5751 | 0.3478 | 0.5751 | 0.7584 | | 0.0555 | 7.9634 | 2174 | 0.5744 | 0.3478 | 0.5744 | 0.7579 | | 0.0555 | 7.9707 | 2176 | 0.5774 | 0.3478 | 0.5774 | 0.7599 | | 0.0555 | 7.9780 | 2178 | 0.5806 | 0.3478 | 0.5806 | 0.7620 | | 0.0555 | 7.9853 | 2180 | 0.5831 | 0.3478 | 0.5831 | 0.7636 | | 0.0555 | 7.9927 | 2182 | 0.5831 | 0.3478 | 0.5831 | 0.7636 | | 0.0555 | 8.0 | 2184 | 0.5836 | 0.3478 | 0.5836 | 0.7640 | | 0.0555 | 8.0073 | 2186 | 0.5885 | 0.3478 | 0.5885 | 0.7671 | | 0.0555 | 8.0147 | 2188 | 0.5972 | 0.3478 | 0.5972 | 0.7728 | | 0.0555 | 8.0220 | 2190 | 0.6014 | 0.3478 | 0.6014 | 0.7755 | | 0.0555 | 8.0293 | 2192 | 0.6106 | 0.3478 | 0.6106 | 0.7814 | | 0.0555 | 8.0366 | 2194 | 0.6189 | 0.3478 | 0.6189 | 0.7867 | | 0.0555 | 8.0440 | 2196 | 0.6244 | 0.3478 | 0.6244 | 0.7902 | | 0.0555 | 8.0513 | 2198 | 0.6226 | 0.3478 | 0.6226 | 0.7891 | | 0.0555 | 8.0586 | 2200 | 0.6172 | 0.3478 | 0.6172 | 0.7856 | | 0.0555 | 8.0659 | 2202 | 0.6123 | 0.3514 | 0.6123 | 0.7825 | | 0.0555 | 8.0733 | 2204 | 0.6068 | 0.4507 | 0.6068 | 0.7790 | | 0.0555 | 8.0806 | 2206 | 0.6067 | 0.4474 | 0.6067 | 0.7789 | | 0.0555 | 8.0879 | 2208 | 0.6093 | 0.4474 | 0.6093 | 0.7806 | | 0.0555 | 8.0952 | 2210 | 0.6127 | 0.4474 | 0.6127 | 0.7828 | | 0.0555 | 8.1026 | 2212 | 0.6125 | 0.4474 | 0.6125 | 0.7826 | | 0.0555 | 8.1099 | 2214 | 0.6125 | 0.4474 | 0.6125 | 0.7826 | | 0.0555 | 8.1172 | 2216 | 0.6110 | 0.4474 | 0.6110 | 0.7817 | | 0.0555 | 8.1245 | 2218 | 0.6084 | 0.4474 | 0.6084 | 0.7800 | | 0.0555 | 8.1319 | 2220 | 0.6068 | 0.4474 | 0.6068 | 0.7790 | | 0.0555 | 8.1392 | 2222 | 0.6060 | 0.4507 | 0.6060 | 0.7785 | | 0.0555 | 8.1465 | 2224 | 0.6050 | 0.4507 | 0.6050 | 0.7778 | | 0.0555 | 8.1538 | 2226 | 0.6020 | 0.5 | 0.6020 | 0.7759 | | 0.0555 | 8.1612 | 2228 | 0.5998 | 0.5 | 0.5998 | 0.7745 | | 0.0555 | 8.1685 | 2230 | 0.5957 | 0.5 | 0.5957 | 0.7718 | | 0.0555 | 8.1758 | 2232 | 0.5928 | 0.5 | 0.5928 | 0.7699 | | 0.0555 | 8.1832 | 2234 | 0.5901 | 0.5075 | 0.5901 | 0.7682 | | 0.0555 | 8.1905 | 2236 | 0.5867 | 0.3824 | 0.5867 | 0.7660 | | 0.0555 | 8.1978 | 2238 | 0.5851 | 0.3478 | 0.5851 | 0.7649 | | 0.0555 | 8.2051 | 2240 | 0.5822 | 0.3478 | 0.5822 | 0.7630 | | 0.0555 | 8.2125 | 2242 | 0.5833 | 0.3478 | 0.5833 | 0.7637 | | 0.0555 | 8.2198 | 2244 | 0.5853 | 0.3478 | 0.5853 | 0.7651 | | 0.0555 | 8.2271 | 2246 | 0.5863 | 0.3478 | 0.5863 | 0.7657 | | 0.0555 | 8.2344 | 2248 | 0.5829 | 0.3824 | 0.5829 | 0.7635 | | 0.0555 | 8.2418 | 2250 | 0.5773 | 0.5075 | 0.5773 | 0.7598 | | 0.0555 | 8.2491 | 2252 | 0.5775 | 0.5 | 0.5775 | 0.7599 | | 0.0555 | 8.2564 | 2254 | 0.5807 | 0.5 | 0.5807 | 0.7620 | | 0.0555 | 8.2637 | 2256 | 0.5820 | 0.5 | 0.5820 | 0.7629 | | 0.0555 | 8.2711 | 2258 | 0.5824 | 0.3836 | 0.5824 | 0.7631 | | 0.0555 | 8.2784 | 2260 | 0.5776 | 0.5 | 0.5776 | 0.7600 | | 0.0555 | 8.2857 | 2262 | 0.5725 | 0.5 | 0.5725 | 0.7567 | | 0.0555 | 8.2930 | 2264 | 0.5685 | 0.5 | 0.5685 | 0.7540 | | 0.0555 | 8.3004 | 2266 | 0.5670 | 0.5 | 0.5670 | 0.7530 | | 0.0555 | 8.3077 | 2268 | 0.5669 | 0.3824 | 0.5669 | 0.7529 | | 0.0555 | 8.3150 | 2270 | 0.5702 | 0.3824 | 0.5702 | 0.7551 | | 0.0555 | 8.3223 | 2272 | 0.5737 | 0.3824 | 0.5737 | 0.7574 | | 0.0555 | 8.3297 | 2274 | 0.5774 | 0.3478 | 0.5774 | 0.7599 | | 0.0555 | 8.3370 | 2276 | 0.5804 | 0.3478 | 0.5804 | 0.7618 | | 0.0555 | 8.3443 | 2278 | 0.5754 | 0.3478 | 0.5754 | 0.7585 | | 0.0555 | 8.3516 | 2280 | 0.5720 | 0.3478 | 0.5720 | 0.7563 | | 0.0555 | 8.3590 | 2282 | 0.5672 | 0.3478 | 0.5672 | 0.7531 | | 0.0555 | 8.3663 | 2284 | 0.5628 | 0.3478 | 0.5628 | 0.7502 | | 0.0555 | 8.3736 | 2286 | 0.5574 | 0.3478 | 0.5574 | 0.7466 | | 0.0555 | 8.3810 | 2288 | 0.5503 | 0.3824 | 0.5503 | 0.7418 | | 0.0555 | 8.3883 | 2290 | 0.5463 | 0.3607 | 0.5463 | 0.7391 | | 0.0555 | 8.3956 | 2292 | 0.5475 | 0.3607 | 0.5475 | 0.7399 | | 0.0555 | 8.4029 | 2294 | 0.5492 | 0.3478 | 0.5492 | 0.7411 | | 0.0555 | 8.4103 | 2296 | 0.5484 | 0.3478 | 0.5484 | 0.7406 | | 0.0555 | 8.4176 | 2298 | 0.5486 | 0.3478 | 0.5486 | 0.7407 | | 0.0555 | 8.4249 | 2300 | 0.5507 | 0.3478 | 0.5507 | 0.7421 | | 0.0555 | 8.4322 | 2302 | 0.5524 | 0.3478 | 0.5524 | 0.7432 | | 0.0555 | 8.4396 | 2304 | 0.5508 | 0.3478 | 0.5508 | 0.7421 | | 0.0555 | 8.4469 | 2306 | 0.5476 | 0.3824 | 0.5476 | 0.7400 | | 0.0555 | 8.4542 | 2308 | 0.5475 | 0.3824 | 0.5475 | 0.7400 | | 0.0555 | 8.4615 | 2310 | 0.5535 | 0.3824 | 0.5535 | 0.7440 | | 0.0555 | 8.4689 | 2312 | 0.5645 | 0.3478 | 0.5645 | 0.7513 | | 0.0555 | 8.4762 | 2314 | 0.5741 | 0.3478 | 0.5741 | 0.7577 | | 0.0555 | 8.4835 | 2316 | 0.5803 | 0.3478 | 0.5803 | 0.7617 | | 0.0555 | 8.4908 | 2318 | 0.5842 | 0.3478 | 0.5842 | 0.7644 | | 0.0555 | 8.4982 | 2320 | 0.5849 | 0.3478 | 0.5849 | 0.7648 | | 0.0555 | 8.5055 | 2322 | 0.5799 | 0.3478 | 0.5799 | 0.7615 | | 0.0555 | 8.5128 | 2324 | 0.5778 | 0.3478 | 0.5778 | 0.7601 | | 0.0555 | 8.5201 | 2326 | 0.5814 | 0.3478 | 0.5814 | 0.7625 | | 0.0555 | 8.5275 | 2328 | 0.5837 | 0.3478 | 0.5837 | 0.7640 | | 0.0555 | 8.5348 | 2330 | 0.5807 | 0.3478 | 0.5807 | 0.7620 | | 0.0555 | 8.5421 | 2332 | 0.5739 | 0.3478 | 0.5739 | 0.7576 | | 0.0555 | 8.5495 | 2334 | 0.5650 | 0.3824 | 0.5650 | 0.7517 | | 0.0555 | 8.5568 | 2336 | 0.5598 | 0.3824 | 0.5598 | 0.7482 | | 0.0555 | 8.5641 | 2338 | 0.5569 | 0.3836 | 0.5569 | 0.7462 | | 0.0555 | 8.5714 | 2340 | 0.5582 | 0.3836 | 0.5582 | 0.7471 | | 0.0555 | 8.5788 | 2342 | 0.5617 | 0.3824 | 0.5617 | 0.7494 | | 0.0555 | 8.5861 | 2344 | 0.5664 | 0.3824 | 0.5664 | 0.7526 | | 0.0555 | 8.5934 | 2346 | 0.5708 | 0.3478 | 0.5708 | 0.7555 | | 0.0555 | 8.6007 | 2348 | 0.5735 | 0.3478 | 0.5735 | 0.7573 | | 0.0555 | 8.6081 | 2350 | 0.5779 | 0.3478 | 0.5779 | 0.7602 | | 0.0555 | 8.6154 | 2352 | 0.5804 | 0.3478 | 0.5804 | 0.7619 | | 0.0555 | 8.6227 | 2354 | 0.5842 | 0.3478 | 0.5842 | 0.7644 | | 0.0555 | 8.6300 | 2356 | 0.5875 | 0.3478 | 0.5875 | 0.7665 | | 0.0555 | 8.6374 | 2358 | 0.5864 | 0.3478 | 0.5864 | 0.7658 | | 0.0555 | 8.6447 | 2360 | 0.5830 | 0.3478 | 0.5830 | 0.7636 | | 0.0555 | 8.6520 | 2362 | 0.5796 | 0.3478 | 0.5796 | 0.7613 | | 0.0555 | 8.6593 | 2364 | 0.5794 | 0.3478 | 0.5794 | 0.7612 | | 0.0555 | 8.6667 | 2366 | 0.5771 | 0.3478 | 0.5771 | 0.7597 | | 0.0555 | 8.6740 | 2368 | 0.5672 | 0.3478 | 0.5672 | 0.7532 | | 0.0555 | 8.6813 | 2370 | 0.5574 | 0.3478 | 0.5574 | 0.7466 | | 0.0555 | 8.6886 | 2372 | 0.5500 | 0.3824 | 0.5500 | 0.7416 | | 0.0555 | 8.6960 | 2374 | 0.5448 | 0.3824 | 0.5448 | 0.7381 | | 0.0555 | 8.7033 | 2376 | 0.5383 | 0.5075 | 0.5383 | 0.7337 | | 0.0555 | 8.7106 | 2378 | 0.5318 | 0.5 | 0.5318 | 0.7292 | | 0.0555 | 8.7179 | 2380 | 0.5312 | 0.5 | 0.5312 | 0.7288 | | 0.0555 | 8.7253 | 2382 | 0.5346 | 0.5 | 0.5346 | 0.7312 | | 0.0555 | 8.7326 | 2384 | 0.5389 | 0.5075 | 0.5389 | 0.7341 | | 0.0555 | 8.7399 | 2386 | 0.5445 | 0.5075 | 0.5445 | 0.7379 | | 0.0555 | 8.7473 | 2388 | 0.5493 | 0.3824 | 0.5493 | 0.7411 | | 0.0555 | 8.7546 | 2390 | 0.5550 | 0.3824 | 0.5550 | 0.7450 | | 0.0555 | 8.7619 | 2392 | 0.5604 | 0.3824 | 0.5604 | 0.7486 | | 0.0555 | 8.7692 | 2394 | 0.5638 | 0.3824 | 0.5638 | 0.7509 | | 0.0555 | 8.7766 | 2396 | 0.5686 | 0.3824 | 0.5686 | 0.7541 | | 0.0555 | 8.7839 | 2398 | 0.5788 | 0.3478 | 0.5788 | 0.7608 | | 0.0555 | 8.7912 | 2400 | 0.5859 | 0.3478 | 0.5859 | 0.7654 | | 0.0555 | 8.7985 | 2402 | 0.5961 | 0.3478 | 0.5961 | 0.7721 | | 0.0555 | 8.8059 | 2404 | 0.6048 | 0.3478 | 0.6048 | 0.7777 | | 0.0555 | 8.8132 | 2406 | 0.6118 | 0.4 | 0.6118 | 0.7821 | | 0.0555 | 8.8205 | 2408 | 0.6112 | 0.4 | 0.6112 | 0.7818 | | 0.0555 | 8.8278 | 2410 | 0.6079 | 0.3478 | 0.6079 | 0.7797 | | 0.0555 | 8.8352 | 2412 | 0.6000 | 0.3478 | 0.6000 | 0.7746 | | 0.0555 | 8.8425 | 2414 | 0.5932 | 0.3478 | 0.5932 | 0.7702 | | 0.0555 | 8.8498 | 2416 | 0.5907 | 0.3478 | 0.5907 | 0.7686 | | 0.0555 | 8.8571 | 2418 | 0.5910 | 0.3478 | 0.5910 | 0.7688 | | 0.0555 | 8.8645 | 2420 | 0.5863 | 0.3478 | 0.5863 | 0.7657 | | 0.0555 | 8.8718 | 2422 | 0.5829 | 0.3824 | 0.5829 | 0.7634 | | 0.0555 | 8.8791 | 2424 | 0.5810 | 0.3824 | 0.5810 | 0.7622 | | 0.0555 | 8.8864 | 2426 | 0.5768 | 0.5075 | 0.5768 | 0.7595 | | 0.0555 | 8.8938 | 2428 | 0.5718 | 0.5075 | 0.5718 | 0.7562 | | 0.0555 | 8.9011 | 2430 | 0.5658 | 0.4507 | 0.5658 | 0.7522 | | 0.0555 | 8.9084 | 2432 | 0.5589 | 0.4507 | 0.5589 | 0.7476 | | 0.0555 | 8.9158 | 2434 | 0.5549 | 0.4507 | 0.5549 | 0.7449 | | 0.0555 | 8.9231 | 2436 | 0.5510 | 0.4507 | 0.5510 | 0.7423 | | 0.0555 | 8.9304 | 2438 | 0.5464 | 0.4507 | 0.5464 | 0.7392 | | 0.0555 | 8.9377 | 2440 | 0.5425 | 0.4375 | 0.5425 | 0.7365 | | 0.0555 | 8.9451 | 2442 | 0.5389 | 0.4407 | 0.5389 | 0.7341 | | 0.0555 | 8.9524 | 2444 | 0.5371 | 0.4407 | 0.5371 | 0.7328 | | 0.0555 | 8.9597 | 2446 | 0.5380 | 0.4407 | 0.5380 | 0.7335 | | 0.0555 | 8.9670 | 2448 | 0.5404 | 0.4407 | 0.5404 | 0.7351 | | 0.0555 | 8.9744 | 2450 | 0.5450 | 0.5075 | 0.5450 | 0.7383 | | 0.0555 | 8.9817 | 2452 | 0.5494 | 0.5075 | 0.5494 | 0.7412 | | 0.0555 | 8.9890 | 2454 | 0.5516 | 0.3824 | 0.5516 | 0.7427 | | 0.0555 | 8.9963 | 2456 | 0.5544 | 0.3824 | 0.5544 | 0.7446 | | 0.0555 | 9.0037 | 2458 | 0.5561 | 0.3824 | 0.5561 | 0.7457 | | 0.0555 | 9.0110 | 2460 | 0.5573 | 0.3824 | 0.5573 | 0.7466 | | 0.0555 | 9.0183 | 2462 | 0.5567 | 0.3824 | 0.5567 | 0.7461 | | 0.0555 | 9.0256 | 2464 | 0.5550 | 0.3824 | 0.5550 | 0.7450 | | 0.0555 | 9.0330 | 2466 | 0.5523 | 0.3824 | 0.5523 | 0.7432 | | 0.0555 | 9.0403 | 2468 | 0.5508 | 0.3824 | 0.5508 | 0.7422 | | 0.0555 | 9.0476 | 2470 | 0.5516 | 0.3824 | 0.5516 | 0.7427 | | 0.0555 | 9.0549 | 2472 | 0.5523 | 0.3824 | 0.5523 | 0.7432 | | 0.0555 | 9.0623 | 2474 | 0.5501 | 0.3824 | 0.5501 | 0.7417 | | 0.0555 | 9.0696 | 2476 | 0.5470 | 0.3824 | 0.5470 | 0.7396 | | 0.0555 | 9.0769 | 2478 | 0.5457 | 0.5075 | 0.5457 | 0.7387 | | 0.0555 | 9.0842 | 2480 | 0.5438 | 0.5 | 0.5438 | 0.7374 | | 0.0555 | 9.0916 | 2482 | 0.5429 | 0.5 | 0.5429 | 0.7368 | | 0.0555 | 9.0989 | 2484 | 0.5433 | 0.5 | 0.5433 | 0.7371 | | 0.0555 | 9.1062 | 2486 | 0.5455 | 0.5 | 0.5455 | 0.7386 | | 0.0555 | 9.1136 | 2488 | 0.5484 | 0.3824 | 0.5484 | 0.7405 | | 0.0555 | 9.1209 | 2490 | 0.5499 | 0.3824 | 0.5499 | 0.7416 | | 0.0555 | 9.1282 | 2492 | 0.5478 | 0.3824 | 0.5478 | 0.7402 | | 0.0555 | 9.1355 | 2494 | 0.5446 | 0.5 | 0.5446 | 0.7379 | | 0.0555 | 9.1429 | 2496 | 0.5405 | 0.5 | 0.5405 | 0.7352 | | 0.0555 | 9.1502 | 2498 | 0.5382 | 0.3607 | 0.5382 | 0.7336 | | 0.0473 | 9.1575 | 2500 | 0.5385 | 0.3607 | 0.5385 | 0.7338 | | 0.0473 | 9.1648 | 2502 | 0.5384 | 0.3607 | 0.5384 | 0.7338 | | 0.0473 | 9.1722 | 2504 | 0.5363 | 0.3607 | 0.5363 | 0.7323 | | 0.0473 | 9.1795 | 2506 | 0.5358 | 0.3607 | 0.5358 | 0.7320 | | 0.0473 | 9.1868 | 2508 | 0.5379 | 0.3607 | 0.5379 | 0.7334 | | 0.0473 | 9.1941 | 2510 | 0.5395 | 0.3607 | 0.5395 | 0.7345 | | 0.0473 | 9.2015 | 2512 | 0.5413 | 0.3607 | 0.5413 | 0.7357 | | 0.0473 | 9.2088 | 2514 | 0.5407 | 0.3607 | 0.5407 | 0.7353 | | 0.0473 | 9.2161 | 2516 | 0.5389 | 0.3607 | 0.5389 | 0.7341 | | 0.0473 | 9.2234 | 2518 | 0.5366 | 0.3607 | 0.5366 | 0.7325 | | 0.0473 | 9.2308 | 2520 | 0.5352 | 0.3607 | 0.5352 | 0.7316 | | 0.0473 | 9.2381 | 2522 | 0.5352 | 0.3607 | 0.5352 | 0.7316 | | 0.0473 | 9.2454 | 2524 | 0.5366 | 0.3607 | 0.5366 | 0.7325 | | 0.0473 | 9.2527 | 2526 | 0.5385 | 0.3607 | 0.5385 | 0.7338 | | 0.0473 | 9.2601 | 2528 | 0.5397 | 0.3607 | 0.5397 | 0.7347 | | 0.0473 | 9.2674 | 2530 | 0.5410 | 0.3607 | 0.5410 | 0.7355 | | 0.0473 | 9.2747 | 2532 | 0.5408 | 0.3607 | 0.5408 | 0.7354 | | 0.0473 | 9.2821 | 2534 | 0.5413 | 0.3607 | 0.5413 | 0.7357 | | 0.0473 | 9.2894 | 2536 | 0.5418 | 0.3607 | 0.5418 | 0.7361 | | 0.0473 | 9.2967 | 2538 | 0.5420 | 0.3607 | 0.5420 | 0.7362 | | 0.0473 | 9.3040 | 2540 | 0.5434 | 0.3636 | 0.5434 | 0.7372 | | 0.0473 | 9.3114 | 2542 | 0.5449 | 0.3607 | 0.5449 | 0.7382 | | 0.0473 | 9.3187 | 2544 | 0.5487 | 0.3607 | 0.5487 | 0.7407 | | 0.0473 | 9.3260 | 2546 | 0.5532 | 0.3824 | 0.5532 | 0.7437 | | 0.0473 | 9.3333 | 2548 | 0.5542 | 0.3824 | 0.5542 | 0.7444 | | 0.0473 | 9.3407 | 2550 | 0.5540 | 0.3607 | 0.5540 | 0.7443 | | 0.0473 | 9.3480 | 2552 | 0.5528 | 0.3607 | 0.5528 | 0.7435 | | 0.0473 | 9.3553 | 2554 | 0.5523 | 0.3607 | 0.5523 | 0.7431 | | 0.0473 | 9.3626 | 2556 | 0.5526 | 0.3607 | 0.5526 | 0.7434 | | 0.0473 | 9.3700 | 2558 | 0.5538 | 0.3607 | 0.5538 | 0.7442 | | 0.0473 | 9.3773 | 2560 | 0.5546 | 0.3607 | 0.5546 | 0.7447 | | 0.0473 | 9.3846 | 2562 | 0.5537 | 0.3607 | 0.5537 | 0.7441 | | 0.0473 | 9.3919 | 2564 | 0.5520 | 0.3607 | 0.5520 | 0.7430 | | 0.0473 | 9.3993 | 2566 | 0.5510 | 0.3607 | 0.5510 | 0.7423 | | 0.0473 | 9.4066 | 2568 | 0.5495 | 0.3607 | 0.5495 | 0.7413 | | 0.0473 | 9.4139 | 2570 | 0.5473 | 0.3607 | 0.5473 | 0.7398 | | 0.0473 | 9.4212 | 2572 | 0.5458 | 0.3607 | 0.5458 | 0.7388 | | 0.0473 | 9.4286 | 2574 | 0.5447 | 0.3607 | 0.5447 | 0.7380 | | 0.0473 | 9.4359 | 2576 | 0.5444 | 0.3607 | 0.5444 | 0.7379 | | 0.0473 | 9.4432 | 2578 | 0.5450 | 0.3607 | 0.5450 | 0.7383 | | 0.0473 | 9.4505 | 2580 | 0.5457 | 0.3607 | 0.5457 | 0.7387 | | 0.0473 | 9.4579 | 2582 | 0.5457 | 0.3607 | 0.5457 | 0.7387 | | 0.0473 | 9.4652 | 2584 | 0.5455 | 0.3607 | 0.5455 | 0.7386 | | 0.0473 | 9.4725 | 2586 | 0.5447 | 0.3607 | 0.5447 | 0.7380 | | 0.0473 | 9.4799 | 2588 | 0.5451 | 0.3607 | 0.5451 | 0.7383 | | 0.0473 | 9.4872 | 2590 | 0.5463 | 0.3607 | 0.5463 | 0.7391 | | 0.0473 | 9.4945 | 2592 | 0.5489 | 0.3226 | 0.5489 | 0.7409 | | 0.0473 | 9.5018 | 2594 | 0.5505 | 0.3226 | 0.5505 | 0.7420 | | 0.0473 | 9.5092 | 2596 | 0.5524 | 0.3226 | 0.5524 | 0.7432 | | 0.0473 | 9.5165 | 2598 | 0.5526 | 0.3226 | 0.5526 | 0.7434 | | 0.0473 | 9.5238 | 2600 | 0.5506 | 0.3226 | 0.5506 | 0.7420 | | 0.0473 | 9.5311 | 2602 | 0.5478 | 0.3226 | 0.5478 | 0.7402 | | 0.0473 | 9.5385 | 2604 | 0.5454 | 0.3226 | 0.5454 | 0.7385 | | 0.0473 | 9.5458 | 2606 | 0.5432 | 0.3226 | 0.5432 | 0.7370 | | 0.0473 | 9.5531 | 2608 | 0.5422 | 0.3226 | 0.5422 | 0.7364 | | 0.0473 | 9.5604 | 2610 | 0.5400 | 0.3607 | 0.5400 | 0.7348 | | 0.0473 | 9.5678 | 2612 | 0.5376 | 0.3607 | 0.5376 | 0.7332 | | 0.0473 | 9.5751 | 2614 | 0.5358 | 0.3607 | 0.5358 | 0.7320 | | 0.0473 | 9.5824 | 2616 | 0.5338 | 0.3607 | 0.5338 | 0.7306 | | 0.0473 | 9.5897 | 2618 | 0.5327 | 0.3607 | 0.5327 | 0.7298 | | 0.0473 | 9.5971 | 2620 | 0.5313 | 0.3607 | 0.5313 | 0.7289 | | 0.0473 | 9.6044 | 2622 | 0.5309 | 0.3607 | 0.5309 | 0.7286 | | 0.0473 | 9.6117 | 2624 | 0.5304 | 0.3607 | 0.5304 | 0.7283 | | 0.0473 | 9.6190 | 2626 | 0.5296 | 0.3607 | 0.5296 | 0.7278 | | 0.0473 | 9.6264 | 2628 | 0.5293 | 0.3607 | 0.5293 | 0.7275 | | 0.0473 | 9.6337 | 2630 | 0.5291 | 0.3607 | 0.5291 | 0.7274 | | 0.0473 | 9.6410 | 2632 | 0.5293 | 0.3607 | 0.5293 | 0.7275 | | 0.0473 | 9.6484 | 2634 | 0.5293 | 0.3607 | 0.5293 | 0.7276 | | 0.0473 | 9.6557 | 2636 | 0.5295 | 0.3607 | 0.5295 | 0.7276 | | 0.0473 | 9.6630 | 2638 | 0.5301 | 0.3607 | 0.5301 | 0.7281 | | 0.0473 | 9.6703 | 2640 | 0.5303 | 0.3607 | 0.5303 | 0.7282 | | 0.0473 | 9.6777 | 2642 | 0.5308 | 0.3607 | 0.5308 | 0.7286 | | 0.0473 | 9.6850 | 2644 | 0.5316 | 0.3607 | 0.5316 | 0.7291 | | 0.0473 | 9.6923 | 2646 | 0.5322 | 0.3607 | 0.5322 | 0.7295 | | 0.0473 | 9.6996 | 2648 | 0.5325 | 0.3607 | 0.5325 | 0.7297 | | 0.0473 | 9.7070 | 2650 | 0.5333 | 0.3607 | 0.5333 | 0.7303 | | 0.0473 | 9.7143 | 2652 | 0.5337 | 0.3607 | 0.5337 | 0.7306 | | 0.0473 | 9.7216 | 2654 | 0.5338 | 0.3607 | 0.5338 | 0.7306 | | 0.0473 | 9.7289 | 2656 | 0.5336 | 0.3607 | 0.5336 | 0.7305 | | 0.0473 | 9.7363 | 2658 | 0.5331 | 0.3607 | 0.5331 | 0.7301 | | 0.0473 | 9.7436 | 2660 | 0.5324 | 0.3607 | 0.5324 | 0.7296 | | 0.0473 | 9.7509 | 2662 | 0.5320 | 0.3607 | 0.5320 | 0.7294 | | 0.0473 | 9.7582 | 2664 | 0.5319 | 0.3607 | 0.5319 | 0.7293 | | 0.0473 | 9.7656 | 2666 | 0.5318 | 0.5 | 0.5318 | 0.7293 | | 0.0473 | 9.7729 | 2668 | 0.5320 | 0.5 | 0.5320 | 0.7294 | | 0.0473 | 9.7802 | 2670 | 0.5320 | 0.5 | 0.5320 | 0.7294 | | 0.0473 | 9.7875 | 2672 | 0.5321 | 0.5 | 0.5321 | 0.7295 | | 0.0473 | 9.7949 | 2674 | 0.5321 | 0.5 | 0.5321 | 0.7295 | | 0.0473 | 9.8022 | 2676 | 0.5324 | 0.5 | 0.5324 | 0.7297 | | 0.0473 | 9.8095 | 2678 | 0.5327 | 0.5 | 0.5327 | 0.7298 | | 0.0473 | 9.8168 | 2680 | 0.5327 | 0.5 | 0.5327 | 0.7299 | | 0.0473 | 9.8242 | 2682 | 0.5324 | 0.5 | 0.5324 | 0.7296 | | 0.0473 | 9.8315 | 2684 | 0.5321 | 0.5 | 0.5321 | 0.7295 | | 0.0473 | 9.8388 | 2686 | 0.5321 | 0.5 | 0.5321 | 0.7295 | | 0.0473 | 9.8462 | 2688 | 0.5320 | 0.5 | 0.5320 | 0.7294 | | 0.0473 | 9.8535 | 2690 | 0.5320 | 0.5 | 0.5320 | 0.7294 | | 0.0473 | 9.8608 | 2692 | 0.5321 | 0.5 | 0.5321 | 0.7295 | | 0.0473 | 9.8681 | 2694 | 0.5322 | 0.5 | 0.5322 | 0.7295 | | 0.0473 | 9.8755 | 2696 | 0.5321 | 0.5 | 0.5321 | 0.7295 | | 0.0473 | 9.8828 | 2698 | 0.5321 | 0.5 | 0.5321 | 0.7295 | | 0.0473 | 9.8901 | 2700 | 0.5322 | 0.5 | 0.5322 | 0.7295 | | 0.0473 | 9.8974 | 2702 | 0.5322 | 0.5 | 0.5322 | 0.7295 | | 0.0473 | 9.9048 | 2704 | 0.5322 | 0.5 | 0.5322 | 0.7295 | | 0.0473 | 9.9121 | 2706 | 0.5322 | 0.5 | 0.5322 | 0.7295 | | 0.0473 | 9.9194 | 2708 | 0.5323 | 0.5 | 0.5323 | 0.7296 | | 0.0473 | 9.9267 | 2710 | 0.5325 | 0.5 | 0.5325 | 0.7297 | | 0.0473 | 9.9341 | 2712 | 0.5326 | 0.5 | 0.5326 | 0.7298 | | 0.0473 | 9.9414 | 2714 | 0.5328 | 0.5 | 0.5328 | 0.7299 | | 0.0473 | 9.9487 | 2716 | 0.5329 | 0.5 | 0.5329 | 0.7300 | | 0.0473 | 9.9560 | 2718 | 0.5330 | 0.5 | 0.5330 | 0.7301 | | 0.0473 | 9.9634 | 2720 | 0.5330 | 0.5 | 0.5330 | 0.7301 | | 0.0473 | 9.9707 | 2722 | 0.5330 | 0.5 | 0.5330 | 0.7301 | | 0.0473 | 9.9780 | 2724 | 0.5330 | 0.5 | 0.5330 | 0.7301 | | 0.0473 | 9.9853 | 2726 | 0.5330 | 0.5 | 0.5330 | 0.7300 | | 0.0473 | 9.9927 | 2728 | 0.5329 | 0.5 | 0.5329 | 0.7300 | | 0.0473 | 10.0 | 2730 | 0.5329 | 0.5 | 0.5329 | 0.7300 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

eric10y/finetuning-sentiment-model-3000-samples

eric10y

2024-11-25T07:30:21Z

105

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T07:21:09Z

--- library_name: transformers license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: finetuning-sentiment-model-3000-samples results: [] ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.46.2 - Pytorch 2.5.1+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

vitumafeni/tiny-crypto-sentiment-analysis

vitumafeni

2024-11-25T07:28:20Z

130

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T07:26:23Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

second-state/FLUX.1-Canny-dev-GGUF

second-state

2024-11-25T07:25:31Z

445

6

null

[ "gguf", "text-to-image", "image-generation", "flux", "en", "base_model:black-forest-labs/FLUX.1-Canny-dev", "base_model:quantized:black-forest-labs/FLUX.1-Canny-dev", "license:other", "region:us" ]

text-to-image

2024-11-25T02:50:33Z

--- base_model: black-forest-labs/FLUX.1-Canny-dev license: other license_name: flux-1-dev-non-commercial-license license_link: LICENSE.md model_creator: black-forest-labs model_name: FLUX.1-Canny-dev quantized_by: Second State Inc. language: - en tags: - text-to-image - image-generation - flux ---   <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">  > [!CAUTION] > T5 and Clip are still not provided in the original model # FLUX.1-Canny-dev-GGUF ## Original Model [black-forest-labs/FLUX.1-Canny-dev](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev) ## Run with LlamaEdge-StableDiffusion - Version: coming soon  ## Quantized GGUF Models | Name | Quant method | Bits | Size | Use case | | ---- | ---- | ---- | ---- | ----- | | [ae.safetensors](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/ae.safetensors) | f32 | 32 | 335 MB | | | [flux1-canny-dev-Q2_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q2_K.gguf) | Q2_K | 2 | 4.15 GB | | | [flux1-canny-dev-Q3_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q3_K.gguf) | Q3_K | 3 | 5.35 GB | | | [flux1-canny-dev-Q4_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q4_0.gguf) | Q4_0 | 4 | 6.93 GB | | | [flux1-canny-dev-Q4_1.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q4_1.gguf) | Q4_1 | 4 | 7.67 GB | | | [flux1-canny-dev-Q4_K.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q4_K.gguf) | Q4_K | 4 | 6.93 GB | | | [flux1-canny-dev-Q5_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q5_0.gguf) | Q5_0 | 5 | 8.40 GB | | | [flux1-canny-dev-Q5_1.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q5_1.gguf) | Q5_1 | 5 | 9.14 GB | | | [flux1-canny-dev-Q8_0.gguf](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev-Q8_0.gguf) | Q8_0 | 8 | 12.6 GB | | | [flux1-canny-dev.safetensors](https://huggingface.co/second-state/FLUX.1-Canny-dev-GGUF/blob/main/flux1-canny-dev.safetensors) | f16 | 16 | 23.8 GB | |  **Quantized with stable-diffusion.cpp `master-c3eeb669`.**

ankit5319/chronos-t5-small-fine-tuned

ankit5319

2024-11-25T07:22:04Z

174

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-11-25T07:21:46Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Shinyaaa/Travel-20-v1-on-RPC-10-v1

Shinyaaa

2024-11-25T07:20:42Z

102

0

transformers

[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

feature-extraction

2024-11-25T07:20:13Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mlx-community/Llama-3.1-Tulu-3-8B-4bit

mlx-community

2024-11-25T07:19:51Z

81

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "mlx", "conversational", "en", "dataset:allenai/RLVR-GSM-MATH-IF-Mixed-Constraints", "base_model:allenai/Llama-3.1-Tulu-3-8B", "base_model:quantized:allenai/Llama-3.1-Tulu-3-8B", "license:llama3.1", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "region:us" ]

text-generation

2024-11-25T07:08:02Z

--- license: llama3.1 language: - en pipeline_tag: text-generation datasets: - allenai/RLVR-GSM-MATH-IF-Mixed-Constraints base_model: allenai/Llama-3.1-Tulu-3-8B library_name: transformers tags: - mlx --- # mlx-community/Llama-3.1-Tulu-3-8B-4bit The Model [mlx-community/Llama-3.1-Tulu-3-8B-4bit](https://huggingface.co/mlx-community/Llama-3.1-Tulu-3-8B-4bit) was converted to MLX format from [allenai/Llama-3.1-Tulu-3-8B](https://huggingface.co/allenai/Llama-3.1-Tulu-3-8B) using mlx-lm version **0.20.0**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama-3.1-Tulu-3-8B-4bit") prompt="hello" if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```

MayBashendy/Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold0

MayBashendy

2024-11-25T07:19:11Z

161

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:aubmindlab/bert-base-arabertv02", "base_model:finetune:aubmindlab/bert-base-arabertv02", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T07:06:32Z

--- library_name: transformers base_model: aubmindlab/bert-base-arabertv02 tags: - generated_from_trainer model-index: - name: Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold0 results: [] ---  # Arabic_FineTuningAraBERT_AugV5_k35_task2_organization_fold0 This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6437 - Qwk: 0.3771 - Mse: 0.6437 - Rmse: 0.8023 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:| | No log | 0.0082 | 2 | 3.8449 | 0.0 | 3.8449 | 1.9608 | | No log | 0.0165 | 4 | 2.5465 | 0.0172 | 2.5465 | 1.5958 | | No log | 0.0247 | 6 | 1.3961 | 0.0491 | 1.3961 | 1.1816 | | No log | 0.0329 | 8 | 1.3041 | 0.0 | 1.3041 | 1.1420 | | No log | 0.0412 | 10 | 1.1781 | 0.0 | 1.1781 | 1.0854 | | No log | 0.0494 | 12 | 1.2873 | 0.0 | 1.2873 | 1.1346 | | No log | 0.0576 | 14 | 1.6711 | -0.0302 | 1.6711 | 1.2927 | | No log | 0.0658 | 16 | 1.5391 | 0.0 | 1.5391 | 1.2406 | | No log | 0.0741 | 18 | 1.4694 | 0.0491 | 1.4694 | 1.2122 | | No log | 0.0823 | 20 | 1.5935 | 0.0491 | 1.5935 | 1.2623 | | No log | 0.0905 | 22 | 1.7652 | -0.0096 | 1.7652 | 1.3286 | | No log | 0.0988 | 24 | 1.9005 | 0.0525 | 1.9004 | 1.3786 | | No log | 0.1070 | 26 | 1.7812 | -0.0328 | 1.7812 | 1.3346 | | No log | 0.1152 | 28 | 1.6600 | 0.0173 | 1.6600 | 1.2884 | | No log | 0.1235 | 30 | 1.5788 | 0.0491 | 1.5788 | 1.2565 | | No log | 0.1317 | 32 | 1.4703 | 0.0 | 1.4703 | 1.2126 | | No log | 0.1399 | 34 | 1.2667 | 0.0 | 1.2667 | 1.1255 | | No log | 0.1481 | 36 | 1.0444 | 0.0 | 1.0444 | 1.0220 | | No log | 0.1564 | 38 | 0.8703 | 0.0 | 0.8703 | 0.9329 | | No log | 0.1646 | 40 | 0.7873 | 0.0335 | 0.7873 | 0.8873 | | No log | 0.1728 | 42 | 0.8284 | 0.0335 | 0.8284 | 0.9102 | | No log | 0.1811 | 44 | 0.9977 | 0.0 | 0.9977 | 0.9988 | | No log | 0.1893 | 46 | 1.3060 | 0.0 | 1.3060 | 1.1428 | | No log | 0.1975 | 48 | 1.4340 | 0.0 | 1.4340 | 1.1975 | | No log | 0.2058 | 50 | 1.3664 | 0.0 | 1.3664 | 1.1689 | | No log | 0.2140 | 52 | 1.2462 | 0.0 | 1.2462 | 1.1163 | | No log | 0.2222 | 54 | 1.1136 | 0.0 | 1.1136 | 1.0553 | | No log | 0.2305 | 56 | 1.2068 | 0.0 | 1.2068 | 1.0986 | | No log | 0.2387 | 58 | 1.1604 | 0.0 | 1.1604 | 1.0772 | | No log | 0.2469 | 60 | 0.9721 | 0.0 | 0.9721 | 0.9860 | | No log | 0.2551 | 62 | 0.8585 | 0.0 | 0.8585 | 0.9265 | | No log | 0.2634 | 64 | 0.8457 | 0.0 | 0.8457 | 0.9196 | | No log | 0.2716 | 66 | 0.8333 | 0.0 | 0.8333 | 0.9129 | | No log | 0.2798 | 68 | 0.9003 | 0.0 | 0.9003 | 0.9489 | | No log | 0.2881 | 70 | 1.0546 | 0.0 | 1.0546 | 1.0269 | | No log | 0.2963 | 72 | 1.1747 | 0.0 | 1.1747 | 1.0838 | | No log | 0.3045 | 74 | 1.2196 | 0.0 | 1.2196 | 1.1044 | | No log | 0.3128 | 76 | 1.1503 | 0.0 | 1.1503 | 1.0725 | | No log | 0.3210 | 78 | 1.0305 | 0.0 | 1.0305 | 1.0151 | | No log | 0.3292 | 80 | 0.9394 | 0.0 | 0.9394 | 0.9692 | | No log | 0.3374 | 82 | 0.9147 | 0.0 | 0.9147 | 0.9564 | | No log | 0.3457 | 84 | 0.9039 | 0.0 | 0.9039 | 0.9507 | | No log | 0.3539 | 86 | 0.9438 | 0.0 | 0.9438 | 0.9715 | | No log | 0.3621 | 88 | 1.1728 | 0.0 | 1.1728 | 1.0830 | | No log | 0.3704 | 90 | 1.5843 | 0.125 | 1.5843 | 1.2587 | | No log | 0.3786 | 92 | 1.5654 | 0.125 | 1.5654 | 1.2512 | | No log | 0.3868 | 94 | 1.2734 | 0.0 | 1.2734 | 1.1284 | | No log | 0.3951 | 96 | 0.9856 | 0.0 | 0.9856 | 0.9928 | | No log | 0.4033 | 98 | 0.7949 | 0.1564 | 0.7949 | 0.8916 | | No log | 0.4115 | 100 | 0.7168 | 0.1192 | 0.7168 | 0.8466 | | No log | 0.4198 | 102 | 0.7223 | 0.2150 | 0.7223 | 0.8499 | | No log | 0.4280 | 104 | 0.8128 | 0.1747 | 0.8128 | 0.9015 | | No log | 0.4362 | 106 | 0.7921 | 0.1747 | 0.7921 | 0.8900 | | No log | 0.4444 | 108 | 0.7928 | 0.3046 | 0.7928 | 0.8904 | | No log | 0.4527 | 110 | 0.7358 | 0.3046 | 0.7358 | 0.8578 | | No log | 0.4609 | 112 | 0.6887 | 0.3695 | 0.6887 | 0.8299 | | No log | 0.4691 | 114 | 0.7364 | 0.1747 | 0.7364 | 0.8581 | | No log | 0.4774 | 116 | 0.7567 | 0.3197 | 0.7567 | 0.8699 | | No log | 0.4856 | 118 | 0.8435 | 0.1564 | 0.8435 | 0.9184 | | No log | 0.4938 | 120 | 0.9194 | 0.0 | 0.9194 | 0.9589 | | No log | 0.5021 | 122 | 0.9321 | 0.0 | 0.9321 | 0.9654 | | No log | 0.5103 | 124 | 0.8488 | 0.2821 | 0.8488 | 0.9213 | | No log | 0.5185 | 126 | 0.7542 | 0.2150 | 0.7542 | 0.8684 | | No log | 0.5267 | 128 | 0.7265 | 0.0339 | 0.7265 | 0.8524 | | No log | 0.5350 | 130 | 0.7171 | 0.1141 | 0.7171 | 0.8468 | | No log | 0.5432 | 132 | 0.7488 | 0.0503 | 0.7488 | 0.8654 | | No log | 0.5514 | 134 | 0.7621 | 0.1141 | 0.7621 | 0.8730 | | No log | 0.5597 | 136 | 0.8116 | -0.0426 | 0.8116 | 0.9009 | | No log | 0.5679 | 138 | 0.8358 | -0.2466 | 0.8358 | 0.9142 | | No log | 0.5761 | 140 | 0.8469 | -0.1099 | 0.8469 | 0.9203 | | No log | 0.5844 | 142 | 0.8581 | -0.0769 | 0.8581 | 0.9263 | | No log | 0.5926 | 144 | 0.8125 | -0.1004 | 0.8125 | 0.9014 | | No log | 0.6008 | 146 | 0.7305 | -0.0048 | 0.7305 | 0.8547 | | No log | 0.6091 | 148 | 0.6800 | 0.1340 | 0.6800 | 0.8246 | | No log | 0.6173 | 150 | 0.6528 | 0.1558 | 0.6528 | 0.8079 | | No log | 0.6255 | 152 | 0.6511 | 0.0339 | 0.6511 | 0.8069 | | No log | 0.6337 | 154 | 0.6960 | -0.0048 | 0.6960 | 0.8342 | | No log | 0.6420 | 156 | 0.6974 | 0.0735 | 0.6974 | 0.8351 | | No log | 0.6502 | 158 | 0.7287 | 0.0916 | 0.7287 | 0.8536 | | No log | 0.6584 | 160 | 0.7260 | 0.0916 | 0.7260 | 0.8521 | | No log | 0.6667 | 162 | 0.7071 | 0.0916 | 0.7071 | 0.8409 | | No log | 0.6749 | 164 | 0.6744 | 0.0916 | 0.6744 | 0.8212 | | No log | 0.6831 | 166 | 0.6773 | -0.0678 | 0.6773 | 0.8230 | | No log | 0.6914 | 168 | 0.7038 | 0.0567 | 0.7038 | 0.8389 | | No log | 0.6996 | 170 | 0.7098 | -0.1053 | 0.7098 | 0.8425 | | No log | 0.7078 | 172 | 0.7049 | 0.0099 | 0.7049 | 0.8396 | | No log | 0.7160 | 174 | 0.7244 | 0.0916 | 0.7244 | 0.8511 | | No log | 0.7243 | 176 | 0.6981 | 0.2759 | 0.6981 | 0.8355 | | No log | 0.7325 | 178 | 0.6879 | 0.1962 | 0.6879 | 0.8294 | | No log | 0.7407 | 180 | 0.6943 | 0.1560 | 0.6943 | 0.8333 | | No log | 0.7490 | 182 | 0.6909 | 0.2373 | 0.6909 | 0.8312 | | No log | 0.7572 | 184 | 0.6780 | 0.0916 | 0.6780 | 0.8234 | | No log | 0.7654 | 186 | 0.6530 | 0.0503 | 0.6530 | 0.8081 | | No log | 0.7737 | 188 | 0.6458 | 0.1560 | 0.6458 | 0.8036 | | No log | 0.7819 | 190 | 0.7552 | 0.3046 | 0.7552 | 0.8690 | | No log | 0.7901 | 192 | 0.8491 | 0.1026 | 0.8491 | 0.9215 | | No log | 0.7984 | 194 | 0.7692 | 0.2150 | 0.7692 | 0.8770 | | No log | 0.8066 | 196 | 0.7988 | 0.0503 | 0.7988 | 0.8937 | | No log | 0.8148 | 198 | 0.9089 | 0.2186 | 0.9089 | 0.9534 | | No log | 0.8230 | 200 | 0.9674 | 0.0909 | 0.9674 | 0.9836 | | No log | 0.8313 | 202 | 0.8486 | 0.1600 | 0.8486 | 0.9212 | | No log | 0.8395 | 204 | 0.7004 | 0.0735 | 0.7004 | 0.8369 | | No log | 0.8477 | 206 | 0.7193 | 0.1755 | 0.7193 | 0.8481 | | No log | 0.8560 | 208 | 0.7631 | 0.2889 | 0.7631 | 0.8735 | | No log | 0.8642 | 210 | 0.7775 | 0.1563 | 0.7775 | 0.8818 | | No log | 0.8724 | 212 | 0.6998 | 0.2553 | 0.6998 | 0.8366 | | No log | 0.8807 | 214 | 0.6873 | 0.2373 | 0.6873 | 0.8290 | | No log | 0.8889 | 216 | 0.7307 | 0.0916 | 0.7307 | 0.8548 | | No log | 0.8971 | 218 | 0.7571 | 0.0503 | 0.7571 | 0.8701 | | No log | 0.9053 | 220 | 0.7653 | 0.1356 | 0.7653 | 0.8748 | | No log | 0.9136 | 222 | 0.8030 | 0.1356 | 0.8030 | 0.8961 | | No log | 0.9218 | 224 | 0.8217 | 0.1765 | 0.8217 | 0.9065 | | No log | 0.9300 | 226 | 0.8519 | 0.0099 | 0.8519 | 0.9230 | | No log | 0.9383 | 228 | 0.8988 | 0.1793 | 0.8988 | 0.9480 | | No log | 0.9465 | 230 | 0.8815 | 0.1793 | 0.8815 | 0.9389 | | No log | 0.9547 | 232 | 0.9057 | 0.1793 | 0.9057 | 0.9517 | | No log | 0.9630 | 234 | 0.9843 | 0.0557 | 0.9843 | 0.9921 | | No log | 0.9712 | 236 | 1.0257 | 0.0833 | 1.0257 | 1.0128 | | No log | 0.9794 | 238 | 0.9546 | 0.2186 | 0.9546 | 0.9770 | | No log | 0.9877 | 240 | 0.8870 | 0.0503 | 0.8870 | 0.9418 | | No log | 0.9959 | 242 | 0.8678 | 0.0916 | 0.8678 | 0.9315 | | No log | 1.0041 | 244 | 0.8923 | 0.0916 | 0.8923 | 0.9446 | | No log | 1.0123 | 246 | 0.8906 | 0.0916 | 0.8906 | 0.9437 | | No log | 1.0206 | 248 | 0.8417 | 0.0916 | 0.8417 | 0.9175 | | No log | 1.0288 | 250 | 0.8226 | 0.0099 | 0.8226 | 0.9070 | | No log | 1.0370 | 252 | 0.8301 | 0.0099 | 0.8301 | 0.9111 | | No log | 1.0453 | 254 | 0.8631 | 0.0916 | 0.8631 | 0.9290 | | No log | 1.0535 | 256 | 0.9591 | 0.0258 | 0.9591 | 0.9793 | | No log | 1.0617 | 258 | 1.0055 | 0.0 | 1.0055 | 1.0028 | | No log | 1.0700 | 260 | 0.9585 | 0.0258 | 0.9585 | 0.9790 | | No log | 1.0782 | 262 | 0.9307 | 0.0916 | 0.9307 | 0.9647 | | No log | 1.0864 | 264 | 0.9216 | 0.2186 | 0.9216 | 0.9600 | | No log | 1.0947 | 266 | 0.8643 | 0.1340 | 0.8643 | 0.9297 | | No log | 1.1029 | 268 | 0.7913 | 0.0916 | 0.7913 | 0.8895 | | No log | 1.1111 | 270 | 0.7503 | 0.0916 | 0.7503 | 0.8662 | | No log | 1.1193 | 272 | 0.7356 | 0.0916 | 0.7356 | 0.8577 | | No log | 1.1276 | 274 | 0.7775 | -0.0294 | 0.7775 | 0.8818 | | No log | 1.1358 | 276 | 0.8031 | 0.0099 | 0.8031 | 0.8961 | | No log | 1.1440 | 278 | 0.8682 | 0.1793 | 0.8682 | 0.9318 | | No log | 1.1523 | 280 | 0.9049 | 0.1793 | 0.9049 | 0.9513 | | No log | 1.1605 | 282 | 0.9445 | 0.25 | 0.9445 | 0.9718 | | No log | 1.1687 | 284 | 1.0603 | 0.1463 | 1.0603 | 1.0297 | | No log | 1.1770 | 286 | 1.0686 | 0.1738 | 1.0686 | 1.0337 | | No log | 1.1852 | 288 | 0.9538 | 0.1072 | 0.9538 | 0.9766 | | No log | 1.1934 | 290 | 0.8921 | 0.1793 | 0.8921 | 0.9445 | | No log | 1.2016 | 292 | 0.8276 | 0.1793 | 0.8276 | 0.9097 | | No log | 1.2099 | 294 | 0.7594 | 0.2533 | 0.7594 | 0.8715 | | No log | 1.2181 | 296 | 0.7813 | 0.0785 | 0.7813 | 0.8839 | | No log | 1.2263 | 298 | 0.8477 | 0.1370 | 0.8477 | 0.9207 | | No log | 1.2346 | 300 | 0.8624 | 0.1773 | 0.8624 | 0.9286 | | No log | 1.2428 | 302 | 0.8222 | 0.1168 | 0.8222 | 0.9067 | | No log | 1.2510 | 304 | 0.7986 | 0.3318 | 0.7986 | 0.8937 | | No log | 1.2593 | 306 | 0.9415 | 0.0557 | 0.9415 | 0.9703 | | No log | 1.2675 | 308 | 1.1720 | 0.1649 | 1.1720 | 1.0826 | | No log | 1.2757 | 310 | 1.2318 | 0.1649 | 1.2318 | 1.1099 | | No log | 1.2840 | 312 | 1.2591 | 0.1276 | 1.2591 | 1.1221 | | No log | 1.2922 | 314 | 1.1760 | 0.0440 | 1.1760 | 1.0844 | | No log | 1.3004 | 316 | 1.0025 | 0.0 | 1.0025 | 1.0013 | | No log | 1.3086 | 318 | 0.8959 | 0.1793 | 0.8959 | 0.9465 | | No log | 1.3169 | 320 | 0.9102 | 0.0099 | 0.9102 | 0.9540 | | No log | 1.3251 | 322 | 0.8541 | 0.0503 | 0.8541 | 0.9242 | | No log | 1.3333 | 324 | 0.7833 | 0.0503 | 0.7833 | 0.8850 | | No log | 1.3416 | 326 | 0.7566 | 0.0916 | 0.7566 | 0.8698 | | No log | 1.3498 | 328 | 0.7682 | 0.0258 | 0.7682 | 0.8765 | | No log | 1.3580 | 330 | 0.7707 | 0.0099 | 0.7707 | 0.8779 | | No log | 1.3663 | 332 | 0.7559 | -0.0678 | 0.7559 | 0.8694 | | No log | 1.3745 | 334 | 0.7305 | -0.0048 | 0.7305 | 0.8547 | | No log | 1.3827 | 336 | 0.6950 | -0.0294 | 0.6950 | 0.8336 | | No log | 1.3909 | 338 | 0.7029 | 0.0503 | 0.7029 | 0.8384 | | No log | 1.3992 | 340 | 0.7535 | 0.2186 | 0.7535 | 0.8680 | | No log | 1.4074 | 342 | 0.7803 | 0.2186 | 0.7803 | 0.8834 | | No log | 1.4156 | 344 | 0.7621 | 0.1793 | 0.7621 | 0.8730 | | No log | 1.4239 | 346 | 0.7755 | 0.1034 | 0.7755 | 0.8806 | | No log | 1.4321 | 348 | 0.7882 | 0.2696 | 0.7882 | 0.8878 | | No log | 1.4403 | 350 | 0.7956 | 0.2533 | 0.7956 | 0.8919 | | No log | 1.4486 | 352 | 0.7774 | 0.2154 | 0.7774 | 0.8817 | | No log | 1.4568 | 354 | 0.7527 | 0.2533 | 0.7527 | 0.8676 | | No log | 1.4650 | 356 | 0.7524 | 0.1409 | 0.7524 | 0.8674 | | No log | 1.4733 | 358 | 0.7902 | 0.2186 | 0.7902 | 0.8889 | | No log | 1.4815 | 360 | 0.8423 | 0.1398 | 0.8423 | 0.9177 | | No log | 1.4897 | 362 | 0.8313 | 0.1398 | 0.8313 | 0.9118 | | No log | 1.4979 | 364 | 0.8102 | 0.2186 | 0.8102 | 0.9001 | | No log | 1.5062 | 366 | 0.8309 | 0.1000 | 0.8309 | 0.9116 | | No log | 1.5144 | 368 | 0.8751 | 0.1398 | 0.8751 | 0.9355 | | No log | 1.5226 | 370 | 0.8400 | 0.2186 | 0.8400 | 0.9165 | | No log | 1.5309 | 372 | 0.7666 | 0.1034 | 0.7666 | 0.8755 | | No log | 1.5391 | 374 | 0.7353 | 0.2533 | 0.7353 | 0.8575 | | No log | 1.5473 | 376 | 0.7245 | 0.2533 | 0.7245 | 0.8512 | | No log | 1.5556 | 378 | 0.7324 | 0.1034 | 0.7324 | 0.8558 | | No log | 1.5638 | 380 | 0.7641 | 0.1409 | 0.7641 | 0.8741 | | No log | 1.5720 | 382 | 0.8041 | 0.1000 | 0.8041 | 0.8967 | | No log | 1.5802 | 384 | 0.8160 | 0.1398 | 0.8160 | 0.9033 | | No log | 1.5885 | 386 | 0.8120 | 0.1398 | 0.8120 | 0.9011 | | No log | 1.5967 | 388 | 0.8348 | 0.1398 | 0.8348 | 0.9137 | | No log | 1.6049 | 390 | 0.8076 | 0.1398 | 0.8076 | 0.8987 | | No log | 1.6132 | 392 | 0.7741 | 0.1398 | 0.7741 | 0.8798 | | No log | 1.6214 | 394 | 0.7262 | -0.0418 | 0.7262 | 0.8522 | | No log | 1.6296 | 396 | 0.6940 | -0.0153 | 0.6940 | 0.8330 | | No log | 1.6379 | 398 | 0.6703 | 0.0099 | 0.6703 | 0.8187 | | No log | 1.6461 | 400 | 0.6676 | 0.0099 | 0.6676 | 0.8170 | | No log | 1.6543 | 402 | 0.6581 | 0.0099 | 0.6581 | 0.8112 | | No log | 1.6626 | 404 | 0.6687 | -0.0294 | 0.6687 | 0.8177 | | No log | 1.6708 | 406 | 0.7005 | -0.0294 | 0.7005 | 0.8369 | | No log | 1.6790 | 408 | 0.7436 | 0.0828 | 0.7436 | 0.8623 | | No log | 1.6872 | 410 | 0.7897 | 0.1000 | 0.7897 | 0.8886 | | No log | 1.6955 | 412 | 0.8040 | 0.0612 | 0.8040 | 0.8967 | | No log | 1.7037 | 414 | 0.8163 | 0.0612 | 0.8163 | 0.9035 | | No log | 1.7119 | 416 | 0.8136 | 0.0233 | 0.8136 | 0.9020 | | No log | 1.7202 | 418 | 0.8150 | 0.0233 | 0.8150 | 0.9028 | | No log | 1.7284 | 420 | 0.8136 | 0.0233 | 0.8136 | 0.9020 | | No log | 1.7366 | 422 | 0.7821 | 0.0233 | 0.7821 | 0.8844 | | No log | 1.7449 | 424 | 0.7412 | -0.0553 | 0.7412 | 0.8609 | | No log | 1.7531 | 426 | 0.7101 | -0.0294 | 0.7101 | 0.8427 | | No log | 1.7613 | 428 | 0.6934 | -0.0294 | 0.6934 | 0.8327 | | No log | 1.7695 | 430 | 0.7020 | -0.0294 | 0.7020 | 0.8378 | | No log | 1.7778 | 432 | 0.7327 | 0.0828 | 0.7327 | 0.8560 | | No log | 1.7860 | 434 | 0.7482 | 0.0828 | 0.7482 | 0.8650 | | No log | 1.7942 | 436 | 0.7700 | 0.1209 | 0.7700 | 0.8775 | | No log | 1.8025 | 438 | 0.7710 | 0.0828 | 0.7710 | 0.8780 | | No log | 1.8107 | 440 | 0.7464 | -0.0553 | 0.7464 | 0.8639 | | No log | 1.8189 | 442 | 0.7121 | -0.0943 | 0.7121 | 0.8438 | | No log | 1.8272 | 444 | 0.6957 | -0.0294 | 0.6957 | 0.8341 | | No log | 1.8354 | 446 | 0.6885 | 0.0339 | 0.6885 | 0.8298 | | No log | 1.8436 | 448 | 0.7024 | -0.0153 | 0.7024 | 0.8381 | | No log | 1.8519 | 450 | 0.7452 | 0.1600 | 0.7452 | 0.8632 | | No log | 1.8601 | 452 | 0.7658 | 0.1398 | 0.7658 | 0.8751 | | No log | 1.8683 | 454 | 0.7684 | 0.1398 | 0.7684 | 0.8766 | | No log | 1.8765 | 456 | 0.7694 | 0.1398 | 0.7694 | 0.8772 | | No log | 1.8848 | 458 | 0.7904 | 0.1398 | 0.7904 | 0.8891 | | No log | 1.8930 | 460 | 0.7767 | 0.1600 | 0.7767 | 0.8813 | | No log | 1.9012 | 462 | 0.7151 | 0.2921 | 0.7151 | 0.8457 | | No log | 1.9095 | 464 | 0.7042 | 0.1783 | 0.7042 | 0.8391 | | No log | 1.9177 | 466 | 0.7133 | 0.1783 | 0.7133 | 0.8446 | | No log | 1.9259 | 468 | 0.7119 | 0.1783 | 0.7119 | 0.8437 | | No log | 1.9342 | 470 | 0.6935 | 0.2154 | 0.6935 | 0.8328 | | No log | 1.9424 | 472 | 0.7027 | 0.2921 | 0.7027 | 0.8383 | | No log | 1.9506 | 474 | 0.7269 | 0.3724 | 0.7269 | 0.8526 | | No log | 1.9588 | 476 | 0.7440 | 0.2186 | 0.7440 | 0.8626 | | No log | 1.9671 | 478 | 0.7271 | 0.2588 | 0.7271 | 0.8527 | | No log | 1.9753 | 480 | 0.6672 | 0.2186 | 0.6672 | 0.8169 | | No log | 1.9835 | 482 | 0.5915 | 0.3467 | 0.5915 | 0.7691 | | No log | 1.9918 | 484 | 0.5644 | 0.1560 | 0.5644 | 0.7512 | | No log | 2.0 | 486 | 0.5817 | 0.2329 | 0.5817 | 0.7627 | | No log | 2.0082 | 488 | 0.6373 | 0.2500 | 0.6373 | 0.7983 | | No log | 2.0165 | 490 | 0.6611 | 0.2500 | 0.6611 | 0.8131 | | No log | 2.0247 | 492 | 0.6433 | 0.2500 | 0.6433 | 0.8021 | | No log | 2.0329 | 494 | 0.6117 | 0.2889 | 0.6117 | 0.7821 | | No log | 2.0412 | 496 | 0.5556 | 0.2725 | 0.5556 | 0.7454 | | No log | 2.0494 | 498 | 0.5430 | 0.2794 | 0.5430 | 0.7369 | | 0.4608 | 2.0576 | 500 | 0.5797 | 0.1985 | 0.5797 | 0.7614 | | 0.4608 | 2.0658 | 502 | 0.5947 | 0.1340 | 0.5947 | 0.7712 | | 0.4608 | 2.0741 | 504 | 0.5880 | 0.1558 | 0.5880 | 0.7668 | | 0.4608 | 2.0823 | 506 | 0.5929 | 0.2794 | 0.5929 | 0.7700 | | 0.4608 | 2.0905 | 508 | 0.5988 | 0.2794 | 0.5988 | 0.7738 | | 0.4608 | 2.0988 | 510 | 0.6130 | 0.1962 | 0.6130 | 0.7829 | | 0.4608 | 2.1070 | 512 | 0.6189 | 0.2373 | 0.6189 | 0.7867 | | 0.4608 | 2.1152 | 514 | 0.6166 | 0.2794 | 0.6166 | 0.7852 | | 0.4608 | 2.1235 | 516 | 0.6265 | 0.3318 | 0.6265 | 0.7915 | | 0.4608 | 2.1317 | 518 | 0.6417 | 0.3333 | 0.6417 | 0.8011 | | 0.4608 | 2.1399 | 520 | 0.6221 | 0.3811 | 0.6221 | 0.7887 | | 0.4608 | 2.1481 | 522 | 0.6069 | 0.4740 | 0.6069 | 0.7791 | | 0.4608 | 2.1564 | 524 | 0.5840 | 0.4740 | 0.5840 | 0.7642 | | 0.4608 | 2.1646 | 526 | 0.5537 | 0.4394 | 0.5537 | 0.7441 | | 0.4608 | 2.1728 | 528 | 0.5402 | 0.4394 | 0.5402 | 0.7350 | | 0.4608 | 2.1811 | 530 | 0.5344 | 0.4394 | 0.5344 | 0.7311 | | 0.4608 | 2.1893 | 532 | 0.5366 | 0.4394 | 0.5366 | 0.7325 | | 0.4608 | 2.1975 | 534 | 0.5417 | 0.3467 | 0.5417 | 0.7360 | | 0.4608 | 2.2058 | 536 | 0.5570 | 0.3077 | 0.5570 | 0.7463 | | 0.4608 | 2.2140 | 538 | 0.5678 | 0.3077 | 0.5678 | 0.7535 | | 0.4608 | 2.2222 | 540 | 0.5726 | 0.4394 | 0.5726 | 0.7567 | | 0.4608 | 2.2305 | 542 | 0.5863 | 0.4394 | 0.5863 | 0.7657 | | 0.4608 | 2.2387 | 544 | 0.5828 | 0.4394 | 0.5828 | 0.7634 | | 0.4608 | 2.2469 | 546 | 0.5640 | 0.4394 | 0.5640 | 0.7510 | | 0.4608 | 2.2551 | 548 | 0.5501 | 0.3077 | 0.5501 | 0.7417 | | 0.4608 | 2.2634 | 550 | 0.5549 | 0.3077 | 0.5549 | 0.7449 | | 0.4608 | 2.2716 | 552 | 0.5648 | 0.3077 | 0.5648 | 0.7515 | | 0.4608 | 2.2798 | 554 | 0.5753 | 0.3077 | 0.5753 | 0.7585 | | 0.4608 | 2.2881 | 556 | 0.5908 | 0.3318 | 0.5908 | 0.7686 | | 0.4608 | 2.2963 | 558 | 0.6116 | 0.3724 | 0.6116 | 0.7821 | | 0.4608 | 2.3045 | 560 | 0.6260 | 0.3724 | 0.6260 | 0.7912 | | 0.4608 | 2.3128 | 562 | 0.6291 | 0.3724 | 0.6291 | 0.7931 | | 0.4608 | 2.3210 | 564 | 0.6709 | 0.3724 | 0.6709 | 0.8191 | | 0.4608 | 2.3292 | 566 | 0.6848 | 0.3724 | 0.6848 | 0.8275 | | 0.4608 | 2.3374 | 568 | 0.6502 | 0.3724 | 0.6502 | 0.8063 | | 0.4608 | 2.3457 | 570 | 0.6072 | 0.3724 | 0.6072 | 0.7792 | | 0.4608 | 2.3539 | 572 | 0.5894 | 0.1765 | 0.5894 | 0.7678 | | 0.4608 | 2.3621 | 574 | 0.5911 | 0.1560 | 0.5911 | 0.7688 | | 0.4608 | 2.3704 | 576 | 0.5899 | 0.1962 | 0.5899 | 0.7680 | | 0.4608 | 2.3786 | 578 | 0.5935 | 0.1765 | 0.5935 | 0.7704 | | 0.4608 | 2.3868 | 580 | 0.6156 | 0.2613 | 0.6156 | 0.7846 | | 0.4608 | 2.3951 | 582 | 0.6616 | 0.3724 | 0.6616 | 0.8134 | | 0.4608 | 2.4033 | 584 | 0.6960 | 0.2186 | 0.6960 | 0.8343 | | 0.4608 | 2.4115 | 586 | 0.6885 | 0.2186 | 0.6885 | 0.8297 | | 0.4608 | 2.4198 | 588 | 0.6483 | 0.3724 | 0.6483 | 0.8052 | | 0.4608 | 2.4280 | 590 | 0.6295 | 0.2613 | 0.6295 | 0.7934 | | 0.4608 | 2.4362 | 592 | 0.6179 | 0.2794 | 0.6179 | 0.7861 | | 0.4608 | 2.4444 | 594 | 0.6188 | 0.1962 | 0.6188 | 0.7866 | | 0.4608 | 2.4527 | 596 | 0.6167 | 0.1962 | 0.6167 | 0.7853 | | 0.4608 | 2.4609 | 598 | 0.6165 | 0.2373 | 0.6165 | 0.7851 | | 0.4608 | 2.4691 | 600 | 0.6327 | 0.1558 | 0.6327 | 0.7954 | | 0.4608 | 2.4774 | 602 | 0.6478 | 0.2186 | 0.6478 | 0.8049 | | 0.4608 | 2.4856 | 604 | 0.6660 | 0.2186 | 0.6660 | 0.8161 | | 0.4608 | 2.4938 | 606 | 0.6790 | 0.2186 | 0.6790 | 0.8240 | | 0.4608 | 2.5021 | 608 | 0.6569 | 0.2759 | 0.6569 | 0.8105 | | 0.4608 | 2.5103 | 610 | 0.6135 | 0.3467 | 0.6135 | 0.7833 | | 0.4608 | 2.5185 | 612 | 0.6081 | 0.3467 | 0.6081 | 0.7798 | | 0.4608 | 2.5267 | 614 | 0.6175 | 0.3467 | 0.6175 | 0.7858 | | 0.4608 | 2.5350 | 616 | 0.6364 | 0.3467 | 0.6364 | 0.7977 | | 0.4608 | 2.5432 | 618 | 0.6775 | 0.2364 | 0.6775 | 0.8231 | | 0.4608 | 2.5514 | 620 | 0.7083 | 0.3396 | 0.7083 | 0.8416 | | 0.4608 | 2.5597 | 622 | 0.7059 | 0.2759 | 0.7059 | 0.8402 | | 0.4608 | 2.5679 | 624 | 0.7097 | 0.2186 | 0.7097 | 0.8425 | | 0.4608 | 2.5761 | 626 | 0.7355 | 0.2000 | 0.7355 | 0.8576 | | 0.4608 | 2.5844 | 628 | 0.7654 | 0.2000 | 0.7654 | 0.8749 | | 0.4608 | 2.5926 | 630 | 0.7487 | 0.2000 | 0.7487 | 0.8653 | | 0.4608 | 2.6008 | 632 | 0.7130 | 0.2000 | 0.7130 | 0.8444 | | 0.4608 | 2.6091 | 634 | 0.6732 | 0.1600 | 0.6732 | 0.8205 | | 0.4608 | 2.6173 | 636 | 0.6652 | 0.1600 | 0.6652 | 0.8156 | | 0.4608 | 2.6255 | 638 | 0.6602 | 0.1600 | 0.6602 | 0.8125 | | 0.4608 | 2.6337 | 640 | 0.6635 | 0.2000 | 0.6635 | 0.8146 | | 0.4608 | 2.6420 | 642 | 0.6839 | 0.2000 | 0.6839 | 0.8270 | | 0.4608 | 2.6502 | 644 | 0.6558 | 0.2000 | 0.6558 | 0.8098 | | 0.4608 | 2.6584 | 646 | 0.6363 | 0.1600 | 0.6363 | 0.7977 | | 0.4608 | 2.6667 | 648 | 0.6270 | 0.1600 | 0.6270 | 0.7918 | | 0.4608 | 2.6749 | 650 | 0.6337 | 0.1600 | 0.6337 | 0.7960 | | 0.4608 | 2.6831 | 652 | 0.6327 | 0.1600 | 0.6327 | 0.7954 | | 0.4608 | 2.6914 | 654 | 0.6530 | 0.1600 | 0.6530 | 0.8081 | | 0.4608 | 2.6996 | 656 | 0.6802 | 0.2000 | 0.6802 | 0.8248 | | 0.4608 | 2.7078 | 658 | 0.7190 | 0.2000 | 0.7190 | 0.8479 | | 0.4608 | 2.7160 | 660 | 0.7140 | 0.2000 | 0.7140 | 0.8450 | | 0.4608 | 2.7243 | 662 | 0.6844 | 0.1600 | 0.6844 | 0.8273 | | 0.4608 | 2.7325 | 664 | 0.6335 | 0.3724 | 0.6335 | 0.7959 | | 0.4608 | 2.7407 | 666 | 0.6120 | 0.1962 | 0.6120 | 0.7823 | | 0.4608 | 2.7490 | 668 | 0.6157 | 0.1560 | 0.6157 | 0.7846 | | 0.4608 | 2.7572 | 670 | 0.6152 | 0.1560 | 0.6152 | 0.7844 | | 0.4608 | 2.7654 | 672 | 0.6157 | 0.1560 | 0.6157 | 0.7847 | | 0.4608 | 2.7737 | 674 | 0.6330 | 0.3318 | 0.6330 | 0.7956 | | 0.4608 | 2.7819 | 676 | 0.6575 | 0.3318 | 0.6575 | 0.8108 | | 0.4608 | 2.7901 | 678 | 0.6984 | 0.1600 | 0.6984 | 0.8357 | | 0.4608 | 2.7984 | 680 | 0.7294 | 0.2000 | 0.7294 | 0.8541 | | 0.4608 | 2.8066 | 682 | 0.7152 | 0.2000 | 0.7152 | 0.8457 | | 0.4608 | 2.8148 | 684 | 0.7148 | 0.2000 | 0.7148 | 0.8455 | | 0.4608 | 2.8230 | 686 | 0.6888 | 0.2000 | 0.6888 | 0.8299 | | 0.4608 | 2.8313 | 688 | 0.6352 | 0.2186 | 0.6352 | 0.7970 | | 0.4608 | 2.8395 | 690 | 0.6025 | 0.3077 | 0.6025 | 0.7762 | | 0.4608 | 2.8477 | 692 | 0.6244 | 0.2150 | 0.6244 | 0.7902 | | 0.4608 | 2.8560 | 694 | 0.6440 | 0.1755 | 0.6440 | 0.8025 | | 0.4608 | 2.8642 | 696 | 0.6430 | 0.1755 | 0.6430 | 0.8019 | | 0.4608 | 2.8724 | 698 | 0.6276 | 0.1560 | 0.6276 | 0.7922 | | 0.4608 | 2.8807 | 700 | 0.6191 | 0.2921 | 0.6191 | 0.7869 | | 0.4608 | 2.8889 | 702 | 0.6311 | 0.3724 | 0.6311 | 0.7944 | | 0.4608 | 2.8971 | 704 | 0.6476 | 0.2000 | 0.6476 | 0.8047 | | 0.4608 | 2.9053 | 706 | 0.6636 | 0.2000 | 0.6636 | 0.8146 | | 0.4608 | 2.9136 | 708 | 0.6691 | 0.2000 | 0.6691 | 0.8180 | | 0.4608 | 2.9218 | 710 | 0.6595 | 0.2000 | 0.6595 | 0.8121 | | 0.4608 | 2.9300 | 712 | 0.6259 | 0.1600 | 0.6259 | 0.7911 | | 0.4608 | 2.9383 | 714 | 0.6047 | 0.3163 | 0.6047 | 0.7776 | | 0.4608 | 2.9465 | 716 | 0.5931 | 0.3724 | 0.5931 | 0.7701 | | 0.4608 | 2.9547 | 718 | 0.5937 | 0.3724 | 0.5937 | 0.7705 | | 0.4608 | 2.9630 | 720 | 0.5991 | 0.3724 | 0.5991 | 0.7740 | | 0.4608 | 2.9712 | 722 | 0.5993 | 0.3724 | 0.5993 | 0.7742 | | 0.4608 | 2.9794 | 724 | 0.6208 | 0.3724 | 0.6208 | 0.7879 | | 0.4608 | 2.9877 | 726 | 0.6729 | 0.2000 | 0.6729 | 0.8203 | | 0.4608 | 2.9959 | 728 | 0.7148 | 0.2000 | 0.7148 | 0.8455 | | 0.4608 | 3.0041 | 730 | 0.7300 | 0.2000 | 0.7300 | 0.8544 | | 0.4608 | 3.0123 | 732 | 0.7470 | 0.2000 | 0.7470 | 0.8643 | | 0.4608 | 3.0206 | 734 | 0.7594 | 0.2000 | 0.7594 | 0.8714 | | 0.4608 | 3.0288 | 736 | 0.7383 | 0.2000 | 0.7383 | 0.8593 | | 0.4608 | 3.0370 | 738 | 0.7335 | 0.2000 | 0.7335 | 0.8565 | | 0.4608 | 3.0453 | 740 | 0.7054 | 0.2186 | 0.7054 | 0.8399 | | 0.4608 | 3.0535 | 742 | 0.6693 | 0.2186 | 0.6693 | 0.8181 | | 0.4608 | 3.0617 | 744 | 0.6355 | 0.2921 | 0.6355 | 0.7972 | | 0.4608 | 3.0700 | 746 | 0.6327 | 0.3318 | 0.6327 | 0.7954 | | 0.4608 | 3.0782 | 748 | 0.6501 | 0.1600 | 0.6501 | 0.8063 | | 0.4608 | 3.0864 | 750 | 0.6755 | 0.2000 | 0.6755 | 0.8219 | | 0.4608 | 3.0947 | 752 | 0.6930 | 0.2000 | 0.6930 | 0.8325 | | 0.4608 | 3.1029 | 754 | 0.7248 | 0.2000 | 0.7248 | 0.8514 | | 0.4608 | 3.1111 | 756 | 0.7025 | 0.2000 | 0.7025 | 0.8382 | | 0.4608 | 3.1193 | 758 | 0.6839 | 0.1600 | 0.6839 | 0.8270 | | 0.4608 | 3.1276 | 760 | 0.6746 | 0.1600 | 0.6746 | 0.8213 | | 0.4608 | 3.1358 | 762 | 0.6942 | 0.2000 | 0.6942 | 0.8332 | | 0.4608 | 3.1440 | 764 | 0.7235 | 0.1398 | 0.7235 | 0.8506 | | 0.4608 | 3.1523 | 766 | 0.7442 | 0.1398 | 0.7442 | 0.8627 | | 0.4608 | 3.1605 | 768 | 0.7152 | 0.1398 | 0.7152 | 0.8457 | | 0.4608 | 3.1687 | 770 | 0.6785 | 0.1600 | 0.6785 | 0.8237 | | 0.4608 | 3.1770 | 772 | 0.6604 | 0.1600 | 0.6604 | 0.8126 | | 0.4608 | 3.1852 | 774 | 0.6512 | 0.2533 | 0.6512 | 0.8070 | | 0.4608 | 3.1934 | 776 | 0.6512 | 0.2533 | 0.6512 | 0.8070 | | 0.4608 | 3.2016 | 778 | 0.6528 | 0.0455 | 0.6528 | 0.8080 | | 0.4608 | 3.2099 | 780 | 0.6800 | 0.1600 | 0.6800 | 0.8246 | | 0.4608 | 3.2181 | 782 | 0.7145 | 0.1000 | 0.7145 | 0.8453 | | 0.4608 | 3.2263 | 784 | 0.7507 | 0.1398 | 0.7507 | 0.8664 | | 0.4608 | 3.2346 | 786 | 0.7818 | 0.1398 | 0.7818 | 0.8842 | | 0.4608 | 3.2428 | 788 | 0.7708 | 0.1000 | 0.7708 | 0.8780 | | 0.4608 | 3.2510 | 790 | 0.7246 | 0.1600 | 0.7246 | 0.8512 | | 0.4608 | 3.2593 | 792 | 0.6926 | 0.1600 | 0.6926 | 0.8322 | | 0.4608 | 3.2675 | 794 | 0.6878 | 0.2154 | 0.6878 | 0.8293 | | 0.4608 | 3.2757 | 796 | 0.6934 | 0.2154 | 0.6934 | 0.8327 | | 0.4608 | 3.2840 | 798 | 0.6951 | 0.0957 | 0.6951 | 0.8337 | | 0.4608 | 3.2922 | 800 | 0.6838 | 0.2154 | 0.6838 | 0.8269 | | 0.4608 | 3.3004 | 802 | 0.6843 | 0.2533 | 0.6843 | 0.8272 | | 0.4608 | 3.3086 | 804 | 0.7108 | 0.1600 | 0.7108 | 0.8431 | | 0.4608 | 3.3169 | 806 | 0.7749 | 0.1398 | 0.7749 | 0.8803 | | 0.4608 | 3.3251 | 808 | 0.8174 | 0.1398 | 0.8174 | 0.9041 | | 0.4608 | 3.3333 | 810 | 0.8305 | 0.1398 | 0.8305 | 0.9113 | | 0.4608 | 3.3416 | 812 | 0.8008 | 0.1398 | 0.8008 | 0.8949 | | 0.4608 | 3.3498 | 814 | 0.7626 | 0.1398 | 0.7626 | 0.8733 | | 0.4608 | 3.3580 | 816 | 0.7119 | 0.2000 | 0.7119 | 0.8438 | | 0.4608 | 3.3663 | 818 | 0.6969 | 0.1600 | 0.6969 | 0.8348 | | 0.4608 | 3.3745 | 820 | 0.7092 | 0.1600 | 0.7092 | 0.8421 | | 0.4608 | 3.3827 | 822 | 0.7331 | 0.2000 | 0.7331 | 0.8562 | | 0.4608 | 3.3909 | 824 | 0.7654 | 0.1398 | 0.7654 | 0.8749 | | 0.4608 | 3.3992 | 826 | 0.7778 | 0.1398 | 0.7778 | 0.8819 | | 0.4608 | 3.4074 | 828 | 0.7790 | 0.1398 | 0.7790 | 0.8826 | | 0.4608 | 3.4156 | 830 | 0.7525 | 0.1398 | 0.7525 | 0.8675 | | 0.4608 | 3.4239 | 832 | 0.7099 | 0.2000 | 0.7099 | 0.8425 | | 0.4608 | 3.4321 | 834 | 0.6679 | 0.2186 | 0.6679 | 0.8172 | | 0.4608 | 3.4403 | 836 | 0.6515 | 0.3318 | 0.6515 | 0.8071 | | 0.4608 | 3.4486 | 838 | 0.6545 | 0.3318 | 0.6545 | 0.8090 | | 0.4608 | 3.4568 | 840 | 0.6770 | 0.2000 | 0.6770 | 0.8228 | | 0.4608 | 3.4650 | 842 | 0.7027 | 0.2000 | 0.7027 | 0.8383 | | 0.4608 | 3.4733 | 844 | 0.7563 | 0.2000 | 0.7563 | 0.8697 | | 0.4608 | 3.4815 | 846 | 0.7959 | 0.1398 | 0.7959 | 0.8921 | | 0.4608 | 3.4897 | 848 | 0.7839 | 0.1398 | 0.7839 | 0.8854 | | 0.4608 | 3.4979 | 850 | 0.7421 | 0.2000 | 0.7421 | 0.8615 | | 0.4608 | 3.5062 | 852 | 0.7016 | 0.2000 | 0.7016 | 0.8376 | | 0.4608 | 3.5144 | 854 | 0.6734 | 0.2000 | 0.6734 | 0.8206 | | 0.4608 | 3.5226 | 856 | 0.6503 | 0.2000 | 0.6503 | 0.8064 | | 0.4608 | 3.5309 | 858 | 0.6411 | 0.2000 | 0.6411 | 0.8007 | | 0.4608 | 3.5391 | 860 | 0.6339 | 0.2000 | 0.6339 | 0.7962 | | 0.4608 | 3.5473 | 862 | 0.6339 | 0.2000 | 0.6339 | 0.7962 | | 0.4608 | 3.5556 | 864 | 0.6477 | 0.2000 | 0.6477 | 0.8048 | | 0.4608 | 3.5638 | 866 | 0.6577 | 0.2000 | 0.6577 | 0.8110 | | 0.4608 | 3.5720 | 868 | 0.6815 | 0.2188 | 0.6815 | 0.8255 | | 0.4608 | 3.5802 | 870 | 0.7363 | 0.3198 | 0.7363 | 0.8581 | | 0.4608 | 3.5885 | 872 | 0.7513 | 0.3198 | 0.7513 | 0.8668 | | 0.4608 | 3.5967 | 874 | 0.7470 | 0.3198 | 0.7470 | 0.8643 | | 0.4608 | 3.6049 | 876 | 0.7090 | 0.2188 | 0.7090 | 0.8420 | | 0.4608 | 3.6132 | 878 | 0.6699 | 0.2000 | 0.6699 | 0.8185 | | 0.4608 | 3.6214 | 880 | 0.6369 | 0.2000 | 0.6369 | 0.7981 | | 0.4608 | 3.6296 | 882 | 0.6287 | 0.2000 | 0.6287 | 0.7929 | | 0.4608 | 3.6379 | 884 | 0.6310 | 0.2000 | 0.6310 | 0.7944 | | 0.4608 | 3.6461 | 886 | 0.6229 | 0.1600 | 0.6229 | 0.7893 | | 0.4608 | 3.6543 | 888 | 0.6169 | 0.1600 | 0.6169 | 0.7855 | | 0.4608 | 3.6626 | 890 | 0.6151 | 0.3724 | 0.6151 | 0.7843 | | 0.4608 | 3.6708 | 892 | 0.6142 | 0.3318 | 0.6142 | 0.7837 | | 0.4608 | 3.6790 | 894 | 0.6169 | 0.2921 | 0.6169 | 0.7854 | | 0.4608 | 3.6872 | 896 | 0.6177 | 0.2921 | 0.6177 | 0.7859 | | 0.4608 | 3.6955 | 898 | 0.6257 | 0.3318 | 0.6257 | 0.7910 | | 0.4608 | 3.7037 | 900 | 0.6257 | 0.3318 | 0.6257 | 0.7910 | | 0.4608 | 3.7119 | 902 | 0.6227 | 0.3318 | 0.6227 | 0.7891 | | 0.4608 | 3.7202 | 904 | 0.6164 | 0.3318 | 0.6164 | 0.7851 | | 0.4608 | 3.7284 | 906 | 0.6184 | 0.3318 | 0.6184 | 0.7864 | | 0.4608 | 3.7366 | 908 | 0.6787 | 0.3107 | 0.6787 | 0.8238 | | 0.4608 | 3.7449 | 910 | 0.7562 | 0.3277 | 0.7562 | 0.8696 | | 0.4608 | 3.7531 | 912 | 0.8121 | 0.3277 | 0.8121 | 0.9011 | | 0.4608 | 3.7613 | 914 | 0.8140 | 0.3107 | 0.8140 | 0.9022 | | 0.4608 | 3.7695 | 916 | 0.8356 | 0.2566 | 0.8356 | 0.9141 | | 0.4608 | 3.7778 | 918 | 0.7919 | 0.3107 | 0.7919 | 0.8899 | | 0.4608 | 3.7860 | 920 | 0.7292 | 0.2000 | 0.7292 | 0.8540 | | 0.4608 | 3.7942 | 922 | 0.6849 | 0.2000 | 0.6849 | 0.8276 | | 0.4608 | 3.8025 | 924 | 0.6415 | 0.2588 | 0.6415 | 0.8010 | | 0.4608 | 3.8107 | 926 | 0.6140 | 0.3724 | 0.6140 | 0.7836 | | 0.4608 | 3.8189 | 928 | 0.6121 | 0.3318 | 0.6121 | 0.7823 | | 0.4608 | 3.8272 | 930 | 0.6138 | 0.3318 | 0.6138 | 0.7834 | | 0.4608 | 3.8354 | 932 | 0.6229 | 0.2186 | 0.6229 | 0.7892 | | 0.4608 | 3.8436 | 934 | 0.6353 | 0.2588 | 0.6353 | 0.7971 | | 0.4608 | 3.8519 | 936 | 0.6542 | 0.2000 | 0.6542 | 0.8088 | | 0.4608 | 3.8601 | 938 | 0.6833 | 0.2000 | 0.6833 | 0.8266 | | 0.4608 | 3.8683 | 940 | 0.7102 | 0.2000 | 0.7102 | 0.8427 | | 0.4608 | 3.8765 | 942 | 0.6991 | 0.2000 | 0.6991 | 0.8361 | | 0.4608 | 3.8848 | 944 | 0.6823 | 0.1600 | 0.6823 | 0.8260 | | 0.4608 | 3.8930 | 946 | 0.6662 | 0.2186 | 0.6662 | 0.8162 | | 0.4608 | 3.9012 | 948 | 0.6658 | 0.2186 | 0.6658 | 0.8160 | | 0.4608 | 3.9095 | 950 | 0.6742 | 0.2186 | 0.6742 | 0.8211 | | 0.4608 | 3.9177 | 952 | 0.6798 | 0.2186 | 0.6798 | 0.8245 | | 0.4608 | 3.9259 | 954 | 0.7083 | 0.1600 | 0.7083 | 0.8416 | | 0.4608 | 3.9342 | 956 | 0.7263 | 0.2000 | 0.7263 | 0.8523 | | 0.4608 | 3.9424 | 958 | 0.7201 | 0.2000 | 0.7201 | 0.8486 | | 0.4608 | 3.9506 | 960 | 0.7136 | 0.2000 | 0.7136 | 0.8448 | | 0.4608 | 3.9588 | 962 | 0.6800 | 0.2186 | 0.6800 | 0.8246 | | 0.4608 | 3.9671 | 964 | 0.6480 | 0.1793 | 0.6480 | 0.8050 | | 0.4608 | 3.9753 | 966 | 0.6376 | 0.1793 | 0.6376 | 0.7985 | | 0.4608 | 3.9835 | 968 | 0.6514 | 0.2186 | 0.6514 | 0.8071 | | 0.4608 | 3.9918 | 970 | 0.6779 | 0.2000 | 0.6779 | 0.8233 | | 0.4608 | 4.0 | 972 | 0.7279 | 0.3198 | 0.7279 | 0.8532 | | 0.4608 | 4.0082 | 974 | 0.7666 | 0.3198 | 0.7666 | 0.8755 | | 0.4608 | 4.0165 | 976 | 0.7730 | 0.3198 | 0.7730 | 0.8792 | | 0.4608 | 4.0247 | 978 | 0.7249 | 0.3198 | 0.7249 | 0.8514 | | 0.4608 | 4.0329 | 980 | 0.6436 | 0.3107 | 0.6436 | 0.8022 | | 0.4608 | 4.0412 | 982 | 0.5838 | 0.2364 | 0.5838 | 0.7641 | | 0.4608 | 4.0494 | 984 | 0.5645 | 0.3865 | 0.5645 | 0.7514 | | 0.4608 | 4.0576 | 986 | 0.5648 | 0.2696 | 0.5648 | 0.7515 | | 0.4608 | 4.0658 | 988 | 0.5706 | 0.2696 | 0.5706 | 0.7554 | | 0.4608 | 4.0741 | 990 | 0.5778 | 0.2696 | 0.5778 | 0.7601 | | 0.4608 | 4.0823 | 992 | 0.5984 | 0.3724 | 0.5984 | 0.7736 | | 0.4608 | 4.0905 | 994 | 0.6383 | 0.1600 | 0.6383 | 0.7989 | | 0.4608 | 4.0988 | 996 | 0.6917 | 0.2000 | 0.6917 | 0.8317 | | 0.4608 | 4.1070 | 998 | 0.7845 | 0.3107 | 0.7845 | 0.8857 | | 0.1067 | 4.1152 | 1000 | 0.8507 | 0.3277 | 0.8507 | 0.9223 | | 0.1067 | 4.1235 | 1002 | 0.8551 | 0.3277 | 0.8551 | 0.9247 | | 0.1067 | 4.1317 | 1004 | 0.8002 | 0.3277 | 0.8002 | 0.8946 | | 0.1067 | 4.1399 | 1006 | 0.7005 | 0.3107 | 0.7005 | 0.8370 | | 0.1067 | 4.1481 | 1008 | 0.6174 | 0.3163 | 0.6174 | 0.7858 | | 0.1067 | 4.1564 | 1010 | 0.5818 | 0.3724 | 0.5818 | 0.7627 | | 0.1067 | 4.1646 | 1012 | 0.5679 | 0.3724 | 0.5679 | 0.7536 | | 0.1067 | 4.1728 | 1014 | 0.5659 | 0.3318 | 0.5659 | 0.7523 | | 0.1067 | 4.1811 | 1016 | 0.5647 | 0.3724 | 0.5647 | 0.7515 | | 0.1067 | 4.1893 | 1018 | 0.5690 | 0.3724 | 0.5690 | 0.7543 | | 0.1067 | 4.1975 | 1020 | 0.5874 | 0.3163 | 0.5874 | 0.7664 | | 0.1067 | 4.2058 | 1022 | 0.6182 | 0.2000 | 0.6182 | 0.7863 | | 0.1067 | 4.2140 | 1024 | 0.6451 | 0.2000 | 0.6451 | 0.8032 | | 0.1067 | 4.2222 | 1026 | 0.6470 | 0.2000 | 0.6470 | 0.8043 | | 0.1067 | 4.2305 | 1028 | 0.6411 | 0.3107 | 0.6411 | 0.8007 | | 0.1067 | 4.2387 | 1030 | 0.6326 | 0.3107 | 0.6326 | 0.7954 | | 0.1067 | 4.2469 | 1032 | 0.6159 | 0.3107 | 0.6159 | 0.7848 | | 0.1067 | 4.2551 | 1034 | 0.5866 | 0.4154 | 0.5866 | 0.7659 | | 0.1067 | 4.2634 | 1036 | 0.5535 | 0.3163 | 0.5535 | 0.7440 | | 0.1067 | 4.2716 | 1038 | 0.5404 | 0.3724 | 0.5404 | 0.7351 | | 0.1067 | 4.2798 | 1040 | 0.5345 | 0.3318 | 0.5345 | 0.7311 | | 0.1067 | 4.2881 | 1042 | 0.5368 | 0.3724 | 0.5368 | 0.7326 | | 0.1067 | 4.2963 | 1044 | 0.5432 | 0.3724 | 0.5432 | 0.7370 | | 0.1067 | 4.3045 | 1046 | 0.5531 | 0.3163 | 0.5531 | 0.7437 | | 0.1067 | 4.3128 | 1048 | 0.5643 | 0.3163 | 0.5643 | 0.7512 | | 0.1067 | 4.3210 | 1050 | 0.5742 | 0.3163 | 0.5742 | 0.7578 | | 0.1067 | 4.3292 | 1052 | 0.5848 | 0.3163 | 0.5848 | 0.7647 | | 0.1067 | 4.3374 | 1054 | 0.5892 | 0.3163 | 0.5892 | 0.7676 | | 0.1067 | 4.3457 | 1056 | 0.5874 | 0.3724 | 0.5874 | 0.7664 | | 0.1067 | 4.3539 | 1058 | 0.6102 | 0.4154 | 0.6102 | 0.7811 | | 0.1067 | 4.3621 | 1060 | 0.6437 | 0.2727 | 0.6437 | 0.8023 | | 0.1067 | 4.3704 | 1062 | 0.6690 | 0.2727 | 0.6690 | 0.8179 | | 0.1067 | 4.3786 | 1064 | 0.7169 | 0.2727 | 0.7169 | 0.8467 | | 0.1067 | 4.3868 | 1066 | 0.7977 | 0.3277 | 0.7977 | 0.8932 | | 0.1067 | 4.3951 | 1068 | 0.9043 | 0.1421 | 0.9043 | 0.9509 | | 0.1067 | 4.4033 | 1070 | 0.9346 | 0.1421 | 0.9346 | 0.9668 | | 0.1067 | 4.4115 | 1072 | 0.8760 | 0.1421 | 0.8760 | 0.9360 | | 0.1067 | 4.4198 | 1074 | 0.7776 | 0.3107 | 0.7776 | 0.8818 | | 0.1067 | 4.4280 | 1076 | 0.6973 | 0.2727 | 0.6973 | 0.8350 | | 0.1067 | 4.4362 | 1078 | 0.6421 | 0.2186 | 0.6421 | 0.8013 | | 0.1067 | 4.4444 | 1080 | 0.6260 | 0.3724 | 0.6260 | 0.7912 | | 0.1067 | 4.4527 | 1082 | 0.6141 | 0.3724 | 0.6141 | 0.7837 | | 0.1067 | 4.4609 | 1084 | 0.5965 | 0.3724 | 0.5965 | 0.7724 | | 0.1067 | 4.4691 | 1086 | 0.5922 | 0.3724 | 0.5922 | 0.7696 | | 0.1067 | 4.4774 | 1088 | 0.5860 | 0.3724 | 0.5860 | 0.7655 | | 0.1067 | 4.4856 | 1090 | 0.5774 | 0.3724 | 0.5774 | 0.7599 | | 0.1067 | 4.4938 | 1092 | 0.5751 | 0.3724 | 0.5751 | 0.7584 | | 0.1067 | 4.5021 | 1094 | 0.5759 | 0.3724 | 0.5759 | 0.7589 | | 0.1067 | 4.5103 | 1096 | 0.5726 | 0.3724 | 0.5726 | 0.7567 | | 0.1067 | 4.5185 | 1098 | 0.5822 | 0.3724 | 0.5822 | 0.7630 | | 0.1067 | 4.5267 | 1100 | 0.5873 | 0.3724 | 0.5873 | 0.7664 | | 0.1067 | 4.5350 | 1102 | 0.5880 | 0.3724 | 0.5880 | 0.7668 | | 0.1067 | 4.5432 | 1104 | 0.5947 | 0.3724 | 0.5947 | 0.7712 | | 0.1067 | 4.5514 | 1106 | 0.6063 | 0.3724 | 0.6063 | 0.7787 | | 0.1067 | 4.5597 | 1108 | 0.6089 | 0.3724 | 0.6089 | 0.7804 | | 0.1067 | 4.5679 | 1110 | 0.6153 | 0.3724 | 0.6153 | 0.7844 | | 0.1067 | 4.5761 | 1112 | 0.6160 | 0.3724 | 0.6160 | 0.7849 | | 0.1067 | 4.5844 | 1114 | 0.6238 | 0.3163 | 0.6238 | 0.7898 | | 0.1067 | 4.5926 | 1116 | 0.6326 | 0.3163 | 0.6326 | 0.7954 | | 0.1067 | 4.6008 | 1118 | 0.6173 | 0.3163 | 0.6173 | 0.7857 | | 0.1067 | 4.6091 | 1120 | 0.6053 | 0.3163 | 0.6053 | 0.7780 | | 0.1067 | 4.6173 | 1122 | 0.6107 | 0.3163 | 0.6107 | 0.7815 | | 0.1067 | 4.6255 | 1124 | 0.6035 | 0.3163 | 0.6035 | 0.7769 | | 0.1067 | 4.6337 | 1126 | 0.6111 | 0.3163 | 0.6111 | 0.7817 | | 0.1067 | 4.6420 | 1128 | 0.6276 | 0.3163 | 0.6276 | 0.7922 | | 0.1067 | 4.6502 | 1130 | 0.6515 | 0.3107 | 0.6515 | 0.8072 | | 0.1067 | 4.6584 | 1132 | 0.6731 | 0.3107 | 0.6731 | 0.8204 | | 0.1067 | 4.6667 | 1134 | 0.6614 | 0.3107 | 0.6614 | 0.8133 | | 0.1067 | 4.6749 | 1136 | 0.6400 | 0.3107 | 0.6400 | 0.8000 | | 0.1067 | 4.6831 | 1138 | 0.6019 | 0.3163 | 0.6019 | 0.7758 | | 0.1067 | 4.6914 | 1140 | 0.5688 | 0.3724 | 0.5688 | 0.7542 | | 0.1067 | 4.6996 | 1142 | 0.5518 | 0.3724 | 0.5518 | 0.7428 | | 0.1067 | 4.7078 | 1144 | 0.5512 | 0.3609 | 0.5512 | 0.7424 | | 0.1067 | 4.7160 | 1146 | 0.5574 | 0.3226 | 0.5574 | 0.7466 | | 0.1067 | 4.7243 | 1148 | 0.5579 | 0.3609 | 0.5579 | 0.7469 | | 0.1067 | 4.7325 | 1150 | 0.5574 | 0.3865 | 0.5574 | 0.7466 | | 0.1067 | 4.7407 | 1152 | 0.5764 | 0.3724 | 0.5764 | 0.7592 | | 0.1067 | 4.7490 | 1154 | 0.6318 | 0.4154 | 0.6318 | 0.7948 | | 0.1067 | 4.7572 | 1156 | 0.6887 | 0.3198 | 0.6887 | 0.8299 | | 0.1067 | 4.7654 | 1158 | 0.7032 | 0.3198 | 0.7032 | 0.8386 | | 0.1067 | 4.7737 | 1160 | 0.6678 | 0.4529 | 0.6678 | 0.8172 | | 0.1067 | 4.7819 | 1162 | 0.6315 | 0.4154 | 0.6315 | 0.7947 | | 0.1067 | 4.7901 | 1164 | 0.6128 | 0.4167 | 0.6128 | 0.7828 | | 0.1067 | 4.7984 | 1166 | 0.5894 | 0.4154 | 0.5894 | 0.7677 | | 0.1067 | 4.8066 | 1168 | 0.5796 | 0.3163 | 0.5796 | 0.7613 | | 0.1067 | 4.8148 | 1170 | 0.5729 | 0.3163 | 0.5729 | 0.7569 | | 0.1067 | 4.8230 | 1172 | 0.5792 | 0.3163 | 0.5792 | 0.7611 | | 0.1067 | 4.8313 | 1174 | 0.5800 | 0.3163 | 0.5800 | 0.7616 | | 0.1067 | 4.8395 | 1176 | 0.5854 | 0.3163 | 0.5854 | 0.7651 | | 0.1067 | 4.8477 | 1178 | 0.5778 | 0.3163 | 0.5778 | 0.7601 | | 0.1067 | 4.8560 | 1180 | 0.5690 | 0.3163 | 0.5690 | 0.7543 | | 0.1067 | 4.8642 | 1182 | 0.5619 | 0.3163 | 0.5619 | 0.7496 | | 0.1067 | 4.8724 | 1184 | 0.5602 | 0.3163 | 0.5602 | 0.7484 | | 0.1067 | 4.8807 | 1186 | 0.5590 | 0.3163 | 0.5591 | 0.7477 | | 0.1067 | 4.8889 | 1188 | 0.5542 | 0.3163 | 0.5542 | 0.7444 | | 0.1067 | 4.8971 | 1190 | 0.5554 | 0.3163 | 0.5554 | 0.7452 | | 0.1067 | 4.9053 | 1192 | 0.5586 | 0.3163 | 0.5586 | 0.7474 | | 0.1067 | 4.9136 | 1194 | 0.5645 | 0.3163 | 0.5645 | 0.7513 | | 0.1067 | 4.9218 | 1196 | 0.5658 | 0.3163 | 0.5658 | 0.7522 | | 0.1067 | 4.9300 | 1198 | 0.5701 | 0.3163 | 0.5701 | 0.7551 | | 0.1067 | 4.9383 | 1200 | 0.5671 | 0.3163 | 0.5671 | 0.7531 | | 0.1067 | 4.9465 | 1202 | 0.5657 | 0.3163 | 0.5657 | 0.7521 | | 0.1067 | 4.9547 | 1204 | 0.5573 | 0.3163 | 0.5573 | 0.7465 | | 0.1067 | 4.9630 | 1206 | 0.5621 | 0.3163 | 0.5621 | 0.7497 | | 0.1067 | 4.9712 | 1208 | 0.5581 | 0.3163 | 0.5581 | 0.7471 | | 0.1067 | 4.9794 | 1210 | 0.5770 | 0.3255 | 0.5770 | 0.7596 | | 0.1067 | 4.9877 | 1212 | 0.6160 | 0.4529 | 0.6160 | 0.7848 | | 0.1067 | 4.9959 | 1214 | 0.6855 | 0.3198 | 0.6855 | 0.8280 | | 0.1067 | 5.0041 | 1216 | 0.7575 | 0.3198 | 0.7575 | 0.8703 | | 0.1067 | 5.0123 | 1218 | 0.7589 | 0.3198 | 0.7589 | 0.8712 | | 0.1067 | 5.0206 | 1220 | 0.7158 | 0.3198 | 0.7158 | 0.8460 | | 0.1067 | 5.0288 | 1222 | 0.6280 | 0.4529 | 0.6280 | 0.7925 | | 0.1067 | 5.0370 | 1224 | 0.5834 | 0.4167 | 0.5834 | 0.7638 | | 0.1067 | 5.0453 | 1226 | 0.5561 | 0.4637 | 0.5561 | 0.7457 | | 0.1067 | 5.0535 | 1228 | 0.5489 | 0.3771 | 0.5489 | 0.7409 | | 0.1067 | 5.0617 | 1230 | 0.5482 | 0.3771 | 0.5482 | 0.7404 | | 0.1067 | 5.0700 | 1232 | 0.5362 | 0.3724 | 0.5362 | 0.7323 | | 0.1067 | 5.0782 | 1234 | 0.5372 | 0.3724 | 0.5372 | 0.7329 | | 0.1067 | 5.0864 | 1236 | 0.5379 | 0.3724 | 0.5379 | 0.7334 | | 0.1067 | 5.0947 | 1238 | 0.5419 | 0.3318 | 0.5419 | 0.7361 | | 0.1067 | 5.1029 | 1240 | 0.5436 | 0.3318 | 0.5436 | 0.7373 | | 0.1067 | 5.1111 | 1242 | 0.5462 | 0.3724 | 0.5462 | 0.7391 | | 0.1067 | 5.1193 | 1244 | 0.5527 | 0.3724 | 0.5527 | 0.7434 | | 0.1067 | 5.1276 | 1246 | 0.5586 | 0.3724 | 0.5586 | 0.7474 | | 0.1067 | 5.1358 | 1248 | 0.5621 | 0.3724 | 0.5621 | 0.7497 | | 0.1067 | 5.1440 | 1250 | 0.5642 | 0.2921 | 0.5642 | 0.7511 | | 0.1067 | 5.1523 | 1252 | 0.5681 | 0.2921 | 0.5681 | 0.7537 | | 0.1067 | 5.1605 | 1254 | 0.5697 | 0.2921 | 0.5697 | 0.7548 | | 0.1067 | 5.1687 | 1256 | 0.5701 | 0.2921 | 0.5701 | 0.7550 | | 0.1067 | 5.1770 | 1258 | 0.5750 | 0.3724 | 0.5750 | 0.7583 | | 0.1067 | 5.1852 | 1260 | 0.5924 | 0.3724 | 0.5924 | 0.7697 | | 0.1067 | 5.1934 | 1262 | 0.6060 | 0.4140 | 0.6060 | 0.7785 | | 0.1067 | 5.2016 | 1264 | 0.6008 | 0.3724 | 0.6008 | 0.7751 | | 0.1067 | 5.2099 | 1266 | 0.5907 | 0.3724 | 0.5907 | 0.7686 | | 0.1067 | 5.2181 | 1268 | 0.5801 | 0.3724 | 0.5801 | 0.7616 | | 0.1067 | 5.2263 | 1270 | 0.5799 | 0.2921 | 0.5799 | 0.7615 | | 0.1067 | 5.2346 | 1272 | 0.5806 | 0.3467 | 0.5806 | 0.7620 | | 0.1067 | 5.2428 | 1274 | 0.5769 | 0.3467 | 0.5769 | 0.7596 | | 0.1067 | 5.2510 | 1276 | 0.5722 | 0.3865 | 0.5722 | 0.7564 | | 0.1067 | 5.2593 | 1278 | 0.5707 | 0.3724 | 0.5707 | 0.7554 | | 0.1067 | 5.2675 | 1280 | 0.5736 | 0.3724 | 0.5736 | 0.7574 | | 0.1067 | 5.2757 | 1282 | 0.5781 | 0.3724 | 0.5781 | 0.7603 | | 0.1067 | 5.2840 | 1284 | 0.5893 | 0.3724 | 0.5893 | 0.7677 | | 0.1067 | 5.2922 | 1286 | 0.6013 | 0.3724 | 0.6013 | 0.7754 | | 0.1067 | 5.3004 | 1288 | 0.6058 | 0.3724 | 0.6058 | 0.7783 | | 0.1067 | 5.3086 | 1290 | 0.6026 | 0.3724 | 0.6026 | 0.7763 | | 0.1067 | 5.3169 | 1292 | 0.6021 | 0.3724 | 0.6021 | 0.7760 | | 0.1067 | 5.3251 | 1294 | 0.6085 | 0.3724 | 0.6085 | 0.7801 | | 0.1067 | 5.3333 | 1296 | 0.6104 | 0.3724 | 0.6104 | 0.7813 | | 0.1067 | 5.3416 | 1298 | 0.6099 | 0.3724 | 0.6099 | 0.7810 | | 0.1067 | 5.3498 | 1300 | 0.6138 | 0.4273 | 0.6138 | 0.7834 | | 0.1067 | 5.3580 | 1302 | 0.6232 | 0.3467 | 0.6232 | 0.7894 | | 0.1067 | 5.3663 | 1304 | 0.6391 | 0.2696 | 0.6391 | 0.7995 | | 0.1067 | 5.3745 | 1306 | 0.6443 | 0.3077 | 0.6443 | 0.8027 | | 0.1067 | 5.3827 | 1308 | 0.6407 | 0.2921 | 0.6407 | 0.8004 | | 0.1067 | 5.3909 | 1310 | 0.6412 | 0.3724 | 0.6412 | 0.8008 | | 0.1067 | 5.3992 | 1312 | 0.6475 | 0.3724 | 0.6475 | 0.8046 | | 0.1067 | 5.4074 | 1314 | 0.6641 | 0.3724 | 0.6641 | 0.8149 | | 0.1067 | 5.4156 | 1316 | 0.6809 | 0.1600 | 0.6809 | 0.8252 | | 0.1067 | 5.4239 | 1318 | 0.6994 | 0.1818 | 0.6994 | 0.8363 | | 0.1067 | 5.4321 | 1320 | 0.6984 | 0.2846 | 0.6984 | 0.8357 | | 0.1067 | 5.4403 | 1322 | 0.6712 | 0.1818 | 0.6712 | 0.8193 | | 0.1067 | 5.4486 | 1324 | 0.6343 | 0.2186 | 0.6343 | 0.7964 | | 0.1067 | 5.4568 | 1326 | 0.6057 | 0.3724 | 0.6057 | 0.7782 | | 0.1067 | 5.4650 | 1328 | 0.5926 | 0.3724 | 0.5926 | 0.7698 | | 0.1067 | 5.4733 | 1330 | 0.5906 | 0.4273 | 0.5906 | 0.7685 | | 0.1067 | 5.4815 | 1332 | 0.5892 | 0.3865 | 0.5892 | 0.7676 | | 0.1067 | 5.4897 | 1334 | 0.5892 | 0.3077 | 0.5892 | 0.7676 | | 0.1067 | 5.4979 | 1336 | 0.5866 | 0.3467 | 0.5866 | 0.7659 | | 0.1067 | 5.5062 | 1338 | 0.5847 | 0.4273 | 0.5847 | 0.7647 | | 0.1067 | 5.5144 | 1340 | 0.5820 | 0.4273 | 0.5820 | 0.7629 | | 0.1067 | 5.5226 | 1342 | 0.5855 | 0.4273 | 0.5855 | 0.7652 | | 0.1067 | 5.5309 | 1344 | 0.5909 | 0.4277 | 0.5909 | 0.7687 | | 0.1067 | 5.5391 | 1346 | 0.5976 | 0.3811 | 0.5976 | 0.7730 | | 0.1067 | 5.5473 | 1348 | 0.6043 | 0.3811 | 0.6043 | 0.7774 | | 0.1067 | 5.5556 | 1350 | 0.5986 | 0.3811 | 0.5986 | 0.7737 | | 0.1067 | 5.5638 | 1352 | 0.5869 | 0.2759 | 0.5869 | 0.7661 | | 0.1067 | 5.5720 | 1354 | 0.5819 | 0.3865 | 0.5819 | 0.7628 | | 0.1067 | 5.5802 | 1356 | 0.5902 | 0.2759 | 0.5902 | 0.7682 | | 0.1067 | 5.5885 | 1358 | 0.5856 | 0.3865 | 0.5856 | 0.7652 | | 0.1067 | 5.5967 | 1360 | 0.5777 | 0.3865 | 0.5777 | 0.7601 | | 0.1067 | 5.6049 | 1362 | 0.5730 | 0.3865 | 0.5730 | 0.7570 | | 0.1067 | 5.6132 | 1364 | 0.5739 | 0.3865 | 0.5739 | 0.7576 | | 0.1067 | 5.6214 | 1366 | 0.5833 | 0.3865 | 0.5833 | 0.7638 | | 0.1067 | 5.6296 | 1368 | 0.5973 | 0.4772 | 0.5973 | 0.7729 | | 0.1067 | 5.6379 | 1370 | 0.6009 | 0.4772 | 0.6009 | 0.7752 | | 0.1067 | 5.6461 | 1372 | 0.6135 | 0.3463 | 0.6135 | 0.7833 | | 0.1067 | 5.6543 | 1374 | 0.6249 | 0.3463 | 0.6249 | 0.7905 | | 0.1067 | 5.6626 | 1376 | 0.6465 | 0.3811 | 0.6465 | 0.8041 | | 0.1067 | 5.6708 | 1378 | 0.6563 | 0.3333 | 0.6563 | 0.8101 | | 0.1067 | 5.6790 | 1380 | 0.6677 | 0.2846 | 0.6677 | 0.8171 | | 0.1067 | 5.6872 | 1382 | 0.6573 | 0.2846 | 0.6573 | 0.8107 | | 0.1067 | 5.6955 | 1384 | 0.6353 | 0.3255 | 0.6353 | 0.7971 | | 0.1067 | 5.7037 | 1386 | 0.6239 | 0.3255 | 0.6239 | 0.7899 | | 0.1067 | 5.7119 | 1388 | 0.6212 | 0.2186 | 0.6212 | 0.7882 | | 0.1067 | 5.7202 | 1390 | 0.6240 | 0.2186 | 0.6240 | 0.7899 | | 0.1067 | 5.7284 | 1392 | 0.6335 | 0.2186 | 0.6335 | 0.7959 | | 0.1067 | 5.7366 | 1394 | 0.6243 | 0.3724 | 0.6243 | 0.7902 | | 0.1067 | 5.7449 | 1396 | 0.6067 | 0.3724 | 0.6067 | 0.7789 | | 0.1067 | 5.7531 | 1398 | 0.6060 | 0.3724 | 0.6060 | 0.7785 | | 0.1067 | 5.7613 | 1400 | 0.6136 | 0.3724 | 0.6136 | 0.7833 | | 0.1067 | 5.7695 | 1402 | 0.6249 | 0.3724 | 0.6249 | 0.7905 | | 0.1067 | 5.7778 | 1404 | 0.6320 | 0.3724 | 0.6320 | 0.7950 | | 0.1067 | 5.7860 | 1406 | 0.6302 | 0.3724 | 0.6302 | 0.7938 | | 0.1067 | 5.7942 | 1408 | 0.6485 | 0.3724 | 0.6485 | 0.8053 | | 0.1067 | 5.8025 | 1410 | 0.6884 | 0.4615 | 0.6884 | 0.8297 | | 0.1067 | 5.8107 | 1412 | 0.7049 | 0.4615 | 0.7049 | 0.8396 | | 0.1067 | 5.8189 | 1414 | 0.7113 | 0.2948 | 0.7113 | 0.8434 | | 0.1067 | 5.8272 | 1416 | 0.6979 | 0.2948 | 0.6979 | 0.8354 | | 0.1067 | 5.8354 | 1418 | 0.6645 | 0.3333 | 0.6645 | 0.8152 | | 0.1067 | 5.8436 | 1420 | 0.6171 | 0.2186 | 0.6171 | 0.7855 | | 0.1067 | 5.8519 | 1422 | 0.5984 | 0.2186 | 0.5984 | 0.7736 | | 0.1067 | 5.8601 | 1424 | 0.5909 | 0.3724 | 0.5909 | 0.7687 | | 0.1067 | 5.8683 | 1426 | 0.5855 | 0.3724 | 0.5855 | 0.7652 | | 0.1067 | 5.8765 | 1428 | 0.5823 | 0.3724 | 0.5823 | 0.7631 | | 0.1067 | 5.8848 | 1430 | 0.5827 | 0.3724 | 0.5827 | 0.7633 | | 0.1067 | 5.8930 | 1432 | 0.5836 | 0.3724 | 0.5836 | 0.7640 | | 0.1067 | 5.9012 | 1434 | 0.5890 | 0.2186 | 0.5890 | 0.7675 | | 0.1067 | 5.9095 | 1436 | 0.5924 | 0.2186 | 0.5924 | 0.7697 | | 0.1067 | 5.9177 | 1438 | 0.5934 | 0.2186 | 0.5934 | 0.7703 | | 0.1067 | 5.9259 | 1440 | 0.5975 | 0.2186 | 0.5975 | 0.7730 | | 0.1067 | 5.9342 | 1442 | 0.5973 | 0.2186 | 0.5973 | 0.7729 | | 0.1067 | 5.9424 | 1444 | 0.6052 | 0.2186 | 0.6052 | 0.7779 | | 0.1067 | 5.9506 | 1446 | 0.6085 | 0.2355 | 0.6085 | 0.7800 | | 0.1067 | 5.9588 | 1448 | 0.6136 | 0.2355 | 0.6136 | 0.7833 | | 0.1067 | 5.9671 | 1450 | 0.6272 | 0.3333 | 0.6272 | 0.7920 | | 0.1067 | 5.9753 | 1452 | 0.6377 | 0.3333 | 0.6377 | 0.7986 | | 0.1067 | 5.9835 | 1454 | 0.6601 | 0.3333 | 0.6601 | 0.8124 | | 0.1067 | 5.9918 | 1456 | 0.6702 | 0.3333 | 0.6702 | 0.8186 | | 0.1067 | 6.0 | 1458 | 0.6696 | 0.3333 | 0.6696 | 0.8183 | | 0.1067 | 6.0082 | 1460 | 0.7106 | 0.3401 | 0.7106 | 0.8430 | | 0.1067 | 6.0165 | 1462 | 0.7502 | 0.3277 | 0.7502 | 0.8662 | | 0.1067 | 6.0247 | 1464 | 0.7732 | 0.3277 | 0.7732 | 0.8793 | | 0.1067 | 6.0329 | 1466 | 0.7905 | 0.3277 | 0.7905 | 0.8891 | | 0.1067 | 6.0412 | 1468 | 0.7390 | 0.3277 | 0.7390 | 0.8596 | | 0.1067 | 6.0494 | 1470 | 0.6955 | 0.3401 | 0.6955 | 0.8339 | | 0.1067 | 6.0576 | 1472 | 0.6746 | 0.3401 | 0.6746 | 0.8214 | | 0.1067 | 6.0658 | 1474 | 0.6916 | 0.3401 | 0.6916 | 0.8316 | | 0.1067 | 6.0741 | 1476 | 0.7171 | 0.3401 | 0.7171 | 0.8468 | | 0.1067 | 6.0823 | 1478 | 0.7460 | 0.3401 | 0.7460 | 0.8637 | | 0.1067 | 6.0905 | 1480 | 0.7604 | 0.4187 | 0.7604 | 0.8720 | | 0.1067 | 6.0988 | 1482 | 0.7358 | 0.3401 | 0.7358 | 0.8578 | | 0.1067 | 6.1070 | 1484 | 0.6898 | 0.3401 | 0.6898 | 0.8305 | | 0.1067 | 6.1152 | 1486 | 0.6483 | 0.4740 | 0.6483 | 0.8052 | | 0.1067 | 6.1235 | 1488 | 0.6281 | 0.4389 | 0.6281 | 0.7925 | | 0.1067 | 6.1317 | 1490 | 0.6149 | 0.4389 | 0.6149 | 0.7842 | | 0.1067 | 6.1399 | 1492 | 0.6114 | 0.4389 | 0.6114 | 0.7819 | | 0.1067 | 6.1481 | 1494 | 0.6122 | 0.4389 | 0.6122 | 0.7825 | | 0.1067 | 6.1564 | 1496 | 0.6148 | 0.4389 | 0.6148 | 0.7841 | | 0.1067 | 6.1646 | 1498 | 0.6254 | 0.3463 | 0.6254 | 0.7909 | | 0.0692 | 6.1728 | 1500 | 0.6406 | 0.3333 | 0.6406 | 0.8004 | | 0.0692 | 6.1811 | 1502 | 0.6690 | 0.3333 | 0.6690 | 0.8179 | | 0.0692 | 6.1893 | 1504 | 0.6879 | 0.3333 | 0.6879 | 0.8294 | | 0.0692 | 6.1975 | 1506 | 0.6737 | 0.3333 | 0.6737 | 0.8208 | | 0.0692 | 6.2058 | 1508 | 0.6619 | 0.3333 | 0.6619 | 0.8136 | | 0.0692 | 6.2140 | 1510 | 0.6404 | 0.3333 | 0.6404 | 0.8002 | | 0.0692 | 6.2222 | 1512 | 0.6208 | 0.2186 | 0.6208 | 0.7879 | | 0.0692 | 6.2305 | 1514 | 0.6093 | 0.2364 | 0.6093 | 0.7806 | | 0.0692 | 6.2387 | 1516 | 0.6077 | 0.3865 | 0.6077 | 0.7795 | | 0.0692 | 6.2469 | 1518 | 0.6043 | 0.3865 | 0.6043 | 0.7774 | | 0.0692 | 6.2551 | 1520 | 0.6001 | 0.3865 | 0.6001 | 0.7747 | | 0.0692 | 6.2634 | 1522 | 0.5978 | 0.3865 | 0.5978 | 0.7732 | | 0.0692 | 6.2716 | 1524 | 0.5987 | 0.2759 | 0.5987 | 0.7738 | | 0.0692 | 6.2798 | 1526 | 0.6101 | 0.2355 | 0.6101 | 0.7811 | | 0.0692 | 6.2881 | 1528 | 0.6292 | 0.3333 | 0.6292 | 0.7932 | | 0.0692 | 6.2963 | 1530 | 0.6504 | 0.3333 | 0.6504 | 0.8065 | | 0.0692 | 6.3045 | 1532 | 0.6731 | 0.2846 | 0.6731 | 0.8204 | | 0.0692 | 6.3128 | 1534 | 0.6776 | 0.2846 | 0.6776 | 0.8231 | | 0.0692 | 6.3210 | 1536 | 0.6742 | 0.2846 | 0.6742 | 0.8211 | | 0.0692 | 6.3292 | 1538 | 0.6550 | 0.3811 | 0.6550 | 0.8093 | | 0.0692 | 6.3374 | 1540 | 0.6390 | 0.3811 | 0.6390 | 0.7993 | | 0.0692 | 6.3457 | 1542 | 0.6197 | 0.3811 | 0.6197 | 0.7872 | | 0.0692 | 6.3539 | 1544 | 0.6014 | 0.4740 | 0.6014 | 0.7755 | | 0.0692 | 6.3621 | 1546 | 0.5895 | 0.4740 | 0.5895 | 0.7678 | | 0.0692 | 6.3704 | 1548 | 0.5917 | 0.4740 | 0.5917 | 0.7692 | | 0.0692 | 6.3786 | 1550 | 0.5937 | 0.3463 | 0.5937 | 0.7705 | | 0.0692 | 6.3868 | 1552 | 0.5984 | 0.3811 | 0.5984 | 0.7736 | | 0.0692 | 6.3951 | 1554 | 0.6104 | 0.3333 | 0.6104 | 0.7813 | | 0.0692 | 6.4033 | 1556 | 0.6300 | 0.3333 | 0.6300 | 0.7937 | | 0.0692 | 6.4115 | 1558 | 0.6186 | 0.3333 | 0.6186 | 0.7865 | | 0.0692 | 6.4198 | 1560 | 0.6094 | 0.3811 | 0.6094 | 0.7806 | | 0.0692 | 6.4280 | 1562 | 0.6105 | 0.3811 | 0.6105 | 0.7814 | | 0.0692 | 6.4362 | 1564 | 0.6063 | 0.2881 | 0.6063 | 0.7787 | | 0.0692 | 6.4444 | 1566 | 0.6054 | 0.2881 | 0.6054 | 0.7781 | | 0.0692 | 6.4527 | 1568 | 0.6045 | 0.2881 | 0.6045 | 0.7775 | | 0.0692 | 6.4609 | 1570 | 0.6082 | 0.2759 | 0.6082 | 0.7798 | | 0.0692 | 6.4691 | 1572 | 0.6141 | 0.2186 | 0.6141 | 0.7836 | | 0.0692 | 6.4774 | 1574 | 0.6320 | 0.2355 | 0.6320 | 0.7950 | | 0.0692 | 6.4856 | 1576 | 0.6476 | 0.2355 | 0.6476 | 0.8047 | | 0.0692 | 6.4938 | 1578 | 0.6600 | 0.1818 | 0.6600 | 0.8124 | | 0.0692 | 6.5021 | 1580 | 0.6625 | 0.2188 | 0.6625 | 0.8139 | | 0.0692 | 6.5103 | 1582 | 0.6635 | 0.2188 | 0.6635 | 0.8145 | | 0.0692 | 6.5185 | 1584 | 0.6506 | 0.2188 | 0.6506 | 0.8066 | | 0.0692 | 6.5267 | 1586 | 0.6407 | 0.1818 | 0.6407 | 0.8004 | | 0.0692 | 6.5350 | 1588 | 0.6252 | 0.2355 | 0.6252 | 0.7907 | | 0.0692 | 6.5432 | 1590 | 0.6079 | 0.2186 | 0.6079 | 0.7797 | | 0.0692 | 6.5514 | 1592 | 0.5948 | 0.2186 | 0.5948 | 0.7712 | | 0.0692 | 6.5597 | 1594 | 0.5883 | 0.4273 | 0.5883 | 0.7670 | | 0.0692 | 6.5679 | 1596 | 0.5897 | 0.3077 | 0.5897 | 0.7679 | | 0.0692 | 6.5761 | 1598 | 0.5946 | 0.3226 | 0.5946 | 0.7711 | | 0.0692 | 6.5844 | 1600 | 0.5944 | 0.3226 | 0.5944 | 0.7710 | | 0.0692 | 6.5926 | 1602 | 0.5929 | 0.3467 | 0.5929 | 0.7700 | | 0.0692 | 6.6008 | 1604 | 0.5930 | 0.3724 | 0.5930 | 0.7701 | | 0.0692 | 6.6091 | 1606 | 0.5982 | 0.3724 | 0.5982 | 0.7734 | | 0.0692 | 6.6173 | 1608 | 0.6042 | 0.3724 | 0.6042 | 0.7773 | | 0.0692 | 6.6255 | 1610 | 0.6068 | 0.3724 | 0.6068 | 0.7790 | | 0.0692 | 6.6337 | 1612 | 0.6094 | 0.3724 | 0.6094 | 0.7806 | | 0.0692 | 6.6420 | 1614 | 0.6169 | 0.2186 | 0.6169 | 0.7854 | | 0.0692 | 6.6502 | 1616 | 0.6346 | 0.2186 | 0.6346 | 0.7966 | | 0.0692 | 6.6584 | 1618 | 0.6467 | 0.2186 | 0.6467 | 0.8042 | | 0.0692 | 6.6667 | 1620 | 0.6422 | 0.2186 | 0.6422 | 0.8014 | | 0.0692 | 6.6749 | 1622 | 0.6255 | 0.2186 | 0.6255 | 0.7909 | | 0.0692 | 6.6831 | 1624 | 0.6098 | 0.3724 | 0.6098 | 0.7809 | | 0.0692 | 6.6914 | 1626 | 0.5949 | 0.3724 | 0.5949 | 0.7713 | | 0.0692 | 6.6996 | 1628 | 0.5834 | 0.3724 | 0.5834 | 0.7638 | | 0.0692 | 6.7078 | 1630 | 0.5793 | 0.3724 | 0.5793 | 0.7611 | | 0.0692 | 6.7160 | 1632 | 0.5767 | 0.3318 | 0.5767 | 0.7594 | | 0.0692 | 6.7243 | 1634 | 0.5749 | 0.2921 | 0.5749 | 0.7582 | | 0.0692 | 6.7325 | 1636 | 0.5739 | 0.2921 | 0.5739 | 0.7576 | | 0.0692 | 6.7407 | 1638 | 0.5732 | 0.2921 | 0.5732 | 0.7571 | | 0.0692 | 6.7490 | 1640 | 0.5720 | 0.3318 | 0.5720 | 0.7563 | | 0.0692 | 6.7572 | 1642 | 0.5721 | 0.3724 | 0.5721 | 0.7564 | | 0.0692 | 6.7654 | 1644 | 0.5810 | 0.3724 | 0.5810 | 0.7623 | | 0.0692 | 6.7737 | 1646 | 0.5880 | 0.2186 | 0.5880 | 0.7668 | | 0.0692 | 6.7819 | 1648 | 0.5938 | 0.2186 | 0.5938 | 0.7706 | | 0.0692 | 6.7901 | 1650 | 0.5990 | 0.2186 | 0.5990 | 0.7740 | | 0.0692 | 6.7984 | 1652 | 0.5994 | 0.3724 | 0.5994 | 0.7742 | | 0.0692 | 6.8066 | 1654 | 0.6008 | 0.3724 | 0.6008 | 0.7751 | | 0.0692 | 6.8148 | 1656 | 0.6042 | 0.3724 | 0.6042 | 0.7773 | | 0.0692 | 6.8230 | 1658 | 0.6091 | 0.2186 | 0.6091 | 0.7805 | | 0.0692 | 6.8313 | 1660 | 0.6180 | 0.2186 | 0.6180 | 0.7861 | | 0.0692 | 6.8395 | 1662 | 0.6167 | 0.2186 | 0.6167 | 0.7853 | | 0.0692 | 6.8477 | 1664 | 0.6168 | 0.2186 | 0.6168 | 0.7854 | | 0.0692 | 6.8560 | 1666 | 0.6187 | 0.2186 | 0.6187 | 0.7866 | | 0.0692 | 6.8642 | 1668 | 0.6188 | 0.2186 | 0.6188 | 0.7867 | | 0.0692 | 6.8724 | 1670 | 0.6151 | 0.2186 | 0.6151 | 0.7843 | | 0.0692 | 6.8807 | 1672 | 0.6070 | 0.2186 | 0.6070 | 0.7791 | | 0.0692 | 6.8889 | 1674 | 0.6057 | 0.2186 | 0.6057 | 0.7783 | | 0.0692 | 6.8971 | 1676 | 0.6044 | 0.1793 | 0.6044 | 0.7774 | | 0.0692 | 6.9053 | 1678 | 0.6025 | 0.1793 | 0.6025 | 0.7762 | | 0.0692 | 6.9136 | 1680 | 0.5999 | 0.1793 | 0.5999 | 0.7745 | | 0.0692 | 6.9218 | 1682 | 0.5964 | 0.3318 | 0.5964 | 0.7723 | | 0.0692 | 6.9300 | 1684 | 0.5980 | 0.3318 | 0.5980 | 0.7733 | | 0.0692 | 6.9383 | 1686 | 0.6003 | 0.1793 | 0.6003 | 0.7748 | | 0.0692 | 6.9465 | 1688 | 0.6075 | 0.1992 | 0.6075 | 0.7794 | | 0.0692 | 6.9547 | 1690 | 0.6167 | 0.1992 | 0.6167 | 0.7853 | | 0.0692 | 6.9630 | 1692 | 0.6238 | 0.1992 | 0.6238 | 0.7898 | | 0.0692 | 6.9712 | 1694 | 0.6318 | 0.1992 | 0.6318 | 0.7949 | | 0.0692 | 6.9794 | 1696 | 0.6347 | 0.1793 | 0.6347 | 0.7967 | | 0.0692 | 6.9877 | 1698 | 0.6336 | 0.1793 | 0.6336 | 0.7960 | | 0.0692 | 6.9959 | 1700 | 0.6399 | 0.2186 | 0.6399 | 0.7999 | | 0.0692 | 7.0041 | 1702 | 0.6433 | 0.1600 | 0.6433 | 0.8020 | | 0.0692 | 7.0123 | 1704 | 0.6346 | 0.2186 | 0.6346 | 0.7966 | | 0.0692 | 7.0206 | 1706 | 0.6267 | 0.2186 | 0.6267 | 0.7916 | | 0.0692 | 7.0288 | 1708 | 0.6184 | 0.2186 | 0.6184 | 0.7864 | | 0.0692 | 7.0370 | 1710 | 0.6157 | 0.2186 | 0.6157 | 0.7846 | | 0.0692 | 7.0453 | 1712 | 0.6129 | 0.1793 | 0.6129 | 0.7829 | | 0.0692 | 7.0535 | 1714 | 0.6149 | 0.2186 | 0.6149 | 0.7842 | | 0.0692 | 7.0617 | 1716 | 0.6210 | 0.1600 | 0.6210 | 0.7880 | | 0.0692 | 7.0700 | 1718 | 0.6209 | 0.2186 | 0.6209 | 0.7880 | | 0.0692 | 7.0782 | 1720 | 0.6126 | 0.1793 | 0.6126 | 0.7827 | | 0.0692 | 7.0864 | 1722 | 0.6086 | 0.1793 | 0.6086 | 0.7801 | | 0.0692 | 7.0947 | 1724 | 0.6144 | 0.1793 | 0.6144 | 0.7839 | | 0.0692 | 7.1029 | 1726 | 0.6169 | 0.1992 | 0.6169 | 0.7854 | | 0.0692 | 7.1111 | 1728 | 0.6278 | 0.1992 | 0.6278 | 0.7923 | | 0.0692 | 7.1193 | 1730 | 0.6458 | 0.2355 | 0.6458 | 0.8036 | | 0.0692 | 7.1276 | 1732 | 0.6782 | 0.2355 | 0.6782 | 0.8235 | | 0.0692 | 7.1358 | 1734 | 0.6941 | 0.1818 | 0.6941 | 0.8331 | | 0.0692 | 7.1440 | 1736 | 0.6847 | 0.2355 | 0.6847 | 0.8275 | | 0.0692 | 7.1523 | 1738 | 0.6607 | 0.2355 | 0.6607 | 0.8129 | | 0.0692 | 7.1605 | 1740 | 0.6388 | 0.2355 | 0.6388 | 0.7993 | | 0.0692 | 7.1687 | 1742 | 0.6237 | 0.1992 | 0.6237 | 0.7898 | | 0.0692 | 7.1770 | 1744 | 0.6138 | 0.3396 | 0.6138 | 0.7835 | | 0.0692 | 7.1852 | 1746 | 0.6140 | 0.3396 | 0.6140 | 0.7836 | | 0.0692 | 7.1934 | 1748 | 0.6138 | 0.3771 | 0.6138 | 0.7835 | | 0.0692 | 7.2016 | 1750 | 0.6196 | 0.2355 | 0.6196 | 0.7871 | | 0.0692 | 7.2099 | 1752 | 0.6268 | 0.2355 | 0.6268 | 0.7917 | | 0.0692 | 7.2181 | 1754 | 0.6321 | 0.2355 | 0.6321 | 0.7950 | | 0.0692 | 7.2263 | 1756 | 0.6372 | 0.2355 | 0.6372 | 0.7982 | | 0.0692 | 7.2346 | 1758 | 0.6410 | 0.2355 | 0.6410 | 0.8007 | | 0.0692 | 7.2428 | 1760 | 0.6502 | 0.2355 | 0.6502 | 0.8064 | | 0.0692 | 7.2510 | 1762 | 0.6506 | 0.2355 | 0.6506 | 0.8066 | | 0.0692 | 7.2593 | 1764 | 0.6459 | 0.2355 | 0.6459 | 0.8037 | | 0.0692 | 7.2675 | 1766 | 0.6486 | 0.2355 | 0.6486 | 0.8053 | | 0.0692 | 7.2757 | 1768 | 0.6505 | 0.2355 | 0.6505 | 0.8065 | | 0.0692 | 7.2840 | 1770 | 0.6471 | 0.3396 | 0.6471 | 0.8044 | | 0.0692 | 7.2922 | 1772 | 0.6450 | 0.3396 | 0.6450 | 0.8031 | | 0.0692 | 7.3004 | 1774 | 0.6465 | 0.3396 | 0.6465 | 0.8041 | | 0.0692 | 7.3086 | 1776 | 0.6521 | 0.3396 | 0.6521 | 0.8076 | | 0.0692 | 7.3169 | 1778 | 0.6630 | 0.1992 | 0.6630 | 0.8143 | | 0.0692 | 7.3251 | 1780 | 0.6659 | 0.1992 | 0.6659 | 0.8160 | | 0.0692 | 7.3333 | 1782 | 0.6630 | 0.3396 | 0.6630 | 0.8143 | | 0.0692 | 7.3416 | 1784 | 0.6656 | 0.1992 | 0.6656 | 0.8158 | | 0.0692 | 7.3498 | 1786 | 0.6680 | 0.2355 | 0.6680 | 0.8173 | | 0.0692 | 7.3580 | 1788 | 0.6584 | 0.3396 | 0.6584 | 0.8114 | | 0.0692 | 7.3663 | 1790 | 0.6572 | 0.3396 | 0.6572 | 0.8107 | | 0.0692 | 7.3745 | 1792 | 0.6563 | 0.3771 | 0.6563 | 0.8101 | | 0.0692 | 7.3827 | 1794 | 0.6627 | 0.3771 | 0.6627 | 0.8141 | | 0.0692 | 7.3909 | 1796 | 0.6650 | 0.2355 | 0.6650 | 0.8155 | | 0.0692 | 7.3992 | 1798 | 0.6579 | 0.2355 | 0.6579 | 0.8111 | | 0.0692 | 7.4074 | 1800 | 0.6494 | 0.3771 | 0.6494 | 0.8059 | | 0.0692 | 7.4156 | 1802 | 0.6377 | 0.3724 | 0.6377 | 0.7985 | | 0.0692 | 7.4239 | 1804 | 0.6364 | 0.3724 | 0.6364 | 0.7978 | | 0.0692 | 7.4321 | 1806 | 0.6459 | 0.3724 | 0.6459 | 0.8037 | | 0.0692 | 7.4403 | 1808 | 0.6677 | 0.2355 | 0.6677 | 0.8171 | | 0.0692 | 7.4486 | 1810 | 0.6834 | 0.2355 | 0.6834 | 0.8267 | | 0.0692 | 7.4568 | 1812 | 0.7022 | 0.2355 | 0.7022 | 0.8380 | | 0.0692 | 7.4650 | 1814 | 0.7093 | 0.2355 | 0.7093 | 0.8422 | | 0.0692 | 7.4733 | 1816 | 0.7180 | 0.2355 | 0.7180 | 0.8474 | | 0.0692 | 7.4815 | 1818 | 0.7246 | 0.3333 | 0.7246 | 0.8513 | | 0.0692 | 7.4897 | 1820 | 0.7286 | 0.3333 | 0.7286 | 0.8536 | | 0.0692 | 7.4979 | 1822 | 0.7242 | 0.3333 | 0.7242 | 0.8510 | | 0.0692 | 7.5062 | 1824 | 0.7173 | 0.3333 | 0.7173 | 0.8470 | | 0.0692 | 7.5144 | 1826 | 0.7155 | 0.3333 | 0.7155 | 0.8459 | | 0.0692 | 7.5226 | 1828 | 0.6998 | 0.2355 | 0.6998 | 0.8365 | | 0.0692 | 7.5309 | 1830 | 0.6859 | 0.2355 | 0.6859 | 0.8282 | | 0.0692 | 7.5391 | 1832 | 0.6722 | 0.2355 | 0.6722 | 0.8199 | | 0.0692 | 7.5473 | 1834 | 0.6616 | 0.3771 | 0.6616 | 0.8134 | | 0.0692 | 7.5556 | 1836 | 0.6468 | 0.3771 | 0.6468 | 0.8043 | | 0.0692 | 7.5638 | 1838 | 0.6428 | 0.3771 | 0.6428 | 0.8018 | | 0.0692 | 7.5720 | 1840 | 0.6488 | 0.3771 | 0.6488 | 0.8055 | | 0.0692 | 7.5802 | 1842 | 0.6507 | 0.3771 | 0.6507 | 0.8067 | | 0.0692 | 7.5885 | 1844 | 0.6520 | 0.3771 | 0.6520 | 0.8075 | | 0.0692 | 7.5967 | 1846 | 0.6585 | 0.3771 | 0.6585 | 0.8115 | | 0.0692 | 7.6049 | 1848 | 0.6589 | 0.3771 | 0.6589 | 0.8117 | | 0.0692 | 7.6132 | 1850 | 0.6589 | 0.3771 | 0.6589 | 0.8117 | | 0.0692 | 7.6214 | 1852 | 0.6684 | 0.2355 | 0.6684 | 0.8175 | | 0.0692 | 7.6296 | 1854 | 0.6832 | 0.2355 | 0.6832 | 0.8265 | | 0.0692 | 7.6379 | 1856 | 0.7081 | 0.3333 | 0.7081 | 0.8415 | | 0.0692 | 7.6461 | 1858 | 0.7205 | 0.2846 | 0.7205 | 0.8488 | | 0.0692 | 7.6543 | 1860 | 0.7250 | 0.2846 | 0.7250 | 0.8515 | | 0.0692 | 7.6626 | 1862 | 0.7194 | 0.2846 | 0.7194 | 0.8482 | | 0.0692 | 7.6708 | 1864 | 0.7073 | 0.3333 | 0.7073 | 0.8410 | | 0.0692 | 7.6790 | 1866 | 0.6927 | 0.2355 | 0.6927 | 0.8323 | | 0.0692 | 7.6872 | 1868 | 0.6672 | 0.2355 | 0.6672 | 0.8168 | | 0.0692 | 7.6955 | 1870 | 0.6434 | 0.3724 | 0.6434 | 0.8022 | | 0.0692 | 7.7037 | 1872 | 0.6320 | 0.3724 | 0.6320 | 0.7950 | | 0.0692 | 7.7119 | 1874 | 0.6248 | 0.3318 | 0.6248 | 0.7905 | | 0.0692 | 7.7202 | 1876 | 0.6213 | 0.3318 | 0.6213 | 0.7882 | | 0.0692 | 7.7284 | 1878 | 0.6196 | 0.3318 | 0.6196 | 0.7871 | | 0.0692 | 7.7366 | 1880 | 0.6225 | 0.3318 | 0.6225 | 0.7890 | | 0.0692 | 7.7449 | 1882 | 0.6360 | 0.3724 | 0.6360 | 0.7975 | | 0.0692 | 7.7531 | 1884 | 0.6573 | 0.3724 | 0.6573 | 0.8108 | | 0.0692 | 7.7613 | 1886 | 0.6798 | 0.3724 | 0.6798 | 0.8245 | | 0.0692 | 7.7695 | 1888 | 0.6981 | 0.3771 | 0.6981 | 0.8355 | | 0.0692 | 7.7778 | 1890 | 0.7134 | 0.2355 | 0.7134 | 0.8446 | | 0.0692 | 7.7860 | 1892 | 0.7096 | 0.2355 | 0.7096 | 0.8424 | | 0.0692 | 7.7942 | 1894 | 0.6947 | 0.2355 | 0.6947 | 0.8335 | | 0.0692 | 7.8025 | 1896 | 0.6689 | 0.2186 | 0.6689 | 0.8179 | | 0.0692 | 7.8107 | 1898 | 0.6435 | 0.3724 | 0.6435 | 0.8022 | | 0.0692 | 7.8189 | 1900 | 0.6295 | 0.3724 | 0.6295 | 0.7934 | | 0.0692 | 7.8272 | 1902 | 0.6151 | 0.3724 | 0.6151 | 0.7843 | | 0.0692 | 7.8354 | 1904 | 0.6045 | 0.3724 | 0.6045 | 0.7775 | | 0.0692 | 7.8436 | 1906 | 0.5975 | 0.3724 | 0.5975 | 0.7730 | | 0.0692 | 7.8519 | 1908 | 0.5937 | 0.3724 | 0.5937 | 0.7705 | | 0.0692 | 7.8601 | 1910 | 0.5954 | 0.3724 | 0.5954 | 0.7716 | | 0.0692 | 7.8683 | 1912 | 0.5982 | 0.3724 | 0.5982 | 0.7735 | | 0.0692 | 7.8765 | 1914 | 0.6021 | 0.3724 | 0.6021 | 0.7759 | | 0.0692 | 7.8848 | 1916 | 0.6086 | 0.3724 | 0.6086 | 0.7802 | | 0.0692 | 7.8930 | 1918 | 0.6219 | 0.3724 | 0.6219 | 0.7886 | | 0.0692 | 7.9012 | 1920 | 0.6473 | 0.3771 | 0.6473 | 0.8046 | | 0.0692 | 7.9095 | 1922 | 0.6686 | 0.3771 | 0.6686 | 0.8177 | | 0.0692 | 7.9177 | 1924 | 0.6928 | 0.3811 | 0.6928 | 0.8324 | | 0.0692 | 7.9259 | 1926 | 0.7079 | 0.4615 | 0.7079 | 0.8413 | | 0.0692 | 7.9342 | 1928 | 0.7041 | 0.3811 | 0.7041 | 0.8391 | | 0.0692 | 7.9424 | 1930 | 0.6896 | 0.3811 | 0.6896 | 0.8304 | | 0.0692 | 7.9506 | 1932 | 0.6779 | 0.3771 | 0.6779 | 0.8234 | | 0.0692 | 7.9588 | 1934 | 0.6610 | 0.3771 | 0.6610 | 0.8130 | | 0.0692 | 7.9671 | 1936 | 0.6406 | 0.3724 | 0.6406 | 0.8004 | | 0.0692 | 7.9753 | 1938 | 0.6288 | 0.3318 | 0.6288 | 0.7930 | | 0.0692 | 7.9835 | 1940 | 0.6253 | 0.3318 | 0.6253 | 0.7907 | | 0.0692 | 7.9918 | 1942 | 0.6240 | 0.3318 | 0.6240 | 0.7899 | | 0.0692 | 8.0 | 1944 | 0.6227 | 0.3318 | 0.6227 | 0.7891 | | 0.0692 | 8.0082 | 1946 | 0.6165 | 0.3318 | 0.6165 | 0.7852 | | 0.0692 | 8.0165 | 1948 | 0.6127 | 0.3724 | 0.6127 | 0.7827 | | 0.0692 | 8.0247 | 1950 | 0.6124 | 0.3724 | 0.6124 | 0.7826 | | 0.0692 | 8.0329 | 1952 | 0.6120 | 0.3724 | 0.6120 | 0.7823 | | 0.0692 | 8.0412 | 1954 | 0.6186 | 0.3724 | 0.6186 | 0.7865 | | 0.0692 | 8.0494 | 1956 | 0.6321 | 0.3724 | 0.6321 | 0.7950 | | 0.0692 | 8.0576 | 1958 | 0.6434 | 0.3724 | 0.6434 | 0.8021 | | 0.0692 | 8.0658 | 1960 | 0.6600 | 0.3771 | 0.6600 | 0.8124 | | 0.0692 | 8.0741 | 1962 | 0.6698 | 0.2355 | 0.6698 | 0.8184 | | 0.0692 | 8.0823 | 1964 | 0.6623 | 0.3771 | 0.6623 | 0.8138 | | 0.0692 | 8.0905 | 1966 | 0.6544 | 0.3724 | 0.6544 | 0.8089 | | 0.0692 | 8.0988 | 1968 | 0.6476 | 0.3724 | 0.6476 | 0.8047 | | 0.0692 | 8.1070 | 1970 | 0.6421 | 0.3724 | 0.6421 | 0.8013 | | 0.0692 | 8.1152 | 1972 | 0.6377 | 0.3724 | 0.6377 | 0.7985 | | 0.0692 | 8.1235 | 1974 | 0.6314 | 0.3318 | 0.6314 | 0.7946 | | 0.0692 | 8.1317 | 1976 | 0.6277 | 0.3318 | 0.6277 | 0.7923 | | 0.0692 | 8.1399 | 1978 | 0.6263 | 0.3318 | 0.6263 | 0.7914 | | 0.0692 | 8.1481 | 1980 | 0.6240 | 0.3318 | 0.6240 | 0.7900 | | 0.0692 | 8.1564 | 1982 | 0.6229 | 0.3318 | 0.6229 | 0.7892 | | 0.0692 | 8.1646 | 1984 | 0.6233 | 0.3318 | 0.6233 | 0.7895 | | 0.0692 | 8.1728 | 1986 | 0.6248 | 0.3318 | 0.6248 | 0.7905 | | 0.0692 | 8.1811 | 1988 | 0.6294 | 0.3724 | 0.6294 | 0.7934 | | 0.0692 | 8.1893 | 1990 | 0.6369 | 0.3724 | 0.6369 | 0.7981 | | 0.0692 | 8.1975 | 1992 | 0.6457 | 0.3724 | 0.6457 | 0.8035 | | 0.0692 | 8.2058 | 1994 | 0.6554 | 0.2355 | 0.6554 | 0.8096 | | 0.0692 | 8.2140 | 1996 | 0.6601 | 0.2355 | 0.6601 | 0.8125 | | 0.0692 | 8.2222 | 1998 | 0.6605 | 0.2355 | 0.6605 | 0.8127 | | 0.049 | 8.2305 | 2000 | 0.6565 | 0.2355 | 0.6565 | 0.8102 | | 0.049 | 8.2387 | 2002 | 0.6439 | 0.2355 | 0.6439 | 0.8024 | | 0.049 | 8.2469 | 2004 | 0.6288 | 0.3724 | 0.6288 | 0.7930 | | 0.049 | 8.2551 | 2006 | 0.6161 | 0.3724 | 0.6161 | 0.7849 | | 0.049 | 8.2634 | 2008 | 0.6110 | 0.3724 | 0.6110 | 0.7816 | | 0.049 | 8.2716 | 2010 | 0.6071 | 0.3318 | 0.6071 | 0.7792 | | 0.049 | 8.2798 | 2012 | 0.6065 | 0.3318 | 0.6065 | 0.7788 | | 0.049 | 8.2881 | 2014 | 0.6065 | 0.3318 | 0.6065 | 0.7788 | | 0.049 | 8.2963 | 2016 | 0.6070 | 0.3318 | 0.6070 | 0.7791 | | 0.049 | 8.3045 | 2018 | 0.6090 | 0.3318 | 0.6090 | 0.7804 | | 0.049 | 8.3128 | 2020 | 0.6126 | 0.3724 | 0.6126 | 0.7827 | | 0.049 | 8.3210 | 2022 | 0.6177 | 0.3724 | 0.6177 | 0.7859 | | 0.049 | 8.3292 | 2024 | 0.6258 | 0.3724 | 0.6258 | 0.7911 | | 0.049 | 8.3374 | 2026 | 0.6331 | 0.3724 | 0.6331 | 0.7957 | | 0.049 | 8.3457 | 2028 | 0.6384 | 0.3724 | 0.6384 | 0.7990 | | 0.049 | 8.3539 | 2030 | 0.6449 | 0.3724 | 0.6449 | 0.8031 | | 0.049 | 8.3621 | 2032 | 0.6463 | 0.3724 | 0.6463 | 0.8039 | | 0.049 | 8.3704 | 2034 | 0.6405 | 0.3724 | 0.6405 | 0.8003 | | 0.049 | 8.3786 | 2036 | 0.6380 | 0.3724 | 0.6380 | 0.7987 | | 0.049 | 8.3868 | 2038 | 0.6386 | 0.3724 | 0.6386 | 0.7992 | | 0.049 | 8.3951 | 2040 | 0.6387 | 0.3724 | 0.6387 | 0.7992 | | 0.049 | 8.4033 | 2042 | 0.6349 | 0.3724 | 0.6349 | 0.7968 | | 0.049 | 8.4115 | 2044 | 0.6330 | 0.3724 | 0.6330 | 0.7956 | | 0.049 | 8.4198 | 2046 | 0.6300 | 0.3724 | 0.6300 | 0.7937 | | 0.049 | 8.4280 | 2048 | 0.6275 | 0.3724 | 0.6275 | 0.7922 | | 0.049 | 8.4362 | 2050 | 0.6270 | 0.3724 | 0.6270 | 0.7918 | | 0.049 | 8.4444 | 2052 | 0.6259 | 0.3724 | 0.6259 | 0.7911 | | 0.049 | 8.4527 | 2054 | 0.6310 | 0.3724 | 0.6310 | 0.7944 | | 0.049 | 8.4609 | 2056 | 0.6363 | 0.3771 | 0.6363 | 0.7977 | | 0.049 | 8.4691 | 2058 | 0.6430 | 0.3771 | 0.6430 | 0.8019 | | 0.049 | 8.4774 | 2060 | 0.6499 | 0.3771 | 0.6499 | 0.8062 | | 0.049 | 8.4856 | 2062 | 0.6491 | 0.3771 | 0.6491 | 0.8056 | | 0.049 | 8.4938 | 2064 | 0.6505 | 0.3771 | 0.6505 | 0.8065 | | 0.049 | 8.5021 | 2066 | 0.6514 | 0.3771 | 0.6514 | 0.8071 | | 0.049 | 8.5103 | 2068 | 0.6522 | 0.3771 | 0.6522 | 0.8076 | | 0.049 | 8.5185 | 2070 | 0.6528 | 0.3771 | 0.6528 | 0.8079 | | 0.049 | 8.5267 | 2072 | 0.6535 | 0.3771 | 0.6535 | 0.8084 | | 0.049 | 8.5350 | 2074 | 0.6525 | 0.3771 | 0.6525 | 0.8078 | | 0.049 | 8.5432 | 2076 | 0.6489 | 0.3771 | 0.6489 | 0.8056 | | 0.049 | 8.5514 | 2078 | 0.6478 | 0.3771 | 0.6478 | 0.8048 | | 0.049 | 8.5597 | 2080 | 0.6491 | 0.3771 | 0.6491 | 0.8057 | | 0.049 | 8.5679 | 2082 | 0.6485 | 0.3771 | 0.6485 | 0.8053 | | 0.049 | 8.5761 | 2084 | 0.6435 | 0.3771 | 0.6435 | 0.8022 | | 0.049 | 8.5844 | 2086 | 0.6410 | 0.3771 | 0.6410 | 0.8006 | | 0.049 | 8.5926 | 2088 | 0.6415 | 0.3771 | 0.6415 | 0.8009 | | 0.049 | 8.6008 | 2090 | 0.6361 | 0.3771 | 0.6361 | 0.7976 | | 0.049 | 8.6091 | 2092 | 0.6311 | 0.3724 | 0.6311 | 0.7944 | | 0.049 | 8.6173 | 2094 | 0.6297 | 0.3724 | 0.6297 | 0.7935 | | 0.049 | 8.6255 | 2096 | 0.6302 | 0.3724 | 0.6302 | 0.7938 | | 0.049 | 8.6337 | 2098 | 0.6291 | 0.3724 | 0.6291 | 0.7931 | | 0.049 | 8.6420 | 2100 | 0.6286 | 0.3771 | 0.6286 | 0.7928 | | 0.049 | 8.6502 | 2102 | 0.6291 | 0.3771 | 0.6291 | 0.7932 | | 0.049 | 8.6584 | 2104 | 0.6278 | 0.3771 | 0.6278 | 0.7923 | | 0.049 | 8.6667 | 2106 | 0.6239 | 0.3771 | 0.6239 | 0.7899 | | 0.049 | 8.6749 | 2108 | 0.6212 | 0.3724 | 0.6212 | 0.7882 | | 0.049 | 8.6831 | 2110 | 0.6200 | 0.3724 | 0.6200 | 0.7874 | | 0.049 | 8.6914 | 2112 | 0.6194 | 0.3724 | 0.6194 | 0.7870 | | 0.049 | 8.6996 | 2114 | 0.6191 | 0.3724 | 0.6191 | 0.7868 | | 0.049 | 8.7078 | 2116 | 0.6220 | 0.3724 | 0.6220 | 0.7886 | | 0.049 | 8.7160 | 2118 | 0.6242 | 0.3724 | 0.6242 | 0.7901 | | 0.049 | 8.7243 | 2120 | 0.6277 | 0.3771 | 0.6277 | 0.7923 | | 0.049 | 8.7325 | 2122 | 0.6324 | 0.3771 | 0.6324 | 0.7952 | | 0.049 | 8.7407 | 2124 | 0.6402 | 0.3771 | 0.6402 | 0.8001 | | 0.049 | 8.7490 | 2126 | 0.6490 | 0.3771 | 0.6490 | 0.8056 | | 0.049 | 8.7572 | 2128 | 0.6551 | 0.3771 | 0.6551 | 0.8094 | | 0.049 | 8.7654 | 2130 | 0.6629 | 0.3811 | 0.6629 | 0.8142 | | 0.049 | 8.7737 | 2132 | 0.6697 | 0.25 | 0.6697 | 0.8184 | | 0.049 | 8.7819 | 2134 | 0.6754 | 0.25 | 0.6754 | 0.8218 | | 0.049 | 8.7901 | 2136 | 0.6786 | 0.25 | 0.6786 | 0.8237 | | 0.049 | 8.7984 | 2138 | 0.6735 | 0.25 | 0.6735 | 0.8207 | | 0.049 | 8.8066 | 2140 | 0.6687 | 0.3811 | 0.6687 | 0.8178 | | 0.049 | 8.8148 | 2142 | 0.6623 | 0.3811 | 0.6623 | 0.8138 | | 0.049 | 8.8230 | 2144 | 0.6538 | 0.3771 | 0.6538 | 0.8086 | | 0.049 | 8.8313 | 2146 | 0.6508 | 0.3771 | 0.6508 | 0.8067 | | 0.049 | 8.8395 | 2148 | 0.6494 | 0.3771 | 0.6494 | 0.8059 | | 0.049 | 8.8477 | 2150 | 0.6483 | 0.3771 | 0.6483 | 0.8052 | | 0.049 | 8.8560 | 2152 | 0.6490 | 0.3771 | 0.6490 | 0.8056 | | 0.049 | 8.8642 | 2154 | 0.6531 | 0.3771 | 0.6531 | 0.8082 | | 0.049 | 8.8724 | 2156 | 0.6534 | 0.3771 | 0.6534 | 0.8084 | | 0.049 | 8.8807 | 2158 | 0.6536 | 0.3771 | 0.6536 | 0.8085 | | 0.049 | 8.8889 | 2160 | 0.6551 | 0.3771 | 0.6551 | 0.8094 | | 0.049 | 8.8971 | 2162 | 0.6585 | 0.3771 | 0.6585 | 0.8115 | | 0.049 | 8.9053 | 2164 | 0.6636 | 0.3771 | 0.6636 | 0.8146 | | 0.049 | 8.9136 | 2166 | 0.6703 | 0.3811 | 0.6703 | 0.8187 | | 0.049 | 8.9218 | 2168 | 0.6764 | 0.25 | 0.6764 | 0.8224 | | 0.049 | 8.9300 | 2170 | 0.6813 | 0.25 | 0.6813 | 0.8254 | | 0.049 | 8.9383 | 2172 | 0.6770 | 0.25 | 0.6770 | 0.8228 | | 0.049 | 8.9465 | 2174 | 0.6734 | 0.25 | 0.6734 | 0.8206 | | 0.049 | 8.9547 | 2176 | 0.6623 | 0.3771 | 0.6623 | 0.8138 | | 0.049 | 8.9630 | 2178 | 0.6515 | 0.3771 | 0.6515 | 0.8071 | | 0.049 | 8.9712 | 2180 | 0.6446 | 0.3771 | 0.6446 | 0.8029 | | 0.049 | 8.9794 | 2182 | 0.6384 | 0.3771 | 0.6384 | 0.7990 | | 0.049 | 8.9877 | 2184 | 0.6327 | 0.3771 | 0.6327 | 0.7954 | | 0.049 | 8.9959 | 2186 | 0.6299 | 0.3771 | 0.6299 | 0.7937 | | 0.049 | 9.0041 | 2188 | 0.6287 | 0.3771 | 0.6287 | 0.7929 | | 0.049 | 9.0123 | 2190 | 0.6276 | 0.3771 | 0.6276 | 0.7922 | | 0.049 | 9.0206 | 2192 | 0.6292 | 0.3771 | 0.6292 | 0.7932 | | 0.049 | 9.0288 | 2194 | 0.6322 | 0.3771 | 0.6322 | 0.7951 | | 0.049 | 9.0370 | 2196 | 0.6348 | 0.3771 | 0.6348 | 0.7967 | | 0.049 | 9.0453 | 2198 | 0.6341 | 0.3771 | 0.6341 | 0.7963 | | 0.049 | 9.0535 | 2200 | 0.6337 | 0.3771 | 0.6337 | 0.7960 | | 0.049 | 9.0617 | 2202 | 0.6330 | 0.3771 | 0.6330 | 0.7956 | | 0.049 | 9.0700 | 2204 | 0.6337 | 0.3771 | 0.6337 | 0.7960 | | 0.049 | 9.0782 | 2206 | 0.6353 | 0.3771 | 0.6353 | 0.7970 | | 0.049 | 9.0864 | 2208 | 0.6354 | 0.3771 | 0.6354 | 0.7971 | | 0.049 | 9.0947 | 2210 | 0.6340 | 0.3771 | 0.6340 | 0.7962 | | 0.049 | 9.1029 | 2212 | 0.6323 | 0.3771 | 0.6323 | 0.7952 | | 0.049 | 9.1111 | 2214 | 0.6307 | 0.3771 | 0.6307 | 0.7942 | | 0.049 | 9.1193 | 2216 | 0.6310 | 0.3771 | 0.6310 | 0.7944 | | 0.049 | 9.1276 | 2218 | 0.6330 | 0.3771 | 0.6330 | 0.7956 | | 0.049 | 9.1358 | 2220 | 0.6362 | 0.3771 | 0.6362 | 0.7976 | | 0.049 | 9.1440 | 2222 | 0.6371 | 0.3771 | 0.6371 | 0.7982 | | 0.049 | 9.1523 | 2224 | 0.6375 | 0.2355 | 0.6375 | 0.7984 | | 0.049 | 9.1605 | 2226 | 0.6362 | 0.2355 | 0.6362 | 0.7976 | | 0.049 | 9.1687 | 2228 | 0.6317 | 0.3771 | 0.6317 | 0.7948 | | 0.049 | 9.1770 | 2230 | 0.6271 | 0.3771 | 0.6271 | 0.7919 | | 0.049 | 9.1852 | 2232 | 0.6244 | 0.3771 | 0.6244 | 0.7902 | | 0.049 | 9.1934 | 2234 | 0.6226 | 0.3771 | 0.6226 | 0.7891 | | 0.049 | 9.2016 | 2236 | 0.6229 | 0.3771 | 0.6229 | 0.7893 | | 0.049 | 9.2099 | 2238 | 0.6242 | 0.3771 | 0.6242 | 0.7901 | | 0.049 | 9.2181 | 2240 | 0.6268 | 0.3771 | 0.6268 | 0.7917 | | 0.049 | 9.2263 | 2242 | 0.6308 | 0.2355 | 0.6308 | 0.7942 | | 0.049 | 9.2346 | 2244 | 0.6343 | 0.2355 | 0.6343 | 0.7964 | | 0.049 | 9.2428 | 2246 | 0.6362 | 0.2355 | 0.6362 | 0.7977 | | 0.049 | 9.2510 | 2248 | 0.6356 | 0.3771 | 0.6356 | 0.7973 | | 0.049 | 9.2593 | 2250 | 0.6348 | 0.3771 | 0.6348 | 0.7967 | | 0.049 | 9.2675 | 2252 | 0.6353 | 0.3771 | 0.6353 | 0.7971 | | 0.049 | 9.2757 | 2254 | 0.6365 | 0.3771 | 0.6365 | 0.7978 | | 0.049 | 9.2840 | 2256 | 0.6386 | 0.3771 | 0.6386 | 0.7991 | | 0.049 | 9.2922 | 2258 | 0.6411 | 0.3771 | 0.6411 | 0.8007 | | 0.049 | 9.3004 | 2260 | 0.6432 | 0.3771 | 0.6432 | 0.8020 | | 0.049 | 9.3086 | 2262 | 0.6466 | 0.3771 | 0.6466 | 0.8041 | | 0.049 | 9.3169 | 2264 | 0.6496 | 0.2355 | 0.6496 | 0.8060 | | 0.049 | 9.3251 | 2266 | 0.6533 | 0.2355 | 0.6533 | 0.8083 | | 0.049 | 9.3333 | 2268 | 0.6567 | 0.2355 | 0.6567 | 0.8104 | | 0.049 | 9.3416 | 2270 | 0.6590 | 0.2355 | 0.6590 | 0.8118 | | 0.049 | 9.3498 | 2272 | 0.6598 | 0.2355 | 0.6598 | 0.8123 | | 0.049 | 9.3580 | 2274 | 0.6620 | 0.2355 | 0.6620 | 0.8136 | | 0.049 | 9.3663 | 2276 | 0.6626 | 0.2355 | 0.6626 | 0.8140 | | 0.049 | 9.3745 | 2278 | 0.6644 | 0.2355 | 0.6644 | 0.8151 | | 0.049 | 9.3827 | 2280 | 0.6670 | 0.2355 | 0.6670 | 0.8167 | | 0.049 | 9.3909 | 2282 | 0.6688 | 0.2355 | 0.6688 | 0.8178 | | 0.049 | 9.3992 | 2284 | 0.6715 | 0.2355 | 0.6715 | 0.8195 | | 0.049 | 9.4074 | 2286 | 0.6731 | 0.2355 | 0.6731 | 0.8204 | | 0.049 | 9.4156 | 2288 | 0.6725 | 0.2355 | 0.6725 | 0.8200 | | 0.049 | 9.4239 | 2290 | 0.6693 | 0.2355 | 0.6693 | 0.8181 | | 0.049 | 9.4321 | 2292 | 0.6665 | 0.2355 | 0.6665 | 0.8164 | | 0.049 | 9.4403 | 2294 | 0.6625 | 0.2355 | 0.6625 | 0.8140 | | 0.049 | 9.4486 | 2296 | 0.6584 | 0.2355 | 0.6584 | 0.8114 | | 0.049 | 9.4568 | 2298 | 0.6572 | 0.2355 | 0.6572 | 0.8107 | | 0.049 | 9.4650 | 2300 | 0.6573 | 0.2355 | 0.6573 | 0.8108 | | 0.049 | 9.4733 | 2302 | 0.6573 | 0.2355 | 0.6573 | 0.8107 | | 0.049 | 9.4815 | 2304 | 0.6582 | 0.2355 | 0.6582 | 0.8113 | | 0.049 | 9.4897 | 2306 | 0.6596 | 0.2355 | 0.6596 | 0.8122 | | 0.049 | 9.4979 | 2308 | 0.6603 | 0.2355 | 0.6603 | 0.8126 | | 0.049 | 9.5062 | 2310 | 0.6614 | 0.2355 | 0.6614 | 0.8133 | | 0.049 | 9.5144 | 2312 | 0.6614 | 0.2355 | 0.6614 | 0.8133 | | 0.049 | 9.5226 | 2314 | 0.6610 | 0.2355 | 0.6610 | 0.8130 | | 0.049 | 9.5309 | 2316 | 0.6613 | 0.2355 | 0.6613 | 0.8132 | | 0.049 | 9.5391 | 2318 | 0.6608 | 0.2355 | 0.6608 | 0.8129 | | 0.049 | 9.5473 | 2320 | 0.6601 | 0.2355 | 0.6601 | 0.8124 | | 0.049 | 9.5556 | 2322 | 0.6588 | 0.2355 | 0.6588 | 0.8117 | | 0.049 | 9.5638 | 2324 | 0.6564 | 0.2355 | 0.6564 | 0.8102 | | 0.049 | 9.5720 | 2326 | 0.6540 | 0.2355 | 0.6540 | 0.8087 | | 0.049 | 9.5802 | 2328 | 0.6524 | 0.2355 | 0.6524 | 0.8077 | | 0.049 | 9.5885 | 2330 | 0.6524 | 0.2355 | 0.6524 | 0.8077 | | 0.049 | 9.5967 | 2332 | 0.6513 | 0.3771 | 0.6513 | 0.8070 | | 0.049 | 9.6049 | 2334 | 0.6504 | 0.3771 | 0.6504 | 0.8065 | | 0.049 | 9.6132 | 2336 | 0.6489 | 0.3771 | 0.6489 | 0.8055 | | 0.049 | 9.6214 | 2338 | 0.6471 | 0.3771 | 0.6471 | 0.8044 | | 0.049 | 9.6296 | 2340 | 0.6455 | 0.3771 | 0.6455 | 0.8034 | | 0.049 | 9.6379 | 2342 | 0.6439 | 0.3771 | 0.6439 | 0.8024 | | 0.049 | 9.6461 | 2344 | 0.6426 | 0.3771 | 0.6426 | 0.8016 | | 0.049 | 9.6543 | 2346 | 0.6421 | 0.3771 | 0.6421 | 0.8013 | | 0.049 | 9.6626 | 2348 | 0.6419 | 0.3396 | 0.6419 | 0.8012 | | 0.049 | 9.6708 | 2350 | 0.6421 | 0.3771 | 0.6421 | 0.8013 | | 0.049 | 9.6790 | 2352 | 0.6414 | 0.3771 | 0.6414 | 0.8009 | | 0.049 | 9.6872 | 2354 | 0.6410 | 0.3771 | 0.6410 | 0.8006 | | 0.049 | 9.6955 | 2356 | 0.6405 | 0.3771 | 0.6405 | 0.8003 | | 0.049 | 9.7037 | 2358 | 0.6402 | 0.3771 | 0.6402 | 0.8001 | | 0.049 | 9.7119 | 2360 | 0.6400 | 0.3771 | 0.6400 | 0.8000 | | 0.049 | 9.7202 | 2362 | 0.6402 | 0.3771 | 0.6402 | 0.8001 | | 0.049 | 9.7284 | 2364 | 0.6404 | 0.3771 | 0.6404 | 0.8003 | | 0.049 | 9.7366 | 2366 | 0.6406 | 0.3771 | 0.6406 | 0.8004 | | 0.049 | 9.7449 | 2368 | 0.6408 | 0.3771 | 0.6408 | 0.8005 | | 0.049 | 9.7531 | 2370 | 0.6414 | 0.3771 | 0.6414 | 0.8008 | | 0.049 | 9.7613 | 2372 | 0.6420 | 0.3771 | 0.6420 | 0.8013 | | 0.049 | 9.7695 | 2374 | 0.6425 | 0.3771 | 0.6425 | 0.8016 | | 0.049 | 9.7778 | 2376 | 0.6432 | 0.3771 | 0.6432 | 0.8020 | | 0.049 | 9.7860 | 2378 | 0.6438 | 0.3771 | 0.6438 | 0.8024 | | 0.049 | 9.7942 | 2380 | 0.6444 | 0.3771 | 0.6444 | 0.8027 | | 0.049 | 9.8025 | 2382 | 0.6450 | 0.3771 | 0.6450 | 0.8031 | | 0.049 | 9.8107 | 2384 | 0.6448 | 0.3771 | 0.6448 | 0.8030 | | 0.049 | 9.8189 | 2386 | 0.6444 | 0.3771 | 0.6444 | 0.8027 | | 0.049 | 9.8272 | 2388 | 0.6439 | 0.3771 | 0.6439 | 0.8025 | | 0.049 | 9.8354 | 2390 | 0.6433 | 0.3771 | 0.6433 | 0.8021 | | 0.049 | 9.8436 | 2392 | 0.6433 | 0.3771 | 0.6433 | 0.8020 | | 0.049 | 9.8519 | 2394 | 0.6430 | 0.3771 | 0.6430 | 0.8019 | | 0.049 | 9.8601 | 2396 | 0.6428 | 0.3771 | 0.6428 | 0.8018 | | 0.049 | 9.8683 | 2398 | 0.6428 | 0.3771 | 0.6428 | 0.8018 | | 0.049 | 9.8765 | 2400 | 0.6432 | 0.3771 | 0.6432 | 0.8020 | | 0.049 | 9.8848 | 2402 | 0.6432 | 0.3771 | 0.6432 | 0.8020 | | 0.049 | 9.8930 | 2404 | 0.6433 | 0.3771 | 0.6433 | 0.8020 | | 0.049 | 9.9012 | 2406 | 0.6436 | 0.3771 | 0.6436 | 0.8023 | | 0.049 | 9.9095 | 2408 | 0.6437 | 0.3771 | 0.6437 | 0.8023 | | 0.049 | 9.9177 | 2410 | 0.6438 | 0.3771 | 0.6438 | 0.8023 | | 0.049 | 9.9259 | 2412 | 0.6436 | 0.3771 | 0.6436 | 0.8023 | | 0.049 | 9.9342 | 2414 | 0.6437 | 0.3771 | 0.6437 | 0.8023 | | 0.049 | 9.9424 | 2416 | 0.6437 | 0.3771 | 0.6437 | 0.8023 | | 0.049 | 9.9506 | 2418 | 0.6439 | 0.3771 | 0.6439 | 0.8024 | | 0.049 | 9.9588 | 2420 | 0.6439 | 0.3771 | 0.6439 | 0.8024 | | 0.049 | 9.9671 | 2422 | 0.6439 | 0.3771 | 0.6439 | 0.8024 | | 0.049 | 9.9753 | 2424 | 0.6438 | 0.3771 | 0.6438 | 0.8024 | | 0.049 | 9.9835 | 2426 | 0.6438 | 0.3771 | 0.6438 | 0.8023 | | 0.049 | 9.9918 | 2428 | 0.6437 | 0.3771 | 0.6437 | 0.8023 | | 0.049 | 10.0 | 2430 | 0.6437 | 0.3771 | 0.6437 | 0.8023 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.0+cu118 - Datasets 2.21.0 - Tokenizers 0.19.1

DoeyLLM/OneLLM-Doey-ChatQA-V1-Llama-3.2-1B

DoeyLLM

2024-11-25T07:17:32Z

89

2

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:nvidia/ChatQA-Training-Data", "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T05:02:44Z

--- license: apache-2.0 datasets: - nvidia/ChatQA-Training-Data language: - en base_model: - meta-llama/Llama-3.2-1B-Instruct pipeline_tag: text-generation library_name: transformers --- ## **Model Summary** This model is a fine-tuned version of **LLaMA 3.2-1B**, optimized using **LoRA (Low-Rank Adaptation)** on the [NVIDIA ChatQA-Training-Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data). It is tailored for conversational AI, question answering, and other instruction-following tasks, with support for sequences up to 1024 tokens. --- ## **Key Features** - **Base Model**: LLaMA 3.2-1B - **Fine-Tuning Framework**: LoRA - **Dataset**: NVIDIA ChatQA-Training-Data - **Max Sequence Length**: 1024 tokens - **Use Case**: Instruction-based tasks, question answering, conversational AI. ## **Model Usage** This fine-tuned model is suitable for: - **Conversational AI**: Chatbots and dialogue agents with improved contextual understanding. - **Question Answering**: Generating concise and accurate answers to user queries. - **Instruction Following**: Responding to structured prompts. - **Long-Context Tasks**: Processing sequences up to 1024 tokens for long-text reasoning. # **How to Use DoeyLLM / OneLLM-Doey-V1-Llama-3.2-1B-Instruct** This guide explains how to use the **DoeyLLM** model on both app (iOS) and PC platforms. --- ## **App: Use with OneLLM** OneLLM brings versatile large language models (LLMs) to your device—Llama, Gemma, Qwen, Mistral, and more. Enjoy private, offline GPT and AI tools tailored to your needs. With OneLLM, experience the capabilities of leading-edge language models directly on your device, all without an internet connection. Get fast, reliable, and intelligent responses, while keeping your data secure with local processing. ### **Quick Start for mobile** ![OneLLM](./OneLLM.png) Follow these steps to integrate the **DoeyLLM** model using the OneLLM app: 1. **Download OneLLM** Get the app from the [App Store](https://apps.apple.com/us/app/onellm-private-ai-gpt-llm/id6737907910) and install it on your iOS device. Or get the app from the [Play Store](https://play.google.com/store/apps/details?id=com.esotech.onellm) and install it on your Android device. 3. **Load the DoeyLLM Model** Use the OneLLM interface to load the DoeyLLM model directly into the app: - Navigate to the **Model Library**. - Search for `DoeyLLM`. - Select the model and tap **Download** to store it locally on your device. 4. **Start Conversing** Once the model is loaded, you can begin interacting with it through the app's chat interface. For example: - Tap the **Chat** tab. - Type your question or prompt, such as: > "Explain the significance of AI in education." - Receive real-time, intelligent responses generated locally. ### **Key Features of OneLLM** - **Versatile Models**: Supports various LLMs, including Llama, Gemma, and Qwen. - **Private & Secure**: All processing occurs locally on your device, ensuring data privacy. - **Offline Capability**: Use the app without requiring an internet connection. - **Fast Performance**: Optimized for mobile devices, delivering low-latency responses. For more details or support, visit the [OneLLM App Store page](https://apps.apple.com/us/app/onellm-private-ai-gpt-llm/id6737907910) and [Play Store](https://play.google.com/store/apps/details?id=com.esotech.onellm). ## **PC: Use with Transformers** The DoeyLLM model can also be used on PC platforms through the `transformers` library, enabling robust and scalable inference for various NLP tasks. ### **Quick Start for PC** Follow these steps to use the model with Transformers: 1. **Install Transformers** Ensure you have `transformers >= 4.43.0` installed. Update or install it via pip: ```bash pip install --upgrade transformers 2. **Load the Model** Use the transformers library to load the model and tokenizer: Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. Make sure to update your transformers installation via `pip install --upgrade transformers`. ```python import torch from transformers import pipeline model_id = "OneLLM-Doey-V1-Llama-3.2-1B" pipe = pipeline( "text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] outputs = pipe( messages, max_new_tokens=256, ) print(outputs[0]["generated_text"][-1]) ``` ## Responsibility & Safety As part of our responsible release strategy, we adopted a three-pronged approach to managing trust and safety risks: Enable developers to deploy helpful, safe, and flexible experiences for their target audience and the use cases supported by the model. Protect developers from adversarial users attempting to exploit the model’s capabilities to potentially cause harm. Provide safeguards for the community to help prevent the misuse of the model.

tl81092/my-drug-model

tl81092

2024-11-25T07:17:11Z

105

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-11-25T07:16:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

JosephNguyen/Qwen2.5-7B-Instruct-finetuned

JosephNguyen

2024-11-25T07:12:02Z

83

1

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/Qwen2.5-7B-Instruct", "base_model:finetune:unsloth/Qwen2.5-7B-Instruct", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-11-25T06:36:15Z

--- base_model: unsloth/Qwen2.5-7B-Instruct tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** JosephNguyen - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

xuYiLi/vit-base-patch16-224-finetuned-flower

xuYiLi

2024-11-25T07:09:06Z

5

0

null

[ "tensorboard", "safetensors", "vit", "image-classification", "pytorch", "huggingpics", "model-index", "region:us" ]

image-classification

2024-11-25T07:08:50Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: vit-base-patch16-224-finetuned-flower results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.907216489315033 --- # vit-base-patch16-224-finetuned-flower Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### crape myrtle ![crape myrtle](images/crape_myrtle.jpg) #### iris ![iris](images/iris.jpg) #### narcissus ![narcissus](images/narcissus.jpg) #### osmanthus ![osmanthus](images/osmanthus.jpg) #### peony ![peony](images/peony.jpg)

Keltezaa/zooey-deschanel

Keltezaa

2024-11-25T07:08:38Z

10

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "migrated", "celebrity", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2024-11-25T07:08:30Z

--- license: other license_name: bespoke-lora-trained-license license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=RentCivit&allowDerivatives=True&allowDifferentLicense=False tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora - migrated - celebrity base_model: black-forest-labs/FLUX.1-dev instance_prompt: Zooey Deschanel widget: - text: ' ' output: url: >- 28183142.jpeg - text: ' ' output: url: >- 28183139.jpeg - text: ' ' output: url: >- 28183138.jpeg - text: ' ' output: url: >- 28183140.jpeg - text: ' ' output: url: >- 28183141.jpeg - text: ' ' output: url: >- 28183143.jpeg - text: ' ' output: url: >- 28183144.jpeg - text: ' ' output: url: >- 28183145.jpeg - text: ' ' output: url: >- 28183146.jpeg - text: ' ' output: url: >- 28183147.jpeg - text: ' ' output: url: >- 28183148.jpeg - text: ' ' output: url: >- 28183150.jpeg - text: 'portrait of Zooey Deschanel shot on a Hasselblad H3D-39. she is wearing a light summer dress at the beach in Italy' output: url: >- 28184076.jpeg --- # Zooey Deschanel <Gallery /> ## Model description <p>Trained on 15 Images for 2500 Steps</p> ## Trigger words You should use `Zooey Deschanel` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/Keltezaa/zooey-deschanel/tree/main) them in the Files & versions tab. ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch device = "cuda" if torch.cuda.is_available() else "cpu" pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to(device) pipeline.load_lora_weights('Keltezaa/zooey-deschanel', weight_name='Zooey_Deschanel_v1.safetensors') image = pipeline('portrait of Zooey Deschanel shot on a Hasselblad H3D-39. she is wearing a light summer dress at the beach in Italy').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

ahmedheakl/asm2asm-qwen2.5coder-0.5b-500k-2ep-tokenizer

ahmedheakl

2024-11-25T07:07:36Z

264

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "trl", "sft", "conversational", "base_model:Qwen/Qwen2.5-Coder-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-Coder-0.5B-Instruct", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-11-23T15:01:30Z

--- base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct library_name: transformers model_name: asm2asm-qwen2.5coder-0.5b-500k-2ep-tokenizer tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for asm2asm-qwen2.5coder-0.5b-500k-2ep-tokenizer This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="ahmedheakl/asm2asm-qwen2.5coder-0.5b-500k-2ep-tokenizer", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/ahmed-heakl/huggingface/runs/eppp58b2) This model was trained with SFT. ### Framework versions - TRL: 0.12.1 - Transformers: 4.46.3 - Pytorch: 2.5.1+cu124 - Datasets: 3.1.0 - Tokenizers: 0.20.3 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

chy2207/robot_care_8b_ver4

chy2207

2024-11-25T07:06:28Z

9

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "base_model:quantized:unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]

null

2024-11-25T06:50:34Z

--- base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** chy2207 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)