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fblgit/UNAversal-8x7B-v1beta
fblgit
2024-03-08T10:28:21Z
1,492
8
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "UNA", "juanako", "MoE", "conversational", "en", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-26T15:58:15Z
--- language: - en license: cc-by-nc-sa-4.0 library_name: transformers tags: - UNA - juanako - mixtral - MoE model-index: - name: UNAversal-8x7B-v1beta results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 69.8 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNAversal-8x7B-v1beta name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 86.9 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNAversal-8x7B-v1beta name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 70.39 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNAversal-8x7B-v1beta name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 71.97 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNAversal-8x7B-v1beta name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNAversal-8x7B-v1beta name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 61.64 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNAversal-8x7B-v1beta name: Open LLM Leaderboard --- # UNAversal - Uniform Neural Alignment (MoE) This is just a beta, a first release so people can start working on franksteins and so. It does achieve high GSM/Math and TQA, so ideally you can merge it with other mixtrals and see what coming out of it Based on [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) ## UNA Details For this model we came out with the most obvious, placing UNA on the router_logit. It does work, but we saw a much better performance on SFT by doing so. So this model DOES have UNA-SFT phase, its highly experimental and it was merely using LLaMA-Factory datasets by example alpaca. As the others: - Can be finetuned further, try 2e-5 or **1e-4 (since its MOE)** - Can be merged, here you will have to improvise and please report findings on a discussion thread. **REMINDER**: please.. cite, it does help on the research and the lab itself, seriously. ## NEED YOUR HELP!! I need a multi-turn trainloop for the Mixtral, that can squeeze the juice out of 8xH100's properly. Please feel free to reach @fblgit either discord or twitter. thanks! # Evals Here there are some, but we also submitted it to the HF eval queue.... ## GSM8k 5-Shot ``` |Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |-----|-------|----------|-----:|-----------|-----:|---|-----:| |gsm8k|Yaml |get-answer| 5|exact_match|0.6603|± | 0.013| ``` ## ARC 25-Shot ``` | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |-------------|-------|------|-----:|--------|-----:|---|-----:| |arc_challenge|Yaml |none | 25|acc |0.6621|± |0.0138| | | |none | 25|acc_norm|0.6962|± |0.0134| ``` ## TruthfulQA 0-Shot (MC2) ``` | Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |--------------|-------|------|-----:|------|-----:|---|-----:| |truthfulqa_mc2|Yaml |none | 0|acc |0.7122|± |0.0141| ``` ## 0-Shots Evals ``` | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |--------------|-------|------|-----:|----------|-----:|---|-----:| |arc_challenge |Yaml |none | 0|acc |0.6101|± |0.0143| | | |none | 0|acc_norm |0.6425|± |0.0140| |arc_easy |Yaml |none | 0|acc |0.8615|± |0.0071| | | |none | 0|acc_norm |0.8375|± |0.0076| |boolq |Yaml |none | 0|acc |0.8624|± |0.0060| |lambada_openai|Yaml |none | 0|perplexity|2.8318|± |0.0507| | | |none | 0|acc |0.7650|± |0.0059| |mathqa |Yaml |none | 0|acc |0.4472|± |0.0091| | | |none | 0|acc_norm |0.4436|± |0.0091| |piqa |Yaml |none | 0|acc |0.8292|± |0.0088| | | |none | 0|acc_norm |0.8422|± |0.0085| |pubmedqa |Yaml |none | 0|acc |0.7920|± |0.0182| |sciq |Yaml |none | 0|acc |0.9630|± |0.0060| | | |none | 0|acc_norm |0.9370|± |0.0077| ``` ## BBH at 0-Shot ``` vllm (pretrained=fblgit/UNAversal-8x7B-v1beta,tensor_parallel_size=2,data_parallel_size=4,gpu_memory_utilization=0.8,dtype=float16), gen_kwargs: (None), limit: None, num_fewshot: 0, batch_size: auto | Tasks |Version| Filter |n-shot| Metric |Value | |Stderr| |----------------------------------------------------------|-------|----------|-----:|-----------|-----:|---|-----:| |bbh |N/A |get-answer| 0|exact_match|0.6752|± |0.1772| | - bbh_cot_fewshot_boolean_expressions |Yaml |get-answer| 0|exact_match|0.8840|± |0.0203| | - bbh_cot_fewshot_causal_judgement |Yaml |get-answer| 0|exact_match|0.6417|± |0.0352| | - bbh_cot_fewshot_date_understanding |Yaml |get-answer| 0|exact_match|0.7600|± |0.0271| | - bbh_cot_fewshot_disambiguation_qa |Yaml |get-answer| 0|exact_match|0.7160|± |0.0286| | - bbh_cot_fewshot_dyck_languages |Yaml |get-answer| 0|exact_match|0.1800|± |0.0243| | - bbh_cot_fewshot_formal_fallacies |Yaml |get-answer| 0|exact_match|0.6520|± |0.0302| | - bbh_cot_fewshot_geometric_shapes |Yaml |get-answer| 0|exact_match|0.3880|± |0.0309| | - bbh_cot_fewshot_hyperbaton |Yaml |get-answer| 0|exact_match|0.9600|± |0.0124| | - bbh_cot_fewshot_logical_deduction_five_objects |Yaml |get-answer| 0|exact_match|0.5360|± |0.0316| | - bbh_cot_fewshot_logical_deduction_seven_objects |Yaml |get-answer| 0|exact_match|0.5040|± |0.0317| | - bbh_cot_fewshot_logical_deduction_three_objects |Yaml |get-answer| 0|exact_match|0.8600|± |0.0220| | - bbh_cot_fewshot_movie_recommendation |Yaml |get-answer| 0|exact_match|0.7840|± |0.0261| | - bbh_cot_fewshot_multistep_arithmetic_two |Yaml |get-answer| 0|exact_match|0.6600|± |0.0300| | - bbh_cot_fewshot_navigate |Yaml |get-answer| 0|exact_match|0.8160|± |0.0246| | - bbh_cot_fewshot_object_counting |Yaml |get-answer| 0|exact_match|0.8360|± |0.0235| | - bbh_cot_fewshot_penguins_in_a_table |Yaml |get-answer| 0|exact_match|0.7329|± |0.0367| | - bbh_cot_fewshot_reasoning_about_colored_objects |Yaml |get-answer| 0|exact_match|0.8120|± |0.0248| | - bbh_cot_fewshot_ruin_names |Yaml |get-answer| 0|exact_match|0.4440|± |0.0315| | - bbh_cot_fewshot_salient_translation_error_detection |Yaml |get-answer| 0|exact_match|0.5200|± |0.0317| | - bbh_cot_fewshot_snarks |Yaml |get-answer| 0|exact_match|0.7135|± |0.0340| | - bbh_cot_fewshot_sports_understanding |Yaml |get-answer| 0|exact_match|0.9400|± |0.0151| | - bbh_cot_fewshot_temporal_sequences |Yaml |get-answer| 0|exact_match|0.7560|± |0.0272| | - bbh_cot_fewshot_tracking_shuffled_objects_five_objects |Yaml |get-answer| 0|exact_match|0.5680|± |0.0314| | - bbh_cot_fewshot_tracking_shuffled_objects_seven_objects|Yaml |get-answer| 0|exact_match|0.6280|± |0.0306| | - bbh_cot_fewshot_tracking_shuffled_objects_three_objects|Yaml |get-answer| 0|exact_match|0.6280|± |0.0306| | - bbh_cot_fewshot_web_of_lies |Yaml |get-answer| 0|exact_match|0.9560|± |0.0130| | - bbh_cot_fewshot_word_sorting |Yaml |get-answer| 0|exact_match|0.3800|± |0.0308| |Groups|Version| Filter |n-shot| Metric |Value | |Stderr| |------|-------|----------|-----:|-----------|-----:|---|-----:| |bbh |N/A |get-answer| 0|exact_match|0.6752|± |0.1772| ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNAversal-8x7B-v1beta) | Metric |Value| |---------------------------------|----:| |Avg. |73.78| |AI2 Reasoning Challenge (25-Shot)|69.80| |HellaSwag (10-Shot) |86.90| |MMLU (5-Shot) |70.39| |TruthfulQA (0-shot) |71.97| |Winogrande (5-shot) |82.00| |GSM8k (5-shot) |61.64|
Rijgersberg/Mistral-7B-v0.1-chat-nl
Rijgersberg
2024-03-08T10:26:32Z
35
3
transformers
[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "GEITje", "conversational", "nl", "dataset:Rijgersberg/no_robots_nl", "dataset:Rijgersberg/ultrachat_10k_nl", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-12T06:26:30Z
--- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - generated_from_trainer - GEITje - conversational model-index: - name: Mistral-7B-v0.1-chat-nl results: [] datasets: - Rijgersberg/no_robots_nl - Rijgersberg/ultrachat_10k_nl language: - nl pipeline_tag: text-generation --- <!-- 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. --> # Mistral-7B-v0.1-chat-nl This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the Rijgersberg/no_robots_nl and Rijgersberg/ultrachat_10k_nl datasets. It achieves the following results on the evaluation set: - Loss: 1.0263 ## Model description In order to investigate the effect of pretraining [Rijgersberg/GEITje-7B](https://huggingface.co/Rijgersberg/GEITje-7B-chat) on the finetuning of [Rijgersberg/GEITje-7B-chat](https://huggingface.co/Rijgersberg/GEITje-7B-chat), I also subjected the base model Mistral 7B v0.1 to the exact same training. This model is called Mistral-7B-v0.1-chat-nl. ## More info Read more about GEITje and GEITje-chat in the [📄 README](https://github.com/Rijgersberg/GEITje/blob/main/README-en.md) on GitHub. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.2404 | 0.2 | 236 | 1.1166 | | 1.2103 | 0.4 | 472 | 1.1101 | | 1.0357 | 0.6 | 708 | 1.0739 | | 1.27 | 0.8 | 944 | 1.0540 | | 1.3557 | 1.0 | 1180 | 1.0330 | | 0.7919 | 1.2 | 1416 | 1.0368 | | 0.8701 | 1.4 | 1652 | 1.0193 | | 0.8851 | 1.6 | 1888 | 1.0009 | | 0.7562 | 1.8 | 2124 | 0.9791 | | 0.6838 | 2.0 | 2360 | 0.9823 | | 0.5011 | 2.2 | 2596 | 1.0271 | | 0.4495 | 2.39 | 2832 | 1.0267 | | 0.5625 | 2.59 | 3068 | 1.0250 | | 0.4486 | 2.79 | 3304 | 1.0262 | | 0.5706 | 2.99 | 3540 | 1.0263 | ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0
fblgit/una-cybertron-7b-v2-bf16
fblgit
2024-03-08T10:26:27Z
1,715
116
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "juanako", "UNA", "cybertron", "fbl", "dataset:fblgit/tree-of-knowledge", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:allenai/ultrafeedback_binarized_cleaned", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-02T00:07:53Z
--- license: apache-2.0 library_name: transformers tags: - juanako - UNA - cybertron - fbl datasets: - fblgit/tree-of-knowledge - Open-Orca/SlimOrca-Dedup - allenai/ultrafeedback_binarized_cleaned model-index: - name: una-cybertron-7b-v2-bf16 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.26 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.85 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 64.63 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 80.98 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 55.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 name: Open LLM Leaderboard --- # Model Card for una-cybertron-7b-v2-bf16 (UNA: Uniform Neural Alignment) We strike back, introducing **Cybertron 7B v2** a 7B MistralAI based model, best on it's series. Trained on SFT, DPO and UNA (Unified Neural Alignment) on multiple datasets. He scores [EXACTLY](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v2-bf16) **#1** with **69.67**+ score on HF LeaderBoard board, **#8** ALL SIZES top score. * v1 Scoring **#1** at 2 December 2023 with 69.43 ..few models were releasse .. but only 1 can survive: CYBERTRON! * v2 Scoring **#1** at 5 December 2023 with 69.67 | Model | Average | ARC (25-s) | HellaSwag (10-s) | MMLU (5-s) | TruthfulQA (MC) (0-s) | Winogrande (5-s) | GSM8K (5-s) | | --- | --- | --- | --- | --- | --- | --- | --- | | [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 60.97 | 59.98 | 83.31 | 64.16 | 42.15 | 78.37 | 37.83 | | [Intel/neural-chat-7b-v3-2](https://huggingface.co/Intel/neural-chat-7b-v3-2) | 68.29 | 67.49 | 83.92 | 63.55 | 59.68 | 79.95 | 55.12 | | [perlthoughts/Chupacabra-7B-v2](https://huggingface.co/perlthoughts/Chupacabra-7B-v2) | 63.54 | 66.47 | 85.17 | 64.49 | 57.6 | 79.16 | 28.35 | | [fblgit/una-cybertron-7b-v1-fp16](https://huggingface.co/fblgit/una-cybertron-7b-v1-fp16) | **69.49** | **68.43** | **85.85** | 63.34 | **63.28** | **80.90** | **55.12** | | [fblgit/una-cybertron-7b-v2-bf16](https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16) | **69.67** | **68.26** | **85.?4** | 63.23 | **64.63** | **81.37** | **55.04** | The model excels in mathematics, logic, reasoning, overall very smart. He can make a deep reasoning over the context and prompt, it gives the impression of not missing details around. ## Model Details Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon). * What is **NOT** UNA? Its not a merged layers model. Is not SLERP or SLURP or similar. * What **is** UNA? A formula & A technique to *TAME* models * When will be released the code and paper? When have time, contribute and it'll be faster. ### Model Description - **Developed by:** [juanako.ai](https://juanako.ai) - **Author:** [Xavier M.]([email protected]) - **Investors** [CONTACT HERE]([email protected]) - **Model type:** MistralAI 7B - **Funded by Cybertron's H100's** with few hours training. ### Prompt The model is very good, works well on almost any prompt but ChatML format and Alpaca System gets the best ``` <|im_start|>system - You are a helpful assistant chatbot trained by MosaicML. - You answer questions. - You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user. - You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|> <|im_start|>user Explain QKV<|im_end|> <|im_start|>assistant ``` ``` ### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat! ### Human: Explain QKV ### Assistant: ``` ``` [Round <|round|>] 问：Explain QKV 答： ``` ``` [Round <|round|>] Question：Explain QKV Answer： ``` ``` Question：Explain QKV Answer： ``` Using Exllamav2_HF set alpha=2.5 for 16K Context **Users also report that exllamav2_HF loader, 8bpw-h8 exl2 quant, simple-1 preset provides good results** ### Framework versions - Transformers 4.35.0-UNA - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1 ### Citations If you find Cybertron, Juanako or any of our models useful, specially if you use it for your big brand.. or you clone/merge my modelsm, cite please: ``` @misc{unacybertron7b, title={Cybertron: Uniform Neural Alignment}, author={Xavier Murias}, year={2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16}}, } ``` Special thanks to @TheBloke & @bartowski for converting the models and their support to the community. Thank you! # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v2-bf16) | Metric |Value| |---------------------------------|----:| |Avg. |69.67| |AI2 Reasoning Challenge (25-Shot)|68.26| |HellaSwag (10-Shot) |85.85| |MMLU (5-Shot) |63.23| |TruthfulQA (0-shot) |64.63| |Winogrande (5-shot) |80.98| |GSM8k (5-shot) |55.04|
fblgit/UNA-POLAR-10.7B-InstructMath-v2
fblgit
2024-03-08T10:26:14Z
1,527
5
transformers
[ "transformers", "safetensors", "llama", "text-generation", "UNA", "SOLAR", "MathPILE", "conversational", "en", "dataset:GAIR/MathPile", "license:cc-by-nc-nd-4.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-01-02T10:13:16Z
--- language: - en license: cc-by-nc-nd-4.0 tags: - UNA - SOLAR - MathPILE datasets: - GAIR/MathPile model-index: - name: UNA-POLAR-10.7B-InstructMath-v2 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 70.73 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.2 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 66.03 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 71.73 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 64.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard --- # UNA-POLAR-10.7B-InstructMath-v2 ## Model description Its a UNA version with DPO over MathPILE Books out of the UNA-SOLAR-10.7B-Instruct-1.0 I used MathPILE OUTSTANDING Dataset of great Mathematic material in order to produce this beautiful model :) ## Intended uses & limitations If your model has inside UNA technology, cite. ## Training and evaluation data UNA-DPO over Attention and MLP's ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2-UNA - Pytorch 2.1.2+cu121 - Datasets 2.16.0 - Tokenizers 0.15. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-POLAR-10.7B-InstructMath-v2) | Metric |Value| |---------------------------------|----:| |Avg. |74.07| |AI2 Reasoning Challenge (25-Shot)|70.73| |HellaSwag (10-Shot) |88.20| |MMLU (5-Shot) |66.03| |TruthfulQA (0-shot) |71.73| |Winogrande (5-shot) |82.95| |GSM8k (5-shot) |64.75|
fblgit/LUNA-SOLARkrautLM-Instruct
fblgit
2024-03-08T10:25:49Z
1,529
8
transformers
[ "transformers", "safetensors", "llama", "text-generation", "finetune", "dpo", "Instruct", "augmentation", "german", "conversational", "en", "de", "dataset:argilla/distilabel-math-preference-dpo", "doi:10.57967/hf/1517", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-22T13:12:53Z
--- language: - en - de license: cc-by-nc-4.0 library_name: transformers tags: - finetune - dpo - Instruct - augmentation - german datasets: - argilla/distilabel-math-preference-dpo pipeline_tag: text-generation model-index: - name: LUNA-SOLARkrautLM-Instruct results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 71.16 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/LUNA-SOLARkrautLM-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.28 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/LUNA-SOLARkrautLM-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 66.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/LUNA-SOLARkrautLM-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 73.37 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/LUNA-SOLARkrautLM-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/LUNA-SOLARkrautLM-Instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 60.88 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/LUNA-SOLARkrautLM-Instruct name: Open LLM Leaderboard --- ![Juanako.AI & SauerkrautLM Productions](https://vago-solutions.de/wp-content/uploads/2023/12/sauerkrautlm-solar.png "LUNA-SOLARkrautLM-Instruct") ## VAGO solutions LUNA-SOLARkrautLM-Instruct Introducing **LUNA-SOLARkrautLM-Instruct** – a UNA-Sauerkraut version of the powerful [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) ! Aligned with **DPO** and tamed with **UNA**. # Table of Contents 1. [Overview of all LUNA-SOLARkrautLM-Instruct models](#all-sauerkrautlm-solar-instruct-models) 2. [Model Details](#model-details) - [Prompt template](#prompt-template) - [Training Dataset](#training-dataset) - [Data Contamination Test](#data-contamination-test-results) 3. [Evaluation](#evaluation) 5. [Disclaimer](#disclaimer) 6. [Contact](#contact) 7. [Collaborations](#collaborations) 8. [Acknowledgement](#acknowledgement) ## Model Details **LUNA-SOLARkrautLM-Instruct** - **Model Type:** LUNA-SOLARkrautLM-Instruct is a UNA Model based on [fblgit/UNA-SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/fblgit/UNA-SOLAR-10.7B-Instruct-v1.0) and the powerful set of [SauerkrautLM-SOLAR-Instruct](https://huggingface.co/VAGOsolutions/SauerkrautLM-SOLAR-Instruct/) - **Language(s):** English, German - **License:** cc-by-nc-4.0 - **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:[email protected]) [Juanako.AI - UNA](mailto:[email protected]) ### Training Dataset: LUNA-SOLARkrautLM-Instruct was trained with mix of German data augmentation and translated data. Aligned through **DPO** with our **new German SauerkrautLM-DPO dataset** based on parts of the SFT SauerkrautLM dataset as chosen answers and [Sauerkraut-7b-HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) as rejected answers. Added with additional **translated Parts of the [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)** (Our dataset do not contain any TruthfulQA prompts - check Data Contamination Test Results) and **[argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo).** We found, that only a simple translation of training data can lead to unnatural German phrasings. Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data. We improved the German language skills on this model. Nevertheless, certain formulations may occur that are not entirely correct. ### Data Contamination Test Results Some models on the HuggingFace leaderboard had problems with wrong data getting mixed in. We checked our SauerkrautLM-DPO dataset with a special test [1] on this model as target model and upstage/SOLAR-10.7B-Instruct-v1.0 as reference model. The HuggingFace team used the same methods [2, 3]. Our results, with `result < 0.1, %:` being well below 0.9, indicate that our dataset is free from contamination. *The data contamination test results of HellaSwag and Winograde will be added once [1] supports them.* | Dataset | ARC | MMLU | TruthfulQA | GSM8K | |------------------------------|-------|-------|-------|-------| | **SauerkrautLM-DPO**| result < 0.1, %: 0.0 |result < 0.1, %: 0.09 | result < 0.1, %: 0.13 | result < 0.1, %: 0.16 | [1] https://github.com/swj0419/detect-pretrain-code-contamination [2] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/474#657f2245365456e362412a06 [3] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/265#657b6debf81f6b44b8966230 ### Prompt Template: ``` <|im_start|>system Du bist LUNA-SOLARkrautLM, ein großes Sprachmodell, das höflich und kompetent antwortet.<|im_end|> <|im_start|>user Wie geht es dir?<|im_end|> <|im_start|>assistant ``` ``` ### User: Hello, how are you? ### Assistant: Hi there! I am an AI language model, so I don't have personal feelings or emotions in the traditional sense. However, I can assure you that my systems and processes are functioning well at this moment, allowing me to provide helpful responses for your queries. How may I assist you today? ``` ## Evaluation ``` hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto |Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |-----|-------|----------|-----:|-----------|-----:|---|-----:| |gsm8k|Yaml |get-answer| 5|exact_match|0.6467|± |0.0132| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 0, batch_size: auto (64) | Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |--------------|-------|------|-----:|------|-----:|---|-----:| |truthfulqa_mc2|Yaml |none | 0|acc |0.7368|± |0.0149| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 25, batch_size: auto (32) | Tasks |Version|Filter|n-shot| Metric |Value| |Stderr| |-------------|-------|------|-----:|--------|----:|---|-----:| |arc_challenge|Yaml |none | 25|acc |0.692|± |0.0135| | | |none | 25|acc_norm|0.715|± |0.0132| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 0, batch_size: auto (64) | Tasks |Version|Filter|n-shot|Metric| Value | |Stderr| |-----------|-------|------|-----:|------|------:|---|-----:| |paws_de |Yaml |none | 0|acc | 0.3965|± |0.0109| |wmt16-en-de|Yaml |none | 0|bleu | 3.5784|± |0.1325| | | |none | 0|ter |64.5707|± |0.4514| | | |none | 0|chrf |45.7068|± |0.3861| |xnli_de |Yaml |none | 0|acc | 0.4129|± |0.0099| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 10, batch_size: auto (32) | Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |---------|-------|------|-----:|--------|-----:|---|-----:| |hellaswag|Yaml |none | 10|acc |0.7131|± |0.0045| | | |none | 10|acc_norm|0.8815|± |0.0032| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto (64) | Tasks |Version|Filter|n-shot|Metric| Value | |Stderr| |-----------|-------|------|-----:|------|------:|---|-----:| |wmt16-de-en|Yaml |none | 5|bleu |14.9310|± |0.8014| | | |none | 5|ter |46.3206|± |0.4087| | | |none | 5|chrf |60.8637|± |0.4436| |wmt16-en-de|Yaml |none | 5|bleu | 6.2016|± |0.2918| | | |none | 5|ter |63.9997|± |0.4591| | | |none | 5|chrf |51.1399|± |0.3978| |xnli_de |Yaml |none | 5|acc | 0.4703|± |0.0100| hf (pretrained=fblgit/LUNA-SOLARkrautLM-Instruct,dtype=float16), gen_kwargs: (), limit: None, num_fewshot: 5, batch_size: auto (16) | Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |---------------------------------------|-------|------|-----:|------|-----:|---|-----:| |mmlu |N/A |none | 0|acc |0.6461|± |0.1215| | - humanities |N/A |none | 5|acc |0.5960|± |0.1200| | - formal_logic |Yaml |none | 5|acc |0.4683|± |0.0446| | - high_school_european_history |Yaml |none | 5|acc |0.8121|± |0.0305| | - high_school_us_history |Yaml |none | 5|acc |0.8480|± |0.0252| | - high_school_world_history |Yaml |none | 5|acc |0.8312|± |0.0244| | - international_law |Yaml |none | 5|acc |0.7851|± |0.0375| | - jurisprudence |Yaml |none | 5|acc |0.7685|± |0.0408| | - logical_fallacies |Yaml |none | 5|acc |0.7423|± |0.0344| | - moral_disputes |Yaml |none | 5|acc |0.7283|± |0.0239| | - moral_scenarios |Yaml |none | 5|acc |0.3899|± |0.0163| | - philosophy |Yaml |none | 5|acc |0.7074|± |0.0258| | - prehistory |Yaml |none | 5|acc |0.7716|± |0.0234| | - professional_law |Yaml |none | 5|acc |0.4824|± |0.0128| | - world_religions |Yaml |none | 5|acc |0.7661|± |0.0325| | - other |N/A |none | 5|acc |0.7097|± |0.0900| | - business_ethics |Yaml |none | 5|acc |0.7700|± |0.0423| | - clinical_knowledge |Yaml |none | 5|acc |0.6792|± |0.0287| | - college_medicine |Yaml |none | 5|acc |0.6647|± |0.0360| | - global_facts |Yaml |none | 5|acc |0.3600|± |0.0482| | - human_aging |Yaml |none | 5|acc |0.6861|± |0.0311| | - management |Yaml |none | 5|acc |0.8350|± |0.0368| | - marketing |Yaml |none | 5|acc |0.8504|± |0.0234| | - medical_genetics |Yaml |none | 5|acc |0.6700|± |0.0473| | - miscellaneous |Yaml |none | 5|acc |0.7893|± |0.0146| | - nutrition |Yaml |none | 5|acc |0.7549|± |0.0246| | - professional_accounting |Yaml |none | 5|acc |0.5213|± |0.0298| | - professional_medicine |Yaml |none | 5|acc |0.7353|± |0.0268| | - virology |Yaml |none | 5|acc |0.5783|± |0.0384| | - social_sciences |N/A |none | 5|acc |0.7501|± |0.0684| | - econometrics |Yaml |none | 5|acc |0.5175|± |0.0470| | - high_school_geography |Yaml |none | 5|acc |0.8485|± |0.0255| | - high_school_government_and_politics|Yaml |none | 5|acc |0.8912|± |0.0225| | - high_school_macroeconomics |Yaml |none | 5|acc |0.6615|± |0.0240| | - high_school_microeconomics |Yaml |none | 5|acc |0.7311|± |0.0288| | - high_school_psychology |Yaml |none | 5|acc |0.8385|± |0.0158| | - human_sexuality |Yaml |none | 5|acc |0.7023|± |0.0401| | - professional_psychology |Yaml |none | 5|acc |0.6683|± |0.0190| | - public_relations |Yaml |none | 5|acc |0.6909|± |0.0443| | - security_studies |Yaml |none | 5|acc |0.7633|± |0.0272| | - sociology |Yaml |none | 5|acc |0.8358|± |0.0262| | - us_foreign_policy |Yaml |none | 5|acc |0.8800|± |0.0327| | - stem |N/A |none | 5|acc |0.5569|± |0.1360| | - abstract_algebra |Yaml |none | 5|acc |0.3800|± |0.0488| | - anatomy |Yaml |none | 5|acc |0.6148|± |0.0420| | - astronomy |Yaml |none | 5|acc |0.7237|± |0.0364| | - college_biology |Yaml |none | 5|acc |0.7708|± |0.0351| | - college_chemistry |Yaml |none | 5|acc |0.4600|± |0.0501| | - college_computer_science |Yaml |none | 5|acc |0.5400|± |0.0501| | - college_mathematics |Yaml |none | 5|acc |0.2700|± |0.0446| | - college_physics |Yaml |none | 5|acc |0.3333|± |0.0469| | - computer_security |Yaml |none | 5|acc |0.7300|± |0.0446| | - conceptual_physics |Yaml |none | 5|acc |0.6213|± |0.0317| | - electrical_engineering |Yaml |none | 5|acc |0.6276|± |0.0403| | - elementary_mathematics |Yaml |none | 5|acc |0.4788|± |0.0257| | - high_school_biology |Yaml |none | 5|acc |0.8065|± |0.0225| | - high_school_chemistry |Yaml |none | 5|acc |0.5123|± |0.0352| | - high_school_computer_science |Yaml |none | 5|acc |0.7000|± |0.0461| | - high_school_mathematics |Yaml |none | 5|acc |0.3889|± |0.0297| | - high_school_physics |Yaml |none | 5|acc |0.3576|± |0.0391| | - high_school_statistics |Yaml |none | 5|acc |0.5926|± |0.0335| | - machine_learning |Yaml |none | 5|acc |0.4554|± |0.0473| | Groups |Version|Filter|n-shot|Metric|Value | |Stderr| |------------------|-------|------|-----:|------|-----:|---|-----:| |mmlu |N/A |none | 0|acc |0.6461|± |0.1215| | - humanities |N/A |none | 5|acc |0.5960|± |0.1200| | - other |N/A |none | 5|acc |0.7097|± |0.0900| | - social_sciences|N/A |none | 5|acc |0.7501|± |0.0684| | - stem |N/A |none | 5|acc |0.5569|± |0.1360| ``` ### MT-Bench ``` ########## Average ########## score model gpt-4 8.990625 gpt-3.5-turbo 7.943750 claude-instant-v1 7.905660 claude-v1 7.900000 UNA-SOLAR-10.7B-Instruct-v1.0 7.521875 LUNA-SOLARkrautLM-Instruct 7.462500 vicuna-33b-v1.3 7.121875 wizardlm-30b 7.009375 Llama-2-70b-chat 6.856250 Llama-2-13b-chat 6.650000 guanaco-33b 6.528125 tulu-30b 6.434375 guanaco-65b 6.409375 oasst-sft-7-llama-30b 6.409375 palm-2-chat-bison-001 6.400000 mpt-30b-chat 6.393750 vicuna-13b-v1.3 6.387500 wizardlm-13b 6.353125 Llama-2-7b-chat 6.268750 vicuna-7b-v1.3 5.996875 baize-v2-13b 5.750000 nous-hermes-13b 5.553459 mpt-7b-chat 5.459119 gpt4all-13b-snoozy 5.452830 koala-13b 5.350000 mpt-30b-instruct 5.218750 falcon-40b-instruct 5.168750 h2ogpt-oasst-open-llama-13b 4.625000 alpaca-13b 4.531250 chatglm-6b 4.500000 oasst-sft-4-pythia-12b 4.318750 rwkv-4-raven-14b 3.984375 dolly-v2-12b 3.275000 fastchat-t5-3b 3.040625 stablelm-tuned-alpha-7b 2.753125 llama-13b 2.606250 ``` ## Disclaimer We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out. However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided. Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models.   ## Contact If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:[email protected]). We are also grateful for your feedback and suggestions.   ## Collaborations We are also keenly seeking support and investment for our startup, [VAGO Solutions](https://huggingface.co/VAGOsolutions), where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us. [Juanako.AI](https://huggingface.co/fblgit) is also seeking support and investment for our startup, we also are open for collaborating with other labs to make awesome models like this one. ## Acknowledgement Big Hug to [VAGO Solutions](https://huggingface.co/VAGOsolutions), we merely used our UNA transformers library on their code and dataset, nothing else. This won't be possible without them, thanks! Many thanks to [argilla](https://huggingface.co/datasets/argilla) and [Huggingface](https://huggingface.co) for providing such valuable datasets to the Open-Source community. And of course a big thanks to [upstage](https://huggingface.co/upstage) for providing the open source community with their latest technology! # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__LUNA-SOLARkrautLM-Instruct) | Metric |Value| |---------------------------------|----:| |Avg. |73.79| |AI2 Reasoning Challenge (25-Shot)|71.16| |HellaSwag (10-Shot) |88.28| |MMLU (5-Shot) |66.11| |TruthfulQA (0-shot) |73.37| |Winogrande (5-shot) |82.95| |GSM8k (5-shot) |60.88|
fblgit/una-cybertron-7b-v1-fp16
fblgit
2024-03-08T10:25:13Z
1,448
5
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "juanako", "UNA", "dataset:fblgit/tree-of-knowledge", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:HuggingFaceH4/ultrafeedback_binarized", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-01T16:29:08Z
--- license: apache-2.0 library_name: transformers tags: - juanako - UNA datasets: - fblgit/tree-of-knowledge - Open-Orca/SlimOrca-Dedup - HuggingFaceH4/ultrafeedback_binarized model-index: - name: una-cybertron-7b-v1-fp16 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 68.43 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v1-fp16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.42 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v1-fp16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 63.34 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v1-fp16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 63.28 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v1-fp16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 81.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v1-fp16 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 55.12 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v1-fp16 name: Open LLM Leaderboard --- # Model Card for una-cybertron-7b-v1 (UNA: Uniform Neural Alignment) We strike back, introducing **Cybertron 7B v1** a 7B MistralAI based model, best on it's series. Trained on SFT, DPO and UNA (Unified Neural Alignment) on multiple datasets. He scores **64.60**+ on HF LeaderTests (without DROP for now). Scoring **#1** at 2 December 2023: | Model | Average | ARC (25-s) | HellaSwag (10-s) | MMLU (5-s) | TruthfulQA (MC) (0-s) | Winogrande (5-s) | GSM8K (5-s) | | --- | --- | --- | --- | --- | --- | --- | --- | | [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 60.97 | 59.98 | 83.31 | 64.16 | 42.15 | 78.37 | 37.83 | | [perlthoughts/Chupacabra-7B-v2](https://huggingface.co/perlthoughts/Chupacabra-7B-v2) | 63.54 | 66.47 | 85.17 | 64.49 | 57.6 | 79.16 | 28.35 | | [fblgit/una-cybertron-7b-v1](https://huggingface.co/fblgit/una-cybertron-7b-v1) | **64.60** | **68.17** | 85.14 | 62.07 | **63.98** | **80.9** | 27.34 | The model excels in mathematics, logic, reasoning, overall very smart. ## Model Details Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon). ### Model Description - **Developed by:** [juanako.ai](https://juanako.ai) - **Author:** [Xavier M.]([email protected]) - **Model type:** MistralAI 7B - **Funded by Cybertron's H100's** ### Prompt The model is very good, works well on almost any prompt but ChatML format and Alpaca System gets the best ``` <|im_start|>system - You are a helpful assistant chatbot trained by MosaicML. - You answer questions. - You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user. - You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|> <|im_start|>user Explain QKV<|im_end|> <|im_start|>assistant ``` ``` ### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat! ### Human: Explain QKV ### Assistant: ``` ``` [Round <|round|>] 问：Explain QKV 答： ``` ``` [Round <|round|>] Question：Explain QKV Answer： ``` ``` Question：Explain QKV Answer： ``` ## Evaluation ``` | Tasks |Version|Shots | Metric |Value | |Stderr| |--------------|-------|------|--------|-----:|---|-----:| |arc_challenge | | 25 |acc_norm|0.6817|± |0.0136| |truthfulqa_mc2| | 0 |acc |0.6398|± |0.0151| |hellaswag | | 10 |acc_norm|0.8492|± |0.0036| |winogrande | | 0 |acc |0.809 |± |0.011 | |gsm8k | | 5 |acc |0.2733|± |0.0137| |mmlu | | 5 |acc |0.6207|± |0.1230| | |average| |acc |0.6456| | | | Groups |Version|Filter|n-shot|Metric|Value | |Stderr| |------------------|-------|------|-----:|------|-----:|---|-----:| |mmlu |N/A |none | 0|acc |0.6207|_ |0.1230| | - humanities |N/A |none | 5|acc |0.5675|_ |0.1125| | - other |N/A |none | 5|acc |0.6933|_ |0.1108| | - social_sciences|N/A |none | 5|acc |0.7270|_ |0.0666| | - stem |N/A |none | 5|acc |0.5249|_ |0.1311| ``` ### Framework versions - Transformers 4.35.0-UNA - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1 ### Citations If you find Cybertron, Juanako or any of our models useful, specially if you use it for your big brand.. cite please: ``` @misc{unacybertron7a, title={Cybertron: Uniform Neural Alignment}, author={Xavier Murias}, year={2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co/fblgit/una-cybertron-7b-v1}}, } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v1-fp16) | Metric |Value| |---------------------------------|----:| |Avg. |69.49| |AI2 Reasoning Challenge (25-Shot)|68.43| |HellaSwag (10-Shot) |85.42| |MMLU (5-Shot) |63.34| |TruthfulQA (0-shot) |63.28| |Winogrande (5-shot) |81.37| |GSM8k (5-shot) |55.12|
megaaziib/Llava-Maid-7B-DPO-GGUF
megaaziib
2024-03-08T10:25:01Z
87
4
null
[ "gguf", "llava", "image-text-to-text", "endpoints_compatible", "region:us", "conversational" ]
image-text-to-text
2024-03-02T08:44:53Z
--- tags: - llava pipeline_tag: image-text-to-text --- System Prompt: ```bash ### INSTRUCTION: if USER provide an <image>, Provide a correct answer for the latest <image> based on USER request and completely ignore the previous <image> and previous answer. ```
fyp-admin/dreambooth_Jupiter_15
fyp-admin
2024-03-08T10:20:42Z
4
0
diffusers
[ "diffusers", "text-to-image", "lora", "stable-diffusion", "stable-diffusion-diffusers", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-03-08T09:33:40Z
--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - diffusers - lora - stable-diffusion - stable-diffusion-diffusers inference: true base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a picture of planet Jupiter in the center, in white color having rusty brownish orange-colored bands through the middle and blue-colored cyclones on the poles. It is present in space which has dark background, embedded with a cluster of small-sized bright stars. --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA DreamBooth - fyp-admin/dreambooth_Jupiter_15 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a picture of planet Jupiter in the center, in white color having rusty brownish orange-colored bands through the middle and blue-colored cyclones on the poles. It is present in space which has dark background, embedded with a cluster of small-sized bright stars. using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
prashantloni/lilt-en-aadhaar
prashantloni
2024-03-08T10:20:04Z
91
0
transformers
[ "transformers", "tensorboard", "safetensors", "lilt", "token-classification", "generated_from_trainer", "base_model:SCUT-DLVCLab/lilt-roberta-en-base", "base_model:finetune:SCUT-DLVCLab/lilt-roberta-en-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
token-classification
2024-03-08T09:53:16Z
--- license: mit base_model: SCUT-DLVCLab/lilt-roberta-en-base tags: - generated_from_trainer model-index: - name: lilt-en-aadhaar 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. --> # lilt-en-aadhaar This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0950 - Adhaar Number: {'precision': 0.9523809523809523, 'recall': 1.0, 'f1': 0.975609756097561, 'number': 20} - Ame: {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13} - Ather Name: {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} - Ather Name Back: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} - Ather Name Front Top: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} - Ddress Back: {'precision': 0.9032258064516129, 'recall': 0.8235294117647058, 'f1': 0.8615384615384616, 'number': 34} - Ddress Front: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} - Ender: {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} - Ob: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} - Obile Number: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} - Ther: {'precision': 0.898876404494382, 'recall': 0.8791208791208791, 'f1': 0.8888888888888888, 'number': 91} - Overall Precision: 0.9256 - Overall Recall: 0.9045 - Overall F1: 0.9149 - Overall Accuracy: 0.9923 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 2500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Adhaar Number | Ame | Ather Name | Ather Name Back | Ather Name Front Top | Ddress Back | Ddress Front | Ender | Ob | Obile Number | Ther | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |:-------------:|:------:|:----:|:---------------:|:-----------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------:|:---------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| | 0.185 | 15.38 | 200 | 0.0832 | {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 20} | {'precision': 0.9166666666666666, 'recall': 0.8461538461538461, 'f1': 0.8799999999999999, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.8484848484848485, 'recall': 0.8235294117647058, 'f1': 0.8358208955223881, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8539325842696629, 'recall': 0.8351648351648352, 'f1': 0.8444444444444446, 'number': 91} | 0.8940 | 0.8818 | 0.8879 | 0.9884 | | 0.0034 | 30.77 | 400 | 0.0860 | {'precision': 0.9047619047619048, 'recall': 0.95, 'f1': 0.9268292682926829, 'number': 20} | {'precision': 0.8461538461538461, 'recall': 0.8461538461538461, 'f1': 0.8461538461538461, 'number': 13} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.8387096774193549, 'recall': 0.7647058823529411, 'f1': 0.7999999999999999, 'number': 34} | {'precision': 0.9411764705882353, 'recall': 1.0, 'f1': 0.9696969696969697, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 0.9285714285714286, 'recall': 1.0, 'f1': 0.962962962962963, 'number': 13} | {'precision': 1.0, 'recall': 0.8, 'f1': 0.888888888888889, 'number': 5} | {'precision': 0.8444444444444444, 'recall': 0.8351648351648352, 'f1': 0.839779005524862, 'number': 91} | 0.8796 | 0.8636 | 0.8716 | 0.9877 | | 0.0011 | 46.15 | 600 | 0.1305 | {'precision': 0.9047619047619048, 'recall': 0.95, 'f1': 0.9268292682926829, 'number': 20} | {'precision': 0.7692307692307693, 'recall': 0.7692307692307693, 'f1': 0.7692307692307693, 'number': 13} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 0.9, 'recall': 1.0, 'f1': 0.9473684210526316, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.8181818181818182, 'recall': 0.7941176470588235, 'f1': 0.8059701492537314, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 0.9285714285714286, 'recall': 1.0, 'f1': 0.962962962962963, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8222222222222222, 'recall': 0.8131868131868132, 'f1': 0.8176795580110496, 'number': 91} | 0.8630 | 0.8591 | 0.8610 | 0.9854 | | 0.0013 | 61.54 | 800 | 0.1075 | {'precision': 0.9523809523809523, 'recall': 1.0, 'f1': 0.975609756097561, 'number': 20} | {'precision': 0.8333333333333334, 'recall': 0.7692307692307693, 'f1': 0.8, 'number': 13} | {'precision': 0.6666666666666666, 'recall': 0.6666666666666666, 'f1': 0.6666666666666666, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.7878787878787878, 'recall': 0.7647058823529411, 'f1': 0.7761194029850745, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8222222222222222, 'recall': 0.8131868131868132, 'f1': 0.8176795580110496, 'number': 91} | 0.875 | 0.8591 | 0.8670 | 0.9838 | | 0.001 | 76.92 | 1000 | 0.1076 | {'precision': 0.9523809523809523, 'recall': 1.0, 'f1': 0.975609756097561, 'number': 20} | {'precision': 0.8333333333333334, 'recall': 0.7692307692307693, 'f1': 0.8, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.9032258064516129, 'recall': 0.8235294117647058, 'f1': 0.8615384615384616, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8636363636363636, 'recall': 0.8351648351648352, 'f1': 0.8491620111731844, 'number': 91} | 0.9061 | 0.8773 | 0.8915 | 0.9892 | | 0.0003 | 92.31 | 1200 | 0.0856 | {'precision': 0.9523809523809523, 'recall': 1.0, 'f1': 0.975609756097561, 'number': 20} | {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.8125, 'recall': 0.7647058823529411, 'f1': 0.787878787878788, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8555555555555555, 'recall': 0.8461538461538461, 'f1': 0.850828729281768, 'number': 91} | 0.8940 | 0.8818 | 0.8879 | 0.9884 | | 0.0001 | 107.69 | 1400 | 0.0950 | {'precision': 0.9523809523809523, 'recall': 1.0, 'f1': 0.975609756097561, 'number': 20} | {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.9032258064516129, 'recall': 0.8235294117647058, 'f1': 0.8615384615384616, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.898876404494382, 'recall': 0.8791208791208791, 'f1': 0.8888888888888888, 'number': 91} | 0.9256 | 0.9045 | 0.9149 | 0.9923 | | 0.0001 | 123.08 | 1600 | 0.1075 | {'precision': 0.9047619047619048, 'recall': 0.95, 'f1': 0.9268292682926829, 'number': 20} | {'precision': 0.8461538461538461, 'recall': 0.8461538461538461, 'f1': 0.8461538461538461, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.9032258064516129, 'recall': 0.8235294117647058, 'f1': 0.8615384615384616, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8764044943820225, 'recall': 0.8571428571428571, 'f1': 0.8666666666666666, 'number': 91} | 0.9070 | 0.8864 | 0.8966 | 0.9908 | | 0.0002 | 138.46 | 1800 | 0.0919 | {'precision': 0.9047619047619048, 'recall': 0.95, 'f1': 0.9268292682926829, 'number': 20} | {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.8484848484848485, 'recall': 0.8235294117647058, 'f1': 0.8358208955223881, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8444444444444444, 'recall': 0.8351648351648352, 'f1': 0.839779005524862, 'number': 91} | 0.8899 | 0.8818 | 0.8858 | 0.9892 | | 0.0001 | 153.85 | 2000 | 0.0953 | {'precision': 0.9047619047619048, 'recall': 0.95, 'f1': 0.9268292682926829, 'number': 20} | {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.8484848484848485, 'recall': 0.8235294117647058, 'f1': 0.8358208955223881, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8444444444444444, 'recall': 0.8351648351648352, 'f1': 0.839779005524862, 'number': 91} | 0.8899 | 0.8818 | 0.8858 | 0.9892 | | 0.0001 | 169.23 | 2200 | 0.0974 | {'precision': 0.9047619047619048, 'recall': 0.95, 'f1': 0.9268292682926829, 'number': 20} | {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.8484848484848485, 'recall': 0.8235294117647058, 'f1': 0.8358208955223881, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8444444444444444, 'recall': 0.8351648351648352, 'f1': 0.839779005524862, 'number': 91} | 0.8899 | 0.8818 | 0.8858 | 0.9892 | | 0.0 | 184.62 | 2400 | 0.1008 | {'precision': 0.9047619047619048, 'recall': 0.95, 'f1': 0.9268292682926829, 'number': 20} | {'precision': 0.9230769230769231, 'recall': 0.9230769230769231, 'f1': 0.9230769230769231, 'number': 13} | {'precision': 0.5, 'recall': 0.6666666666666666, 'f1': 0.5714285714285715, 'number': 3} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 9} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 4} | {'precision': 0.8484848484848485, 'recall': 0.8235294117647058, 'f1': 0.8358208955223881, 'number': 34} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 16} | {'precision': 1.0, 'recall': 0.8333333333333334, 'f1': 0.9090909090909091, 'number': 12} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 13} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 5} | {'precision': 0.8444444444444444, 'recall': 0.8351648351648352, 'f1': 0.839779005524862, 'number': 91} | 0.8899 | 0.8818 | 0.8858 | 0.9892 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ndieckow/q-Taxi-v3
ndieckow
2024-03-08T10:01:41Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T10:01:39Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="ndieckow/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Asahina2K/AsahinaMix
Asahina2K
2024-03-08T09:52:49Z
0
0
null
[ "text-to-image", "stable-diffusion", "safetensors", "stable-diffusion-xl", "en", "base_model:cagliostrolab/animagine-xl-3.0", "base_model:finetune:cagliostrolab/animagine-xl-3.0", "license:other", "region:us" ]
text-to-image
2024-02-27T11:40:39Z
--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en tags: - text-to-image - stable-diffusion - safetensors - stable-diffusion-xl base_model: cagliostrolab/animagine-xl-3.0 --- <style> .title-container { display: flex; justify-content: center; align-items: center; height: 100vh; /* Adjust this value to position the title vertically */ } .title { font-size: 2.5em; text-align: center; color: #333; font-family: 'Helvetica Neue', sans-serif; text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #8efdff, #ab735c); -webkit-background-clip: text; -webkit-text-fill-color: transparent; } </style> <h1 class="title"> <span>AsahinaMix</span> </h1> **AsahinaMix** is a merge model, and has two branches of merge models, AsaMix which focuses on Anime style while [HinaMix](https://huggingface.co/Asahina2K/AsahinaMix/resolve/main/HinaMix/HinaMix.safetensors) focuses on 2.5D anime style. ## Model Details AsaMix (Still WIP Comming soon ^^) - **Developed by**: [Asahina2k](https://twitter.com/Asahina2k) - **Model type**: Diffusion-based text-to-image generative model - **Model Description**: Generate high-quality anime images from textual prompts - **License**: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) - **Merged from model**: [Animagine XL 3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0) ## Model Details HinaMix - **Developed by**: [Asahina2k](https://twitter.com/Asahina2k) - **Model type**: Diffusion-based text-to-image generative model - **Model Description**: Generate high-quality anime images from textual prompts - **License**: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) - **Merged from model**: [Animagine XL 3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0), [RealCartoon-XL](https://civitai.com/models/125907/realcartoon-xl), [bluePencilXL](https://civitai.com/models/119012), [Lah | Mysterious SDXL](https://civitai.com/models/118441), [SwampMachine](https://civitai.com/models/286574) ## Recommended settings AsaMix and HinaMix have same recommended settings To guide the model towards generating high-aesthetic images, use negative prompts like: ``` (worst quality, low quality, lowres), (interlocked fingers, badly drawn hands and fingers, anatomically incorrect hands), blurry, watermark, ``` For higher quality outcomes, prepend prompts with: ``` (very aethetic, best quality, ultra detailed), intricate details, ``` ### Multi Aspect Resolution This model supports generating images at the following dimensions: | Dimensions | Aspect Ratio | |-------------------|-----------------| | `1024 x 1024` | 1:1 Square | | `1152 x 896` | 9:7 | | `896 x 1152` | 7:9 | | `1216 x 832` | 19:13 | | `832 x 1216` | 13:19 | | `1344 x 768` | 7:4 Horizontal | | `768 x 1344` | 4:7 Vertical | | `1536 x 640` | 12:5 Horizontal | | `640 x 1536` | 5:12 Vertical | ## Hires.fix Setting - Upscaler : [4x-YandereNeoXL](https://nmkd.de/?esrgan) - Hires step : 10-20 - Denoising : 0.2-0.4 or 0.55 for latent upscaler ## Merge parameters for HinaMix 1. Animagine XL 3.0 merged to [RealCartoonXL V6](https://civitai.com/models/125907/realcartoon-xl) to get 2.5D body using MBW (0,1,0.8,0.5,0.25,0,0,0,0,0,0,0.3,0.5,0.71,1,0.56,0.71,1,0.83,0.1) 2. (1) merged with [Blue Pencil XL v4.0.1](https://civitai.com/models/119012/bluepencil-xl) to get anime touch using MBW (0,0.11,0.22,0.33,0.44,0.55,0.44,0.33,0.22,0.11,0,0.11,0.22,0.33,0.44,0.55,0.44,0.33,0.22,0.11) 3. (2) merge with [Lah | Mysterious SDXL](https://civitai.com/models/118441) to get manhua fantasy style using MBW (0,1,0.8,0.5,0.25,0,0,0,0,0,0,0.3,0.5,0.71,1,0.56,0.71,1,0.83,0.1) 4. (3) merge with [SwampMachine](https://civitai.com/models/286574) for final anime touch using MBW (0,0.11,0.22,0.33,0.44,0.55,0.44,0.33,0.22,0.11,0,0.11,0.22,0.33,0.44,0.55,0.44,0.33,0.22,0.11) 5. HinaMix ## License AsahinaMix now uses the [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) inherited from Animagine XL 3.0, compatible with Stable Diffusion models. Key points: 1. **Modification Sharing:** If you modify AsahinaMix, you must share both your changes and the original license. 2. **Source Code Accessibility:** If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too. 3. **Distribution Terms:** Any distribution must be under this license or another with similar rules. 4. **Compliance:** Non-compliance must be fixed within 30 days to avoid license termination, emphasizing transparency and adherence to open-source values. The choice of this license aims to keep AsahinaMix open and modifiable, aligning with open source community spirit. It protects contributors and users, encouraging a collaborative, ethical open-source community. This ensures the model not only benefits from communal input but also respects open-source development freedoms.
Gargaz/brain.ai
Gargaz
2024-03-08T09:42:32Z
13
0
null
[ "gguf", "text2text-generation", "en", "doi:10.57967/hf/1872", "license:apache-2.0", "endpoints_compatible", "region:us" ]
text2text-generation
2024-03-08T06:38:34Z
--- license: apache-2.0 language: - en pipeline_tag: text2text-generation ---
Luliyanng/finetuning-sentiment-model-3000-samples
Luliyanng
2024-03-08T09:32:46Z
90
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
2024-03-08T09:19:20Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 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. It achieves the following results on the evaluation set: - Loss: 0.3268 - Accuracy: 0.87 - F1: 0.8746 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ndieckow/q-FrozenLake-v1-4x4-noSlippery
ndieckow
2024-03-08T09:31:36Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T09:31:33Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="ndieckow/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
srimandebugged/ppo_Lunarlander
srimandebugged
2024-03-08T09:31:10Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T09:30:12Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 263.73 +/- 12.28 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
sekhharr/detr_finetuned_v5_last_best_checkpoint
sekhharr
2024-03-08T09:30:36Z
175
0
transformers
[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
object-detection
2024-03-08T09:30:21Z
--- 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]
sekhharr/detr_finetuned_v5_last_checkpoint
sekhharr
2024-03-08T09:30:20Z
174
0
transformers
[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
object-detection
2024-03-08T09:30:00Z
--- 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. 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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]
2bytescorp/2b_mt_opennmt_v0.1
2bytescorp
2024-03-08T09:26:23Z
2
0
transformers(OpenNMT)
[ "transformers(OpenNMT)", "translation", "ko", "en", "license:cc-by-4.0", "region:us" ]
translation
2024-03-08T08:43:21Z
--- library_name: transformers(OpenNMT) license: cc-by-4.0 language: - ko - en tags: - translation --- # Model Card for Model ID - **Git repo:** https://github.com/2bytes-platform/2b-nmt <!-- Provide a quick summary of what the model is/does. --> ## Model Details - **Base Model:** Pre-training - **Model Description:** This model can be used for translation. - **Developed by:** Platform Develop Div. at the 2Bytescorp Korea. - **Model Type:** Translation - **Language(s):** - Source Language: English - Target Language: Korean ## Training Info - **Training Step/epoch:** 400,000 steps ## Dataset - **Train Dataset:** 12,000,000 - **Test Dataset:** 1,000,000 - **Valid Dataset:** 1,000,000 - #### Training Data * dataset: Our own Korea/English dataset. ## How to Get Started With the Model (Inference) ```python import ctranslate2 import pyonmttok import sys if len(sys.argv) < 2: sentence = "I sincerely apologize for not providing the best taste and quality." else: sentence = sys.argv[1] tokenizer = pyonmttok.Tokenizer("conservative", joiner_annotate=True) tokens = tokenizer(sentence) model = "/home/techops/data/nmt_data/clean_data_files_v1/ctranslate2/model_4m" # model = "/home/techops/data/nmt_data/ctranslate_model/en_ko/100m_300000" translator = ctranslate2.Translator(model_path=model, device="cpu") outputs = translator.translate_batch([tokens], beam_size=5, num_hypotheses=2, sampling_temperature=0.8, replace_unknowns=True) translated = outputs[0].hypotheses[0] t_s = tokenizer.detokenize(translated) print(t_s.replace("@@", "")) >>> (nmt) [techops@inf-ai-nmt-a01 (screen: ) /data/NMT/2b_nmt/ctranslate]$ python ctran_translate.py 최고의 맛과 품질을 제공하지 못한 점에 대해 진심으로 사과드립니다. ```
dong9ry/nuclear-v1.4b
dong9ry
2024-03-08T09:20:12Z
72
0
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T09:14:17Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: nuclear-v1.4b 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. --> # nuclear-v1.4b This model is a fine-tuned version of [dong9ry/nuclear-v1.1b](https://huggingface.co/dong9ry/nuclear-v1.1b) 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.13.3
openkg/aijudge
openkg
2024-03-08T09:15:56Z
94
3
transformers
[ "transformers", "pytorch", "gpt2", "text-generation", "Court View", "Legal Judgment Prediction", "Explainable", "GPT2", "zh", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-07-01T14:43:32Z
--- license: apache-2.0 language: - zh tags: - Court View - Legal Judgment Prediction - Explainable - GPT2 --- # AI Judge ---- ## Model Description <p align = "justify"> The advent of ChatGPT and GPT-4 have brought groundbreaking progress in the realm of natural language processing, with its astonishing generative capabilities. Nevertheless, the training and deployment of such large-scale language models are exceedingly costly. Furthermore, experience has shown that these models struggle to deliver satisfactory performance in specific domains, such as knowledge-intensive scenarios like jurisprudence. Common limitations include knowledge hallucinations, inability to accurately apply legal provisions, and generating overly vague content. </p> <p align = "justify">To alleviate the aforementioned challenges, we have trained a series of language models based on Chinese legal corpora, known as JurisLMs. These models have been further pre-trained on various types of legal documents, such as Chinese laws and regulations, consultations, and judgment document. AI Judge is one such model within the JurisLMs family, derived from the GPT-2 model that has further pre-training on legal judgment documents, combined with an article selection model (a BERT-based classifier) for fine-tuning, resulting in an explainable legal judgment model. Compared to existing models, AI Judge not only provides sentencing outcomes but also offers corresponding judicial perspectives. </p> ## Model Usage ```python import torch from transformers import BertTokenizer, GPT2LMHeadModel, TextGenerationPipeline fact_description = "1、2013年6月25日9时许，被告人丁某某在平阴县中医院建筑工地工人宿舍，窃取被害人胡某（男，43岁）现金1500元，在逃离现场时被工地工人抓获，丁某某将窃取的现金返还被害人。2、2013年7月12日14时许，被告人丁某某在平阴县府前街文鼎嘉苑建筑工地工人宿舍，窃取被害人陈某（男，31岁）及王某（男，25岁）现金850元，在逃跑时被抓获，丁某某将盗窃现金返还被害人。本院认为，" model_name = "openkg/aijudge" device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") tokenizer = BertTokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name).to(device) generator = TextGenerationPipeline(model, tokenizer, device=0) generator.tokenizer.pad_token_id = generator.model.config.eos_token_id prediction = generator(fact_description, max_length=1024, num_beams=1, top_p=0.7, num_return_sequences=1, eos_token_id=50256, pad_token_id=generator.model.config.eos_token_id) court_view = prediction[0]["generated_text"].replace(" ", "").split("。本院认为，")[1].split("<生成结束>")[0] print(court_view) ``` ## Comparison For detailed comparisons, please refer to [(JurisLMs)](https://github.com/seudl/JurisLMs) ## Acknowledged Limitations Despite being significantly ameliorated through professional annotation and evaluation, JurisGPT2 inevitably retains certain limitations, including but not limited to: - Potential oversight of crucial facts - Possible logical errors in multiple parties - Potential inaccuracies in conclusions - Possibility of outdated legal provisions ## Disclaimer <p align = "justify">This project is strictly for academic research purposes and is prohibited for commercial use. When utilizing third-party technologies, adhere to the corresponding open-source licenses. The accuracy of the content generated by this project is subject to factors such as algorithms, randomness, and quantification precision, and therefore, cannot be guaranteed. The project assumes no legal liability for any content produced by the model and shall not be held responsible for any damages resulting from the use of related resources and output. Due to the time constraints of the R&D group, timely technical support is unfortunately not feasible.</p> ## Contributors Sheng Bi, Haofen Wang, Tianxing Wu, Guilin Qi
openkg/ailawyer
openkg
2024-03-08T09:15:21Z
9
3
transformers
[ "transformers", "llama", "text-generation", "Legal QA", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-generation
2023-07-01T15:24:53Z
--- license: apache-2.0 language: - zh tags: - Legal QA --- ## Project Description <p align = "justify"> The advent of ChatGPT, specifically GPT-4, has engendered groundbreaking strides in the realm of natural language processing, with its generative capabilities inducing profound impressions. However, empirical observations suggest that these models often falter in specific domains, notably in knowledge-intensive areas such as law, where common limitations manifest as knowledge hallucinations, inability to accurately apply legal provisions, and the generation of excessively abstract content. </p> <p align = "justify"> To mitigate the aforementioned challenges, we have trained a series of language models, namely JurisLMs, on Chinese legal corpora. These models have been further pretrained on diverse datasets including legislations, legal consultations, and judicial documents, tailored to distinct scenarios. Among these, AI Judge, a model fine-tuned after further pretraining of GPT-2 on legal corpora and combined with a <u>legal provision application model</u> (a classifier based on BERT), is an <font color=#FF000>explainable legal decision prediction model</font>. Existing decision making models typically yield predictions but fail to rationalize them. To address this, AI Judge not only provides verdict predictions but also corresponding court views. Leveraging a similar framework, we have trained an <font color=#FF000>intelligent legal consultation model</font>, AI Lawyer, based on Chinese LLaMA. Owing to the scarcity of consultation corpora annotated with legal provisions, we have employed <u>Active Learning</u> to fine-tune a <u>legal provision application model</u> on a limited dataset, enabling AI Lawyer to answer queries by correctly applying corresponding judicial perspectives.</p> ## AI Lawyer Demo and Usage <!---<div align=center><img src="./images/ailawyer_framework.png"></div> <center style="font-size:14px;color:#C0C0C0;text-decoration:underline">AI Lawyer 框架</center> <br>---> ```python #!/usr/bin/env python3 # -*- coding: utf-8 -*- import torch from peft import PeftModel from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig def generate_prompt(instruction, input=None): if input: return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Input: {input} ### Response: """ else: return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {instruction} ### Response: """ base_model = "save_merge_weight_directory" lora_weights = "ailawyer_lora" # download from https://huggingface.co/openkg/ailawyer instruction = "假设你是一名律师，请分析如下案例，并提供专业的法律服务。" _input = "去年三月份包工头欠我和另外两个工友一共七万多元，然后一直拖着不给，也找不到人，或者是见面了就说没钱。现在要怎么做才能要到钱？" tokenizer = LlamaTokenizer.from_pretrained(base_model) model = LlamaForCausalLM.from_pretrained(base_model, load_in_8bit=False, torch_dtype=torch.float16, device_map="auto") model = PeftModel.from_pretrained(model, lora_weights, torch_dtype=torch.float16).half() model.config.pad_token_id = tokenizer.pad_token_id = 0 model.config.bos_token_id = 1 model.config.eos_token_id = 2 model.eval() prompt = generate_prompt(instruction, _input) inputs = tokenizer(prompt, return_tensors="pt") input_ids = inputs["input_ids"].to("cuda") generation_config = GenerationConfig(temperature=0.1, top_p=0.75, top_k=1, num_beams=1) with torch.no_grad(): generation_output = model.generate( input_ids=input_ids, generation_config=generation_config, return_dict_in_generate=True, output_scores=True, max_new_tokens=500, ) output_ids = generation_output.sequences[0] output = tokenizer.decode(output_ids) print(output.split("### Response:")[1].strip()) # Response: 根据《保障农民工工资支付条例》第十六条　用人单位拖欠农民工工资的，应当依法予以清偿。因此，拖欠农民工工资属于违法行为，劳动者有权要求用工单位承担工资清偿责任，建议劳动者收集拖欠工资的证据，比如合同书，工资欠条，与工地负责人通话录音，短信微信聊天记录，工友证人证言等向劳动监察大队举报，要求责令有关单位支付工资，也可以向法院起诉要求判决支付农民工工资。可以向法律援助中心申请免费的法律援助，指派法律援助律师代为诉讼维权，可以向12345政府服务热线投诉。</s> ``` ## Environment - RAM 30G+, GPU 32G+ - python>=3.9 - pip install -r requirements.txt ## Model Merging ### Step 1: Download the original LLaMa 13B including: - consolidated.*.pth - tokenizer.model - params.json ### Step 2: Download Chinese-LLaMA-Alpaca 13B weights and save as chinese_llama_alpaca_lora_weight_directory - HF：https://huggingface.co/ziqingyang/chinese-llama-lora-13b/tree/main - Baidu Pan：https://pan.baidu.com/s/1BxFhYhDMipW7LwI58cGmQQ?pwd=ef3t including: adapter_config.json、adapter_model.bin、special_tokens_map.json、tokenizer.model、tokenizer_config.json ### Step 3: Convert the original LLaMA to HF format ```python python convert_llama_weights_to_hf.py \ --input_dir origin_llama_weight_directory \ --model_size 13B \ --output_dir origin_llama_hf_weight_directory ``` - input_dir: the original LLaMa directory - output_dir: the directory where the converted LLaMA ### Step 4: Merge LoRA weights to generate base model ```python python merge_llama_with_chinese_lora_to_hf.py \ --base_model origin_llama_hf_weight_directory \ --lora_model chinese_llama_alpaca_lora_weight_directory \ --output_dir save_merge_weight_directory ``` - base_model：origin_llama_hf_weight_directory in Step 3 - lora_model：chinese_llama_alpaca_lora_weight_directory in Step 2 - output_dir：the directory where the full model weights ## Deployment Download the LoRA weights for this project and save as ailawyer_lora. ### Web UI Deployment Local deployment using Gradio Web UI, deployed on GPU 0 as follows: ```shell CUDA_VISIBLE_DEVICES=0 python web_demo_llama_13B.py \ --base_model save_merge_weight_directory \ --lora_weights ailawyer_lora ``` - base_model save_merge_weight_directory in Step 4 ## Disclaimer <p align = "justify"> This project is exclusively for academic research purposes and strictly prohibited for commercial use. The accuracy of the content generated by this project is subject to factors such as algorithms, randomness, and quantitative precision, hence difficult to guarantee. Although utmost efforts have been made to ensure the accuracy and timeliness of the data used, the characteristics of language models may still cause a lag in information and legal developments. Therefore, this project assumes no legal liability for any content output by the model, nor does it assume responsibility for any losses that may arise from the use of related resources and output results. Machines should not and cannot replace the process of seeking professional legal advice. In the event of specific legal issues or cases, it is recommended to consult a qualified lawyer or legal professional to obtain personalized advice. </p> ## Contributors Sheng Bi, Haofen Wang, Tianxing Wu, Guilin Qi
tagawayskintagremover/tagawayproskintagremover
tagawayskintagremover
2024-03-08T09:08:52Z
0
0
sentence-transformers
[ "sentence-transformers", "Tag Away Pro Skin Tag Remover", "en", "license:bsd", "region:us" ]
null
2024-03-08T09:08:19Z
--- license: bsd language: - en library_name: sentence-transformers tags: - Tag Away Pro Skin Tag Remover --- [Tag Away Pro Skin Tag Remover](https://atozsupplement.com/tag-away-pro-skin-tag-remover/) Expanded Hydration: Fixings like hyaluronic corrosive and glycerin profoundly hydrate the skin, plumping it up and limiting the presence of dryness and parchedness lines.Evened Complexion: Hostile to maturing serums might incorporate fixings like L-ascorbic acid, niacinamide, or alpha hydroxy acids (AHAs) that assist with blurring dull spots, hyperpigmentation, and advance an all the more even complexion. VISIT HERE FOR OFFICIAL WEBSITE:-https://atozsupplement.com/tag-away-pro-skin-tag-remover/
humung/komt-mistral-7b-v1-vlending-cs-v0.2
humung
2024-03-08T09:06:34Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-03-08T09:06:28Z
--- 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]
haryoaw/scenario-TCR-XLMV-1_data-AmazonScience_massive_all_1_1
haryoaw
2024-03-08T09:02:11Z
94
0
transformers
[ "transformers", "pytorch", "xlm-roberta", "text-classification", "generated_from_trainer", "dataset:massive", "base_model:facebook/xlm-v-base", "base_model:finetune:facebook/xlm-v-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-03-08T08:59:30Z
--- license: mit base_model: facebook/xlm-v-base tags: - generated_from_trainer datasets: - massive metrics: - accuracy - f1 model-index: - name: scenario-TCR-XLMV-1_data-AmazonScience_massive_all_1_1 results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: all_1.1 split: validation args: all_1.1 metrics: - name: Accuracy type: accuracy value: 0.8472984221877483 - name: F1 type: f1 value: 0.8225956665149763 --- <!-- 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. --> # scenario-TCR-XLMV-1_data-AmazonScience_massive_all_1_1 This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.7886 - Accuracy: 0.8473 - F1: 0.8226 ## 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: 32 - eval_batch_size: 32 - seed: 47 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | 0.587 | 0.27 | 5000 | 0.7148 | 0.8166 | 0.7696 | | 0.456 | 0.53 | 10000 | 0.6624 | 0.8415 | 0.8006 | | 0.3711 | 0.8 | 15000 | 0.6803 | 0.8394 | 0.8064 | | 0.2846 | 1.07 | 20000 | 0.7409 | 0.8406 | 0.8119 | | 0.2698 | 1.34 | 25000 | 0.7120 | 0.8428 | 0.8129 | | 0.2589 | 1.6 | 30000 | 0.7179 | 0.8478 | 0.8300 | | 0.246 | 1.87 | 35000 | 0.7383 | 0.8455 | 0.8119 | | 0.2079 | 2.14 | 40000 | 0.7911 | 0.8503 | 0.8162 | | 0.2157 | 2.41 | 45000 | 0.7775 | 0.8434 | 0.8251 | | 0.2111 | 2.67 | 50000 | 0.7737 | 0.8455 | 0.8196 | | 0.2014 | 2.94 | 55000 | 0.7886 | 0.8473 | 0.8226 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3
digiplay/HK_Loras
digiplay
2024-03-08T09:02:05Z
0
0
null
[ "license:other", "region:us" ]
null
2024-03-08T08:41:55Z
--- license: other --- Models info : **HongKong by Night - Film Color** ❤️ Hongkong_byNight_Film_Color.safetensors ✏️ Trigger Words:Hongkong street, night 🔗 https://civitai.com/models/91185/hongkong-by-night-film-color **港风风格HongKong Style** 📌 HongKongStyleV1beta.safetensors 📌 HongKongStyleV1.safetensors ✏️ Trigger Words: HongKong, HongKong style 🔗 https://civitai.com/models/107331?modelVersionId=131066
hoangthethief/chatbot_question_classification
hoangthethief
2024-03-08T09:00:55Z
90
0
transformers
[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:Supabase/gte-small", "base_model:finetune:Supabase/gte-small", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-03-08T09:00:36Z
--- license: mit base_model: Supabase/gte-small tags: - generated_from_trainer metrics: - accuracy model-index: - name: v_best_model 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. --> # v_best_model This model is a fine-tuned version of [Supabase/gte-small](https://huggingface.co/Supabase/gte-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0734 - Accuracy: 0.9990 ## 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: 0.0001 - 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 - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.2837 | 1.0 | 62 | 0.6846 | 0.9231 | | 0.448 | 2.0 | 124 | 0.2268 | 0.9808 | | 0.1566 | 3.0 | 186 | 0.1397 | 0.9808 | | 0.0879 | 4.0 | 248 | 0.1302 | 0.9808 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.17.1 - Tokenizers 0.15.0
fyp-admin/dreambooth_Venus_15
fyp-admin
2024-03-08T08:56:13Z
1
0
diffusers
[ "diffusers", "text-to-image", "lora", "stable-diffusion", "stable-diffusion-diffusers", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-03-08T08:16:02Z
--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - diffusers - lora - stable-diffusion - stable-diffusion-diffusers inference: true base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a picture of planet Venus in the center, in golden orange color and white hues on the poles. It is present in space which has dark background, embedded with a cluster of small-sized bright stars. --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA DreamBooth - fyp-admin/dreambooth_Venus_15 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a picture of planet Venus in the center, in golden orange color and white hues on the poles. It is present in space which has dark background, embedded with a cluster of small-sized bright stars. using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Senthilkumar-M/distilbert_finetune_own_data_model
Senthilkumar-M
2024-03-08T08:54:35Z
55
0
transformers
[ "transformers", "tensorboard", "safetensors", "distilbert", "token-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" ]
token-classification
2024-02-29T04:40:22Z
--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert_finetune_own_data_model 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_finetune_own_data_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0618 - Precision: 0.8889 - Recall: 0.8889 - F1: 0.8889 - Accuracy: 0.9773 ## 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 23 | 0.3117 | 1.0 | 0.6667 | 0.8 | 0.9091 | | No log | 2.0 | 46 | 0.1638 | 0.7778 | 0.7778 | 0.7778 | 0.9318 | | No log | 3.0 | 69 | 0.1322 | 0.875 | 0.7778 | 0.8235 | 0.9545 | | No log | 4.0 | 92 | 0.0582 | 0.8889 | 0.8889 | 0.8889 | 0.9773 | | No log | 5.0 | 115 | 0.1196 | 0.8889 | 0.8889 | 0.8889 | 0.9773 | | No log | 6.0 | 138 | 0.0607 | 0.8889 | 0.8889 | 0.8889 | 0.9773 | | No log | 7.0 | 161 | 0.0918 | 0.8889 | 0.8889 | 0.8889 | 0.9773 | | No log | 8.0 | 184 | 0.0512 | 0.8889 | 0.8889 | 0.8889 | 0.9773 | | No log | 9.0 | 207 | 0.0521 | 0.8889 | 0.8889 | 0.8889 | 0.9773 | | No log | 10.0 | 230 | 0.0618 | 0.8889 | 0.8889 | 0.8889 | 0.9773 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
melino2000/falcon-7b-quantize
melino2000
2024-03-08T08:49:43Z
89
0
transformers
[ "transformers", "safetensors", "falcon", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]
text-generation
2024-03-08T08:47:23Z
--- 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]
psroy/results
psroy
2024-03-08T08:49:34Z
1
0
peft
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]
null
2024-02-28T08:35:03Z
--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: NousResearch/Llama-2-7b-chat-hf 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 [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
GraydientPlatformAPI/cheyenne16-xl
GraydientPlatformAPI
2024-03-08T08:46:50Z
30
3
diffusers
[ "diffusers", "safetensors", "text-to-image", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]
text-to-image
2024-03-08T08:26:29Z
--- library_name: diffusers pipeline_tag: text-to-image ---
dchatca/vistral-economics-v3.2
dchatca
2024-03-08T08:42:40Z
4
0
peft
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:Viet-Mistral/Vistral-7B-Chat", "base_model:adapter:Viet-Mistral/Vistral-7B-Chat", "license:afl-3.0", "region:us" ]
null
2024-03-07T17:38:34Z
--- license: afl-3.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: Viet-Mistral/Vistral-7B-Chat 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 [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat) on Summary 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
yzzky/mstral7b-ft-autotrain-1
yzzky
2024-03-08T08:33:10Z
0
0
null
[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T08:33:06Z
--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
dong9ry/nuclear-v1.3b
dong9ry
2024-03-08T08:31:39Z
71
0
transformers
[ "transformers", "pytorch", "gpt_neox", "text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T08:24:52Z
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: nuclear-v1.3b 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. --> # nuclear-v1.3b This model is a fine-tuned version of [EleutherAI/polyglot-ko-1.3b](https://huggingface.co/EleutherAI/polyglot-ko-1.3b) 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 64 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.29.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.13.3
Rardilit/Gaitonde-v1
Rardilit
2024-03-08T08:29:22Z
92
0
transformers
[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T08:24:04Z
--- 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]
CC-AI-Labs/nord-triplet-hsm-bert-base-cased
CC-AI-Labs
2024-03-08T08:10:11Z
48
0
sentence-transformers
[ "sentence-transformers", "tf", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]
sentence-similarity
2024-03-08T07:58:58Z
--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 66 with parameters: ``` {'batch_size': 128, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.BatchHardSoftMarginTripletLoss.BatchHardSoftMarginTripletLoss` Parameters of the fit()-Method: ``` { "epochs": 30, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 8e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 198, "weight_decay": 0 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
etri-xainlp/SOLAR-10.7B-merge-dpo
etri-xainlp
2024-03-08T08:09:50Z
2,301
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "merge", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-04T01:13:06Z
--- license: cc-by-nc-4.0 tags: - merge --- # etri-xainlp/SOLAR-10.7B-merge-dpo ## Model Details **Model Developers** ETRI xainlp team **Input** text only. **Output** text only. **Model Architecture** We used MergeKit to merge Model heavytail/kullm-solar into Model upstage/SOLAR-10.7B-Instruct-v1.0 as the base. **Base Model** [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) **Merge Model** [heavytail/kullm-solar](https://huggingface.co/heavytail/kullm-solar) **Training Dataset** - dpo+lora: 90k user preference set - We use A100 GPU 80GB * 1, when training.
hellosimple/bert-base-uncased-2022-habana
hellosimple
2024-03-08T08:01:38Z
0
0
transformers
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-03-08T08:01:37Z
--- 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]
nielsr/DUSt3R_ViTLarge_BaseDecoder_512_dpt
nielsr
2024-03-08T07:57:56Z
1,575
2
transformers
[ "transformers", "safetensors", "vision", "endpoints_compatible", "region:us" ]
null
2024-03-06T21:37:25Z
--- tags: - vision --- ## DUSt3R # Model info Project page: https://dust3r.europe.naverlabs.com/ # How to use Here's how to load the model (after [installing](https://github.com/naver/dust3r?tab=readme-ov-file#installation) the dust3r package): ```python from dust3r.model import AsymmetricCroCo3DStereo import torch model = AsymmetricCroCo3DStereo.from_pretrained("nielsr/DUSt3R_ViTLarge_BaseDecoder_512_dpt") device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) ``` Next, one can run inference as follows: ``` from dust3r.inference import inference from dust3r.utils.image import load_images from dust3r.image_pairs import make_pairs from dust3r.cloud_opt import global_aligner, GlobalAlignerMode if __name__ == '__main__': batch_size = 1 schedule = 'cosine' lr = 0.01 niter = 300 # load_images can take a list of images or a directory images = load_images(['croco/assets/Chateau1.png', 'croco/assets/Chateau2.png'], size=512) pairs = make_pairs(images, scene_graph='complete', prefilter=None, symmetrize=True) output = inference(pairs, model, device, batch_size=batch_size) # at this stage, you have the raw dust3r predictions view1, pred1 = output['view1'], output['pred1'] view2, pred2 = output['view2'], output['pred2'] # here, view1, pred1, view2, pred2 are dicts of lists of len(2) # -> because we symmetrize we have (im1, im2) and (im2, im1) pairs # in each view you have: # an integer image identifier: view1['idx'] and view2['idx'] # the img: view1['img'] and view2['img'] # the image shape: view1['true_shape'] and view2['true_shape'] # an instance string output by the dataloader: view1['instance'] and view2['instance'] # pred1 and pred2 contains the confidence values: pred1['conf'] and pred2['conf'] # pred1 contains 3D points for view1['img'] in view1['img'] space: pred1['pts3d'] # pred2 contains 3D points for view2['img'] in view1['img'] space: pred2['pts3d_in_other_view'] # next we'll use the global_aligner to align the predictions # depending on your task, you may be fine with the raw output and not need it # with only two input images, you could use GlobalAlignerMode.PairViewer: it would just convert the output # if using GlobalAlignerMode.PairViewer, no need to run compute_global_alignment scene = global_aligner(output, device=device, mode=GlobalAlignerMode.PointCloudOptimizer) loss = scene.compute_global_alignment(init="mst", niter=niter, schedule=schedule, lr=lr) # retrieve useful values from scene: imgs = scene.imgs focals = scene.get_focals() poses = scene.get_im_poses() pts3d = scene.get_pts3d() confidence_masks = scene.get_masks() # visualize reconstruction scene.show() # find 2D-2D matches between the two images from dust3r.utils.geometry import find_reciprocal_matches, xy_grid pts2d_list, pts3d_list = [], [] for i in range(2): conf_i = confidence_masks[i].cpu().numpy() pts2d_list.append(xy_grid(*imgs[i].shape[:2][::-1])[conf_i]) # imgs[i].shape[:2] = (H, W) pts3d_list.append(pts3d[i].detach().cpu().numpy()[conf_i]) reciprocal_in_P2, nn2_in_P1, num_matches = find_reciprocal_matches(*pts3d_list) print(f'found {num_matches} matches') matches_im1 = pts2d_list[1][reciprocal_in_P2] matches_im0 = pts2d_list[0][nn2_in_P1][reciprocal_in_P2] # visualize a few matches import numpy as np from matplotlib import pyplot as pl n_viz = 10 match_idx_to_viz = np.round(np.linspace(0, num_matches-1, n_viz)).astype(int) viz_matches_im0, viz_matches_im1 = matches_im0[match_idx_to_viz], matches_im1[match_idx_to_viz] H0, W0, H1, W1 = *imgs[0].shape[:2], *imgs[1].shape[:2] img0 = np.pad(imgs[0], ((0, max(H1 - H0, 0)), (0, 0), (0, 0)), 'constant', constant_values=0) img1 = np.pad(imgs[1], ((0, max(H0 - H1, 0)), (0, 0), (0, 0)), 'constant', constant_values=0) img = np.concatenate((img0, img1), axis=1) pl.figure() pl.imshow(img) cmap = pl.get_cmap('jet') for i in range(n_viz): (x0, y0), (x1, y1) = viz_matches_im0[i].T, viz_matches_im1[i].T pl.plot([x0, x1 + W0], [y0, y1], '-+', color=cmap(i / (n_viz - 1)), scalex=False, scaley=False) pl.show(block=True) ``` ### BibTeX entry and citation info ```bibtex @journal{dust3r2023, title={{DUSt3R: Geometric 3D Vision Made Easy}}, author={{Wang, Shuzhe and Leroy, Vincent and Cabon, Yohann and Chidlovskii, Boris and Revaud Jerome}}, journal={arXiv preprint 2312.14132}, year={2023}} ```
llmixer/BigWeave-v29-122b
llmixer
2024-03-08T07:55:32Z
48
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "merge", "frankenmerge", "122b", "en", "base_model:152334H/miqu-1-70b-sf", "base_model:finetune:152334H/miqu-1-70b-sf", "license:unknown", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-07T23:31:58Z
--- base_model: - 152334H/miqu-1-70b-sf license: unknown language: - en pipeline_tag: text-generation tags: - merge - frankenmerge - 122b --- # BigWeave v29 122b <img src="https://cdn-uploads.huggingface.co/production/uploads/65a6db055c58475cf9e6def1/4CbbAN-X7ZWj702JrcCGH.png" width=600> The BigWeave models aim to experimentally identify merge settings for increasing model performance. The version number merely tracks various attempts and is not a quality indicator. Only results demonstrating good performance are retained and shared. # Prompting Format Chatml, Mistral, Vicuna. # Merge process This is a self-merge of 152334H/miqu-1-70b-sf. Layers are repeated in groups of 4 with a 2 layer overlap. The first and last 8/9 layers are not repeated. Merge configuration: ``` slices: - sources: - model: 152334H/miqu-1-70b-sf layer_range: [0,11] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [9,13] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [11,15] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [13,17] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [15,19] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [17,21] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [19,23] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [21,25] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [23,27] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [25,29] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [27,31] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [29,33] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [31,35] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [33,37] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [35,39] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [37,41] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [39,43] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [41,45] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [43,47] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [45,49] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [47,51] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [49,53] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [51,55] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [53,57] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [55,59] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [57,61] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [59,63] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [61,65] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [63,67] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [65,69] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [67,71] - sources: - model: 152334H/miqu-1-70b-sf layer_range: [69,80] merge_method: passthrough dtype: float16 ```
VikrantRamesh/Falcon-CN
VikrantRamesh
2024-03-08T07:52:27Z
90
0
transformers
[ "transformers", "tensorboard", "safetensors", "falcon", "feature-extraction", "generated_from_trainer", "custom_code", "base_model:tiiuae/falcon-7b", "base_model:quantized:tiiuae/falcon-7b", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]
feature-extraction
2024-03-08T05:59:09Z
--- license: apache-2.0 base_model: tiiuae/falcon-7b tags: - generated_from_trainer model-index: - name: Falcon-CN 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. --> # Falcon-CN This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.2484 ## 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: 0.0002 - 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 - training_steps: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.5969 | 0.24 | 10 | 2.5105 | | 2.332 | 0.49 | 20 | 2.4691 | | 2.418 | 0.73 | 30 | 2.4289 | | 2.4031 | 0.98 | 40 | 2.4040 | | 2.3109 | 1.22 | 50 | 2.3807 | | 2.3516 | 1.46 | 60 | 2.3600 | | 2.2906 | 1.71 | 70 | 2.3406 | | 2.3594 | 1.95 | 80 | 2.3265 | | 2.2031 | 2.2 | 90 | 2.3151 | | 2.25 | 2.44 | 100 | 2.3039 | | 2.2148 | 2.68 | 110 | 2.2911 | | 2.2594 | 2.93 | 120 | 2.2803 | | 2.1844 | 3.17 | 130 | 2.2752 | | 2.0914 | 3.41 | 140 | 2.2714 | | 2.2008 | 3.66 | 150 | 2.2624 | | 2.2109 | 3.9 | 160 | 2.2586 | | 2.1648 | 4.15 | 170 | 2.2548 | | 2.1484 | 4.39 | 180 | 2.2535 | | 2.193 | 4.63 | 190 | 2.2484 | | 2.1219 | 4.88 | 200 | 2.2484 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Kudod/hoa-1b4_model_nmt_test
Kudod
2024-03-08T07:51:37Z
0
0
peft
[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:vlsp-2023-vllm/hoa-1b4", "base_model:adapter:vlsp-2023-vllm/hoa-1b4", "license:bigscience-bloom-rail-1.0", "region:us" ]
null
2024-03-08T07:42:11Z
--- license: bigscience-bloom-rail-1.0 library_name: peft tags: - generated_from_trainer base_model: vlsp-2023-vllm/hoa-1b4 model-index: - name: hoa-1b4_model_nmt_test 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. --> # hoa-1b4_model_nmt_test This model is a fine-tuned version of [vlsp-2023-vllm/hoa-1b4](https://huggingface.co/vlsp-2023-vllm/hoa-1b4) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0045 ## 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: 4e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 21 | 2.8255 | | No log | 2.0 | 42 | 2.3028 | | No log | 3.0 | 63 | 1.8727 | | No log | 4.0 | 84 | 1.5161 | | No log | 5.0 | 105 | 1.2181 | | No log | 6.0 | 126 | 0.9991 | | No log | 7.0 | 147 | 0.7980 | | No log | 8.0 | 168 | 0.6372 | | No log | 9.0 | 189 | 0.5075 | | No log | 10.0 | 210 | 0.4042 | | No log | 11.0 | 231 | 0.3321 | | No log | 12.0 | 252 | 0.2716 | | No log | 13.0 | 273 | 0.2143 | | No log | 14.0 | 294 | 0.1740 | | No log | 15.0 | 315 | 0.1397 | | No log | 16.0 | 336 | 0.1263 | | No log | 17.0 | 357 | 0.0990 | | No log | 18.0 | 378 | 0.0853 | | No log | 19.0 | 399 | 0.0678 | | No log | 20.0 | 420 | 0.0546 | | No log | 21.0 | 441 | 0.0476 | | No log | 22.0 | 462 | 0.0441 | | No log | 23.0 | 483 | 0.0367 | | 0.7202 | 24.0 | 504 | 0.0292 | | 0.7202 | 25.0 | 525 | 0.0241 | | 0.7202 | 26.0 | 546 | 0.0227 | | 0.7202 | 27.0 | 567 | 0.0207 | | 0.7202 | 28.0 | 588 | 0.0186 | | 0.7202 | 29.0 | 609 | 0.0168 | | 0.7202 | 30.0 | 630 | 0.0139 | | 0.7202 | 31.0 | 651 | 0.0126 | | 0.7202 | 32.0 | 672 | 0.0113 | | 0.7202 | 33.0 | 693 | 0.0113 | | 0.7202 | 34.0 | 714 | 0.0107 | | 0.7202 | 35.0 | 735 | 0.0099 | | 0.7202 | 36.0 | 756 | 0.0087 | | 0.7202 | 37.0 | 777 | 0.0085 | | 0.7202 | 38.0 | 798 | 0.0080 | | 0.7202 | 39.0 | 819 | 0.0077 | | 0.7202 | 40.0 | 840 | 0.0072 | | 0.7202 | 41.0 | 861 | 0.0071 | | 0.7202 | 42.0 | 882 | 0.0070 | | 0.7202 | 43.0 | 903 | 0.0068 | | 0.7202 | 44.0 | 924 | 0.0064 | | 0.7202 | 45.0 | 945 | 0.0063 | | 0.7202 | 46.0 | 966 | 0.0061 | | 0.7202 | 47.0 | 987 | 0.0061 | | 0.0146 | 48.0 | 1008 | 0.0060 | | 0.0146 | 49.0 | 1029 | 0.0058 | | 0.0146 | 50.0 | 1050 | 0.0059 | | 0.0146 | 51.0 | 1071 | 0.0067 | | 0.0146 | 52.0 | 1092 | 0.0056 | | 0.0146 | 53.0 | 1113 | 0.0055 | | 0.0146 | 54.0 | 1134 | 0.0055 | | 0.0146 | 55.0 | 1155 | 0.0053 | | 0.0146 | 56.0 | 1176 | 0.0055 | | 0.0146 | 57.0 | 1197 | 0.0055 | | 0.0146 | 58.0 | 1218 | 0.0057 | | 0.0146 | 59.0 | 1239 | 0.0053 | | 0.0146 | 60.0 | 1260 | 0.0052 | | 0.0146 | 61.0 | 1281 | 0.0052 | | 0.0146 | 62.0 | 1302 | 0.0051 | | 0.0146 | 63.0 | 1323 | 0.0050 | | 0.0146 | 64.0 | 1344 | 0.0049 | | 0.0146 | 65.0 | 1365 | 0.0050 | | 0.0146 | 66.0 | 1386 | 0.0049 | | 0.0146 | 67.0 | 1407 | 0.0049 | | 0.0146 | 68.0 | 1428 | 0.0050 | | 0.0146 | 69.0 | 1449 | 0.0049 | | 0.0146 | 70.0 | 1470 | 0.0049 | | 0.0146 | 71.0 | 1491 | 0.0048 | | 0.0064 | 72.0 | 1512 | 0.0048 | | 0.0064 | 73.0 | 1533 | 0.0047 | | 0.0064 | 74.0 | 1554 | 0.0048 | | 0.0064 | 75.0 | 1575 | 0.0048 | | 0.0064 | 76.0 | 1596 | 0.0047 | | 0.0064 | 77.0 | 1617 | 0.0047 | | 0.0064 | 78.0 | 1638 | 0.0047 | | 0.0064 | 79.0 | 1659 | 0.0047 | | 0.0064 | 80.0 | 1680 | 0.0048 | | 0.0064 | 81.0 | 1701 | 0.0046 | | 0.0064 | 82.0 | 1722 | 0.0046 | | 0.0064 | 83.0 | 1743 | 0.0046 | | 0.0064 | 84.0 | 1764 | 0.0046 | | 0.0064 | 85.0 | 1785 | 0.0046 | | 0.0064 | 86.0 | 1806 | 0.0046 | | 0.0064 | 87.0 | 1827 | 0.0046 | | 0.0064 | 88.0 | 1848 | 0.0046 | | 0.0064 | 89.0 | 1869 | 0.0046 | | 0.0064 | 90.0 | 1890 | 0.0046 | | 0.0064 | 91.0 | 1911 | 0.0045 | | 0.0064 | 92.0 | 1932 | 0.0045 | | 0.0064 | 93.0 | 1953 | 0.0045 | | 0.0064 | 94.0 | 1974 | 0.0045 | | 0.0064 | 95.0 | 1995 | 0.0045 | | 0.0052 | 96.0 | 2016 | 0.0045 | | 0.0052 | 97.0 | 2037 | 0.0045 | | 0.0052 | 98.0 | 2058 | 0.0045 | | 0.0052 | 99.0 | 2079 | 0.0045 | | 0.0052 | 100.0 | 2100 | 0.0045 | ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.5 - Tokenizers 0.15.2
zongxiao/ppo-LunarLander-v2-local3
zongxiao
2024-03-08T07:50:26Z
0
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T07:50:16Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -260.90 +/- 76.91 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
sujayC66/bart_samsum
sujayC66
2024-03-08T07:50:25Z
93
0
transformers
[ "transformers", "tensorboard", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-large-cnn", "base_model:finetune:facebook/bart-large-cnn", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2024-03-08T07:47:38Z
--- license: mit base_model: facebook/bart-large-cnn tags: - generated_from_trainer model-index: - name: bart_samsum 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. --> # bart_samsum This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Mena55/videomae-base-finetuned-kinetics_m_v11
Mena55
2024-03-08T07:49:54Z
49
0
transformers
[ "transformers", "tensorboard", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:MCG-NJU/videomae-base-finetuned-kinetics", "base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
video-classification
2024-03-08T07:10:02Z
--- license: cc-by-nc-4.0 base_model: MCG-NJU/videomae-base-finetuned-kinetics tags: - generated_from_trainer model-index: - name: videomae-base-finetuned-kinetics_m_v11 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. --> # videomae-base-finetuned-kinetics_m_v11 This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0009 - eval_accuracy: 1.0 - eval_runtime: 26.6647 - eval_samples_per_second: 0.75 - eval_steps_per_second: 0.188 - epoch: 2.2 - step: 261 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 430 ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ingeol/q2d_5
ingeol
2024-03-08T07:49:38Z
47
0
sentence-transformers
[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2024-03-08T07:48:21Z
--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/q2d_5 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/q2d_5') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/q2d_5') model = AutoModel.from_pretrained('ingeol/q2d_5') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/q2d_5) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 7797 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 5, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, '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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
lapp0/open_hermes_query_expansion
lapp0
2024-03-08T07:46:16Z
0
0
peft
[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B", "license:apache-2.0", "region:us" ]
null
2024-03-08T01:25:27Z
--- license: apache-2.0 library_name: peft tags: - trl - sft - unsloth - generated_from_trainer base_model: teknium/OpenHermes-2.5-Mistral-7B model-index: - name: open_hermes_query_expansion 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. --> # open_hermes_query_expansion This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0409 ## 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: 0.0001 - 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: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7964 | 1.0 | 178 | 0.5460 | | 0.4356 | 2.0 | 356 | 0.1952 | | 0.1109 | 3.0 | 534 | 0.0663 | | 0.0279 | 4.0 | 712 | 0.0390 | | 0.0023 | 5.0 | 890 | 0.0409 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
lwit/om_de_en_nctx_model
lwit
2024-03-08T07:45:42Z
91
0
transformers
[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:facebook/mbart-large-50-many-to-many-mmt", "base_model:finetune:facebook/mbart-large-50-many-to-many-mmt", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2024-03-08T07:13:56Z
--- base_model: facebook/mbart-large-50-many-to-many-mmt tags: - generated_from_trainer model-index: - name: om_de_en_nctx_model 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. --> # om_de_en_nctx_model This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0640 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9692 | 1.0 | 514 | 0.0673 | | 0.0419 | 2.0 | 1028 | 0.0640 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
KUKU0404/pokemon-lora
KUKU0404
2024-03-08T07:44:32Z
2
0
diffusers
[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]
text-to-image
2024-03-08T06:08:20Z
--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora inference: true base_model: runwayml/stable-diffusion-v1-5 --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - KUKU0404/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Duxiaoman-DI/XuanYuan2-70B-Chat-8bit
Duxiaoman-DI
2024-03-08T07:43:52Z
14
1
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "bitsandbytes", "region:us" ]
text-generation
2024-02-04T12:19:24Z
--- license: llama2 --- ## 介绍 XuanYuan2-70B系列模型是在[XuanYuan-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B)基座模型基础上，使用更多高质量的语料进行继续预训练和指令微调，并进行基于人类反馈的强化训练而得到。相比第一代XuanYuan-70B系列模型，第二代模型在通用性、安全性和金融能力上都得到了明显提高，模型输出更加符合人类偏好。同时，第二代模型支持的上下文长度达到16k，能够更好处理长文本输入，适用范围更为广泛。模型细节请参考文档：[Report](https://github.com/Duxiaoman-DI/XuanYuan/blob/main/xuanyuan2_70b_report.md) XuanYuan2-70B系列共包含4个模型，包括基座模型XuanYuan2-70B，chat模型XuanYuan2-70B-Chat，chat模型的量化版本XuanYuan2-70B-Chat-8bit和XuanYuan2-70B-Chat-4bit。各个模型的下载链接为： | 基座模型 | Chat模型 | 8-bit量化Chat模型 | 4-bit量化Chat模型 | | ------------------------------------------------------------ | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | | 🤗 [XuanYuan2-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B) | 🤗 [XuanYuan2-70B-Chat](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat) | 🤗 [XuanYuan2-70B-Chat-8bit](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat-8bit ) | 🤗 [XuanYuan2-70B-Chat-4bit](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat-4bit) | 主要特点： - 使用更多高质量的数据进行继续预训练和指令微调，各项能力持续提升 - 支持的上下文长度达到了16k，使用范围更广 - 基于人类的反馈信息进行强化训练，进一步对齐了人类偏好 ## 模型训练 在XuanYuan-70B基座模型的基础上，我们持续加入更高质量的预训练数据进行训练。同时为了兼顾训练效率和长文本建模，提出了一种**数据分桶的动态预训练方法**。基于数据分桶方式，我们在第一代XuanYuan-70B基座模型的基础上额外训练了大量tokens得到XuanYuan2-70B基座模型，模型的中文理解、金融知识等指标评测均达到不同幅度的提升。 基于XuanYuan2-70B基座模型，我们重新利用更多高质量的指令微调数据来进行指令对齐，主要提升的方向是通用与金融类型的指令数据质量和多样性。 对于指令微调后的模型，我们构建高质量的偏好数据和prompt数据，进行了基于人类反馈的强化训练（Reinforcement learning with human feedback，RLHF），进一步对齐了模型与人类的偏好，使模型表现能更符合人类需求。模型在通用性、安全性、金融领域内的表现有了较明显的提升。 ## 性能评测 类似XuanYuan-70B，我们也对XuanYuan2-70B进行了通用性评测和金融评测。 ### 通用评测 通用评测的目标是观察XuanYuan2-70B在使用更多高质量数据进行继续预训练后，英文能力是否得到了保持，中文能力是否得到了增强。同样，我们也选择MMLU来测试模型在英文场景下的通用能力，同时使用CEVAL和CMMLU来测试模型在中文场景下的各项能力。评测结果如下表所示。从表中可以看出，相比XuanYuan-70B，XuanYuan2-70B的中文能力得到了进一步提升，同时英文能力也没有出现明显的下降，整体表现符合预期。这一方面证明了我们所做的各项优化的有效性，另一方面也显示出了XuanYuan2-70B强大的通用能力。值得注意的是，榜单结果并不完全代表模型的实际性能表现，即便在CEVAL和CMMLU上我们的评测结果超过了GPT4，但实际中我们模型的表现和GPT4还存在明显的差距，我们将继续优化和提升轩辕模型的各项能力。 | 模型 | MMLU | CEVAL | CMMLU | | ------------- | --------- | -------- | --------- | | LLaMA2-70B | 68.9 | 52.1 | 53.11 | | XuanYuan-70B | 70.9 | 71.9 | 71.10 | | XuanYuan2-70B | 70.8 | **72.7** | **72.7** | | GPT4 | **83.93** | 68.4 | 70.95 | ### 金融评测 我们在[FinanceIQ](https://github.com/Duxiaoman-DI/XuanYuan/tree/main/FinanceIQ)上评测了模型的金融能力。FinanceIQ是一个专业的金融领域评测集，其涵盖了10个金融大类及36个金融小类，总计7173个单项选择题，某种程度上可客观反应模型的金融能力。评测结果如下表所示。从表中结果可以看出，经过继续优化训练后，XuanYuan2-70B的综合金融能力得到了进一步提升，这再次证明了我们所做的一系列优化的有效性。同时我们也发现一些细分类目上模型的能力出现了一定程度的退化，这说明模型仍存在一定的优化空间，我们将继续优化提升轩辕模型的金融能力。 | 模型 | 平均分 | 注册会计师 | 银行从业资格 | 证券从业资格 | 基金从业资格 | 保险从业资格 | 经济师 | 税务师 | 期货从业资格 | 理财规划师 | 精算师 | | ------------- | --------- | -------- | ---------- | ---------- | ----------- | --------- | ----- | ----- | ---------- | -------- | ----- | | XuanYuan-70B | 67.56 | 69.49 | 76.40 | 69.56 | 74.89 | 67.82 | 84.81 | 58.4 | 71.59 | 65.15 | 37.50 | | XuanYuan2-70B | **67.83** | 68.63 | 69.72 | 79.1 | 71.51 | 69.68 | 84.81 | 58.2 | 72.98 | 71.86 | 31.82 | | GPT4 | 60.05 | 52.33 | 68.72 | 64.8 | 68.81 | 68.68 | 75.58 | 46.93 | 63.51 | 63.84 | 27.27 | ## 快速使用 XuanYuan2-70B系列模型的硬件需求、软件依赖、Base及Chat模型使用方法和XuanYuan-70B系列模型一致。请参考[XuanYuan-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B)系列模型的介绍内容。 为降低硬件需求，我们也提供了XuanYuan2-70B-Chat模型的8bit和4bit量化版本。 ### 8bit模型 在8bit量化算法上，我们使用目前社区广泛使用的bitsandbytes库。经测试，8bit量化对模型的性能损失很低。8bit模型的使用方式如下所示（需注意promopt格式，我们在训练时设置了system message）： ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer model_name_or_path = "/your/model/path" tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=False, legacy=True) model = LlamaForCausalLM.from_pretrained(model_name_or_path,torch_dtype=torch.float16, device_map="auto") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=1.1) outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True) print(f"输出: {outputs}") ``` ### 4bit模型： 在4bit量化算法上，我们使用[auto-gptq](https://github.com/PanQiWei/AutoGPTQ)工具。4bit模型使用方式如下所示，同样，需要对齐我们的prompt格式： ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer from auto_gptq import AutoGPTQForCausalLM model_name_or_path = "/your/model/path" tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=False, legacy=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,torch_dtype=torch.float16, device_map="auto") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=1.1) outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True) print(f"输出: {outputs}") ``` ### 在vLLM下使用4bit模型： 普通HuggingFace的推理脚本运行gptq量化的4bit模型时，推理的速度很慢，并不实用。而最新版本的vLLM已经支持包含gptq在内的多种量化模型的加载，vLLM依靠量化的加速算子以及pagedAttention，continue batching以及一些调度机制，可以实现至少10倍的推理吞吐的提升。 您可以安装最新版本的vLLM并使用以下脚本使用我们的4bit量化模型： ```python from vllm import LLM, SamplingParams sampling_params = SamplingParams(temperature=0.7, top_p=0.95,max_tokens=256) llm = LLM(model="/your/model/path", quantization="gptq", dtype="float16") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") result = llm.generate(prompt, sampling_params) result_output = [[output.outputs[0].text, output.outputs[0].token_ids] for output in result] print(f"输出：{result_output[0]}") ``` ### 生成速度评估 我们测试了不同模型（量化前和量化后）在不同推理方式（HuggingFace、vLLM）下的生成速度，结果如下所示： * 全量70B模型推理吞吐是： 8.26 token/s * 4bit 70B模型推理吞吐是： 0.70 token/s * 8bit 70B模型推理吞吐是： 3.05 token/s * 4bit 70B模型vllm推理吞吐是： 60.32 token/s * 全量70B模型vllm推理吞吐是： 41.80 token/s 在所有测试中，我们均设置batchsize=1。上述前三项都是普通HuggingFace推理脚本的测试结果，可以看到量化后模型推理速度并无提升。最后两项是vLLM的推理测试结果，比起HuggingFace推理，可以看出vLLM可用性更高，模型生成速度均有显著提升。
Duxiaoman-DI/XuanYuan2-70B-Chat
Duxiaoman-DI
2024-03-08T07:42:38Z
43
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-02-04T17:26:11Z
--- license: llama2 --- ## 介绍 XuanYuan2-70B系列模型是在[XuanYuan-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B)基座模型基础上，使用更多高质量的语料进行继续预训练和指令微调，并进行基于人类反馈的强化训练而得到。相比第一代XuanYuan-70B系列模型，第二代模型在通用性、安全性和金融能力上都得到了明显提高，模型输出更加符合人类偏好。同时，第二代模型支持的上下文长度达到16k，能够更好处理长文本输入，适用范围更为广泛。模型细节请参考文档：[Report](https://github.com/Duxiaoman-DI/XuanYuan/blob/main/xuanyuan2_70b_report.md) XuanYuan2-70B系列共包含4个模型，包括基座模型XuanYuan2-70B，chat模型XuanYuan2-70B-Chat，chat模型的量化版本XuanYuan2-70B-Chat-8bit和XuanYuan2-70B-Chat-4bit。各个模型的下载链接为： | 基座模型 | Chat模型 | 8-bit量化Chat模型 | 4-bit量化Chat模型 | | ------------------------------------------------------------ | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | | 🤗 [XuanYuan2-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B) | 🤗 [XuanYuan2-70B-Chat](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat) | 🤗 [XuanYuan2-70B-Chat-8bit](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat-8bit ) | 🤗 [XuanYuan2-70B-Chat-4bit](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat-4bit) | 主要特点： - 使用更多高质量的数据进行继续预训练和指令微调，各项能力持续提升 - 支持的上下文长度达到了16k，使用范围更广 - 基于人类的反馈信息进行强化训练，进一步对齐了人类偏好 ## 模型训练 在XuanYuan-70B基座模型的基础上，我们持续加入更高质量的预训练数据进行训练。同时为了兼顾训练效率和长文本建模，提出了一种**数据分桶的动态预训练方法**。基于数据分桶方式，我们在第一代XuanYuan-70B基座模型的基础上额外训练了大量tokens得到XuanYuan2-70B基座模型，模型的中文理解、金融知识等指标评测均达到不同幅度的提升。 基于XuanYuan2-70B基座模型，我们重新利用更多高质量的指令微调数据来进行指令对齐，主要提升的方向是通用与金融类型的指令数据质量和多样性。 对于指令微调后的模型，我们构建高质量的偏好数据和prompt数据，进行了基于人类反馈的强化训练（Reinforcement learning with human feedback，RLHF），进一步对齐了模型与人类的偏好，使模型表现能更符合人类需求。模型在通用性、安全性、金融领域内的表现有了较明显的提升。 ## 性能评测 类似XuanYuan-70B，我们也对XuanYuan2-70B进行了通用性评测和金融评测。 ### 通用评测 通用评测的目标是观察XuanYuan2-70B在使用更多高质量数据进行继续预训练后，英文能力是否得到了保持，中文能力是否得到了增强。同样，我们也选择MMLU来测试模型在英文场景下的通用能力，同时使用CEVAL和CMMLU来测试模型在中文场景下的各项能力。评测结果如下表所示。从表中可以看出，相比XuanYuan-70B，XuanYuan2-70B的中文能力得到了进一步提升，同时英文能力也没有出现明显的下降，整体表现符合预期。这一方面证明了我们所做的各项优化的有效性，另一方面也显示出了XuanYuan2-70B强大的通用能力。值得注意的是，榜单结果并不完全代表模型的实际性能表现，即便在CEVAL和CMMLU上我们的评测结果超过了GPT4，但实际中我们模型的表现和GPT4还存在明显的差距，我们将继续优化和提升轩辕模型的各项能力。 | 模型 | MMLU | CEVAL | CMMLU | | ------------- | --------- | -------- | --------- | | LLaMA2-70B | 68.9 | 52.1 | 53.11 | | XuanYuan-70B | 70.9 | 71.9 | 71.10 | | XuanYuan2-70B | 70.8 | **72.7** | **72.7** | | GPT4 | **83.93** | 68.4 | 70.95 | ### 金融评测 我们在[FinanceIQ](https://github.com/Duxiaoman-DI/XuanYuan/tree/main/FinanceIQ)上评测了模型的金融能力。FinanceIQ是一个专业的金融领域评测集，其涵盖了10个金融大类及36个金融小类，总计7173个单项选择题，某种程度上可客观反应模型的金融能力。评测结果如下表所示。从表中结果可以看出，经过继续优化训练后，XuanYuan2-70B的综合金融能力得到了进一步提升，这再次证明了我们所做的一系列优化的有效性。同时我们也发现一些细分类目上模型的能力出现了一定程度的退化，这说明模型仍存在一定的优化空间，我们将继续优化提升轩辕模型的金融能力。 | 模型 | 平均分 | 注册会计师 | 银行从业资格 | 证券从业资格 | 基金从业资格 | 保险从业资格 | 经济师 | 税务师 | 期货从业资格 | 理财规划师 | 精算师 | | ------------- | --------- | -------- | ---------- | ---------- | ----------- | --------- | ----- | ----- | ---------- | -------- | ----- | | XuanYuan-70B | 67.56 | 69.49 | 76.40 | 69.56 | 74.89 | 67.82 | 84.81 | 58.4 | 71.59 | 65.15 | 37.50 | | XuanYuan2-70B | **67.83** | 68.63 | 69.72 | 79.1 | 71.51 | 69.68 | 84.81 | 58.2 | 72.98 | 71.86 | 31.82 | | GPT4 | 60.05 | 52.33 | 68.72 | 64.8 | 68.81 | 68.68 | 75.58 | 46.93 | 63.51 | 63.84 | 27.27 | ## 快速使用 XuanYuan2-70B系列模型的硬件需求、软件依赖、Base及Chat模型使用方法和XuanYuan-70B系列模型一致。请参考[XuanYuan-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B)系列模型的介绍内容。 为降低硬件需求，我们也提供了XuanYuan2-70B-Chat模型的8bit和4bit量化版本。 ### 8bit模型 在8bit量化算法上，我们使用目前社区广泛使用的bitsandbytes库。经测试，8bit量化对模型的性能损失很低。8bit模型的使用方式如下所示（需注意promopt格式，我们在训练时设置了system message）： ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer model_name_or_path = "/your/model/path" tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=False, legacy=True) model = LlamaForCausalLM.from_pretrained(model_name_or_path,torch_dtype=torch.float16, device_map="auto") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=1.1) outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True) print(f"输出: {outputs}") ``` ### 4bit模型： 在4bit量化算法上，我们使用[auto-gptq](https://github.com/PanQiWei/AutoGPTQ)工具。4bit模型使用方式如下所示，同样，需要对齐我们的prompt格式： ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer from auto_gptq import AutoGPTQForCausalLM model_name_or_path = "/your/model/path" tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=False, legacy=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,torch_dtype=torch.float16, device_map="auto") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=1.1) outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True) print(f"输出: {outputs}") ``` ### 在vLLM下使用4bit模型： 普通HuggingFace的推理脚本运行gptq量化的4bit模型时，推理的速度很慢，并不实用。而最新版本的vLLM已经支持包含gptq在内的多种量化模型的加载，vLLM依靠量化的加速算子以及pagedAttention，continue batching以及一些调度机制，可以实现至少10倍的推理吞吐的提升。 您可以安装最新版本的vLLM并使用以下脚本使用我们的4bit量化模型： ```python from vllm import LLM, SamplingParams sampling_params = SamplingParams(temperature=0.7, top_p=0.95,max_tokens=256) llm = LLM(model="/your/model/path", quantization="gptq", dtype="float16") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") result = llm.generate(prompt, sampling_params) result_output = [[output.outputs[0].text, output.outputs[0].token_ids] for output in result] print(f"输出：{result_output[0]}") ``` ### 生成速度评估 我们测试了不同模型（量化前和量化后）在不同推理方式（HuggingFace、vLLM）下的生成速度，结果如下所示： * 全量70B模型推理吞吐是： 8.26 token/s * 4bit 70B模型推理吞吐是： 0.70 token/s * 8bit 70B模型推理吞吐是： 3.05 token/s * 4bit 70B模型vllm推理吞吐是： 60.32 token/s * 全量70B模型vllm推理吞吐是： 41.80 token/s 在所有测试中，我们均设置batchsize=1。上述前三项都是普通HuggingFace推理脚本的测试结果，可以看到量化后模型推理速度并无提升。最后两项是vLLM的推理测试结果，比起HuggingFace推理，可以看出vLLM可用性更高，模型生成速度均有显著提升。
zongxiao/ppo-LunarLander-v2-local2
zongxiao
2024-03-08T07:41:12Z
4
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T07:41:01Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -336.87 +/- 321.97 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
Duxiaoman-DI/XuanYuan2-70B
Duxiaoman-DI
2024-03-08T07:38:21Z
0
0
null
[ "safetensors", "license:llama2", "region:us" ]
null
2024-02-04T08:47:21Z
--- license: llama2 --- ## 介绍 XuanYuan2-70B系列模型是在[XuanYuan-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B)基座模型基础上，使用更多高质量的语料进行继续预训练和指令微调，并进行基于人类反馈的强化训练而得到。相比第一代XuanYuan-70B系列模型，第二代模型在通用性、安全性和金融能力上都得到了明显提高，模型输出更加符合人类偏好。同时，第二代模型支持的上下文长度达到16k，能够更好处理长文本输入，适用范围更为广泛。模型细节请参考文档：[Report](https://github.com/Duxiaoman-DI/XuanYuan/blob/main/xuanyuan2_70b_report.md) XuanYuan2-70B系列共包含4个模型，包括基座模型XuanYuan2-70B，chat模型XuanYuan2-70B-Chat，chat模型的量化版本XuanYuan2-70B-Chat-8bit和XuanYuan2-70B-Chat-4bit。各个模型的下载链接为： | 基座模型 | Chat模型 | 8-bit量化Chat模型 | 4-bit量化Chat模型 | | ------------------------------------------------------------ | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------- | | 🤗 [XuanYuan2-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B) | 🤗 [XuanYuan2-70B-Chat](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat) | 🤗 [XuanYuan2-70B-Chat-8bit](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat-8bit ) | 🤗 [XuanYuan2-70B-Chat-4bit](https://huggingface.co/Duxiaoman-DI/XuanYuan2-70B-Chat-4bit) | 主要特点： - 使用更多高质量的数据进行继续预训练和指令微调，各项能力持续提升 - 支持的上下文长度达到了16k，使用范围更广 - 基于人类的反馈信息进行强化训练，进一步对齐了人类偏好 ## 模型训练 在XuanYuan-70B基座模型的基础上，我们持续加入更高质量的预训练数据进行训练。同时为了兼顾训练效率和长文本建模，提出了一种**数据分桶的动态预训练方法**。基于数据分桶方式，我们在第一代XuanYuan-70B基座模型的基础上额外训练了大量tokens得到XuanYuan2-70B基座模型，模型的中文理解、金融知识等指标评测均达到不同幅度的提升。 基于XuanYuan2-70B基座模型，我们重新利用更多高质量的指令微调数据来进行指令对齐，主要提升的方向是通用与金融类型的指令数据质量和多样性。 对于指令微调后的模型，我们构建高质量的偏好数据和prompt数据，进行了基于人类反馈的强化训练（Reinforcement learning with human feedback，RLHF），进一步对齐了模型与人类的偏好，使模型表现能更符合人类需求。模型在通用性、安全性、金融领域内的表现有了较明显的提升。 ## 性能评测 类似XuanYuan-70B，我们也对XuanYuan2-70B进行了通用性评测和金融评测。 ### 通用评测 通用评测的目标是观察XuanYuan2-70B在使用更多高质量数据进行继续预训练后，英文能力是否得到了保持，中文能力是否得到了增强。同样，我们也选择MMLU来测试模型在英文场景下的通用能力，同时使用CEVAL和CMMLU来测试模型在中文场景下的各项能力。评测结果如下表所示。从表中可以看出，相比XuanYuan-70B，XuanYuan2-70B的中文能力得到了进一步提升，同时英文能力也没有出现明显的下降，整体表现符合预期。这一方面证明了我们所做的各项优化的有效性，另一方面也显示出了XuanYuan2-70B强大的通用能力。值得注意的是，榜单结果并不完全代表模型的实际性能表现，即便在CEVAL和CMMLU上我们的评测结果超过了GPT4，但实际中我们模型的表现和GPT4还存在明显的差距，我们将继续优化和提升轩辕模型的各项能力。 | 模型 | MMLU | CEVAL | CMMLU | | ------------- | --------- | -------- | --------- | | LLaMA2-70B | 68.9 | 52.1 | 53.11 | | XuanYuan-70B | 70.9 | 71.9 | 71.10 | | XuanYuan2-70B | 70.8 | **72.7** | **72.7** | | GPT4 | **83.93** | 68.4 | 70.95 | ### 金融评测 我们在[FinanceIQ](https://github.com/Duxiaoman-DI/XuanYuan/tree/main/FinanceIQ)上评测了模型的金融能力。FinanceIQ是一个专业的金融领域评测集，其涵盖了10个金融大类及36个金融小类，总计7173个单项选择题，某种程度上可客观反应模型的金融能力。评测结果如下表所示。从表中结果可以看出，经过继续优化训练后，XuanYuan2-70B的综合金融能力得到了进一步提升，这再次证明了我们所做的一系列优化的有效性。同时我们也发现一些细分类目上模型的能力出现了一定程度的退化，这说明模型仍存在一定的优化空间，我们将继续优化提升轩辕模型的金融能力。 | 模型 | 平均分 | 注册会计师 | 银行从业资格 | 证券从业资格 | 基金从业资格 | 保险从业资格 | 经济师 | 税务师 | 期货从业资格 | 理财规划师 | 精算师 | | ------------- | --------- | -------- | ---------- | ---------- | ----------- | --------- | ----- | ----- | ---------- | -------- | ----- | | XuanYuan-70B | 67.56 | 69.49 | 76.40 | 69.56 | 74.89 | 67.82 | 84.81 | 58.4 | 71.59 | 65.15 | 37.50 | | XuanYuan2-70B | **67.83** | 68.63 | 69.72 | 79.1 | 71.51 | 69.68 | 84.81 | 58.2 | 72.98 | 71.86 | 31.82 | | GPT4 | 60.05 | 52.33 | 68.72 | 64.8 | 68.81 | 68.68 | 75.58 | 46.93 | 63.51 | 63.84 | 27.27 | ## 快速使用 XuanYuan2-70B系列模型的硬件需求、软件依赖、Base及Chat模型使用方法和XuanYuan-70B系列模型一致。请参考[XuanYuan-70B](https://huggingface.co/Duxiaoman-DI/XuanYuan-70B)系列模型的介绍内容。 为降低硬件需求，我们也提供了XuanYuan2-70B-Chat模型的8bit和4bit量化版本。 ### 8bit模型 在8bit量化算法上，我们使用目前社区广泛使用的bitsandbytes库。经测试，8bit量化对模型的性能损失很低。8bit模型的使用方式如下所示（需注意promopt格式，我们在训练时设置了system message）： ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer model_name_or_path = "/your/model/path" tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=False, legacy=True) model = LlamaForCausalLM.from_pretrained(model_name_or_path,torch_dtype=torch.float16, device_map="auto") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=1.1) outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True) print(f"输出: {outputs}") ``` ### 4bit模型： 在4bit量化算法上，我们使用[auto-gptq](https://github.com/PanQiWei/AutoGPTQ)工具。4bit模型使用方式如下所示，同样，需要对齐我们的prompt格式： ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer from auto_gptq import AutoGPTQForCausalLM model_name_or_path = "/your/model/path" tokenizer = LlamaTokenizer.from_pretrained(model_name_or_path, use_fast=False, legacy=True) model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,torch_dtype=torch.float16, device_map="auto") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=256, repetition_penalty=1.1) outputs = tokenizer.decode(outputs.cpu()[0][len(inputs.input_ids[0]):], skip_special_tokens=True) print(f"输出: {outputs}") ``` ### 在vLLM下使用4bit模型： 普通HuggingFace的推理脚本运行gptq量化的4bit模型时，推理的速度很慢，并不实用。而最新版本的vLLM已经支持包含gptq在内的多种量化模型的加载，vLLM依靠量化的加速算子以及pagedAttention，continue batching以及一些调度机制，可以实现至少10倍的推理吞吐的提升。 您可以安装最新版本的vLLM并使用以下脚本使用我们的4bit量化模型： ```python from vllm import LLM, SamplingParams sampling_params = SamplingParams(temperature=0.7, top_p=0.95,max_tokens=256) llm = LLM(model="/your/model/path", quantization="gptq", dtype="float16") system_message = "以下是用户和人工智能助手之间的对话。用户以Human开头，人工智能助手以Assistant开头，会对人类提出的问题给出有帮助、高质量、详细和礼貌的回答，并且总是拒绝参与 与不道德、不安全、有争议、政治敏感等相关的话题、问题和指示。\n" seps = [" ", "</s>"] roles = ["Human", "Assistant"] content = "介绍下你自己" prompt = system_message + seps[0] + roles[0] + ": " + content + seps[0] + roles[1] + ":" print(f"输入: {content}") result = llm.generate(prompt, sampling_params) result_output = [[output.outputs[0].text, output.outputs[0].token_ids] for output in result] print(f"输出：{result_output[0]}") ``` ### 生成速度评估 我们测试了不同模型（量化前和量化后）在不同推理方式（HuggingFace、vLLM）下的生成速度，结果如下所示： * 全量70B模型推理吞吐是： 8.26 token/s * 4bit 70B模型推理吞吐是： 0.70 token/s * 8bit 70B模型推理吞吐是： 3.05 token/s * 4bit 70B模型vllm推理吞吐是： 60.32 token/s * 全量70B模型vllm推理吞吐是： 41.80 token/s 在所有测试中，我们均设置batchsize=1。上述前三项都是普通HuggingFace推理脚本的测试结果，可以看到量化后模型推理速度并无提升。最后两项是vLLM的推理测试结果，比起HuggingFace推理，可以看出vLLM可用性更高，模型生成速度均有显著提升。
dhiya96/t5-base-finetuned-stocknews_1900_100
dhiya96
2024-03-08T07:36:41Z
6
0
transformers
[ "transformers", "tensorboard", "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
2024-03-08T05:23:35Z
--- license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-base-finetuned-stocknews_1900_100 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. --> # t5-base-finetuned-stocknews_1900_100 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: 1.2997 - Rouge1: 16.6203 - Rouge2: 8.7831 - Rougel: 13.9116 - Rougelsum: 15.4831 - Gen Len: 19.0 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 102 | 1.5488 | 14.6381 | 6.8963 | 12.1802 | 13.6527 | 19.0 | | No log | 2.0 | 204 | 1.4139 | 15.0451 | 6.9216 | 12.6068 | 14.1445 | 19.0 | | No log | 3.0 | 306 | 1.3627 | 15.3864 | 7.115 | 12.6537 | 14.267 | 19.0 | | No log | 4.0 | 408 | 1.3288 | 15.6891 | 7.5106 | 13.0451 | 14.6203 | 19.0 | | 1.8685 | 5.0 | 510 | 1.3087 | 15.8071 | 7.6382 | 13.103 | 14.7587 | 19.0 | | 1.8685 | 6.0 | 612 | 1.2938 | 15.6775 | 7.6448 | 13.0823 | 14.6034 | 19.0 | | 1.8685 | 7.0 | 714 | 1.2870 | 15.7672 | 7.89 | 13.3325 | 14.7821 | 19.0 | | 1.8685 | 8.0 | 816 | 1.2779 | 16.1616 | 8.1642 | 13.4471 | 15.0305 | 19.0 | | 1.8685 | 9.0 | 918 | 1.2731 | 16.3679 | 8.4804 | 13.7618 | 15.3468 | 19.0 | | 1.1991 | 10.0 | 1020 | 1.2695 | 16.2821 | 8.456 | 13.7692 | 15.2461 | 19.0 | | 1.1991 | 11.0 | 1122 | 1.2647 | 16.4056 | 8.5019 | 13.7217 | 15.3711 | 19.0 | | 1.1991 | 12.0 | 1224 | 1.2667 | 16.4259 | 8.6692 | 13.8396 | 15.4122 | 19.0 | | 1.1991 | 13.0 | 1326 | 1.2654 | 16.6988 | 8.9574 | 14.0239 | 15.6864 | 19.0 | | 1.1991 | 14.0 | 1428 | 1.2648 | 16.7394 | 9.0588 | 14.0529 | 15.6644 | 19.0 | | 1.0382 | 15.0 | 1530 | 1.2642 | 16.6864 | 9.106 | 13.9046 | 15.5687 | 19.0 | | 1.0382 | 16.0 | 1632 | 1.2662 | 16.6786 | 8.8288 | 13.9603 | 15.5724 | 19.0 | | 1.0382 | 17.0 | 1734 | 1.2651 | 16.7446 | 8.9211 | 13.9999 | 15.6617 | 19.0 | | 1.0382 | 18.0 | 1836 | 1.2702 | 16.6361 | 8.8503 | 14.0324 | 15.546 | 19.0 | | 1.0382 | 19.0 | 1938 | 1.2676 | 16.7046 | 9.0089 | 14.073 | 15.6342 | 19.0 | | 0.9273 | 20.0 | 2040 | 1.2732 | 16.4339 | 8.6714 | 13.8422 | 15.44 | 19.0 | | 0.9273 | 21.0 | 2142 | 1.2743 | 16.5655 | 8.7747 | 13.839 | 15.4958 | 19.0 | | 0.9273 | 22.0 | 2244 | 1.2781 | 16.7749 | 8.9154 | 14.1216 | 15.6395 | 19.0 | | 0.9273 | 23.0 | 2346 | 1.2814 | 16.535 | 8.7436 | 13.971 | 15.5056 | 19.0 | | 0.9273 | 24.0 | 2448 | 1.2795 | 16.6612 | 8.7045 | 14.0096 | 15.5692 | 19.0 | | 0.8539 | 25.0 | 2550 | 1.2844 | 16.6083 | 8.6106 | 13.9202 | 15.5641 | 19.0 | | 0.8539 | 26.0 | 2652 | 1.2817 | 16.6449 | 8.8127 | 14.0562 | 15.5792 | 19.0 | | 0.8539 | 27.0 | 2754 | 1.2856 | 16.6185 | 8.7475 | 14.0134 | 15.5439 | 19.0 | | 0.8539 | 28.0 | 2856 | 1.2868 | 16.4913 | 8.7293 | 13.9367 | 15.4702 | 19.0 | | 0.8539 | 29.0 | 2958 | 1.2905 | 16.4887 | 8.6461 | 13.8893 | 15.4342 | 19.0 | | 0.8006 | 30.0 | 3060 | 1.2893 | 16.5861 | 8.695 | 13.9081 | 15.4307 | 19.0 | | 0.8006 | 31.0 | 3162 | 1.2919 | 16.5972 | 8.8314 | 13.9069 | 15.4967 | 19.0 | | 0.8006 | 32.0 | 3264 | 1.2940 | 16.5957 | 8.789 | 13.9202 | 15.4839 | 19.0 | | 0.8006 | 33.0 | 3366 | 1.2946 | 16.6313 | 8.8011 | 13.9684 | 15.5256 | 19.0 | | 0.8006 | 34.0 | 3468 | 1.2945 | 16.6711 | 8.8915 | 14.0228 | 15.5394 | 19.0 | | 0.7598 | 35.0 | 3570 | 1.2970 | 16.67 | 8.891 | 13.9749 | 15.5174 | 19.0 | | 0.7598 | 36.0 | 3672 | 1.2975 | 16.6223 | 8.7522 | 13.9528 | 15.4761 | 19.0 | | 0.7598 | 37.0 | 3774 | 1.2987 | 16.6444 | 8.8594 | 13.9567 | 15.5117 | 19.0 | | 0.7598 | 38.0 | 3876 | 1.2993 | 16.6444 | 8.8594 | 13.9567 | 15.5117 | 19.0 | | 0.7598 | 39.0 | 3978 | 1.2996 | 16.6196 | 8.8108 | 13.9213 | 15.4806 | 19.0 | | 0.7463 | 40.0 | 4080 | 1.2997 | 16.6203 | 8.7831 | 13.9116 | 15.4831 | 19.0 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
mitkox/Genstruct-7B-mlx
mitkox
2024-03-08T07:35:25Z
66
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "Mistral", "instruct", "finetune", "synthetic", "mlx", "conversational", "en", "base_model:mistralai/Mistral-7B-v0.1", "base_model:finetune:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T07:31:05Z
--- language: - en license: apache-2.0 library_name: transformers tags: - Mistral - instruct - finetune - synthetic - mlx base_model: mistralai/Mistral-7B-v0.1 --- # mitkox/Genstruct-7B-mlx This model was converted to MLX format from [`NousResearch/Genstruct-7B`](). Refer to the [original model card](https://huggingface.co/NousResearch/Genstruct-7B) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mitkox/Genstruct-7B-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
neofung/m3e-ernie-xbase-zh
neofung
2024-03-08T07:23:02Z
0
0
sentence-transformers
[ "sentence-transformers", "sentence-similarity", "mteb", "zh", "en", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
sentence-similarity
2024-03-04T06:11:16Z
--- language: - zh - en tags: - sentence-transformers - sentence-similarity - mteb model-index: - name: zh results: - task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics: - type: cos_sim_pearson value: 36.28363608508365 - type: cos_sim_spearman value: 37.39698005114737 - type: euclidean_pearson value: 36.407377294778186 - type: euclidean_spearman value: 37.396959945459166 - type: manhattan_pearson value: 36.30818480805082 - type: manhattan_spearman value: 37.28435580456356 - task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics: - type: cos_sim_pearson value: 39.918566602029536 - type: cos_sim_spearman value: 42.163555979292155 - type: euclidean_pearson value: 43.24429263158407 - type: euclidean_spearman value: 42.16355485217486 - type: manhattan_pearson value: 43.23108002349145 - type: manhattan_spearman value: 42.156854810425834 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.788000000000004 - type: f1 value: 44.518439064691925 - task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 metrics: - type: cos_sim_pearson value: 67.03414409142314 - type: cos_sim_spearman value: 70.95560250546684 - type: euclidean_pearson value: 69.35644910492917 - type: euclidean_spearman value: 70.95560250269956 - type: manhattan_pearson value: 69.32201332479197 - type: manhattan_spearman value: 70.92406185691 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 metrics: - type: v_measure value: 39.31955168227449 - task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f metrics: - type: v_measure value: 37.8418274237459 - task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: 8d7f1e942507dac42dc58017c1a001c3717da7df metrics: - type: map value: 80.66118119519746 - type: mrr value: 83.47972222222222 - task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: 23d186750531a14a0357ca22cd92d712fd512ea0 metrics: - type: map value: 79.31430375371524 - type: mrr value: 82.10194444444444 - task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics: - type: map_at_1 value: 16.672 - type: map_at_10 value: 26.273000000000003 - type: map_at_100 value: 28.044999999999998 - type: map_at_1000 value: 28.208 - type: map_at_3 value: 22.989 - type: map_at_5 value: 24.737000000000002 - type: mrr_at_1 value: 26.257 - type: mrr_at_10 value: 34.358 - type: mrr_at_100 value: 35.436 - type: mrr_at_1000 value: 35.513 - type: mrr_at_3 value: 31.954 - type: mrr_at_5 value: 33.234 - type: ndcg_at_1 value: 26.257 - type: ndcg_at_10 value: 32.326 - type: ndcg_at_100 value: 39.959 - type: ndcg_at_1000 value: 43.163000000000004 - type: ndcg_at_3 value: 27.700999999999997 - type: ndcg_at_5 value: 29.514000000000003 - type: precision_at_1 value: 26.257 - type: precision_at_10 value: 7.607 - type: precision_at_100 value: 1.388 - type: precision_at_1000 value: 0.179 - type: precision_at_3 value: 16.162000000000003 - type: precision_at_5 value: 11.933 - type: recall_at_1 value: 16.672 - type: recall_at_10 value: 42.135 - type: recall_at_100 value: 74.417 - type: recall_at_1000 value: 96.417 - type: recall_at_3 value: 28.416999999999998 - type: recall_at_5 value: 33.873999999999995 - task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 metrics: - type: cos_sim_accuracy value: 61.11846061334937 - type: cos_sim_ap value: 65.68356716139071 - type: cos_sim_f1 value: 68.15213842637937 - type: cos_sim_precision value: 52.35109717868338 - type: cos_sim_recall value: 97.61515080664017 - type: dot_accuracy value: 61.11846061334937 - type: dot_ap value: 65.68369552204702 - type: dot_f1 value: 68.15213842637937 - type: dot_precision value: 52.35109717868338 - type: dot_recall value: 97.61515080664017 - type: euclidean_accuracy value: 61.11846061334937 - type: euclidean_ap value: 65.68356789608616 - type: euclidean_f1 value: 68.15213842637937 - type: euclidean_precision value: 52.35109717868338 - type: euclidean_recall value: 97.61515080664017 - type: manhattan_accuracy value: 61.17859290438966 - type: manhattan_ap value: 65.68230365595265 - type: manhattan_f1 value: 68.14029363784665 - type: manhattan_precision value: 52.32368783665289 - type: manhattan_recall value: 97.66191255552957 - type: max_accuracy value: 61.17859290438966 - type: max_ap value: 65.68369552204702 - type: max_f1 value: 68.15213842637937 - task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics: - type: map_at_1 value: 51.054 - type: map_at_10 value: 61.926 - type: map_at_100 value: 62.514 - type: map_at_1000 value: 62.529 - type: map_at_3 value: 59.272999999999996 - type: map_at_5 value: 60.943000000000005 - type: mrr_at_1 value: 51.212 - type: mrr_at_10 value: 61.916000000000004 - type: mrr_at_100 value: 62.495999999999995 - type: mrr_at_1000 value: 62.511 - type: mrr_at_3 value: 59.326 - type: mrr_at_5 value: 60.958999999999996 - type: ndcg_at_1 value: 51.212 - type: ndcg_at_10 value: 67.223 - type: ndcg_at_100 value: 69.92699999999999 - type: ndcg_at_1000 value: 70.307 - type: ndcg_at_3 value: 61.873 - type: ndcg_at_5 value: 64.883 - type: precision_at_1 value: 51.212 - type: precision_at_10 value: 8.472 - type: precision_at_100 value: 0.9730000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 23.253 - type: precision_at_5 value: 15.448 - type: recall_at_1 value: 51.054 - type: recall_at_10 value: 83.825 - type: recall_at_100 value: 96.207 - type: recall_at_1000 value: 99.157 - type: recall_at_3 value: 69.31 - type: recall_at_5 value: 76.66 - task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics: - type: map_at_1 value: 21.247 - type: map_at_10 value: 64.793 - type: map_at_100 value: 68.62899999999999 - type: map_at_1000 value: 68.718 - type: map_at_3 value: 44.192 - type: map_at_5 value: 55.435 - type: mrr_at_1 value: 76.7 - type: mrr_at_10 value: 84.22 - type: mrr_at_100 value: 84.341 - type: mrr_at_1000 value: 84.346 - type: mrr_at_3 value: 83.42500000000001 - type: mrr_at_5 value: 83.902 - type: ndcg_at_1 value: 76.7 - type: ndcg_at_10 value: 75.271 - type: ndcg_at_100 value: 80.62 - type: ndcg_at_1000 value: 81.45 - type: ndcg_at_3 value: 72.803 - type: ndcg_at_5 value: 71.694 - type: precision_at_1 value: 76.7 - type: precision_at_10 value: 36.925000000000004 - type: precision_at_100 value: 4.675 - type: precision_at_1000 value: 0.48700000000000004 - type: precision_at_3 value: 65.383 - type: precision_at_5 value: 55.15 - type: recall_at_1 value: 21.247 - type: recall_at_10 value: 78.38300000000001 - type: recall_at_100 value: 94.759 - type: recall_at_1000 value: 98.907 - type: recall_at_3 value: 48.04 - type: recall_at_5 value: 62.883 - task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics: - type: map_at_1 value: 42.0 - type: map_at_10 value: 52.691 - type: map_at_100 value: 53.456 - type: map_at_1000 value: 53.480000000000004 - type: map_at_3 value: 49.583 - type: map_at_5 value: 51.723 - type: mrr_at_1 value: 42.0 - type: mrr_at_10 value: 52.691 - type: mrr_at_100 value: 53.456 - type: mrr_at_1000 value: 53.480000000000004 - type: mrr_at_3 value: 49.583 - type: mrr_at_5 value: 51.723 - type: ndcg_at_1 value: 42.0 - type: ndcg_at_10 value: 58.243 - type: ndcg_at_100 value: 61.907999999999994 - type: ndcg_at_1000 value: 62.483999999999995 - type: ndcg_at_3 value: 52.03 - type: ndcg_at_5 value: 55.85099999999999 - type: precision_at_1 value: 42.0 - type: precision_at_10 value: 7.580000000000001 - type: precision_at_100 value: 0.928 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 19.7 - type: precision_at_5 value: 13.66 - type: recall_at_1 value: 42.0 - type: recall_at_10 value: 75.8 - type: recall_at_100 value: 92.80000000000001 - type: recall_at_1000 value: 97.2 - type: recall_at_3 value: 59.099999999999994 - type: recall_at_5 value: 68.30000000000001 - task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: 421605374b29664c5fc098418fe20ada9bd55f8a metrics: - type: accuracy value: 44.86340900346287 - type: f1 value: 31.324006049353713 - task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: b7c64bd89eb87f8ded463478346f76731f07bf8b metrics: - type: accuracy value: 88.48030018761726 - type: ap value: 59.392058006606476 - type: f1 value: 83.61333024672861 - task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: 17f9b096f80380fce5ed12a9be8be7784b337daf metrics: - type: cos_sim_pearson value: 66.36852873686233 - type: cos_sim_spearman value: 73.27371960661353 - type: euclidean_pearson value: 71.38209904858738 - type: euclidean_spearman value: 73.27373512049904 - type: manhattan_pearson value: 71.51557697058817 - type: manhattan_spearman value: 73.38956581066971 - task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6 metrics: - type: map value: 19.57107231987867 - type: mrr value: 18.224603174603175 - task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics: - type: map_at_1 value: 43.785000000000004 - type: map_at_10 value: 53.278000000000006 - type: map_at_100 value: 53.946000000000005 - type: map_at_1000 value: 53.983000000000004 - type: map_at_3 value: 50.846999999999994 - type: map_at_5 value: 52.286 - type: mrr_at_1 value: 45.559 - type: mrr_at_10 value: 54.129000000000005 - type: mrr_at_100 value: 54.732 - type: mrr_at_1000 value: 54.766999999999996 - type: mrr_at_3 value: 51.885999999999996 - type: mrr_at_5 value: 53.212 - type: ndcg_at_1 value: 45.559 - type: ndcg_at_10 value: 57.909 - type: ndcg_at_100 value: 61.068999999999996 - type: ndcg_at_1000 value: 62.09400000000001 - type: ndcg_at_3 value: 53.125 - type: ndcg_at_5 value: 55.614 - type: precision_at_1 value: 45.559 - type: precision_at_10 value: 7.617 - type: precision_at_100 value: 0.9199999999999999 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 20.707 - type: precision_at_5 value: 13.730999999999998 - type: recall_at_1 value: 43.785000000000004 - type: recall_at_10 value: 71.543 - type: recall_at_100 value: 86.197 - type: recall_at_1000 value: 94.305 - type: recall_at_3 value: 58.677 - type: recall_at_5 value: 64.62599999999999 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 61.29455279085406 - type: f1 value: 58.42865357114413 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 66.89979825151312 - type: f1 value: 66.6125514843663 - task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics: - type: map_at_1 value: 44.7 - type: map_at_10 value: 51.307 - type: map_at_100 value: 52.002 - type: map_at_1000 value: 52.06699999999999 - type: map_at_3 value: 49.55 - type: map_at_5 value: 50.544999999999995 - type: mrr_at_1 value: 44.9 - type: mrr_at_10 value: 51.415 - type: mrr_at_100 value: 52.111 - type: mrr_at_1000 value: 52.175000000000004 - type: mrr_at_3 value: 49.683 - type: mrr_at_5 value: 50.653000000000006 - type: ndcg_at_1 value: 44.7 - type: ndcg_at_10 value: 54.778000000000006 - type: ndcg_at_100 value: 58.526 - type: ndcg_at_1000 value: 60.187999999999995 - type: ndcg_at_3 value: 51.129999999999995 - type: ndcg_at_5 value: 52.933 - type: precision_at_1 value: 44.7 - type: precision_at_10 value: 6.58 - type: precision_at_100 value: 0.8420000000000001 - type: precision_at_1000 value: 0.097 - type: precision_at_3 value: 18.567 - type: precision_at_5 value: 12.02 - type: recall_at_1 value: 44.7 - type: recall_at_10 value: 65.8 - type: recall_at_100 value: 84.2 - type: recall_at_1000 value: 97.2 - type: recall_at_3 value: 55.7 - type: recall_at_5 value: 60.099999999999994 - task: type: Retrieval dataset: type: Shitao/MLDR name: MTEB MultiLongDocRetrieval (zh) config: zh split: test revision: None metrics: - type: map_at_1 value: 7.625 - type: map_at_10 value: 10.238 - type: map_at_100 value: 10.885 - type: map_at_1000 value: 10.958 - type: map_at_3 value: 9.292 - type: map_at_5 value: 9.91 - type: mrr_at_1 value: 7.625 - type: mrr_at_10 value: 10.238 - type: mrr_at_100 value: 10.885 - type: mrr_at_1000 value: 10.958 - type: mrr_at_3 value: 9.292 - type: mrr_at_5 value: 9.91 - type: ndcg_at_1 value: 7.625 - type: ndcg_at_10 value: 11.784 - type: ndcg_at_100 value: 15.133 - type: ndcg_at_1000 value: 17.511 - type: ndcg_at_3 value: 9.857000000000001 - type: ndcg_at_5 value: 10.981 - type: precision_at_1 value: 7.625 - type: precision_at_10 value: 1.675 - type: precision_at_100 value: 0.329 - type: precision_at_1000 value: 0.053 - type: precision_at_3 value: 3.833 - type: precision_at_5 value: 2.85 - type: recall_at_1 value: 7.625 - type: recall_at_10 value: 16.75 - type: recall_at_100 value: 32.875 - type: recall_at_1000 value: 52.625 - type: recall_at_3 value: 11.5 - type: recall_at_5 value: 14.249999999999998 - task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a metrics: - type: accuracy value: 78.45666666666666 - type: f1 value: 78.06393644109178 - task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: 66e76a618a34d6d565d5538088562851e6daa7ec metrics: - type: cos_sim_accuracy value: 59.88088792636708 - type: cos_sim_ap value: 59.993466246406854 - type: cos_sim_f1 value: 69.33333333333334 - type: cos_sim_precision value: 54.23122765196663 - type: cos_sim_recall value: 96.09292502639916 - type: dot_accuracy value: 59.88088792636708 - type: dot_ap value: 59.99351215786742 - type: dot_f1 value: 69.33333333333334 - type: dot_precision value: 54.23122765196663 - type: dot_recall value: 96.09292502639916 - type: euclidean_accuracy value: 59.88088792636708 - type: euclidean_ap value: 59.993466246406854 - type: euclidean_f1 value: 69.33333333333334 - type: euclidean_precision value: 54.23122765196663 - type: euclidean_recall value: 96.09292502639916 - type: manhattan_accuracy value: 59.989171629669734 - type: manhattan_ap value: 60.06745167956717 - type: manhattan_f1 value: 69.37381404174573 - type: manhattan_precision value: 54.14691943127961 - type: manhattan_recall value: 96.51531151003168 - type: max_accuracy value: 59.989171629669734 - type: max_ap value: 60.06745167956717 - type: max_f1 value: 69.37381404174573 - task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: e610f2ebd179a8fda30ae534c3878750a96db120 metrics: - type: accuracy value: 92.58 - type: ap value: 90.58624365698103 - type: f1 value: 92.56998002261557 - task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 metrics: - type: cos_sim_pearson value: 15.428347645738844 - type: cos_sim_spearman value: 18.54916824520863 - type: euclidean_pearson value: 18.525706701701317 - type: euclidean_spearman value: 18.564855538117524 - type: manhattan_pearson value: 18.54511262151164 - type: manhattan_spearman value: 18.587848451111213 - task: type: PairClassification dataset: type: paws-x name: MTEB PawsX (zh) config: zh split: test revision: 8a04d940a42cd40658986fdd8e3da561533a3646 metrics: - type: cos_sim_accuracy value: 60.3 - type: cos_sim_ap value: 57.92869006380703 - type: cos_sim_f1 value: 62.31681786461968 - type: cos_sim_precision value: 45.283975659229206 - type: cos_sim_recall value: 99.88814317673378 - type: dot_accuracy value: 60.3 - type: dot_ap value: 57.7632607916169 - type: dot_f1 value: 62.31681786461968 - type: dot_precision value: 45.283975659229206 - type: dot_recall value: 99.88814317673378 - type: euclidean_accuracy value: 60.3 - type: euclidean_ap value: 57.92869006380703 - type: euclidean_f1 value: 62.31681786461968 - type: euclidean_precision value: 45.283975659229206 - type: euclidean_recall value: 99.88814317673378 - type: manhattan_accuracy value: 60.25 - type: manhattan_ap value: 57.929597845689706 - type: manhattan_f1 value: 62.31681786461968 - type: manhattan_precision value: 45.283975659229206 - type: manhattan_recall value: 99.88814317673378 - type: max_accuracy value: 60.3 - type: max_ap value: 57.929597845689706 - type: max_f1 value: 62.31681786461968 - task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 metrics: - type: cos_sim_pearson value: 28.445664430656038 - type: cos_sim_spearman value: 29.599326690902288 - type: euclidean_pearson value: 27.900455284977017 - type: euclidean_spearman value: 29.599947224705264 - type: manhattan_pearson value: 28.101656918683116 - type: manhattan_spearman value: 29.78083888978687 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics: - type: cos_sim_pearson value: 61.13774633735679 - type: cos_sim_spearman value: 65.43749616084263 - type: euclidean_pearson value: 63.42122949030793 - type: euclidean_spearman value: 65.43749616084263 - type: manhattan_pearson value: 63.78466267937151 - type: manhattan_spearman value: 65.4252196465631 - task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0 metrics: - type: cos_sim_pearson value: 66.43725663481563 - type: cos_sim_spearman value: 66.91073455354187 - type: euclidean_pearson value: 67.25178758750022 - type: euclidean_spearman value: 66.91129699608939 - type: manhattan_pearson value: 67.33381999971951 - type: manhattan_spearman value: 66.9990458174529 - task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: 76631901a18387f85eaa53e5450019b87ad58ef9 metrics: - type: map value: 64.31327281684898 - type: mrr value: 73.58095291829211 - task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: 8731a845f1bf500a4f111cf1070785c793d10e64 metrics: - type: map_at_1 value: 20.961 - type: map_at_10 value: 59.065 - type: map_at_100 value: 63.544 - type: map_at_1000 value: 63.681 - type: map_at_3 value: 40.849999999999994 - type: map_at_5 value: 50.268 - type: mrr_at_1 value: 74.934 - type: mrr_at_10 value: 80.571 - type: mrr_at_100 value: 80.814 - type: mrr_at_1000 value: 80.82300000000001 - type: mrr_at_3 value: 79.449 - type: mrr_at_5 value: 80.13 - type: ndcg_at_1 value: 74.934 - type: ndcg_at_10 value: 69.215 - type: ndcg_at_100 value: 75.61099999999999 - type: ndcg_at_1000 value: 77.03999999999999 - type: ndcg_at_3 value: 70.04899999999999 - type: ndcg_at_5 value: 68.50699999999999 - type: precision_at_1 value: 74.934 - type: precision_at_10 value: 35.569 - type: precision_at_100 value: 4.757 - type: precision_at_1000 value: 0.509 - type: precision_at_3 value: 61.802 - type: precision_at_5 value: 51.882 - type: recall_at_1 value: 20.961 - type: recall_at_10 value: 69.626 - type: recall_at_100 value: 89.464 - type: recall_at_1000 value: 96.721 - type: recall_at_3 value: 43.608999999999995 - type: recall_at_5 value: 55.724 - task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: 317f262bf1e6126357bbe89e875451e4b0938fe4 metrics: - type: accuracy value: 50.01800000000001 - type: f1 value: 48.262341643251936 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: 5798586b105c0434e4f0fe5e767abe619442cf93 metrics: - type: v_measure value: 60.68748256831344 - task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d metrics: - type: v_measure value: 56.73298697800912 - task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics: - type: map_at_1 value: 46.9 - type: map_at_10 value: 57.849 - type: map_at_100 value: 58.532 - type: map_at_1000 value: 58.553 - type: map_at_3 value: 55.467 - type: map_at_5 value: 56.92700000000001 - type: mrr_at_1 value: 46.9 - type: mrr_at_10 value: 57.849 - type: mrr_at_100 value: 58.532 - type: mrr_at_1000 value: 58.553 - type: mrr_at_3 value: 55.467 - type: mrr_at_5 value: 56.92700000000001 - type: ndcg_at_1 value: 46.9 - type: ndcg_at_10 value: 63.09 - type: ndcg_at_100 value: 66.43 - type: ndcg_at_1000 value: 66.949 - type: ndcg_at_3 value: 58.226 - type: ndcg_at_5 value: 60.838 - type: precision_at_1 value: 46.9 - type: precision_at_10 value: 7.95 - type: precision_at_100 value: 0.951 - type: precision_at_1000 value: 0.099 - type: precision_at_3 value: 22.067 - type: precision_at_5 value: 14.499999999999998 - type: recall_at_1 value: 46.9 - type: recall_at_10 value: 79.5 - type: recall_at_100 value: 95.1 - type: recall_at_1000 value: 99.1 - type: recall_at_3 value: 66.2 - type: recall_at_5 value: 72.5 - task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: 339287def212450dcaa9df8c22bf93e9980c7023 metrics: - type: accuracy value: 89.09 - type: ap value: 74.68093732384233 - type: f1 value: 87.7768409829789 ---
Supreeth40/finetuned-bartB-samsum
Supreeth40
2024-03-08T07:15:43Z
80
0
transformers
[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "base_model:finetune:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text2text-generation
2024-03-08T06:11:50Z
--- license: apache-2.0 base_model: facebook/bart-base tags: - generated_from_trainer model-index: - name: finetuned-bartB-samsum 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. --> # finetuned-bartB-samsum This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3250 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7398 | 0.54 | 1000 | 0.3636 | | 0.3869 | 1.09 | 2000 | 0.3406 | | 0.3327 | 1.63 | 3000 | 0.3334 | | 0.309 | 2.17 | 4000 | 0.3325 | | 0.2776 | 2.71 | 5000 | 0.3262 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
tsavage68/mistralit2_550_STEPS_5e8_SFT_SFT
tsavage68
2024-03-08T07:12:24Z
7
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T07:08:30Z
--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: mistralit2_550_STEPS_5e8_SFT_SFT 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. --> # mistralit2_550_STEPS_5e8_SFT_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5702 ## 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-08 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 550 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4484 | 0.1 | 50 | 1.4370 | | 1.3055 | 0.2 | 100 | 1.2880 | | 1.1109 | 0.29 | 150 | 1.0891 | | 0.9328 | 0.39 | 200 | 0.9223 | | 0.7797 | 0.49 | 250 | 0.7676 | | 0.6719 | 0.59 | 300 | 0.6567 | | 0.5895 | 0.68 | 350 | 0.5927 | | 0.5801 | 0.78 | 400 | 0.5714 | | 0.5676 | 0.88 | 450 | 0.5703 | | 0.5723 | 0.98 | 500 | 0.5702 | | 0.5668 | 1.07 | 550 | 0.5702 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
Rardilit/Gaitonde
Rardilit
2024-03-08T07:02:37Z
92
0
transformers
[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T06:58:34Z
--- 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]
Ashwini1412/wav2vec2-nepali-itr-10
Ashwini1412
2024-03-08T07:00:28Z
5
0
transformers
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
automatic-speech-recognition
2024-03-08T03:58:09Z
--- 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]
usmanxia/resonance-it-ft
usmanxia
2024-03-08T06:59:59Z
6
0
transformers
[ "transformers", "safetensors", "gguf", "gemma", "text-generation", "conversational", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-06T15:07:59Z
--- library_name: transformers tags: [] widget: - messages: - role: user content: How does the brain work? inference: parameters: max_new_tokens: 200 extra_gated_heading: "Access Resonance on Hugging Face" extra_gated_prompt: "To access Resonance on Hugging Face, you’re required to review and agree to Resonance’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately." extra_gated_button_content: "Acknowledge license" license: other --- # Resonance Model Card This model card corresponds to the 2B instruct version of the Resonance model. **Terms of Use**: **Authors**: AI Reseaerch Lab, NUST ## Model Information Summary description and brief definition of inputs and outputs. ### Description Resonance is a family of lightweight, state-of-the-art open models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Resonance models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("usmanxia/resonance-2b-it") model = AutoModelForCausalLM.from_pretrained("usmanxia/resonance-2b-it") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("usmanxia/resonance-2b-it") model = AutoModelForCausalLM.from_pretrained("usmanxia/resonance-2b-it", device_map="auto") input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a GPU using different precisions * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("usmanxia/resonance-2b-it") model = AutoModelForCausalLM.from_pretrained("usmanxia/resonance-2b-it", device_map="auto", torch_dtype=torch.float16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("usmanxia/resonance-2b-it") model = AutoModelForCausalLM.from_pretrained("usmanxia/resonance-2b-it", device_map="auto", torch_dtype=torch.bfloat16) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("usmanxia/resonance-2b-it") model = AutoModelForCausalLM.from_pretrained("usmanxia/resonance-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("usmanxia/resonance-2b-it") model = AutoModelForCausalLM.from_pretrained("usmanxia/resonance-2b-it", quantization_config=quantization_config) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations * _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` ### Chat Template The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "usmanxia/resonance-it" dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype=dtype, ) chat = [ { "role": "user", "content": "Write a hello world program" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` At this point, the prompt contains the following text: ``` <bos><start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model ``` As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with the `<end_of_turn>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. After the prompt is ready, generation can be performed like this: ```py inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) ``` ### Inputs and outputs * **Input:** Text string, such as a question, a prompt, or a document to be summarized. * **Output:** Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.
genne/nhn_dpo_v3_leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT_DPO
genne
2024-03-08T06:55:07Z
4
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT", "base_model:finetune:ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T06:49:14Z
--- license: cc-by-nc-4.0 base_model: ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: nhn_dpo_v3_leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT_DPO 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. --> # nhn_dpo_v3_leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT_DPO This model is a fine-tuned version of [ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT](https://huggingface.co/ENERGY-DRINK-LOVE/leaderboard_inst_v1.3_deup_LDCC-SOLAR-10.7B_SFT) 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: 5e-07 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 7 - gradient_accumulation_steps: 8 - total_train_batch_size: 56 - total_eval_batch_size: 56 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1+cu118 - Datasets 2.17.1 - Tokenizers 0.15.2
manimaranpa07/finetuned_bart_mnli_08th_march_1
manimaranpa07
2024-03-08T06:47:06Z
484
0
transformers
[ "transformers", "safetensors", "bart", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-03-08T06:45:21Z
--- 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]
LoneStriker/MixTAO-7Bx2-MoE-v8.1-6.0bpw-h6-exl2
LoneStriker
2024-03-08T06:34:18Z
6
1
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T06:30:17Z
--- license: apache-2.0 tags: - moe model-index: - name: MixTAO-7Bx2-MoE-v8.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.22 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 78.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 87.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard --- # MixTAO-7Bx2-MoE MixTAO-7Bx2-MoE is a Mixure of Experts (MoE). This model is mainly used for large model technology experiments, and increasingly perfect iterations will eventually create high-level large language models. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-v8.1) | Metric |Value| |---------------------------------|----:| |Avg. |77.50| |AI2 Reasoning Challenge (25-Shot)|73.81| |HellaSwag (10-Shot) |89.22| |MMLU (5-Shot) |64.92| |TruthfulQA (0-shot) |78.57| |Winogrande (5-shot) |87.37| |GSM8k (5-shot) |71.11|
dlwlgus53/q-FrozenLake-v1-4x4-noSlippery
dlwlgus53
2024-03-08T06:33:19Z
0
0
null
[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T06:33:17Z
--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="dlwlgus53/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
Ayush2312/llama2-7B-orca-colab
Ayush2312
2024-03-08T06:31:20Z
5
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-06T10:44:45Z
Fine-tuned llama 2 7b with processed open orca dataset (Ayush2312/deduplicated_orca_post_processed): data processing: 1. Remove output token less than 100 tokens in reponse 2. Do cosine similarity on examples with threshold 0.95 3. python codes for data processing: step 1: ``` from datasets import load_dataset, Dataset from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity # Load your dataset from Hugging Face dataset = load_dataset("Ayush2312/orca-1m-gpt4", split='train[:7000]') # Tokenize your text data texts = dataset['system_prompt'] + dataset['question'] + dataset['response'] # Filter out instructions with less than 100 tokens in response filtered_texts = [] for i, response in enumerate(dataset['response']): if len(response.split()) >= 100: filtered_texts.append({'system_prompt': dataset['system_prompt'][i], 'question': dataset['question'][i], 'response': response}) # TF-IDF Vectorization for deduplication texts = [text['system_prompt'] + ' ' + text['question'] + ' ' + text['response'] for text in filtered_texts] vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(texts) # Calculate cosine similarity for deduplication cos_sim_matrix = cosine_similarity(tfidf_matrix, tfidf_matrix) # Deduplicate the data based on cosine similarity deduplicated_indices = set() for i in range(len(cos_sim_matrix)): if i not in deduplicated_indices: for j in range(i + 1, len(cos_sim_matrix)): if cos_sim_matrix[i, j] > 0.95: deduplicated_indices.add(j) # Create a new dataset with the deduplicated data deduplicated_texts = [filtered_texts[i] for i in range(len(filtered_texts)) if i not in deduplicated_indices] deduplicated_texts_dict = {key: [item[key] for item in deduplicated_texts] for key in filtered_texts[0].keys()} deduplicated_dataset = Dataset.from_dict(deduplicated_texts_dict) # Publish the dataset on Hugging Face deduplicated_dataset.push_to_hub("deduplicated_orca_processed") ``` step 2: ``` from datasets import Dataset, load_dataset # Load your Hugging Face dataset dataset = load_dataset("Ayush2312/deduplicated_orca_processed")['train'][:1000] # Define the default instruction default_instruction = "### Instruction: Below is a conversation between a human and an AI agent. Write a summary of the conversation." # Define the function to format each example def format_example(example): input_text = "### Input:\n" if "response" in example: input_text += "\n".join([f" {example[role]}" for role in ["question"]]) else: input_text += "\n".join([f" {example[role]}" for role in ["question"]]) response_text = example["response"] if "response" in example else "" instruction = "### Instruction: " + example["system_prompt"] if not example["system_prompt"].strip(): instruction = default_instruction # Fill empty or missing instruction with default return { "formatted_example": f"{instruction}\n\n{input_text}\n\n### Response:\n{response_text}" } # Convert the dictionary to a Dataset object dataset = Dataset.from_dict(dataset) # Apply the function to format each example formatted_dataset = dataset.map(format_example) # Upload the new dataset to Hugging Face formatted_dataset.push_to_hub("deduplicated_orca_post_processed") ```
LoneStriker/MixTAO-7Bx2-MoE-v8.1-5.0bpw-h6-exl2
LoneStriker
2024-03-08T06:30:15Z
6
1
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T06:26:52Z
--- license: apache-2.0 tags: - moe model-index: - name: MixTAO-7Bx2-MoE-v8.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.22 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 78.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 87.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard --- # MixTAO-7Bx2-MoE MixTAO-7Bx2-MoE is a Mixure of Experts (MoE). This model is mainly used for large model technology experiments, and increasingly perfect iterations will eventually create high-level large language models. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-v8.1) | Metric |Value| |---------------------------------|----:| |Avg. |77.50| |AI2 Reasoning Challenge (25-Shot)|73.81| |HellaSwag (10-Shot) |89.22| |MMLU (5-Shot) |64.92| |TruthfulQA (0-shot) |78.57| |Winogrande (5-shot) |87.37| |GSM8k (5-shot) |71.11|
AIFT/Finance-KcELECTRA-base-v1.0
AIFT
2024-03-08T06:30:07Z
112
1
transformers
[ "transformers", "pytorch", "electra", "fill-mask", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us" ]
fill-mask
2024-01-02T06:20:06Z
--- license: cc-by-nc-sa-4.0 pipeline_tag: fill-mask --- <img src ="https://pds.saramin.co.kr/company/logo/202110/15/r0zx8u_zrd4-xpn1m0_logo.JPG" height="780" width="450"> <br/> <br> <h1>Finance-KcELECTRA-v1.0</h1> <br> 구어체의 금융 관련 질의를 데이터로 활용하여 "beomi/KcELECTRA-base"에서 이름을 착안하여 Finance-KcELECTRA-base-v1.0으로 지었습니다. <br> <b><h2><학습 말뭉치 구축></h2></b> 1. 네이버 신문 기사 데이터 (카드, 보험, 은행 키워드 기사 각 15만 건) 2. 일반적인 성능을 위해 한국어 위키 텍스트 말뭉치 사용 (https://ko-nlp.github.io/Korpora) 3. 자체 보유 중인 <b>구어체의</b> 금융 관련 FAQ 지식 및 금융 채팅 대화 데이터 활용 <b><h2><베이스 모델></h2></b> "ELECTRA-Base"의 모델 사이즈를 사용하였습니다. <b><h2><성능 비교></h2></b> 자체 제작한 2607개 분류의 약 10만개의 질의세트를 학습하여 평가 진행 <br> 테스트셋은 각 분류당 1개의 질문 2607개로 학습 시 진행한 결과는 아래와 같습니다. <br> Finance-KcELECTRA-base-v1 <br> 2000STEPS -- acc = 0.7909474491752972 <br> 4000STEPS -- acc = 0.9673954737245877 <br> 6000STEPS -- <b>acc = 0.984656693517453</b> <br> <br> KcELECTRA-base <br> 2000STEPS -- acc = 0.5124664365170695 <br> 4000STEPS -- acc = 0.9136939010356732 <br> 6000STEPS -- acc = 0.9612581511315689 <br> <br> 초기 1epoch에 기존 Kc-ELECTRA에 비해서 빠르게 학습이 수행되는 것을 확인하였습니다. <br> 이후에도 성능이 조금이나마 앞선 것을 확인할 수 있었습니다.
cookinai/Blitz-v0.2
cookinai
2024-03-08T06:28:08Z
105
0
transformers
[ "transformers", "pytorch", "mistral", "text-generation", "unsloth", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T06:08:53Z
--- library_name: transformers tags: - unsloth license: cc-by-4.0 --- # Base finetune of [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on my [Kugelblitz Dataset](https://huggingface.co/datasets/cookinai/kugelblitz-alpha-v0.1) ![Kugelblitz](https://huggingface.co/cookinai/Blitz-v0.1/resolve/main/kugelblitz_black_hole.png) Trained on 3 epochs rather than 1 this time. V0.3 coming soon # Pretty alpha v0.3 should be more stable ![Unsloth_is_awesome](https://raw.githubusercontent.com/unslothai/unsloth/main/images/made%20with%20unsloth.png)
LoneStriker/MixTAO-7Bx2-MoE-v8.1-4.0bpw-h6-exl2
LoneStriker
2024-03-08T06:26:50Z
5
0
transformers
[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T06:24:03Z
--- license: apache-2.0 tags: - moe model-index: - name: MixTAO-7Bx2-MoE-v8.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.22 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 78.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 87.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard --- # MixTAO-7Bx2-MoE MixTAO-7Bx2-MoE is a Mixure of Experts (MoE). This model is mainly used for large model technology experiments, and increasingly perfect iterations will eventually create high-level large language models. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-v8.1) | Metric |Value| |---------------------------------|----:| |Avg. |77.50| |AI2 Reasoning Challenge (25-Shot)|73.81| |HellaSwag (10-Shot) |89.22| |MMLU (5-Shot) |64.92| |TruthfulQA (0-shot) |78.57| |Winogrande (5-shot) |87.37| |GSM8k (5-shot) |71.11|
iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF
iAkashPaul
2024-03-08T06:21:17Z
13
3
null
[ "gguf", "gemma", "llama.cpp", "indic", "license:mit", "endpoints_compatible", "region:us" ]
null
2024-03-06T10:15:05Z
--- license: mit tags: - gemma - gguf - llama.cpp - indic --- # GGUF for Indic-gemma-2b-finetuned-sft-Navarasa This model from [Telugu-LLM-Labs](https://huggingface.co/Telugu-LLM-Labs/Indic-gemma-2b-finetuned-sft-Navarasa) is based on google/gemma-2b and has been LoRA finetuned on 9 Indian languages and English instruction datasets ```bash git clone https://huggingface.co/iAkashPaul/Indic-gemma-2b-finetuned-sft-Navarasa-GGUF # & cd into it, update paths accordingly # build llama.cpp for your hardware https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#build ./main --file prompt.md --lora ./models/ggml-adapter-model.bin --lora-base ./models/indic-llm_Q8.gguf ./main --file prompt.md -m ./models/merged_indic_llm_Q8.gguf -ngl 99 ``` ## Prompt template for Instruction adherence- Save this to a file(ex. prompt.md) & load it with the main executable. ```markdown ### Instruction: Translate following sentence to Kannada. ### Input: This model is developed by Telugu LLM Labs ## Response: ``` ## Performance * LORA+BASE (not merged) * ``` ./server --lora ./models/ggml-adapter-model.bin --lora-base ./models/indic-llm_Q8.gguf -m ./models/indic-llm_Q8.gguf ``` * ![](indic-llm-q8.jpg) * Merged model * ``` ./server -ngl 20 -m ./models/merged_indic_llm_Q8.gguf ``` * ![](Q8-75tok.png)
Q-bert/MambaHermes-3B
Q-bert
2024-03-08T06:18:34Z
18
10
transformers
[ "transformers", "pytorch", "mamba", "text-generation", "mamba-hf", "custom_code", "en", "arxiv:2312.00752", "license:wtfpl", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-28T17:20:48Z
--- license: wtfpl language: - en tags: - mamba-hf --- # MambaHermes-3B <img src="https://cdn-uploads.huggingface.co/production/uploads/63da3d7ae697e5898cb86854/A3BYIH-q7G5vz4NlsPlGJ.jpeg" width="300" height="300" alt="mamba-hf"> Mamba Models with hf_integration. For modeling codes: [**mamba-hf**](https://github.com/LegallyCoder/mamba-hf) # Usage: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM CHAT_TEMPLATE_ID = "HuggingFaceH4/zephyr-7b-beta" device = "cuda:0" if torch.cuda.is_available() else "cpu" model_name = "Q-bert/MambaHermes-3B" eos_token = "<|endoftext|>" tokenizer = AutoTokenizer.from_pretrained(model_name) tokenizer.eos_token = eos_token tokenizer.pad_token = tokenizer.eos_token tokenizer.chat_template = AutoTokenizer.from_pretrained(CHAT_TEMPLATE_ID).chat_template model = AutoModelForCausalLM.from_pretrained( model_name, device_map=device, trust_remote_code=True) messages = [] prompt = "Tell me 5 sites to visit in Spain" messages.append(dict(role="user", content=prompt)) input_ids = tokenizer.apply_chat_template( messages, return_tensors="pt", add_generation_prompt=True ).to(device) out = model.generate( input_ids=input_ids, max_length=2000, temperature=0.9, top_p=0.7, eos_token_id=tokenizer.eos_token_id, ) decoded = tokenizer.batch_decode(out) assistant_message = ( decoded[0].split("<|assistant|>\n")[-1].replace(tokenizer.eos_token, "") ) print(assistant_message) ``` # For Training: ```python from transformers import Trainer ,TrainingArguments import torch import os class MambaTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): input_ids = inputs.pop("input_ids") lm_logits = model(input_ids)[0] labels = input_ids.to(lm_logits.device) shift_logits = lm_logits[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = torch.nn.CrossEntropyLoss() lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)) return lm_loss ``` You must use this class for training. And fp16 must be **False**. # Credits: https://huggingface.co/state-spaces https://huggingface.co/clibrain/mamba-2.8b-instruct-openhermes Special thanks to Albert Gu and Tri Dao for their articles. (https://arxiv.org/abs/2312.00752)
LoneStriker/MixTAO-7Bx2-MoE-v8.1-GGUF
LoneStriker
2024-03-08T06:18:12Z
0
3
null
[ "gguf", "moe", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
null
2024-03-08T05:58:44Z
--- license: apache-2.0 tags: - moe model-index: - name: MixTAO-7Bx2-MoE-v8.1 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 73.81 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 89.22 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.92 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 78.57 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 87.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 71.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=zhengr/MixTAO-7Bx2-MoE-v8.1 name: Open LLM Leaderboard --- # MixTAO-7Bx2-MoE MixTAO-7Bx2-MoE is a Mixure of Experts (MoE). This model is mainly used for large model technology experiments, and increasingly perfect iterations will eventually create high-level large language models. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_zhengr__MixTAO-7Bx2-MoE-v8.1) | Metric |Value| |---------------------------------|----:| |Avg. |77.50| |AI2 Reasoning Challenge (25-Shot)|73.81| |HellaSwag (10-Shot) |89.22| |MMLU (5-Shot) |64.92| |TruthfulQA (0-shot) |78.57| |Winogrande (5-shot) |87.37| |GSM8k (5-shot) |71.11|
Q-bert/Mamba-1B
Q-bert
2024-03-08T06:16:31Z
112
27
transformers
[ "transformers", "pytorch", "mamba", "text-generation", "mamba-hf", "custom_code", "en", "arxiv:2312.00752", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-23T08:13:58Z
--- license: apache-2.0 language: - en tags: - mamba-hf --- # Mamba-1B <img src="https://cdn-uploads.huggingface.co/production/uploads/63da3d7ae697e5898cb86854/A3BYIH-q7G5vz4NlsPlGJ.jpeg" width="300" height="300" alt="mamba-hf"> Mamba Models with hf_integration. For modeling codes: [**mamba-hf**](https://github.com/LegallyCoder/mamba-hf) # Usage: ```python from transformers import AutoModelForCausalLM , AutoTokenizer model = AutoModelForCausalLM.from_pretrained('Q-bert/Mamba-1B', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained('Q-bert/Mamba-1B') text = "Hi" input_ids = tokenizer.encode(text, return_tensors="pt") output = model.generate(input_ids, max_length=20, num_beams=5, no_repeat_ngram_size=2) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text) ``` > Hi, I'm looking for a new job. I've been working at a company for about a year now. # For Training: ```python from transformers import Trainer ,TrainingArguments import torch import os class MambaTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): input_ids = inputs.pop("input_ids") lm_logits = model(input_ids)[0] labels = input_ids.to(lm_logits.device) shift_logits = lm_logits[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = torch.nn.CrossEntropyLoss() lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)) return lm_loss ``` You must use this class for training. And fp16 must be **False**. # Credits: https://huggingface.co/state-spaces Special thanks to Albert Gu and Tri Dao for their articles. (https://arxiv.org/abs/2312.00752)
Q-bert/Mamba-790M
Q-bert
2024-03-08T06:16:12Z
106
2
transformers
[ "transformers", "pytorch", "mamba", "text-generation", "mamba-hf", "custom_code", "en", "arxiv:2312.00752", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-23T08:06:38Z
--- license: apache-2.0 language: - en tags: - mamba-hf --- # Mamba-790M <img src="https://cdn-uploads.huggingface.co/production/uploads/63da3d7ae697e5898cb86854/A3BYIH-q7G5vz4NlsPlGJ.jpeg" width="300" height="300" alt="mamba-hf"> Mamba Models with hf_integration. For modeling codes: [**mamba-hf**](https://github.com/LegallyCoder/mamba-hf) # Usage: ```python from transformers import AutoModelForCausalLM , AutoTokenizer model = AutoModelForCausalLM.from_pretrained('Q-bert/Mamba-790M', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained('Q-bert/Mamba-790M') text = "Hi" input_ids = tokenizer.encode(text, return_tensors="pt") output = model.generate(input_ids, max_length=20, num_beams=5, no_repeat_ngram_size=2) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text) ``` > Hi, I'm looking for a new job. I've been working at a company for about a year now. # For Training: ```python from transformers import Trainer ,TrainingArguments import torch import os class MambaTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): input_ids = inputs.pop("input_ids") lm_logits = model(input_ids)[0] labels = input_ids.to(lm_logits.device) shift_logits = lm_logits[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = torch.nn.CrossEntropyLoss() lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)) return lm_loss ``` You must use this class for training. And fp16 must be **False**. # Credits: https://huggingface.co/state-spaces Special thanks to Albert Gu and Tri Dao for their articles. (https://arxiv.org/abs/2312.00752)
Q-bert/Mamba-370M
Q-bert
2024-03-08T06:15:48Z
23
4
transformers
[ "transformers", "pytorch", "mamba", "text-generation", "mamba-hf", "custom_code", "en", "arxiv:2312.00752", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2023-12-23T08:02:00Z
--- license: apache-2.0 language: - en tags: - mamba-hf --- # Mamba-370M <img src="https://cdn-uploads.huggingface.co/production/uploads/63da3d7ae697e5898cb86854/A3BYIH-q7G5vz4NlsPlGJ.jpeg" width="300" height="300" alt="mamba-hf"> Mamba Models with hf_integration. For modeling codes: [**mamba-hf**](https://github.com/LegallyCoder/mamba-hf) # Usage: ```python from transformers import AutoModelForCausalLM , AutoTokenizer model = AutoModelForCausalLM.from_pretrained('Q-bert/Mamba-370M', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained('Q-bert/Mamba-370M') text = "Hi" input_ids = tokenizer.encode(text, return_tensors="pt") output = model.generate(input_ids, max_length=20, num_beams=5, no_repeat_ngram_size=2) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) print(generated_text) ``` > Hi, I'm looking for a new job. I've been working at a company for about a year now. # For Training: ```python from transformers import Trainer ,TrainingArguments import torch import os class MambaTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): input_ids = inputs.pop("input_ids") lm_logits = model(input_ids)[0] labels = input_ids.to(lm_logits.device) shift_logits = lm_logits[:, :-1, :].contiguous() labels = labels[:, 1:].contiguous() loss_fct = torch.nn.CrossEntropyLoss() lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)) return lm_loss ``` You must use this class for training. And fp16 must be **False**. # Credits: https://huggingface.co/state-spaces Special thanks to Albert Gu and Tri Dao for their articles. (https://arxiv.org/abs/2312.00752)
Vannsh/Taxi-v3
Vannsh
2024-03-08T06:15:05Z
0
0
null
[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T06:14:57Z
--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Vannsh/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
tsavage68/mistralit2_1000_STEPS_5e8_SFT_SFT
tsavage68
2024-03-08T06:12:28Z
5
0
transformers
[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T05:45:33Z
--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: mistralit2_1000_STEPS_5e8_SFT_SFT 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. --> # mistralit2_1000_STEPS_5e8_SFT_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3690 ## 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-08 - train_batch_size: 4 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 1000 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.4484 | 0.1 | 50 | 1.4370 | | 1.3055 | 0.2 | 100 | 1.2880 | | 1.1102 | 0.29 | 150 | 1.0868 | | 0.9133 | 0.39 | 200 | 0.8993 | | 0.7102 | 0.49 | 250 | 0.6891 | | 0.5453 | 0.59 | 300 | 0.5207 | | 0.4238 | 0.68 | 350 | 0.4248 | | 0.4008 | 0.78 | 400 | 0.3916 | | 0.3768 | 0.88 | 450 | 0.3790 | | 0.3766 | 0.98 | 500 | 0.3744 | | 0.3672 | 1.07 | 550 | 0.3718 | | 0.3752 | 1.17 | 600 | 0.3702 | | 0.3828 | 1.27 | 650 | 0.3694 | | 0.3502 | 1.37 | 700 | 0.3691 | | 0.3676 | 1.46 | 750 | 0.3690 | | 0.3717 | 1.56 | 800 | 0.3690 | | 0.3695 | 1.66 | 850 | 0.3690 | | 0.3727 | 1.76 | 900 | 0.3690 | | 0.3854 | 1.86 | 950 | 0.3690 | | 0.3768 | 1.95 | 1000 | 0.3690 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.0.0+cu117 - Datasets 2.18.0 - Tokenizers 0.15.2
Weni/ZeroShot-3.3.31-Mistral-7b-Multilanguage-3.2.0
Weni
2024-03-08T06:07:43Z
0
0
peft
[ "peft", "safetensors", "mistral", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]
null
2024-03-07T19:10:26Z
--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: ZeroShot-3.3.31-Mistral-7b-Multilanguage-3.2.0 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. --> # ZeroShot-3.3.31-Mistral-7b-Multilanguage-3.2.0 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0584 ## 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.1649 | 0.03 | 100 | 0.1309 | | 0.1186 | 0.06 | 200 | 0.1111 | | 0.1197 | 0.09 | 300 | 0.1349 | | 0.1052 | 0.12 | 400 | 0.1160 | | 0.1162 | 0.16 | 500 | 0.1013 | | 0.1138 | 0.19 | 600 | 0.1214 | | 0.1011 | 0.22 | 700 | 0.1184 | | 0.1123 | 0.25 | 800 | 0.1169 | | 0.1112 | 0.28 | 900 | 0.1144 | | 0.1174 | 0.31 | 1000 | 0.0976 | | 0.1222 | 0.34 | 1100 | 0.0975 | | 0.0972 | 0.37 | 1200 | 0.0949 | | 0.0809 | 0.4 | 1300 | 0.0935 | | 0.0841 | 0.43 | 1400 | 0.0904 | | 0.0835 | 0.47 | 1500 | 0.0911 | | 0.1003 | 0.5 | 1600 | 0.0816 | | 0.0875 | 0.53 | 1700 | 0.0770 | | 0.099 | 0.56 | 1800 | 0.0833 | | 0.0697 | 0.59 | 1900 | 0.0797 | | 0.0958 | 0.62 | 2000 | 0.0774 | | 0.0594 | 0.65 | 2100 | 0.0748 | | 0.0886 | 0.68 | 2200 | 0.0651 | | 0.0583 | 0.71 | 2300 | 0.0678 | | 0.05 | 0.74 | 2400 | 0.0639 | | 0.0696 | 0.78 | 2500 | 0.0612 | | 0.0615 | 0.81 | 2600 | 0.0625 | | 0.0493 | 0.84 | 2700 | 0.0610 | | 0.0661 | 0.87 | 2800 | 0.0584 | | 0.0469 | 0.9 | 2900 | 0.0593 | | 0.0701 | 0.93 | 3000 | 0.0588 | | 0.0768 | 0.96 | 3100 | 0.0587 | | 0.0611 | 0.99 | 3200 | 0.0584 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Anmol1902/my_awesome_opus_books_model
Anmol1902
2024-03-08T06:06:06Z
89
0
transformers
[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text2text-generation
2024-03-08T05:07:51Z
--- license: apache-2.0 base_model: google-t5/t5-small tags: - generated_from_trainer metrics: - bleu model-index: - name: my_awesome_opus_books_model 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. --> # my_awesome_opus_books_model This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0937 - Bleu: 14.231 - Gen Len: 14.7356 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| | 2.3526 | 1.0 | 6355 | 2.1326 | 13.9842 | 14.6763 | | 2.2938 | 2.0 | 12710 | 2.0937 | 14.231 | 14.7356 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
clp/leagaleasy-mistral-7b-instruct-v0.2-v1
clp
2024-03-08T06:04:16Z
0
0
peft
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]
null
2024-03-07T00:37:52Z
--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer datasets: - generator base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: leagaleasy-mistral-7b-instruct-v0.2-v1 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. --> # leagaleasy-mistral-7b-instruct-v0.2-v1 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator 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: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
quirky-lats-at-mats/BobzillaV1
quirky-lats-at-mats
2024-03-08T05:59:37Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-03-08T05:56:40Z
--- 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/Qwen1.5-14B-Chat-4bit
mlx-community
2024-03-08T05:37:24Z
11
1
mlx
[ "mlx", "safetensors", "qwen2", "chat", "text-generation", "conversational", "en", "license:other", "region:us" ]
text-generation
2024-03-07T07:45:04Z
--- language: - en license: other tags: - chat - mlx license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen1.5-14B-Chat/blob/main/LICENSE pipeline_tag: text-generation --- # mlx-community/Qwen1.5-14B-Chat-4bit This model was converted to MLX format from [`Qwen/Qwen1.5-14B-Chat`](). Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-14B-Chat) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen1.5-14B-Chat-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
powerpuf-bot/wangchanberta-th-wiki-qa_hyp-params
powerpuf-bot
2024-03-08T05:36:01Z
39
0
transformers
[ "transformers", "tensorboard", "safetensors", "camembert", "question-answering", "generated_from_trainer", "th", "base_model:Thammarak/wangchanBERTa-QA-thaiqa_squad", "base_model:finetune:Thammarak/wangchanBERTa-QA-thaiqa_squad", "endpoints_compatible", "region:us" ]
question-answering
2023-12-26T19:34:58Z
--- base_model: Thammarak/wangchanBERTa-QA-thaiqa_squad tags: - generated_from_trainer model-index: - name: WangchanBERTa-QA-thaiwiki results: [] language: - th pipeline_tag: question-answering --- # WangchanBERTa-QA-thaiwiki This model is a fine-tuned version of [Thammarak/wangchanBERTa-QA-thaiqa_squad](https://huggingface.co/Thammarak/wangchanBERTa-QA-thaiqa_squad) on the [Thai Wiki QA - NSC2020 dataset](https://copycatch.in.th/corpus/thai-wikiqa-nsc2020.html). It achieves the following results on the evaluation set: - Loss: 0.0026 ## 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: 3.3419271605136403e-06 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06 - lr_scheduler_type: linear - num_epochs: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 7.148 | 0.0 | 10 | 4.2283 | | 0.0015 | 1.0 | 2120 | 0.0706 | | 0.0804 | 2.0 | 4240 | 0.0317 | | 0.0044 | 3.0 | 6370 | 0.0193 | | 0.0834 | 4.0 | 8500 | 0.0155 | | 0.0762 | 5.0 | 10620 | 0.0102 | | 0.0914 | 5.0 | 10630 | 0.0102 | | 0.0034 | 6.0 | 12740 | 0.0078 | | 0.0101 | 7.0 | 14880 | 0.0062 | | 0.0001 | 7.01 | 14890 | 0.0062 | | 0.0006 | 7.01 | 14900 | 0.0062 | | 0.0373 | 7.02 | 14910 | 0.0063 | | 0.0001 | 7.02 | 14920 | 0.0064 | | 0.0005 | 7.03 | 14930 | 0.0066 | | 0.0001 | 7.03 | 14940 | 0.0067 | | 0.1267 | 7.04 | 14950 | 0.0067 | | 0.0012 | 7.04 | 14960 | 0.0064 | | 0.0005 | 7.04 | 14970 | 0.0064 | | 0.0704 | 7.05 | 14980 | 0.0064 | | 0.0001 | 7.05 | 14990 | 0.0064 | | 0.0008 | 7.06 | 15000 | 0.0064 | | 0.0181 | 7.06 | 15010 | 0.0064 | | 0.0001 | 7.07 | 15020 | 0.0064 | | 0.0 | 7.07 | 15030 | 0.0064 | | 0.1274 | 7.08 | 15040 | 0.0059 | | 0.0001 | 7.08 | 15050 | 0.0058 | | 0.0522 | 7.09 | 15060 | 0.0056 | | 0.0111 | 7.09 | 15070 | 0.0055 | | 0.0016 | 7.1 | 15080 | 0.0056 | | 0.0092 | 7.1 | 15090 | 0.0056 | | 0.0032 | 7.11 | 15100 | 0.0057 | | 0.0004 | 7.11 | 15110 | 0.0058 | | 0.0038 | 7.12 | 15120 | 0.0059 | | 0.0011 | 7.12 | 15130 | 0.0060 | | 0.0068 | 7.12 | 15140 | 0.0061 | | 0.0313 | 7.13 | 15150 | 0.0062 | | 0.0002 | 7.13 | 15160 | 0.0062 | | 0.0001 | 7.14 | 15170 | 0.0063 | | 0.1072 | 7.14 | 15180 | 0.0061 | | 0.1635 | 7.15 | 15190 | 0.0057 | | 0.0002 | 7.15 | 15200 | 0.0056 | | 0.0001 | 7.16 | 15210 | 0.0056 | | 0.0 | 7.16 | 15220 | 0.0057 | | 0.2542 | 7.17 | 15230 | 0.0053 | | 0.0002 | 7.17 | 15240 | 0.0052 | | 0.0006 | 7.18 | 15250 | 0.0052 | | 0.0009 | 7.18 | 15260 | 0.0054 | | 0.0002 | 7.19 | 15270 | 0.0056 | | 0.0 | 7.19 | 15280 | 0.0056 | | 0.1518 | 7.2 | 15290 | 0.0054 | | 0.0001 | 7.2 | 15300 | 0.0052 | | 0.0011 | 7.2 | 15310 | 0.0053 | | 0.0819 | 7.21 | 15320 | 0.0052 | | 0.0001 | 7.21 | 15330 | 0.0052 | | 0.0001 | 7.22 | 15340 | 0.0052 | | 0.0017 | 7.22 | 15350 | 0.0052 | | 0.1814 | 7.23 | 15360 | 0.0053 | | 0.0084 | 7.23 | 15370 | 0.0053 | | 0.0004 | 7.24 | 15380 | 0.0054 | | 0.0007 | 7.24 | 15390 | 0.0055 | | 0.0 | 7.25 | 15400 | 0.0056 | | 0.0 | 7.25 | 15410 | 0.0056 | | 0.0017 | 7.26 | 15420 | 0.0056 | | 0.0004 | 7.26 | 15430 | 0.0057 | | 0.0001 | 7.27 | 15440 | 0.0057 | | 0.0585 | 7.27 | 15450 | 0.0055 | | 0.0 | 7.28 | 15460 | 0.0054 | | 0.0008 | 7.28 | 15470 | 0.0054 | | 0.0607 | 7.28 | 15480 | 0.0054 | | 0.0097 | 7.29 | 15490 | 0.0054 | | 0.0133 | 7.29 | 15500 | 0.0054 | | 0.0001 | 7.3 | 15510 | 0.0054 | | 0.0241 | 7.3 | 15520 | 0.0053 | | 0.0001 | 7.31 | 15530 | 0.0054 | | 0.0001 | 7.31 | 15540 | 0.0054 | | 0.0799 | 7.32 | 15550 | 0.0055 | | 0.0746 | 7.32 | 15560 | 0.0055 | | 0.0881 | 7.33 | 15570 | 0.0055 | | 0.0363 | 7.33 | 15580 | 0.0056 | | 0.0082 | 7.34 | 15590 | 0.0056 | | 0.0485 | 7.34 | 15600 | 0.0057 | | 0.0004 | 7.35 | 15610 | 0.0059 | | 0.0 | 7.35 | 15620 | 0.0061 | | 0.0001 | 7.36 | 15630 | 0.0061 | | 0.0 | 7.36 | 15640 | 0.0062 | | 0.0489 | 7.36 | 15650 | 0.0061 | | 0.0038 | 7.37 | 15660 | 0.0060 | | 0.0001 | 7.37 | 15670 | 0.0059 | | 0.0005 | 7.38 | 15680 | 0.0058 | | 0.0007 | 7.38 | 15690 | 0.0057 | | 0.0001 | 7.39 | 15700 | 0.0057 | | 0.0 | 7.39 | 15710 | 0.0057 | | 0.0094 | 7.4 | 15720 | 0.0057 | | 0.0001 | 7.4 | 15730 | 0.0057 | | 0.0011 | 7.41 | 15740 | 0.0058 | | 0.099 | 7.41 | 15750 | 0.0056 | | 0.0102 | 7.42 | 15760 | 0.0055 | | 0.1448 | 7.42 | 15770 | 0.0053 | | 0.0001 | 7.43 | 15780 | 0.0051 | | 0.0001 | 7.43 | 15790 | 0.0051 | | 0.0001 | 7.44 | 15800 | 0.0052 | | 0.074 | 7.44 | 15810 | 0.0053 | | 0.0002 | 7.44 | 15820 | 0.0055 | | 0.01 | 7.45 | 15830 | 0.0056 | | 0.012 | 7.45 | 15840 | 0.0056 | | 0.0002 | 7.46 | 15850 | 0.0056 | | 0.0106 | 7.46 | 15860 | 0.0056 | | 0.0006 | 7.47 | 15870 | 0.0057 | | 0.0002 | 7.47 | 15880 | 0.0058 | | 0.0354 | 7.48 | 15890 | 0.0060 | | 0.0154 | 7.48 | 15900 | 0.0062 | | 0.0001 | 7.49 | 15910 | 0.0062 | | 0.0002 | 7.49 | 15920 | 0.0063 | | 0.0021 | 7.5 | 15930 | 0.0063 | | 0.0076 | 7.5 | 15940 | 0.0062 | | 0.0001 | 7.51 | 15950 | 0.0063 | | 0.0001 | 7.51 | 15960 | 0.0063 | | 0.1369 | 7.52 | 15970 | 0.0062 | | 0.0001 | 7.52 | 15980 | 0.0062 | | 0.09 | 7.52 | 15990 | 0.0061 | | 0.0138 | 7.53 | 16000 | 0.0060 | | 0.0006 | 7.53 | 16010 | 0.0061 | | 0.0001 | 7.54 | 16020 | 0.0061 | | 0.0 | 7.54 | 16030 | 0.0062 | | 0.0016 | 7.55 | 16040 | 0.0061 | | 0.0172 | 7.55 | 16050 | 0.0060 | | 0.0959 | 7.56 | 16060 | 0.0056 | | 0.0001 | 7.56 | 16070 | 0.0048 | | 0.0001 | 7.57 | 16080 | 0.0046 | | 0.0952 | 7.57 | 16090 | 0.0046 | | 0.001 | 7.58 | 16100 | 0.0046 | | 0.0054 | 7.58 | 16110 | 0.0047 | | 0.0714 | 7.59 | 16120 | 0.0048 | | 0.001 | 7.59 | 16130 | 0.0047 | | 0.0 | 7.6 | 16140 | 0.0048 | | 0.0006 | 7.6 | 16150 | 0.0048 | | 0.1022 | 7.6 | 16160 | 0.0049 | | 0.0576 | 7.61 | 16170 | 0.0050 | | 0.0834 | 7.61 | 16180 | 0.0048 | | 0.1453 | 7.62 | 16190 | 0.0047 | | 0.098 | 7.62 | 16200 | 0.0046 | | 0.0002 | 7.63 | 16210 | 0.0044 | | 0.0014 | 7.63 | 16220 | 0.0044 | | 0.1971 | 7.64 | 16230 | 0.0043 | | 0.0368 | 7.64 | 16240 | 0.0053 | | 0.0005 | 7.65 | 16250 | 0.0063 | | 0.0001 | 7.65 | 16260 | 0.0063 | | 0.0004 | 7.66 | 16270 | 0.0063 | | 0.0003 | 7.66 | 16280 | 0.0064 | | 0.0001 | 7.67 | 16290 | 0.0065 | | 0.0166 | 7.67 | 16300 | 0.0066 | | 0.0653 | 7.68 | 16310 | 0.0068 | | 0.0001 | 7.68 | 16320 | 0.0069 | | 0.0503 | 7.68 | 16330 | 0.0070 | | 0.1309 | 7.69 | 16340 | 0.0068 | | 0.0397 | 7.69 | 16350 | 0.0068 | | 0.08 | 7.7 | 16360 | 0.0089 | | 0.0391 | 7.7 | 16370 | 0.0088 | | 0.1459 | 7.71 | 16380 | 0.0087 | | 0.0102 | 7.71 | 16390 | 0.0099 | | 0.0 | 7.72 | 16400 | 0.0099 | | 0.0001 | 7.72 | 16410 | 0.0107 | | 0.0 | 7.73 | 16420 | 0.0108 | | 0.0002 | 7.73 | 16430 | 0.0108 | | 0.0069 | 7.74 | 16440 | 0.0108 | | 0.0 | 7.74 | 16450 | 0.0108 | | 0.0001 | 7.75 | 16460 | 0.0108 | | 0.1238 | 7.75 | 16470 | 0.0106 | | 0.0037 | 7.76 | 16480 | 0.0104 | | 0.0003 | 7.76 | 16490 | 0.0104 | | 0.0007 | 7.76 | 16500 | 0.0104 | | 0.1338 | 7.77 | 16510 | 0.0132 | | 0.0688 | 7.77 | 16520 | 0.0178 | | 0.0472 | 7.78 | 16530 | 0.0215 | | 0.0232 | 7.78 | 16540 | 0.0259 | | 0.0001 | 7.79 | 16550 | 0.0295 | | 0.2246 | 7.79 | 16560 | 0.0274 | | 0.0403 | 7.8 | 16570 | 0.0214 | | 0.2483 | 7.8 | 16580 | 0.0205 | | 0.0914 | 7.81 | 16590 | 0.0214 | | 0.0003 | 7.81 | 16600 | 0.0216 | | 0.1359 | 7.82 | 16610 | 0.0216 | | 0.1143 | 7.82 | 16620 | 0.0215 | | 0.0001 | 7.83 | 16630 | 0.0190 | | 0.0003 | 7.83 | 16640 | 0.0182 | | 0.0034 | 7.84 | 16650 | 0.0183 | | 0.0341 | 7.84 | 16660 | 0.0166 | | 0.0733 | 7.84 | 16670 | 0.0130 | | 0.0329 | 7.85 | 16680 | 0.0121 | | 0.0073 | 7.85 | 16690 | 0.0121 | | 0.034 | 7.86 | 16700 | 0.0130 | | 0.0002 | 7.86 | 16710 | 0.0139 | | 0.0002 | 7.87 | 16720 | 0.0140 | | 0.1277 | 7.87 | 16730 | 0.0134 | | 0.0001 | 7.88 | 16740 | 0.0123 | | 0.0002 | 7.88 | 16750 | 0.0123 | | 0.0871 | 7.89 | 16760 | 0.0122 | | 0.0804 | 7.89 | 16770 | 0.0122 | | 0.041 | 7.9 | 16780 | 0.0123 | | 0.008 | 7.9 | 16790 | 0.0122 | | 0.0345 | 7.91 | 16800 | 0.0113 | | 0.0169 | 7.91 | 16810 | 0.0104 | | 0.0001 | 7.92 | 16820 | 0.0078 | | 0.1148 | 7.92 | 16830 | 0.0078 | | 0.0009 | 7.92 | 16840 | 0.0079 | | 0.0032 | 7.93 | 16850 | 0.0079 | | 0.0004 | 7.93 | 16860 | 0.0079 | | 0.0001 | 7.94 | 16870 | 0.0080 | | 0.0001 | 7.94 | 16880 | 0.0080 | | 0.0003 | 7.95 | 16890 | 0.0080 | | 0.0003 | 7.95 | 16900 | 0.0072 | | 0.0 | 7.96 | 16910 | 0.0072 | | 0.0013 | 7.96 | 16920 | 0.0073 | | 0.0198 | 7.97 | 16930 | 0.0072 | | 0.0003 | 7.97 | 16940 | 0.0071 | | 0.0004 | 7.98 | 16950 | 0.0071 | | 0.0001 | 7.98 | 16960 | 0.0071 | | 0.0 | 7.99 | 16970 | 0.0072 | | 0.0011 | 7.99 | 16980 | 0.0070 | | 0.0001 | 8.0 | 16990 | 0.0070 | | 0.0002 | 8.0 | 17000 | 0.0070 | | 0.0 | 8.0 | 17010 | 0.0070 | | 0.0132 | 8.01 | 17020 | 0.0069 | | 0.0 | 8.01 | 17030 | 0.0069 | | 0.0679 | 8.02 | 17040 | 0.0071 | | 0.0 | 8.02 | 17050 | 0.0072 | | 0.0002 | 8.03 | 17060 | 0.0072 | | 0.0 | 8.03 | 17070 | 0.0072 | | 0.0001 | 8.04 | 17080 | 0.0073 | | 0.0 | 8.04 | 17090 | 0.0073 | | 0.2347 | 8.05 | 17100 | 0.0071 | | 0.0436 | 8.05 | 17110 | 0.0081 | | 0.0001 | 8.06 | 17120 | 0.0081 | | 0.0064 | 8.06 | 17130 | 0.0098 | | 0.0462 | 8.07 | 17140 | 0.0079 | | 0.0037 | 8.07 | 17150 | 0.0079 | | 0.0896 | 8.08 | 17160 | 0.0076 | | 0.0025 | 8.08 | 17170 | 0.0075 | | 0.0315 | 8.08 | 17180 | 0.0074 | | 0.0002 | 8.09 | 17190 | 0.0075 | | 0.0962 | 8.09 | 17200 | 0.0075 | | 0.2005 | 8.1 | 17210 | 0.0073 | | 0.0724 | 8.1 | 17220 | 0.0071 | | 0.0778 | 8.11 | 17230 | 0.0071 | | 0.0469 | 8.11 | 17240 | 0.0088 | | 0.0003 | 8.12 | 17250 | 0.0088 | | 0.0004 | 8.12 | 17260 | 0.0089 | | 0.0757 | 8.13 | 17270 | 0.0096 | | 0.1558 | 8.13 | 17280 | 0.0095 | | 0.0007 | 8.14 | 17290 | 0.0094 | | 0.2009 | 8.14 | 17300 | 0.0090 | | 0.0001 | 8.15 | 17310 | 0.0078 | | 0.0006 | 8.15 | 17320 | 0.0078 | | 0.0004 | 8.16 | 17330 | 0.0078 | | 0.0001 | 8.16 | 17340 | 0.0079 | | 0.0803 | 8.16 | 17350 | 0.0079 | | 0.0372 | 8.17 | 17360 | 0.0089 | | 0.1616 | 8.17 | 17370 | 0.0088 | | 0.0014 | 8.18 | 17380 | 0.0078 | | 0.0009 | 8.18 | 17390 | 0.0061 | | 0.0382 | 8.19 | 17400 | 0.0061 | | 0.0001 | 8.19 | 17410 | 0.0052 | | 0.0001 | 8.2 | 17420 | 0.0052 | | 0.0005 | 8.2 | 17430 | 0.0054 | | 0.0001 | 8.21 | 17440 | 0.0054 | | 0.0013 | 8.21 | 17450 | 0.0055 | | 0.0069 | 8.22 | 17460 | 0.0056 | | 0.0104 | 8.22 | 17470 | 0.0056 | | 0.0006 | 8.23 | 17480 | 0.0055 | | 0.0002 | 8.23 | 17490 | 0.0056 | | 0.035 | 8.24 | 17500 | 0.0056 | | 0.1617 | 8.24 | 17510 | 0.0055 | | 0.1127 | 8.24 | 17520 | 0.0054 | | 0.0001 | 8.25 | 17530 | 0.0054 | | 0.0001 | 8.25 | 17540 | 0.0054 | | 0.0003 | 8.26 | 17550 | 0.0053 | | 0.0019 | 8.26 | 17560 | 0.0053 | | 0.0793 | 8.27 | 17570 | 0.0055 | | 0.0001 | 8.27 | 17580 | 0.0057 | | 0.0 | 8.28 | 17590 | 0.0058 | | 0.0 | 8.28 | 17600 | 0.0058 | | 0.0001 | 8.29 | 17610 | 0.0058 | | 0.0002 | 8.29 | 17620 | 0.0058 | | 0.0034 | 8.3 | 17630 | 0.0058 | | 0.0002 | 8.3 | 17640 | 0.0058 | | 0.0204 | 8.31 | 17650 | 0.0059 | | 0.0022 | 8.31 | 17660 | 0.0060 | | 0.0 | 8.32 | 17670 | 0.0060 | | 0.1065 | 8.32 | 17680 | 0.0060 | | 0.0003 | 8.32 | 17690 | 0.0060 | | 0.0392 | 8.33 | 17700 | 0.0060 | | 0.0001 | 8.33 | 17710 | 0.0060 | | 0.0856 | 8.34 | 17720 | 0.0059 | | 0.0001 | 8.34 | 17730 | 0.0058 | | 0.0 | 8.35 | 17740 | 0.0057 | | 0.0032 | 8.35 | 17750 | 0.0057 | | 0.0001 | 8.36 | 17760 | 0.0056 | | 0.0001 | 8.36 | 17770 | 0.0056 | | 0.0001 | 8.37 | 17780 | 0.0056 | | 0.0062 | 8.37 | 17790 | 0.0055 | | 0.0014 | 8.38 | 17800 | 0.0055 | | 0.0001 | 8.38 | 17810 | 0.0055 | | 0.0701 | 8.39 | 17820 | 0.0055 | | 0.0679 | 8.39 | 17830 | 0.0055 | | 0.0375 | 8.4 | 17840 | 0.0064 | | 0.0028 | 8.4 | 17850 | 0.0065 | | 0.0232 | 8.4 | 17860 | 0.0056 | | 0.0104 | 8.41 | 17870 | 0.0056 | | 0.0 | 8.41 | 17880 | 0.0056 | | 0.0352 | 8.42 | 17890 | 0.0057 | | 0.0791 | 8.42 | 17900 | 0.0057 | | 0.0001 | 8.43 | 17910 | 0.0057 | | 0.073 | 8.43 | 17920 | 0.0057 | | 0.0001 | 8.44 | 17930 | 0.0058 | | 0.0777 | 8.44 | 17940 | 0.0059 | | 0.0 | 8.45 | 17950 | 0.0059 | | 0.0026 | 8.45 | 17960 | 0.0059 | | 0.0 | 8.46 | 17970 | 0.0059 | | 0.0 | 8.46 | 17980 | 0.0058 | | 0.0333 | 8.47 | 17990 | 0.0057 | | 0.0555 | 8.47 | 18000 | 0.0057 | | 0.1599 | 8.48 | 18010 | 0.0057 | | 0.0363 | 8.48 | 18020 | 0.0057 | | 0.0378 | 8.48 | 18030 | 0.0057 | | 0.0711 | 8.49 | 18040 | 0.0057 | | 0.0438 | 8.49 | 18050 | 0.0056 | | 0.0455 | 8.5 | 18060 | 0.0056 | | 0.0001 | 8.5 | 18070 | 0.0056 | | 0.0 | 8.51 | 18080 | 0.0056 | | 0.038 | 8.51 | 18090 | 0.0056 | | 0.0001 | 8.52 | 18100 | 0.0053 | | 0.0041 | 8.52 | 18110 | 0.0053 | | 0.0028 | 8.53 | 18120 | 0.0053 | | 0.0 | 8.53 | 18130 | 0.0053 | | 0.0335 | 8.54 | 18140 | 0.0053 | | 0.0005 | 8.54 | 18150 | 0.0054 | | 0.0544 | 8.55 | 18160 | 0.0054 | | 0.0001 | 8.55 | 18170 | 0.0054 | | 0.0001 | 8.56 | 18180 | 0.0054 | | 0.0008 | 8.56 | 18190 | 0.0054 | | 0.0001 | 8.56 | 18200 | 0.0054 | | 0.0564 | 8.57 | 18210 | 0.0053 | | 0.1649 | 8.57 | 18220 | 0.0053 | | 0.0003 | 8.58 | 18230 | 0.0054 | | 0.0001 | 8.58 | 18240 | 0.0054 | | 0.0171 | 8.59 | 18250 | 0.0054 | | 0.031 | 8.59 | 18260 | 0.0055 | | 0.2706 | 8.6 | 18270 | 0.0053 | | 0.037 | 8.6 | 18280 | 0.0052 | | 0.0004 | 8.61 | 18290 | 0.0052 | | 0.0736 | 8.61 | 18300 | 0.0052 | | 0.0051 | 8.62 | 18310 | 0.0052 | | 0.0006 | 8.62 | 18320 | 0.0052 | | 0.0324 | 8.63 | 18330 | 0.0053 | | 0.0054 | 8.63 | 18340 | 0.0052 | | 0.0036 | 8.64 | 18350 | 0.0052 | | 0.1031 | 8.64 | 18360 | 0.0050 | | 0.0002 | 8.64 | 18370 | 0.0050 | | 0.0001 | 8.65 | 18380 | 0.0050 | | 0.0001 | 8.65 | 18390 | 0.0050 | | 0.1069 | 8.66 | 18400 | 0.0050 | | 0.1139 | 8.66 | 18410 | 0.0051 | | 0.0002 | 8.67 | 18420 | 0.0052 | | 0.0002 | 8.67 | 18430 | 0.0052 | | 0.0001 | 8.68 | 18440 | 0.0053 | | 0.1413 | 8.68 | 18450 | 0.0051 | | 0.0001 | 8.69 | 18460 | 0.0051 | | 0.0002 | 8.69 | 18470 | 0.0051 | | 0.0 | 8.7 | 18480 | 0.0051 | | 0.0001 | 8.7 | 18490 | 0.0051 | | 0.1362 | 8.71 | 18500 | 0.0052 | | 0.0001 | 8.71 | 18510 | 0.0052 | | 0.0049 | 8.72 | 18520 | 0.0052 | | 0.0747 | 8.72 | 18530 | 0.0052 | | 0.0004 | 8.72 | 18540 | 0.0052 | | 0.0001 | 8.73 | 18550 | 0.0053 | | 0.127 | 8.73 | 18560 | 0.0052 | | 0.0913 | 8.74 | 18570 | 0.0050 | | 0.21 | 8.74 | 18580 | 0.0049 | | 0.0001 | 8.75 | 18590 | 0.0049 | | 0.0002 | 8.75 | 18600 | 0.0049 | | 0.0855 | 8.76 | 18610 | 0.0049 | | 0.0042 | 8.76 | 18620 | 0.0048 | | 0.0017 | 8.77 | 18630 | 0.0049 | | 0.0791 | 8.77 | 18640 | 0.0051 | | 0.0001 | 8.78 | 18650 | 0.0051 | | 0.0398 | 8.78 | 18660 | 0.0052 | | 0.1381 | 8.79 | 18670 | 0.0052 | | 0.0015 | 8.79 | 18680 | 0.0052 | | 0.0 | 8.8 | 18690 | 0.0051 | | 0.0001 | 8.8 | 18700 | 0.0051 | | 0.073 | 8.8 | 18710 | 0.0052 | | 0.0003 | 8.81 | 18720 | 0.0052 | | 0.0376 | 8.81 | 18730 | 0.0053 | | 0.0368 | 8.82 | 18740 | 0.0053 | | 0.0338 | 8.82 | 18750 | 0.0054 | | 0.1429 | 8.83 | 18760 | 0.0054 | | 0.0979 | 8.83 | 18770 | 0.0053 | | 0.0001 | 8.84 | 18780 | 0.0052 | | 0.1374 | 8.84 | 18790 | 0.0051 | | 0.0001 | 8.85 | 18800 | 0.0050 | | 0.0005 | 8.85 | 18810 | 0.0051 | | 0.0774 | 8.86 | 18820 | 0.0051 | | 0.0389 | 8.86 | 18830 | 0.0052 | | 0.0366 | 8.87 | 18840 | 0.0052 | | 0.0725 | 8.87 | 18850 | 0.0061 | | 0.0004 | 8.88 | 18860 | 0.0062 | | 0.1598 | 8.88 | 18870 | 0.0062 | | 0.0001 | 8.88 | 18880 | 0.0062 | | 0.0698 | 8.89 | 18890 | 0.0063 | | 0.035 | 8.89 | 18900 | 0.0063 | | 0.074 | 8.9 | 18910 | 0.0054 | | 0.1915 | 8.9 | 18920 | 0.0054 | | 0.0006 | 8.91 | 18930 | 0.0045 | | 0.0765 | 8.91 | 18940 | 0.0054 | | 0.0367 | 8.92 | 18950 | 0.0053 | | 0.0002 | 8.92 | 18960 | 0.0054 | | 0.0726 | 8.93 | 18970 | 0.0054 | | 0.0002 | 8.93 | 18980 | 0.0054 | | 0.1149 | 8.94 | 18990 | 0.0053 | | 0.0001 | 8.94 | 19000 | 0.0053 | | 0.0001 | 8.95 | 19010 | 0.0052 | | 0.0001 | 8.95 | 19020 | 0.0053 | | 0.0001 | 8.96 | 19030 | 0.0053 | | 0.0001 | 8.96 | 19040 | 0.0053 | | 0.0001 | 8.96 | 19050 | 0.0053 | | 0.0375 | 8.97 | 19060 | 0.0053 | | 0.0328 | 8.97 | 19070 | 0.0053 | | 0.0001 | 8.98 | 19080 | 0.0054 | | 0.0001 | 8.98 | 19090 | 0.0054 | | 0.032 | 8.99 | 19100 | 0.0054 | | 0.0005 | 8.99 | 19110 | 0.0055 | | 0.0001 | 9.0 | 19120 | 0.0055 | | 0.0403 | 9.0 | 19130 | 0.0055 | | 0.0 | 9.01 | 19140 | 0.0047 | | 0.0381 | 9.01 | 19150 | 0.0047 | | 0.0001 | 9.02 | 19160 | 0.0047 | | 0.0781 | 9.02 | 19170 | 0.0046 | | 0.0279 | 9.03 | 19180 | 0.0046 | | 0.0001 | 9.03 | 19190 | 0.0046 | | 0.0226 | 9.04 | 19200 | 0.0036 | | 0.1095 | 9.04 | 19210 | 0.0034 | | 0.0361 | 9.04 | 19220 | 0.0032 | | 0.0675 | 9.05 | 19230 | 0.0031 | | 0.0009 | 9.05 | 19240 | 0.0031 | | 0.0004 | 9.06 | 19250 | 0.0031 | | 0.0002 | 9.06 | 19260 | 0.0032 | | 0.0805 | 9.07 | 19270 | 0.0031 | | 0.0001 | 9.07 | 19280 | 0.0030 | | 0.1095 | 9.08 | 19290 | 0.0029 | | 0.0958 | 9.08 | 19300 | 0.0028 | | 0.0128 | 9.09 | 19310 | 0.0028 | | 0.016 | 9.09 | 19320 | 0.0028 | | 0.0004 | 9.1 | 19330 | 0.0029 | | 0.0532 | 9.1 | 19340 | 0.0030 | | 0.0001 | 9.11 | 19350 | 0.0030 | | 0.0008 | 9.11 | 19360 | 0.0031 | | 0.0708 | 9.12 | 19370 | 0.0032 | | 0.0001 | 9.12 | 19380 | 0.0033 | | 0.0001 | 9.12 | 19390 | 0.0033 | | 0.0001 | 9.13 | 19400 | 0.0033 | | 0.001 | 9.13 | 19410 | 0.0033 | | 0.0003 | 9.14 | 19420 | 0.0033 | | 0.0373 | 9.14 | 19430 | 0.0033 | | 0.0678 | 9.15 | 19440 | 0.0042 | | 0.0708 | 9.15 | 19450 | 0.0043 | | 0.0003 | 9.16 | 19460 | 0.0035 | | 0.0001 | 9.16 | 19470 | 0.0035 | | 0.0861 | 9.17 | 19480 | 0.0035 | | 0.001 | 9.17 | 19490 | 0.0035 | | 0.0001 | 9.18 | 19500 | 0.0034 | | 0.0 | 9.18 | 19510 | 0.0034 | | 0.0001 | 9.19 | 19520 | 0.0034 | | 0.0 | 9.19 | 19530 | 0.0035 | | 0.0 | 9.2 | 19540 | 0.0035 | | 0.0 | 9.2 | 19550 | 0.0035 | | 0.0003 | 9.2 | 19560 | 0.0035 | | 0.0012 | 9.21 | 19570 | 0.0035 | | 0.0 | 9.21 | 19580 | 0.0035 | | 0.0 | 9.22 | 19590 | 0.0035 | | 0.0372 | 9.22 | 19600 | 0.0035 | | 0.0002 | 9.23 | 19610 | 0.0035 | | 0.1244 | 9.23 | 19620 | 0.0035 | | 0.0001 | 9.24 | 19630 | 0.0035 | | 0.0863 | 9.24 | 19640 | 0.0035 | | 0.0 | 9.25 | 19650 | 0.0034 | | 0.0001 | 9.25 | 19660 | 0.0034 | | 0.0 | 9.26 | 19670 | 0.0034 | | 0.0676 | 9.26 | 19680 | 0.0034 | | 0.0001 | 9.27 | 19690 | 0.0034 | | 0.0001 | 9.27 | 19700 | 0.0051 | | 0.005 | 9.28 | 19710 | 0.0052 | | 0.0254 | 9.28 | 19720 | 0.0052 | | 0.0544 | 9.28 | 19730 | 0.0051 | | 0.0728 | 9.29 | 19740 | 0.0034 | | 0.0004 | 9.29 | 19750 | 0.0034 | | 0.0 | 9.3 | 19760 | 0.0034 | | 0.0004 | 9.3 | 19770 | 0.0034 | | 0.0001 | 9.31 | 19780 | 0.0035 | | 0.0007 | 9.31 | 19790 | 0.0035 | | 0.071 | 9.32 | 19800 | 0.0035 | | 0.231 | 9.32 | 19810 | 0.0052 | | 0.0002 | 9.33 | 19820 | 0.0051 | | 0.0 | 9.33 | 19830 | 0.0034 | | 0.0002 | 9.34 | 19840 | 0.0034 | | 0.0094 | 9.34 | 19850 | 0.0052 | | 0.0001 | 9.35 | 19860 | 0.0051 | | 0.0003 | 9.35 | 19870 | 0.0051 | | 0.0745 | 9.36 | 19880 | 0.0052 | | 0.0002 | 9.36 | 19890 | 0.0053 | | 0.0001 | 9.36 | 19900 | 0.0054 | | 0.0035 | 9.37 | 19910 | 0.0054 | | 0.2589 | 9.37 | 19920 | 0.0053 | | 0.0 | 9.38 | 19930 | 0.0052 | | 0.0124 | 9.38 | 19940 | 0.0052 | | 0.0413 | 9.39 | 19950 | 0.0053 | | 0.0001 | 9.39 | 19960 | 0.0053 | | 0.1248 | 9.4 | 19970 | 0.0053 | | 0.0698 | 9.4 | 19980 | 0.0053 | | 0.0017 | 9.41 | 19990 | 0.0053 | | 0.0102 | 9.41 | 20000 | 0.0053 | | 0.0 | 9.42 | 20010 | 0.0054 | | 0.0001 | 9.42 | 20020 | 0.0054 | | 0.0 | 9.43 | 20030 | 0.0054 | | 0.0001 | 9.43 | 20040 | 0.0054 | | 0.0004 | 9.44 | 20050 | 0.0054 | | 0.0001 | 9.44 | 20060 | 0.0055 | | 0.11 | 9.44 | 20070 | 0.0054 | | 0.1102 | 9.45 | 20080 | 0.0054 | | 0.0335 | 9.45 | 20090 | 0.0054 | | 0.1011 | 9.46 | 20100 | 0.0054 | | 0.0001 | 9.46 | 20110 | 0.0055 | | 0.0006 | 9.47 | 20120 | 0.0054 | | 0.0734 | 9.47 | 20130 | 0.0054 | | 0.0 | 9.48 | 20140 | 0.0054 | | 0.0004 | 9.48 | 20150 | 0.0054 | | 0.0001 | 9.49 | 20160 | 0.0053 | | 0.0 | 9.49 | 20170 | 0.0053 | | 0.0967 | 9.5 | 20180 | 0.0050 | | 0.0001 | 9.5 | 20190 | 0.0047 | | 0.0 | 9.51 | 20200 | 0.0047 | | 0.0 | 9.51 | 20210 | 0.0046 | | 0.0248 | 9.52 | 20220 | 0.0046 | | 0.0001 | 9.52 | 20230 | 0.0029 | | 0.0006 | 9.52 | 20240 | 0.0029 | | 0.012 | 9.53 | 20250 | 0.0029 | | 0.0 | 9.53 | 20260 | 0.0027 | | 0.0 | 9.54 | 20270 | 0.0027 | | 0.0001 | 9.54 | 20280 | 0.0027 | | 0.1061 | 9.55 | 20290 | 0.0027 | | 0.0435 | 9.55 | 20300 | 0.0028 | | 0.0707 | 9.56 | 20310 | 0.0028 | | 0.0001 | 9.56 | 20320 | 0.0028 | | 0.0019 | 9.57 | 20330 | 0.0029 | | 0.0 | 9.57 | 20340 | 0.0029 | | 0.0001 | 9.58 | 20350 | 0.0029 | | 0.0 | 9.58 | 20360 | 0.0029 | | 0.0 | 9.59 | 20370 | 0.0029 | | 0.0001 | 9.59 | 20380 | 0.0029 | | 0.0001 | 9.6 | 20390 | 0.0030 | | 0.0001 | 9.6 | 20400 | 0.0030 | | 0.0397 | 9.6 | 20410 | 0.0031 | | 0.0703 | 9.61 | 20420 | 0.0031 | | 0.0001 | 9.61 | 20430 | 0.0031 | | 0.0002 | 9.62 | 20440 | 0.0032 | | 0.0001 | 9.62 | 20450 | 0.0032 | | 0.0686 | 9.63 | 20460 | 0.0032 | | 0.0658 | 9.63 | 20470 | 0.0032 | | 0.0008 | 9.64 | 20480 | 0.0032 | | 0.1567 | 9.64 | 20490 | 0.0030 | | 0.0973 | 9.65 | 20500 | 0.0026 | | 0.0001 | 9.65 | 20510 | 0.0025 | | 0.0747 | 9.66 | 20520 | 0.0025 | | 0.0005 | 9.66 | 20530 | 0.0025 | | 0.0703 | 9.67 | 20540 | 0.0026 | | 0.0001 | 9.67 | 20550 | 0.0026 | | 0.0344 | 9.68 | 20560 | 0.0026 | | 0.0678 | 9.68 | 20570 | 0.0027 | | 0.0105 | 9.68 | 20580 | 0.0027 | | 0.0 | 9.69 | 20590 | 0.0028 | | 0.0001 | 9.69 | 20600 | 0.0028 | | 0.0854 | 9.7 | 20610 | 0.0027 | | 0.0001 | 9.7 | 20620 | 0.0027 | | 0.0001 | 9.71 | 20630 | 0.0027 | | 0.074 | 9.71 | 20640 | 0.0028 | | 0.022 | 9.72 | 20650 | 0.0028 | | 0.0001 | 9.72 | 20660 | 0.0029 | | 0.0001 | 9.73 | 20670 | 0.0029 | | 0.0003 | 9.73 | 20680 | 0.0030 | | 0.0001 | 9.74 | 20690 | 0.0030 | | 0.0701 | 9.74 | 20700 | 0.0030 | | 0.0878 | 9.75 | 20710 | 0.0028 | | 0.0 | 9.75 | 20720 | 0.0027 | | 0.0001 | 9.76 | 20730 | 0.0027 | | 0.0003 | 9.76 | 20740 | 0.0028 | | 0.0002 | 9.76 | 20750 | 0.0029 | | 0.001 | 9.77 | 20760 | 0.0029 | | 0.0 | 9.77 | 20770 | 0.0029 | | 0.0137 | 9.78 | 20780 | 0.0028 | | 0.0003 | 9.78 | 20790 | 0.0028 | | 0.2638 | 9.79 | 20800 | 0.0028 | | 0.0112 | 9.79 | 20810 | 0.0026 | | 0.0001 | 9.8 | 20820 | 0.0025 | | 0.0001 | 9.8 | 20830 | 0.0025 | | 0.0004 | 9.81 | 20840 | 0.0025 | | 0.092 | 9.81 | 20850 | 0.0024 | | 0.0003 | 9.82 | 20860 | 0.0024 | | 0.0795 | 9.82 | 20870 | 0.0024 | | 0.0 | 9.83 | 20880 | 0.0042 | | 0.0767 | 9.83 | 20890 | 0.0043 | | 0.0 | 9.84 | 20900 | 0.0043 | | 0.0004 | 9.84 | 20910 | 0.0043 | | 0.0001 | 9.84 | 20920 | 0.0043 | | 0.0809 | 9.85 | 20930 | 0.0044 | | 0.0002 | 9.85 | 20940 | 0.0044 | | 0.0333 | 9.86 | 20950 | 0.0043 | | 0.1653 | 9.86 | 20960 | 0.0042 | | 0.0 | 9.87 | 20970 | 0.0042 | | 0.0141 | 9.87 | 20980 | 0.0042 | | 0.0 | 9.88 | 20990 | 0.0042 | | 0.0006 | 9.88 | 21000 | 0.0044 | | 0.0381 | 9.89 | 21010 | 0.0045 | | 0.0 | 9.89 | 21020 | 0.0045 | | 0.0372 | 9.9 | 21030 | 0.0046 | | 0.0 | 9.9 | 21040 | 0.0046 | | 0.1523 | 9.91 | 21050 | 0.0045 | | 0.0062 | 9.91 | 21060 | 0.0045 | | 0.0928 | 9.92 | 21070 | 0.0045 | | 0.0674 | 9.92 | 21080 | 0.0043 | | 0.0 | 9.92 | 21090 | 0.0042 | | 0.0748 | 9.93 | 21100 | 0.0041 | | 0.0001 | 9.93 | 21110 | 0.0041 | | 0.0001 | 9.94 | 21120 | 0.0041 | | 0.1564 | 9.94 | 21130 | 0.0041 | | 0.0 | 9.95 | 21140 | 0.0041 | | 0.0361 | 9.95 | 21150 | 0.0041 | | 0.0001 | 9.96 | 21160 | 0.0041 | | 0.0012 | 9.96 | 21170 | 0.0041 | | 0.036 | 9.97 | 21180 | 0.0042 | | 0.0221 | 9.97 | 21190 | 0.0042 | | 0.0722 | 9.98 | 21200 | 0.0041 | | 0.0002 | 9.98 | 21210 | 0.0041 | | 0.1203 | 9.99 | 21220 | 0.0040 | | 0.0004 | 9.99 | 21230 | 0.0040 | | 0.0045 | 10.0 | 21240 | 0.0040 | | 0.1223 | 10.0 | 21250 | 0.0039 | | 0.0001 | 10.0 | 21260 | 0.0039 | | 0.0734 | 10.01 | 21270 | 0.0039 | | 0.0001 | 10.01 | 21280 | 0.0039 | | 0.0001 | 10.02 | 21290 | 0.0039 | | 0.0091 | 10.02 | 21300 | 0.0040 | | 0.0004 | 10.03 | 21310 | 0.0040 | | 0.0761 | 10.03 | 21320 | 0.0039 | | 0.2287 | 10.04 | 21330 | 0.0037 | | 0.0005 | 10.04 | 21340 | 0.0037 | | 0.0318 | 10.05 | 21350 | 0.0038 | | 0.0 | 10.05 | 21360 | 0.0038 | | 0.0001 | 10.06 | 21370 | 0.0038 | | 0.0004 | 10.06 | 21380 | 0.0038 | | 0.0 | 10.07 | 21390 | 0.0038 | | 0.0162 | 10.07 | 21400 | 0.0038 | | 0.0008 | 10.08 | 21410 | 0.0039 | | 0.0 | 10.08 | 21420 | 0.0040 | | 0.0 | 10.08 | 21430 | 0.0040 | | 0.057 | 10.09 | 21440 | 0.0037 | | 0.0365 | 10.09 | 21450 | 0.0034 | | 0.0 | 10.1 | 21460 | 0.0031 | | 0.0019 | 10.1 | 21470 | 0.0030 | | 0.0 | 10.11 | 21480 | 0.0031 | | 0.0044 | 10.11 | 21490 | 0.0031 | | 0.0001 | 10.12 | 21500 | 0.0032 | | 0.0 | 10.12 | 21510 | 0.0032 | | 0.1701 | 10.13 | 21520 | 0.0032 | | 0.0002 | 10.13 | 21530 | 0.0032 | | 0.0001 | 10.14 | 21540 | 0.0033 | | 0.0012 | 10.14 | 21550 | 0.0033 | | 0.0024 | 10.15 | 21560 | 0.0033 | | 0.047 | 10.15 | 21570 | 0.0031 | | 0.0002 | 10.16 | 21580 | 0.0031 | | 0.0004 | 10.16 | 21590 | 0.0031 | | 0.0049 | 10.16 | 21600 | 0.0031 | | 0.0001 | 10.17 | 21610 | 0.0031 | | 0.0001 | 10.17 | 21620 | 0.0032 | | 0.0001 | 10.18 | 21630 | 0.0032 | | 0.007 | 10.18 | 21640 | 0.0032 | | 0.0 | 10.19 | 21650 | 0.0032 | | 0.0 | 10.19 | 21660 | 0.0032 | | 0.0132 | 10.2 | 21670 | 0.0032 | | 0.0744 | 10.2 | 21680 | 0.0031 | | 0.0001 | 10.21 | 21690 | 0.0031 | | 0.0863 | 10.21 | 21700 | 0.0031 | | 0.0001 | 10.22 | 21710 | 0.0030 | | 0.0001 | 10.22 | 21720 | 0.0030 | | 0.074 | 10.23 | 21730 | 0.0030 | | 0.0001 | 10.23 | 21740 | 0.0030 | | 0.0001 | 10.24 | 21750 | 0.0030 | | 0.0004 | 10.24 | 21760 | 0.0030 | | 0.035 | 10.24 | 21770 | 0.0030 | | 0.0011 | 10.25 | 21780 | 0.0031 | | 0.0005 | 10.25 | 21790 | 0.0031 | | 0.0001 | 10.26 | 21800 | 0.0031 | | 0.0779 | 10.26 | 21810 | 0.0031 | | 0.0499 | 10.27 | 21820 | 0.0031 | | 0.0013 | 10.27 | 21830 | 0.0031 | | 0.1404 | 10.28 | 21840 | 0.0030 | | 0.0001 | 10.28 | 21850 | 0.0030 | | 0.0002 | 10.29 | 21860 | 0.0030 | | 0.1122 | 10.29 | 21870 | 0.0030 | | 0.0001 | 10.3 | 21880 | 0.0030 | | 0.004 | 10.3 | 21890 | 0.0030 | | 0.0002 | 10.31 | 21900 | 0.0030 | | 0.0001 | 10.31 | 21910 | 0.0030 | | 0.0001 | 10.32 | 21920 | 0.0030 | | 0.0001 | 10.32 | 21930 | 0.0030 | | 0.0 | 10.32 | 21940 | 0.0030 | | 0.0 | 10.33 | 21950 | 0.0030 | | 0.0 | 10.33 | 21960 | 0.0031 | | 0.0005 | 10.34 | 21970 | 0.0031 | | 0.0711 | 10.34 | 21980 | 0.0031 | | 0.0013 | 10.35 | 21990 | 0.0032 | | 0.0 | 10.35 | 22000 | 0.0032 | | 0.0385 | 10.36 | 22010 | 0.0032 | | 0.0 | 10.36 | 22020 | 0.0031 | | 0.0 | 10.37 | 22030 | 0.0031 | | 0.003 | 10.37 | 22040 | 0.0032 | | 0.0777 | 10.38 | 22050 | 0.0032 | | 0.0065 | 10.38 | 22060 | 0.0050 | | 0.0011 | 10.39 | 22070 | 0.0050 | | 0.0002 | 10.39 | 22080 | 0.0049 | | 0.0761 | 10.4 | 22090 | 0.0049 | | 0.0003 | 10.4 | 22100 | 0.0050 | | 0.0813 | 10.4 | 22110 | 0.0050 | | 0.0 | 10.41 | 22120 | 0.0048 | | 0.0001 | 10.41 | 22130 | 0.0048 | | 0.0001 | 10.42 | 22140 | 0.0048 | | 0.0876 | 10.42 | 22150 | 0.0047 | | 0.0014 | 10.43 | 22160 | 0.0047 | | 0.0 | 10.43 | 22170 | 0.0047 | | 0.0001 | 10.44 | 22180 | 0.0047 | | 0.0008 | 10.44 | 22190 | 0.0047 | | 0.0 | 10.45 | 22200 | 0.0047 | | 0.0001 | 10.45 | 22210 | 0.0047 | | 0.0001 | 10.46 | 22220 | 0.0047 | | 0.0 | 10.46 | 22230 | 0.0048 | | 0.0 | 10.47 | 22240 | 0.0048 | | 0.0055 | 10.47 | 22250 | 0.0048 | | 0.0003 | 10.48 | 22260 | 0.0048 | | 0.0 | 10.48 | 22270 | 0.0049 | | 0.0001 | 10.48 | 22280 | 0.0049 | | 0.0123 | 10.49 | 22290 | 0.0049 | | 0.0 | 10.49 | 22300 | 0.0049 | | 0.2534 | 10.5 | 22310 | 0.0047 | | 0.0001 | 10.5 | 22320 | 0.0046 | | 0.0373 | 10.51 | 22330 | 0.0046 | | 0.0003 | 10.51 | 22340 | 0.0046 | | 0.0538 | 10.52 | 22350 | 0.0046 | | 0.0001 | 10.52 | 22360 | 0.0045 | | 0.0414 | 10.53 | 22370 | 0.0044 | | 0.0003 | 10.53 | 22380 | 0.0044 | | 0.0003 | 10.54 | 22390 | 0.0044 | | 0.0032 | 10.54 | 22400 | 0.0044 | | 0.0004 | 10.55 | 22410 | 0.0044 | | 0.0 | 10.55 | 22420 | 0.0045 | | 0.0359 | 10.56 | 22430 | 0.0047 | | 0.0 | 10.56 | 22440 | 0.0047 | | 0.0276 | 10.56 | 22450 | 0.0047 | | 0.0009 | 10.57 | 22460 | 0.0047 | | 0.004 | 10.57 | 22470 | 0.0047 | | 0.0466 | 10.58 | 22480 | 0.0047 | | 0.0003 | 10.58 | 22490 | 0.0046 | | 0.0004 | 10.59 | 22500 | 0.0046 | | 0.0 | 10.59 | 22510 | 0.0046 | | 0.0332 | 10.6 | 22520 | 0.0046 | | 0.0001 | 10.6 | 22530 | 0.0047 | | 0.0001 | 10.61 | 22540 | 0.0047 | | 0.0055 | 10.61 | 22550 | 0.0047 | | 0.0006 | 10.62 | 22560 | 0.0048 | | 0.0 | 10.62 | 22570 | 0.0048 | | 0.0 | 10.63 | 22580 | 0.0048 | | 0.0005 | 10.63 | 22590 | 0.0049 | | 0.0642 | 10.64 | 22600 | 0.0048 | | 0.0002 | 10.64 | 22610 | 0.0048 | | 0.0 | 10.64 | 22620 | 0.0048 | | 0.0001 | 10.65 | 22630 | 0.0049 | | 0.0038 | 10.65 | 22640 | 0.0049 | | 0.0201 | 10.66 | 22650 | 0.0049 | | 0.0011 | 10.66 | 22660 | 0.0049 | | 0.027 | 10.67 | 22670 | 0.0049 | | 0.0001 | 10.67 | 22680 | 0.0049 | | 0.0 | 10.68 | 22690 | 0.0049 | | 0.0 | 10.68 | 22700 | 0.0050 | | 0.0 | 10.69 | 22710 | 0.0050 | | 0.0 | 10.69 | 22720 | 0.0050 | | 0.0 | 10.7 | 22730 | 0.0050 | | 0.0001 | 10.7 | 22740 | 0.0050 | | 0.0001 | 10.71 | 22750 | 0.0050 | | 0.0114 | 10.71 | 22760 | 0.0050 | | 0.0001 | 10.72 | 22770 | 0.0050 | | 0.0 | 10.72 | 22780 | 0.0050 | | 0.033 | 10.72 | 22790 | 0.0050 | | 0.0002 | 10.73 | 22800 | 0.0051 | | 0.0 | 10.73 | 22810 | 0.0051 | | 0.0 | 10.74 | 22820 | 0.0052 | | 0.0001 | 10.74 | 22830 | 0.0052 | | 0.0001 | 10.75 | 22840 | 0.0052 | | 0.0001 | 10.75 | 22850 | 0.0052 | | 0.0 | 10.76 | 22860 | 0.0052 | | 0.0679 | 10.76 | 22870 | 0.0052 | | 0.0001 | 10.77 | 22880 | 0.0035 | | 0.0 | 10.77 | 22890 | 0.0036 | | 0.0024 | 10.78 | 22900 | 0.0036 | | 0.0363 | 10.78 | 22910 | 0.0036 | | 0.0001 | 10.79 | 22920 | 0.0037 | | 0.0 | 10.79 | 22930 | 0.0037 | | 0.0131 | 10.8 | 22940 | 0.0037 | | 0.0033 | 10.8 | 22950 | 0.0036 | | 0.0001 | 10.8 | 22960 | 0.0036 | | 0.0006 | 10.81 | 22970 | 0.0036 | | 0.0684 | 10.81 | 22980 | 0.0036 | | 0.0 | 10.82 | 22990 | 0.0036 | | 0.0003 | 10.82 | 23000 | 0.0036 | | 0.0001 | 10.83 | 23010 | 0.0036 | | 0.0 | 10.83 | 23020 | 0.0036 | | 0.0725 | 10.84 | 23030 | 0.0036 | | 0.0 | 10.84 | 23040 | 0.0053 | | 0.0002 | 10.85 | 23050 | 0.0053 | | 0.0001 | 10.85 | 23060 | 0.0053 | | 0.0001 | 10.86 | 23070 | 0.0053 | | 0.0 | 10.86 | 23080 | 0.0054 | | 0.0475 | 10.87 | 23090 | 0.0053 | | 0.0012 | 10.87 | 23100 | 0.0053 | | 0.0 | 10.88 | 23110 | 0.0052 | | 0.0003 | 10.88 | 23120 | 0.0053 | | 0.0007 | 10.88 | 23130 | 0.0054 | | 0.0001 | 10.89 | 23140 | 0.0054 | | 0.0022 | 10.89 | 23150 | 0.0054 | | 0.0 | 10.9 | 23160 | 0.0054 | | 0.0 | 10.9 | 23170 | 0.0053 | | 0.0713 | 10.91 | 23180 | 0.0054 | | 0.0001 | 10.91 | 23190 | 0.0054 | | 0.0162 | 10.92 | 23200 | 0.0055 | | 0.0056 | 10.92 | 23210 | 0.0056 | | 0.0 | 10.93 | 23220 | 0.0056 | | 0.0542 | 10.93 | 23230 | 0.0055 | | 0.1384 | 10.94 | 23240 | 0.0036 | | 0.0032 | 10.94 | 23250 | 0.0035 | | 0.0 | 10.95 | 23260 | 0.0034 | | 0.0 | 10.95 | 23270 | 0.0034 | | 0.0001 | 10.96 | 23280 | 0.0034 | | 0.0001 | 10.96 | 23290 | 0.0034 | | 0.0 | 10.96 | 23300 | 0.0034 | | 0.0002 | 10.97 | 23310 | 0.0034 | | 0.0001 | 10.97 | 23320 | 0.0033 | | 0.0 | 10.98 | 23330 | 0.0033 | | 0.0001 | 10.98 | 23340 | 0.0033 | | 0.0 | 10.99 | 23350 | 0.0034 | | 0.0723 | 10.99 | 23360 | 0.0034 | | 0.0001 | 11.0 | 23370 | 0.0034 | | 0.0047 | 11.0 | 23380 | 0.0051 | | 0.0491 | 11.01 | 23390 | 0.0051 | | 0.0607 | 11.01 | 23400 | 0.0050 | | 0.0 | 11.02 | 23410 | 0.0050 | | 0.0012 | 11.02 | 23420 | 0.0049 | | 0.018 | 11.03 | 23430 | 0.0031 | | 0.0 | 11.03 | 23440 | 0.0031 | | 0.0 | 11.04 | 23450 | 0.0031 | | 0.0 | 11.04 | 23460 | 0.0031 | | 0.0003 | 11.04 | 23470 | 0.0031 | | 0.0 | 11.05 | 23480 | 0.0032 | | 0.0001 | 11.05 | 23490 | 0.0032 | | 0.0001 | 11.06 | 23500 | 0.0032 | | 0.0002 | 11.06 | 23510 | 0.0032 | | 0.0112 | 11.07 | 23520 | 0.0032 | | 0.0353 | 11.07 | 23530 | 0.0032 | | 0.0 | 11.08 | 23540 | 0.0032 | | 0.0176 | 11.08 | 23550 | 0.0032 | | 0.0 | 11.09 | 23560 | 0.0032 | | 0.1067 | 11.09 | 23570 | 0.0031 | | 0.073 | 11.1 | 23580 | 0.0030 | | 0.0026 | 11.1 | 23590 | 0.0030 | | 0.0001 | 11.11 | 23600 | 0.0030 | | 0.0002 | 11.11 | 23610 | 0.0030 | | 0.0004 | 11.12 | 23620 | 0.0032 | | 0.0001 | 11.12 | 23630 | 0.0033 | | 0.0001 | 11.12 | 23640 | 0.0034 | | 0.0353 | 11.13 | 23650 | 0.0034 | | 0.0062 | 11.13 | 23660 | 0.0033 | | 0.0 | 11.14 | 23670 | 0.0032 | | 0.0001 | 11.14 | 23680 | 0.0032 | | 0.0275 | 11.15 | 23690 | 0.0029 | | 0.0809 | 11.15 | 23700 | 0.0028 | | 0.0001 | 11.16 | 23710 | 0.0030 | | 0.0001 | 11.16 | 23720 | 0.0030 | | 0.0001 | 11.17 | 23730 | 0.0048 | | 0.0 | 11.17 | 23740 | 0.0049 | | 0.0 | 11.18 | 23750 | 0.0049 | | 0.0001 | 11.18 | 23760 | 0.0049 | | 0.0 | 11.19 | 23770 | 0.0049 | | 0.0004 | 11.19 | 23780 | 0.0049 | | 0.0 | 11.2 | 23790 | 0.0032 | | 0.0005 | 11.2 | 23800 | 0.0049 | | 0.0247 | 11.2 | 23810 | 0.0032 | | 0.0 | 11.21 | 23820 | 0.0032 | | 0.0 | 11.21 | 23830 | 0.0032 | | 0.0 | 11.22 | 23840 | 0.0032 | | 0.0033 | 11.22 | 23850 | 0.0032 | | 0.0 | 11.23 | 23860 | 0.0032 | | 0.0716 | 11.23 | 23870 | 0.0031 | | 0.0006 | 11.24 | 23880 | 0.0031 | | 0.0 | 11.24 | 23890 | 0.0031 | | 0.0 | 11.25 | 23900 | 0.0031 | | 0.072 | 11.25 | 23910 | 0.0028 | | 0.0 | 11.26 | 23920 | 0.0025 | | 0.0019 | 11.26 | 23930 | 0.0023 | | 0.0 | 11.27 | 23940 | 0.0023 | | 0.0008 | 11.27 | 23950 | 0.0023 | | 0.0725 | 11.28 | 23960 | 0.0023 | | 0.0003 | 11.28 | 23970 | 0.0023 | | 0.0004 | 11.28 | 23980 | 0.0023 | | 0.0002 | 11.29 | 23990 | 0.0024 | | 0.0002 | 11.29 | 24000 | 0.0024 | | 0.0979 | 11.3 | 24010 | 0.0025 | | 0.0001 | 11.3 | 24020 | 0.0026 | | 0.0001 | 11.31 | 24030 | 0.0026 | | 0.0002 | 11.31 | 24040 | 0.0026 | | 0.0 | 11.32 | 24050 | 0.0027 | | 0.1879 | 11.32 | 24060 | 0.0026 | | 0.0 | 11.33 | 24070 | 0.0025 | | 0.0004 | 11.33 | 24080 | 0.0025 | | 0.0012 | 11.34 | 24090 | 0.0024 | | 0.0001 | 11.34 | 24100 | 0.0024 | | 0.0 | 11.35 | 24110 | 0.0024 | | 0.0051 | 11.35 | 24120 | 0.0025 | | 0.0003 | 11.36 | 24130 | 0.0025 | | 0.0 | 11.36 | 24140 | 0.0025 | | 0.0 | 11.36 | 24150 | 0.0026 | | 0.0 | 11.37 | 24160 | 0.0026 | | 0.0 | 11.37 | 24170 | 0.0026 | | 0.0001 | 11.38 | 24180 | 0.0026 | | 0.0001 | 11.38 | 24190 | 0.0026 | | 0.0 | 11.39 | 24200 | 0.0026 | | 0.1551 | 11.39 | 24210 | 0.0025 | | 0.0208 | 11.4 | 24220 | 0.0025 | | 0.0012 | 11.4 | 24230 | 0.0025 | | 0.0013 | 11.41 | 24240 | 0.0026 | | 0.0 | 11.41 | 24250 | 0.0026 | | 0.0 | 11.42 | 24260 | 0.0026 | | 0.0001 | 11.42 | 24270 | 0.0026 | | 0.0 | 11.43 | 24280 | 0.0026 | | 0.0762 | 11.43 | 24290 | 0.0026 | | 0.0742 | 11.44 | 24300 | 0.0025 | | 0.0001 | 11.44 | 24310 | 0.0025 | | 0.0023 | 11.44 | 24320 | 0.0025 | | 0.0 | 11.45 | 24330 | 0.0025 | | 0.008 | 11.45 | 24340 | 0.0025 | | 0.0 | 11.46 | 24350 | 0.0025 | | 0.0178 | 11.46 | 24360 | 0.0025 | | 0.0001 | 11.47 | 24370 | 0.0025 | | 0.0408 | 11.47 | 24380 | 0.0025 | | 0.0 | 11.48 | 24390 | 0.0025 | | 0.0387 | 11.48 | 24400 | 0.0025 | | 0.0412 | 11.49 | 24410 | 0.0025 | | 0.0 | 11.49 | 24420 | 0.0025 | | 0.0001 | 11.5 | 24430 | 0.0025 | | 0.0001 | 11.5 | 24440 | 0.0025 | | 0.0001 | 11.51 | 24450 | 0.0025 | | 0.0001 | 11.51 | 24460 | 0.0025 | | 0.0598 | 11.52 | 24470 | 0.0024 | | 0.0004 | 11.52 | 24480 | 0.0024 | | 0.0001 | 11.52 | 24490 | 0.0024 | | 0.0251 | 11.53 | 24500 | 0.0024 | | 0.0009 | 11.53 | 24510 | 0.0032 | | 0.0028 | 11.54 | 24520 | 0.0032 | | 0.1783 | 11.54 | 24530 | 0.0032 | | 0.0473 | 11.55 | 24540 | 0.0032 | | 0.0 | 11.55 | 24550 | 0.0032 | | 0.0072 | 11.56 | 24560 | 0.0031 | | 0.0736 | 11.56 | 24570 | 0.0048 | | 0.0012 | 11.57 | 24580 | 0.0048 | | 0.1372 | 11.57 | 24590 | 0.0048 | | 0.0001 | 11.58 | 24600 | 0.0048 | | 0.0001 | 11.58 | 24610 | 0.0048 | | 0.0001 | 11.59 | 24620 | 0.0048 | | 0.0001 | 11.59 | 24630 | 0.0048 | | 0.0002 | 11.6 | 24640 | 0.0048 | | 0.0011 | 11.6 | 24650 | 0.0048 | | 0.0001 | 11.6 | 24660 | 0.0049 | | 0.0197 | 11.61 | 24670 | 0.0049 | | 0.0001 | 11.61 | 24680 | 0.0049 | | 0.0391 | 11.62 | 24690 | 0.0049 | | 0.0047 | 11.62 | 24700 | 0.0049 | | 0.0001 | 11.63 | 24710 | 0.0049 | | 0.0226 | 11.63 | 24720 | 0.0049 | | 0.0001 | 11.64 | 24730 | 0.0050 | | 0.0 | 11.64 | 24740 | 0.0050 | | 0.0378 | 11.65 | 24750 | 0.0050 | | 0.0044 | 11.65 | 24760 | 0.0050 | | 0.0001 | 11.66 | 24770 | 0.0050 | | 0.0 | 11.66 | 24780 | 0.0050 | | 0.0002 | 11.67 | 24790 | 0.0050 | | 0.0209 | 11.67 | 24800 | 0.0050 | | 0.0005 | 11.68 | 24810 | 0.0051 | | 0.0001 | 11.68 | 24820 | 0.0051 | | 0.0357 | 11.68 | 24830 | 0.0051 | | 0.0873 | 11.69 | 24840 | 0.0050 | | 0.0002 | 11.69 | 24850 | 0.0050 | | 0.0001 | 11.7 | 24860 | 0.0050 | | 0.0005 | 11.7 | 24870 | 0.0050 | | 0.0003 | 11.71 | 24880 | 0.0050 | | 0.0002 | 11.71 | 24890 | 0.0050 | | 0.0002 | 11.72 | 24900 | 0.0050 | | 0.0 | 11.72 | 24910 | 0.0050 | | 0.0001 | 11.73 | 24920 | 0.0050 | | 0.0002 | 11.73 | 24930 | 0.0050 | | 0.0 | 11.74 | 24940 | 0.0041 | | 0.002 | 11.74 | 24950 | 0.0041 | | 0.0032 | 11.75 | 24960 | 0.0050 | | 0.0736 | 11.75 | 24970 | 0.0050 | | 0.0001 | 11.76 | 24980 | 0.0050 | | 0.0 | 11.76 | 24990 | 0.0050 | | 0.0 | 11.76 | 25000 | 0.0050 | | 0.0 | 11.77 | 25010 | 0.0050 | | 0.0656 | 11.77 | 25020 | 0.0050 | | 0.1332 | 11.78 | 25030 | 0.0050 | | 0.0001 | 11.78 | 25040 | 0.0050 | | 0.0002 | 11.79 | 25050 | 0.0050 | | 0.0 | 11.79 | 25060 | 0.0050 | | 0.0 | 11.8 | 25070 | 0.0050 | | 0.0 | 11.8 | 25080 | 0.0050 | | 0.0981 | 11.81 | 25090 | 0.0050 | | 0.0 | 11.81 | 25100 | 0.0050 | | 0.0001 | 11.82 | 25110 | 0.0050 | | 0.0004 | 11.82 | 25120 | 0.0050 | | 0.0001 | 11.83 | 25130 | 0.0050 | | 0.0001 | 11.83 | 25140 | 0.0050 | | 0.0 | 11.84 | 25150 | 0.0050 | | 0.0303 | 11.84 | 25160 | 0.0033 | | 0.0008 | 11.84 | 25170 | 0.0033 | | 0.0 | 11.85 | 25180 | 0.0051 | | 0.0923 | 11.85 | 25190 | 0.0050 | | 0.0001 | 11.86 | 25200 | 0.0050 | | 0.0824 | 11.86 | 25210 | 0.0050 | | 0.0 | 11.87 | 25220 | 0.0050 | | 0.003 | 11.87 | 25230 | 0.0050 | | 0.0 | 11.88 | 25240 | 0.0032 | | 0.0001 | 11.88 | 25250 | 0.0032 | | 0.0 | 11.89 | 25260 | 0.0032 | | 0.0786 | 11.89 | 25270 | 0.0032 | | 0.0 | 11.9 | 25280 | 0.0050 | | 0.0 | 11.9 | 25290 | 0.0050 | | 0.0001 | 11.91 | 25300 | 0.0050 | | 0.0 | 11.91 | 25310 | 0.0050 | | 0.0001 | 11.92 | 25320 | 0.0050 | | 0.0 | 11.92 | 25330 | 0.0050 | | 0.0 | 11.92 | 25340 | 0.0050 | | 0.0 | 11.93 | 25350 | 0.0050 | | 0.0 | 11.93 | 25360 | 0.0050 | | 0.0003 | 11.94 | 25370 | 0.0050 | | 0.0 | 11.94 | 25380 | 0.0050 | | 0.0013 | 11.95 | 25390 | 0.0033 | | 0.099 | 11.95 | 25400 | 0.0033 | | 0.0 | 11.96 | 25410 | 0.0033 | | 0.0145 | 11.96 | 25420 | 0.0050 | | 0.0012 | 11.97 | 25430 | 0.0050 | | 0.0 | 11.97 | 25440 | 0.0050 | | 0.0 | 11.98 | 25450 | 0.0050 | | 0.0024 | 11.98 | 25460 | 0.0050 | | 0.0522 | 11.99 | 25470 | 0.0041 | | 0.0004 | 11.99 | 25480 | 0.0041 | | 0.0 | 12.0 | 25490 | 0.0041 | | 0.0377 | 12.0 | 25500 | 0.0041 | | 0.0002 | 12.0 | 25510 | 0.0024 | | 0.0009 | 12.01 | 25520 | 0.0024 | | 0.0281 | 12.01 | 25530 | 0.0023 | | 0.0 | 12.02 | 25540 | 0.0023 | | 0.088 | 12.02 | 25550 | 0.0023 | | 0.017 | 12.03 | 25560 | 0.0023 | | 0.0721 | 12.03 | 25570 | 0.0023 | | 0.0271 | 12.04 | 25580 | 0.0022 | | 0.0008 | 12.04 | 25590 | 0.0023 | | 0.0208 | 12.05 | 25600 | 0.0023 | | 0.0001 | 12.05 | 25610 | 0.0023 | | 0.0 | 12.06 | 25620 | 0.0023 | | 0.0002 | 12.06 | 25630 | 0.0023 | | 0.0003 | 12.07 | 25640 | 0.0023 | | 0.2283 | 12.07 | 25650 | 0.0022 | | 0.0007 | 12.08 | 25660 | 0.0021 | | 0.0464 | 12.08 | 25670 | 0.0021 | | 0.0001 | 12.08 | 25680 | 0.0021 | | 0.0012 | 12.09 | 25690 | 0.0021 | | 0.0 | 12.09 | 25700 | 0.0021 | | 0.0002 | 12.1 | 25710 | 0.0021 | | 0.0336 | 12.1 | 25720 | 0.0021 | | 0.001 | 12.11 | 25730 | 0.0021 | | 0.0001 | 12.11 | 25740 | 0.0022 | | 0.0 | 12.12 | 25750 | 0.0022 | | 0.0 | 12.12 | 25760 | 0.0022 | | 0.0264 | 12.13 | 25770 | 0.0022 | | 0.1619 | 12.13 | 25780 | 0.0039 | | 0.0212 | 12.14 | 25790 | 0.0038 | | 0.0 | 12.14 | 25800 | 0.0038 | | 0.0003 | 12.15 | 25810 | 0.0038 | | 0.0005 | 12.15 | 25820 | 0.0039 | | 0.0002 | 12.16 | 25830 | 0.0039 | | 0.0001 | 12.16 | 25840 | 0.0040 | | 0.0001 | 12.16 | 25850 | 0.0040 | | 0.0004 | 12.17 | 25860 | 0.0040 | | 0.0 | 12.17 | 25870 | 0.0040 | | 0.0002 | 12.18 | 25880 | 0.0040 | | 0.0001 | 12.18 | 25890 | 0.0041 | | 0.002 | 12.19 | 25900 | 0.0040 | | 0.0 | 12.19 | 25910 | 0.0040 | | 0.0001 | 12.2 | 25920 | 0.0040 | | 0.0825 | 12.2 | 25930 | 0.0040 | | 0.0932 | 12.21 | 25940 | 0.0039 | | 0.0001 | 12.21 | 25950 | 0.0039 | | 0.0008 | 12.22 | 25960 | 0.0039 | | 0.0001 | 12.22 | 25970 | 0.0039 | | 0.0026 | 12.23 | 25980 | 0.0039 | | 0.0 | 12.23 | 25990 | 0.0039 | | 0.0004 | 12.24 | 26000 | 0.0040 | | 0.0001 | 12.24 | 26010 | 0.0041 | | 0.0681 | 12.24 | 26020 | 0.0041 | | 0.0 | 12.25 | 26030 | 0.0041 | | 0.0001 | 12.25 | 26040 | 0.0041 | | 0.0741 | 12.26 | 26050 | 0.0041 | | 0.0001 | 12.26 | 26060 | 0.0041 | | 0.0701 | 12.27 | 26070 | 0.0041 | | 0.0001 | 12.27 | 26080 | 0.0041 | | 0.0001 | 12.28 | 26090 | 0.0041 | | 0.0033 | 12.28 | 26100 | 0.0041 | | 0.0712 | 12.29 | 26110 | 0.0041 | | 0.0 | 12.29 | 26120 | 0.0040 | | 0.0007 | 12.3 | 26130 | 0.0040 | | 0.0002 | 12.3 | 26140 | 0.0040 | | 0.0704 | 12.31 | 26150 | 0.0041 | | 0.0003 | 12.31 | 26160 | 0.0041 | | 0.0 | 12.32 | 26170 | 0.0042 | | 0.0 | 12.32 | 26180 | 0.0042 | | 0.0007 | 12.32 | 26190 | 0.0042 | | 0.0 | 12.33 | 26200 | 0.0041 | | 0.0 | 12.33 | 26210 | 0.0041 | | 0.0001 | 12.34 | 26220 | 0.0041 | | 0.0 | 12.34 | 26230 | 0.0041 | | 0.0001 | 12.35 | 26240 | 0.0041 | | 0.0003 | 12.35 | 26250 | 0.0041 | | 0.0003 | 12.36 | 26260 | 0.0041 | | 0.0003 | 12.36 | 26270 | 0.0041 | | 0.0001 | 12.37 | 26280 | 0.0041 | | 0.0005 | 12.37 | 26290 | 0.0041 | | 0.0 | 12.38 | 26300 | 0.0041 | | 0.0001 | 12.38 | 26310 | 0.0041 | | 0.0723 | 12.39 | 26320 | 0.0041 | | 0.0008 | 12.39 | 26330 | 0.0041 | | 0.0383 | 12.4 | 26340 | 0.0041 | | 0.0001 | 12.4 | 26350 | 0.0042 | | 0.0 | 12.4 | 26360 | 0.0042 | | 0.0 | 12.41 | 26370 | 0.0042 | | 0.1803 | 12.41 | 26380 | 0.0041 | | 0.0705 | 12.42 | 26390 | 0.0039 | | 0.0 | 12.42 | 26400 | 0.0039 | | 0.0001 | 12.43 | 26410 | 0.0039 | | 0.0 | 12.43 | 26420 | 0.0039 | | 0.0726 | 12.44 | 26430 | 0.0046 | | 0.0001 | 12.44 | 26440 | 0.0047 | | 0.0006 | 12.45 | 26450 | 0.0046 | | 0.0004 | 12.45 | 26460 | 0.0039 | | 0.0001 | 12.46 | 26470 | 0.0039 | | 0.0001 | 12.46 | 26480 | 0.0039 | | 0.0022 | 12.47 | 26490 | 0.0039 | | 0.0 | 12.47 | 26500 | 0.0039 | | 0.0 | 12.48 | 26510 | 0.0039 | | 0.0005 | 12.48 | 26520 | 0.0039 | | 0.0907 | 12.48 | 26530 | 0.0039 | | 0.0088 | 12.49 | 26540 | 0.0039 | | 0.0 | 12.49 | 26550 | 0.0039 | | 0.0002 | 12.5 | 26560 | 0.0039 | | 0.0005 | 12.5 | 26570 | 0.0039 | | 0.0448 | 12.51 | 26580 | 0.0039 | | 0.0001 | 12.51 | 26590 | 0.0039 | | 0.0014 | 12.52 | 26600 | 0.0040 | | 0.0012 | 12.52 | 26610 | 0.0040 | | 0.0 | 12.53 | 26620 | 0.0040 | | 0.0003 | 12.53 | 26630 | 0.0049 | | 0.0208 | 12.54 | 26640 | 0.0048 | | 0.0001 | 12.54 | 26650 | 0.0048 | | 0.0001 | 12.55 | 26660 | 0.0048 | | 0.0 | 12.55 | 26670 | 0.0048 | | 0.0 | 12.56 | 26680 | 0.0048 | | 0.0082 | 12.56 | 26690 | 0.0048 | | 0.0 | 12.56 | 26700 | 0.0048 | | 0.0 | 12.57 | 26710 | 0.0048 | | 0.0001 | 12.57 | 26720 | 0.0048 | | 0.0 | 12.58 | 26730 | 0.0048 | | 0.0 | 12.58 | 26740 | 0.0048 | | 0.0457 | 12.59 | 26750 | 0.0048 | | 0.0007 | 12.59 | 26760 | 0.0048 | | 0.073 | 12.6 | 26770 | 0.0048 | | 0.0001 | 12.6 | 26780 | 0.0048 | | 0.0002 | 12.61 | 26790 | 0.0048 | | 0.0 | 12.61 | 26800 | 0.0048 | | 0.0001 | 12.62 | 26810 | 0.0048 | | 0.0001 | 12.62 | 26820 | 0.0048 | | 0.0 | 12.63 | 26830 | 0.0048 | | 0.0368 | 12.63 | 26840 | 0.0048 | | 0.0702 | 12.64 | 26850 | 0.0039 | | 0.0 | 12.64 | 26860 | 0.0039 | | 0.0292 | 12.64 | 26870 | 0.0039 | | 0.0001 | 12.65 | 26880 | 0.0039 | | 0.0 | 12.65 | 26890 | 0.0039 | | 0.0 | 12.66 | 26900 | 0.0039 | | 0.0985 | 12.66 | 26910 | 0.0039 | | 0.0 | 12.67 | 26920 | 0.0021 | | 0.0002 | 12.67 | 26930 | 0.0021 | | 0.0004 | 12.68 | 26940 | 0.0021 | | 0.0001 | 12.68 | 26950 | 0.0021 | | 0.0008 | 12.69 | 26960 | 0.0021 | | 0.0 | 12.69 | 26970 | 0.0021 | | 0.0363 | 12.7 | 26980 | 0.0021 | | 0.0 | 12.7 | 26990 | 0.0021 | | 0.0 | 12.71 | 27000 | 0.0021 | | 0.0694 | 12.71 | 27010 | 0.0021 | | 0.0 | 12.72 | 27020 | 0.0021 | | 0.0 | 12.72 | 27030 | 0.0021 | | 0.1096 | 12.72 | 27040 | 0.0020 | | 0.0006 | 12.73 | 27050 | 0.0021 | | 0.0001 | 12.73 | 27060 | 0.0021 | | 0.0002 | 12.74 | 27070 | 0.0021 | | 0.0001 | 12.74 | 27080 | 0.0022 | | 0.0 | 12.75 | 27090 | 0.0022 | | 0.0007 | 12.75 | 27100 | 0.0021 | | 0.042 | 12.76 | 27110 | 0.0021 | | 0.0387 | 12.76 | 27120 | 0.0021 | | 0.0 | 12.77 | 27130 | 0.0021 | | 0.0017 | 12.77 | 27140 | 0.0021 | | 0.0742 | 12.78 | 27150 | 0.0022 | | 0.0695 | 12.78 | 27160 | 0.0022 | | 0.0361 | 12.79 | 27170 | 0.0022 | | 0.0 | 12.79 | 27180 | 0.0021 | | 0.0001 | 12.8 | 27190 | 0.0021 | | 0.0006 | 12.8 | 27200 | 0.0021 | | 0.0001 | 12.8 | 27210 | 0.0021 | | 0.0001 | 12.81 | 27220 | 0.0021 | | 0.0 | 12.81 | 27230 | 0.0004 | | 0.0 | 12.82 | 27240 | 0.0004 | | 0.0 | 12.82 | 27250 | 0.0004 | | 0.0002 | 12.83 | 27260 | 0.0004 | | 0.1472 | 12.83 | 27270 | 0.0021 | | 0.0003 | 12.84 | 27280 | 0.0020 | | 0.0402 | 12.84 | 27290 | 0.0003 | | 0.0001 | 12.85 | 27300 | 0.0003 | | 0.0004 | 12.85 | 27310 | 0.0003 | | 0.0001 | 12.86 | 27320 | 0.0003 | | 0.0757 | 12.86 | 27330 | 0.0003 | | 0.0 | 12.87 | 27340 | 0.0003 | | 0.0001 | 12.87 | 27350 | 0.0003 | | 0.0196 | 12.88 | 27360 | 0.0003 | | 0.0001 | 12.88 | 27370 | 0.0003 | | 0.0016 | 12.88 | 27380 | 0.0003 | | 0.0 | 12.89 | 27390 | 0.0003 | | 0.0001 | 12.89 | 27400 | 0.0003 | | 0.0149 | 12.9 | 27410 | 0.0003 | | 0.0018 | 12.9 | 27420 | 0.0004 | | 0.0005 | 12.91 | 27430 | 0.0004 | | 0.0 | 12.91 | 27440 | 0.0004 | | 0.0 | 12.92 | 27450 | 0.0004 | | 0.0 | 12.92 | 27460 | 0.0004 | | 0.0 | 12.93 | 27470 | 0.0004 | | 0.0009 | 12.93 | 27480 | 0.0004 | | 0.0001 | 12.94 | 27490 | 0.0004 | | 0.1069 | 12.94 | 27500 | 0.0004 | | 0.0 | 12.95 | 27510 | 0.0004 | | 0.0139 | 12.95 | 27520 | 0.0004 | | 0.0 | 12.96 | 27530 | 0.0004 | | 0.0082 | 12.96 | 27540 | 0.0004 | | 0.0 | 12.96 | 27550 | 0.0004 | | 0.1047 | 12.97 | 27560 | 0.0004 | | 0.0 | 12.97 | 27570 | 0.0021 | | 0.0003 | 12.98 | 27580 | 0.0021 | | 0.0 | 12.98 | 27590 | 0.0021 | | 0.001 | 12.99 | 27600 | 0.0021 | | 0.06 | 12.99 | 27610 | 0.0021 | | 0.0003 | 13.0 | 27620 | 0.0021 | | 0.0 | 13.0 | 27630 | 0.0021 | | 0.0001 | 13.01 | 27640 | 0.0021 | | 0.0 | 13.01 | 27650 | 0.0021 | | 0.0 | 13.02 | 27660 | 0.0021 | | 0.0149 | 13.02 | 27670 | 0.0021 | | 0.0003 | 13.03 | 27680 | 0.0021 | | 0.1025 | 13.03 | 27690 | 0.0021 | | 0.0001 | 13.04 | 27700 | 0.0021 | | 0.0001 | 13.04 | 27710 | 0.0020 | | 0.0 | 13.04 | 27720 | 0.0020 | | 0.0001 | 13.05 | 27730 | 0.0020 | | 0.0751 | 13.05 | 27740 | 0.0020 | | 0.0021 | 13.06 | 27750 | 0.0020 | | 0.0002 | 13.06 | 27760 | 0.0021 | | 0.0862 | 13.07 | 27770 | 0.0021 | | 0.0001 | 13.07 | 27780 | 0.0020 | | 0.0001 | 13.08 | 27790 | 0.0020 | | 0.0344 | 13.08 | 27800 | 0.0020 | | 0.0353 | 13.09 | 27810 | 0.0020 | | 0.0001 | 13.09 | 27820 | 0.0020 | | 0.0048 | 13.1 | 27830 | 0.0020 | | 0.0221 | 13.1 | 27840 | 0.0020 | | 0.0001 | 13.11 | 27850 | 0.0020 | | 0.0001 | 13.11 | 27860 | 0.0020 | | 0.0002 | 13.12 | 27870 | 0.0020 | | 0.0055 | 13.12 | 27880 | 0.0020 | | 0.0001 | 13.12 | 27890 | 0.0020 | | 0.0 | 13.13 | 27900 | 0.0020 | | 0.0011 | 13.13 | 27910 | 0.0020 | | 0.0815 | 13.14 | 27920 | 0.0020 | | 0.0001 | 13.14 | 27930 | 0.0021 | | 0.0138 | 13.15 | 27940 | 0.0021 | | 0.0002 | 13.15 | 27950 | 0.0021 | | 0.0 | 13.16 | 27960 | 0.0021 | | 0.0 | 13.16 | 27970 | 0.0021 | | 0.0 | 13.17 | 27980 | 0.0021 | | 0.0001 | 13.17 | 27990 | 0.0021 | | 0.0428 | 13.18 | 28000 | 0.0021 | | 0.0 | 13.18 | 28010 | 0.0021 | | 0.0 | 13.19 | 28020 | 0.0021 | | 0.0 | 13.19 | 28030 | 0.0021 | | 0.0004 | 13.2 | 28040 | 0.0022 | | 0.0 | 13.2 | 28050 | 0.0022 | | 0.0213 | 13.2 | 28060 | 0.0022 | | 0.0123 | 13.21 | 28070 | 0.0022 | | 0.0705 | 13.21 | 28080 | 0.0022 | | 0.0002 | 13.22 | 28090 | 0.0022 | | 0.0001 | 13.22 | 28100 | 0.0022 | | 0.0 | 13.23 | 28110 | 0.0022 | | 0.0003 | 13.23 | 28120 | 0.0022 | | 0.0 | 13.24 | 28130 | 0.0022 | | 0.0 | 13.24 | 28140 | 0.0005 | | 0.0004 | 13.25 | 28150 | 0.0005 | | 0.0001 | 13.25 | 28160 | 0.0005 | | 0.0088 | 13.26 | 28170 | 0.0005 | | 0.0001 | 13.26 | 28180 | 0.0004 | | 0.0001 | 13.27 | 28190 | 0.0004 | | 0.0281 | 13.27 | 28200 | 0.0004 | | 0.0727 | 13.28 | 28210 | 0.0004 | | 0.0001 | 13.28 | 28220 | 0.0004 | | 0.0006 | 13.28 | 28230 | 0.0004 | | 0.0001 | 13.29 | 28240 | 0.0005 | | 0.0048 | 13.29 | 28250 | 0.0005 | | 0.0 | 13.3 | 28260 | 0.0004 | | 0.0 | 13.3 | 28270 | 0.0004 | | 0.0051 | 13.31 | 28280 | 0.0004 | | 0.0036 | 13.31 | 28290 | 0.0004 | | 0.0 | 13.32 | 28300 | 0.0004 | | 0.0005 | 13.32 | 28310 | 0.0004 | | 0.0001 | 13.33 | 28320 | 0.0005 | | 0.0 | 13.33 | 28330 | 0.0005 | | 0.0387 | 13.34 | 28340 | 0.0005 | | 0.0 | 13.34 | 28350 | 0.0004 | | 0.0001 | 13.35 | 28360 | 0.0004 | | 0.0 | 13.35 | 28370 | 0.0004 | | 0.0 | 13.36 | 28380 | 0.0004 | | 0.0003 | 13.36 | 28390 | 0.0004 | | 0.0 | 13.36 | 28400 | 0.0004 | | 0.0 | 13.37 | 28410 | 0.0005 | | 0.0001 | 13.37 | 28420 | 0.0005 | | 0.0001 | 13.38 | 28430 | 0.0005 | | 0.0001 | 13.38 | 28440 | 0.0005 | | 0.0 | 13.39 | 28450 | 0.0005 | | 0.0 | 13.39 | 28460 | 0.0005 | | 0.0001 | 13.4 | 28470 | 0.0005 | | 0.017 | 13.4 | 28480 | 0.0005 | | 0.0 | 13.41 | 28490 | 0.0005 | | 0.0001 | 13.41 | 28500 | 0.0005 | | 0.0 | 13.42 | 28510 | 0.0005 | | 0.0 | 13.42 | 28520 | 0.0005 | | 0.0 | 13.43 | 28530 | 0.0005 | | 0.0 | 13.43 | 28540 | 0.0005 | | 0.0001 | 13.44 | 28550 | 0.0005 | | 0.0 | 13.44 | 28560 | 0.0005 | | 0.0 | 13.44 | 28570 | 0.0005 | | 0.0259 | 13.45 | 28580 | 0.0005 | | 0.0035 | 13.45 | 28590 | 0.0005 | | 0.0004 | 13.46 | 28600 | 0.0005 | | 0.0 | 13.46 | 28610 | 0.0004 | | 0.0006 | 13.47 | 28620 | 0.0004 | | 0.0 | 13.47 | 28630 | 0.0004 | | 0.0 | 13.48 | 28640 | 0.0004 | | 0.0022 | 13.48 | 28650 | 0.0004 | | 0.0005 | 13.49 | 28660 | 0.0004 | | 0.0195 | 13.49 | 28670 | 0.0004 | | 0.0001 | 13.5 | 28680 | 0.0004 | | 0.0369 | 13.5 | 28690 | 0.0004 | | 0.0071 | 13.51 | 28700 | 0.0004 | | 0.0 | 13.51 | 28710 | 0.0004 | | 0.0031 | 13.52 | 28720 | 0.0004 | | 0.0002 | 13.52 | 28730 | 0.0004 | | 0.001 | 13.52 | 28740 | 0.0004 | | 0.0003 | 13.53 | 28750 | 0.0004 | | 0.0761 | 13.53 | 28760 | 0.0022 | | 0.0004 | 13.54 | 28770 | 0.0022 | | 0.0007 | 13.54 | 28780 | 0.0022 | | 0.0002 | 13.55 | 28790 | 0.0022 | | 0.0001 | 13.55 | 28800 | 0.0022 | | 0.0616 | 13.56 | 28810 | 0.0022 | | 0.0 | 13.56 | 28820 | 0.0022 | | 0.0005 | 13.57 | 28830 | 0.0022 | | 0.0248 | 13.57 | 28840 | 0.0022 | | 0.0905 | 13.58 | 28850 | 0.0004 | | 0.0002 | 13.58 | 28860 | 0.0004 | | 0.0007 | 13.59 | 28870 | 0.0003 | | 0.0 | 13.59 | 28880 | 0.0003 | | 0.0 | 13.6 | 28890 | 0.0003 | | 0.0363 | 13.6 | 28900 | 0.0003 | | 0.0003 | 13.6 | 28910 | 0.0003 | | 0.0001 | 13.61 | 28920 | 0.0003 | | 0.0032 | 13.61 | 28930 | 0.0003 | | 0.0001 | 13.62 | 28940 | 0.0003 | | 0.0001 | 13.62 | 28950 | 0.0003 | | 0.0 | 13.63 | 28960 | 0.0003 | | 0.0002 | 13.63 | 28970 | 0.0003 | | 0.0721 | 13.64 | 28980 | 0.0003 | | 0.0 | 13.64 | 28990 | 0.0003 | | 0.0001 | 13.65 | 29000 | 0.0003 | | 0.003 | 13.65 | 29010 | 0.0003 | | 0.0 | 13.66 | 29020 | 0.0003 | | 0.0 | 13.66 | 29030 | 0.0003 | | 0.0008 | 13.67 | 29040 | 0.0003 | | 0.0 | 13.67 | 29050 | 0.0003 | | 0.0 | 13.68 | 29060 | 0.0003 | | 0.0 | 13.68 | 29070 | 0.0003 | | 0.0 | 13.68 | 29080 | 0.0003 | | 0.0041 | 13.69 | 29090 | 0.0020 | | 0.0 | 13.69 | 29100 | 0.0020 | | 0.0001 | 13.7 | 29110 | 0.0020 | | 0.0754 | 13.7 | 29120 | 0.0020 | | 0.0316 | 13.71 | 29130 | 0.0037 | | 0.0897 | 13.71 | 29140 | 0.0019 | | 0.044 | 13.72 | 29150 | 0.0018 | | 0.0 | 13.72 | 29160 | 0.0018 | | 0.0001 | 13.73 | 29170 | 0.0018 | | 0.0 | 13.73 | 29180 | 0.0018 | | 0.0001 | 13.74 | 29190 | 0.0018 | | 0.0023 | 13.74 | 29200 | 0.0018 | | 0.0 | 13.75 | 29210 | 0.0018 | | 0.0048 | 13.75 | 29220 | 0.0019 | | 0.0021 | 13.76 | 29230 | 0.0019 | | 0.0024 | 13.76 | 29240 | 0.0019 | | 0.0191 | 13.76 | 29250 | 0.0018 | | 0.0001 | 13.77 | 29260 | 0.0018 | | 0.0 | 13.77 | 29270 | 0.0018 | | 0.0004 | 13.78 | 29280 | 0.0019 | | 0.1205 | 13.78 | 29290 | 0.0019 | | 0.0001 | 13.79 | 29300 | 0.0019 | | 0.0 | 13.79 | 29310 | 0.0019 | | 0.0719 | 13.8 | 29320 | 0.0019 | | 0.0 | 13.8 | 29330 | 0.0036 | | 0.0 | 13.81 | 29340 | 0.0036 | | 0.0 | 13.81 | 29350 | 0.0036 | | 0.0 | 13.82 | 29360 | 0.0036 | | 0.0392 | 13.82 | 29370 | 0.0019 | | 0.032 | 13.83 | 29380 | 0.0019 | | 0.0 | 13.83 | 29390 | 0.0001 | | 0.0065 | 13.84 | 29400 | 0.0001 | | 0.0001 | 13.84 | 29410 | 0.0019 | | 0.0028 | 13.84 | 29420 | 0.0019 | | 0.009 | 13.85 | 29430 | 0.0019 | | 0.0 | 13.85 | 29440 | 0.0019 | | 0.0 | 13.86 | 29450 | 0.0018 | | 0.0 | 13.86 | 29460 | 0.0018 | | 0.0403 | 13.87 | 29470 | 0.0018 | | 0.0 | 13.87 | 29480 | 0.0018 | | 0.0 | 13.88 | 29490 | 0.0018 | | 0.0001 | 13.88 | 29500 | 0.0018 | | 0.0134 | 13.89 | 29510 | 0.0018 | | 0.078 | 13.89 | 29520 | 0.0036 | | 0.0 | 13.9 | 29530 | 0.0036 | | 0.0121 | 13.9 | 29540 | 0.0036 | | 0.0 | 13.91 | 29550 | 0.0036 | | 0.0 | 13.91 | 29560 | 0.0036 | | 0.0 | 13.92 | 29570 | 0.0036 | | 0.0 | 13.92 | 29580 | 0.0036 | | 0.0002 | 13.92 | 29590 | 0.0036 | | 0.0003 | 13.93 | 29600 | 0.0036 | | 0.048 | 13.93 | 29610 | 0.0044 | | 0.0 | 13.94 | 29620 | 0.0044 | | 0.0001 | 13.94 | 29630 | 0.0044 | | 0.0001 | 13.95 | 29640 | 0.0044 | | 0.0 | 13.95 | 29650 | 0.0044 | | 0.0005 | 13.96 | 29660 | 0.0044 | | 0.0 | 13.96 | 29670 | 0.0044 | | 0.1103 | 13.97 | 29680 | 0.0044 | | 0.0 | 13.97 | 29690 | 0.0044 | | 0.0379 | 13.98 | 29700 | 0.0044 | | 0.0 | 13.98 | 29710 | 0.0044 | | 0.0013 | 13.99 | 29720 | 0.0044 | | 0.0001 | 13.99 | 29730 | 0.0044 | | 0.0016 | 14.0 | 29740 | 0.0044 | | 0.0006 | 14.0 | 29750 | 0.0044 | | 0.0269 | 14.0 | 29760 | 0.0044 | | 0.0 | 14.01 | 29770 | 0.0044 | | 0.0 | 14.01 | 29780 | 0.0044 | | 0.0 | 14.02 | 29790 | 0.0044 | | 0.0002 | 14.02 | 29800 | 0.0044 | | 0.0001 | 14.03 | 29810 | 0.0044 | | 0.0 | 14.03 | 29820 | 0.0044 | | 0.0 | 14.04 | 29830 | 0.0044 | | 0.0739 | 14.04 | 29840 | 0.0044 | | 0.0002 | 14.05 | 29850 | 0.0044 | | 0.0081 | 14.05 | 29860 | 0.0044 | | 0.085 | 14.06 | 29870 | 0.0044 | | 0.0 | 14.06 | 29880 | 0.0044 | | 0.0023 | 14.07 | 29890 | 0.0044 | | 0.0001 | 14.07 | 29900 | 0.0044 | | 0.0 | 14.08 | 29910 | 0.0044 | | 0.0 | 14.08 | 29920 | 0.0044 | | 0.0 | 14.08 | 29930 | 0.0044 | | 0.0011 | 14.09 | 29940 | 0.0044 | | 0.0001 | 14.09 | 29950 | 0.0044 | | 0.0001 | 14.1 | 29960 | 0.0044 | | 0.0 | 14.1 | 29970 | 0.0044 | | 0.0006 | 14.11 | 29980 | 0.0044 | | 0.0033 | 14.11 | 29990 | 0.0044 | | 0.0744 | 14.12 | 30000 | 0.0062 | | 0.0364 | 14.12 | 30010 | 0.0062 | | 0.0 | 14.13 | 30020 | 0.0079 | | 0.0 | 14.13 | 30030 | 0.0079 | | 0.0229 | 14.14 | 30040 | 0.0062 | | 0.0001 | 14.14 | 30050 | 0.0062 | | 0.0005 | 14.15 | 30060 | 0.0062 | | 0.0 | 14.15 | 30070 | 0.0062 | | 0.0 | 14.16 | 30080 | 0.0062 | | 0.0275 | 14.16 | 30090 | 0.0062 | | 0.001 | 14.16 | 30100 | 0.0044 | | 0.0002 | 14.17 | 30110 | 0.0044 | | 0.0 | 14.17 | 30120 | 0.0044 | | 0.0 | 14.18 | 30130 | 0.0044 | | 0.0 | 14.18 | 30140 | 0.0044 | | 0.0001 | 14.19 | 30150 | 0.0044 | | 0.0 | 14.19 | 30160 | 0.0044 | | 0.071 | 14.2 | 30170 | 0.0044 | | 0.0001 | 14.2 | 30180 | 0.0044 | | 0.0002 | 14.21 | 30190 | 0.0044 | | 0.0 | 14.21 | 30200 | 0.0044 | | 0.0001 | 14.22 | 30210 | 0.0044 | | 0.0348 | 14.22 | 30220 | 0.0044 | | 0.0 | 14.23 | 30230 | 0.0044 | | 0.0013 | 14.23 | 30240 | 0.0044 | | 0.0323 | 14.24 | 30250 | 0.0044 | | 0.0 | 14.24 | 30260 | 0.0044 | | 0.0003 | 14.24 | 30270 | 0.0044 | | 0.0 | 14.25 | 30280 | 0.0044 | | 0.0122 | 14.25 | 30290 | 0.0044 | | 0.0001 | 14.26 | 30300 | 0.0044 | | 0.0009 | 14.26 | 30310 | 0.0044 | | 0.0 | 14.27 | 30320 | 0.0044 | | 0.0 | 14.27 | 30330 | 0.0044 | | 0.0001 | 14.28 | 30340 | 0.0044 | | 0.0336 | 14.28 | 30350 | 0.0044 | | 0.0006 | 14.29 | 30360 | 0.0027 | | 0.0451 | 14.29 | 30370 | 0.0027 | | 0.0345 | 14.3 | 30380 | 0.0027 | | 0.025 | 14.3 | 30390 | 0.0027 | | 0.0002 | 14.31 | 30400 | 0.0027 | | 0.0377 | 14.31 | 30410 | 0.0027 | | 0.0001 | 14.32 | 30420 | 0.0027 | | 0.0 | 14.32 | 30430 | 0.0027 | | 0.0001 | 14.32 | 30440 | 0.0027 | | 0.0 | 14.33 | 30450 | 0.0027 | | 0.0 | 14.33 | 30460 | 0.0027 | | 0.0 | 14.34 | 30470 | 0.0026 | | 0.0001 | 14.34 | 30480 | 0.0027 | | 0.0 | 14.35 | 30490 | 0.0027 | | 0.0001 | 14.35 | 30500 | 0.0027 | | 0.0742 | 14.36 | 30510 | 0.0027 | | 0.0044 | 14.36 | 30520 | 0.0027 | | 0.0001 | 14.37 | 30530 | 0.0027 | | 0.0001 | 14.37 | 30540 | 0.0027 | | 0.1246 | 14.38 | 30550 | 0.0027 | | 0.0 | 14.38 | 30560 | 0.0027 | | 0.0003 | 14.39 | 30570 | 0.0027 | | 0.0066 | 14.39 | 30580 | 0.0027 | | 0.0 | 14.4 | 30590 | 0.0027 | | 0.0 | 14.4 | 30600 | 0.0027 | | 0.0001 | 14.4 | 30610 | 0.0027 | | 0.0 | 14.41 | 30620 | 0.0027 | | 0.0 | 14.41 | 30630 | 0.0027 | | 0.0343 | 14.42 | 30640 | 0.0027 | | 0.0298 | 14.42 | 30650 | 0.0026 | | 0.1028 | 14.43 | 30660 | 0.0026 | | 0.0019 | 14.43 | 30670 | 0.0026 | | 0.0374 | 14.44 | 30680 | 0.0026 | | 0.0 | 14.44 | 30690 | 0.0026 | | 0.0 | 14.45 | 30700 | 0.0026 | | 0.0 | 14.45 | 30710 | 0.0026 | | 0.0019 | 14.46 | 30720 | 0.0026 | | 0.0 | 14.46 | 30730 | 0.0026 | | 0.0001 | 14.47 | 30740 | 0.0026 | | 0.0 | 14.47 | 30750 | 0.0026 | | 0.0001 | 14.48 | 30760 | 0.0026 | | 0.0 | 14.48 | 30770 | 0.0026 | | 0.0 | 14.48 | 30780 | 0.0026 | | 0.0065 | 14.49 | 30790 | 0.0026 | | 0.0 | 14.49 | 30800 | 0.0026 | | 0.0 | 14.5 | 30810 | 0.0026 | | 0.0023 | 14.5 | 30820 | 0.0026 | | 0.0047 | 14.51 | 30830 | 0.0026 | | 0.0001 | 14.51 | 30840 | 0.0026 | | 0.0001 | 14.52 | 30850 | 0.0026 | | 0.0001 | 14.52 | 30860 | 0.0026 | | 0.001 | 14.53 | 30870 | 0.0026 | | 0.0 | 14.53 | 30880 | 0.0026 | | 0.0944 | 14.54 | 30890 | 0.0026 | | 0.0001 | 14.54 | 30900 | 0.0026 | | 0.0001 | 14.55 | 30910 | 0.0026 | | 0.0 | 14.55 | 30920 | 0.0026 | | 0.0006 | 14.56 | 30930 | 0.0026 | | 0.0 | 14.56 | 30940 | 0.0027 | | 0.0 | 14.56 | 30950 | 0.0027 | | 0.0 | 14.57 | 30960 | 0.0027 | | 0.0001 | 14.57 | 30970 | 0.0027 | | 0.0 | 14.58 | 30980 | 0.0027 | | 0.0 | 14.58 | 30990 | 0.0027 | | 0.0005 | 14.59 | 31000 | 0.0027 | | 0.0 | 14.59 | 31010 | 0.0027 | | 0.0 | 14.6 | 31020 | 0.0027 | | 0.0002 | 14.6 | 31030 | 0.0027 | | 0.0124 | 14.61 | 31040 | 0.0027 | | 0.0004 | 14.61 | 31050 | 0.0027 | | 0.0725 | 14.62 | 31060 | 0.0027 | | 0.0001 | 14.62 | 31070 | 0.0027 | | 0.076 | 14.63 | 31080 | 0.0027 | | 0.0 | 14.63 | 31090 | 0.0027 | | 0.0352 | 14.64 | 31100 | 0.0027 | | 0.0025 | 14.64 | 31110 | 0.0027 | | 0.0023 | 14.64 | 31120 | 0.0027 | | 0.0 | 14.65 | 31130 | 0.0027 | | 0.0 | 14.65 | 31140 | 0.0027 | | 0.0011 | 14.66 | 31150 | 0.0027 | | 0.0002 | 14.66 | 31160 | 0.0027 | | 0.0 | 14.67 | 31170 | 0.0027 | | 0.0054 | 14.67 | 31180 | 0.0027 | | 0.0 | 14.68 | 31190 | 0.0027 | | 0.0678 | 14.68 | 31200 | 0.0027 | | 0.0088 | 14.69 | 31210 | 0.0027 | | 0.0395 | 14.69 | 31220 | 0.0027 | | 0.004 | 14.7 | 31230 | 0.0009 | | 0.0 | 14.7 | 31240 | 0.0009 | | 0.0001 | 14.71 | 31250 | 0.0009 | | 0.0 | 14.71 | 31260 | 0.0009 | | 0.0799 | 14.72 | 31270 | 0.0009 | | 0.0 | 14.72 | 31280 | 0.0009 | | 0.0004 | 14.72 | 31290 | 0.0009 | | 0.072 | 14.73 | 31300 | 0.0009 | | 0.0014 | 14.73 | 31310 | 0.0009 | | 0.004 | 14.74 | 31320 | 0.0009 | | 0.0038 | 14.74 | 31330 | 0.0009 | | 0.0 | 14.75 | 31340 | 0.0009 | | 0.0335 | 14.75 | 31350 | 0.0009 | | 0.0654 | 14.76 | 31360 | 0.0009 | | 0.0001 | 14.76 | 31370 | 0.0009 | | 0.0 | 14.77 | 31380 | 0.0009 | | 0.0009 | 14.77 | 31390 | 0.0009 | | 0.0001 | 14.78 | 31400 | 0.0009 | | 0.0013 | 14.78 | 31410 | 0.0009 | | 0.0007 | 14.79 | 31420 | 0.0009 | | 0.0001 | 14.79 | 31430 | 0.0009 | | 0.0001 | 14.8 | 31440 | 0.0009 | | 0.0 | 14.8 | 31450 | 0.0009 | | 0.0001 | 14.8 | 31460 | 0.0009 | | 0.0001 | 14.81 | 31470 | 0.0009 | | 0.0 | 14.81 | 31480 | 0.0009 | | 0.0 | 14.82 | 31490 | 0.0009 | | 0.0002 | 14.82 | 31500 | 0.0009 | | 0.1111 | 14.83 | 31510 | 0.0009 | | 0.0001 | 14.83 | 31520 | 0.0009 | | 0.0045 | 14.84 | 31530 | 0.0009 | | 0.0354 | 14.84 | 31540 | 0.0009 | | 0.0414 | 14.85 | 31550 | 0.0009 | | 0.0 | 14.85 | 31560 | 0.0009 | | 0.0 | 14.86 | 31570 | 0.0009 | | 0.0157 | 14.86 | 31580 | 0.0009 | | 0.0386 | 14.87 | 31590 | 0.0009 | | 0.0 | 14.87 | 31600 | 0.0009 | | 0.0 | 14.88 | 31610 | 0.0009 | | 0.0001 | 14.88 | 31620 | 0.0009 | | 0.0689 | 14.88 | 31630 | 0.0009 | | 0.0 | 14.89 | 31640 | 0.0009 | | 0.0002 | 14.89 | 31650 | 0.0009 | | 0.0 | 14.9 | 31660 | 0.0009 | | 0.0409 | 14.9 | 31670 | 0.0009 | | 0.0726 | 14.91 | 31680 | 0.0009 | | 0.0002 | 14.91 | 31690 | 0.0009 | | 0.0 | 14.92 | 31700 | 0.0009 | | 0.0019 | 14.92 | 31710 | 0.0009 | | 0.0001 | 14.93 | 31720 | 0.0009 | | 0.0761 | 14.93 | 31730 | 0.0009 | | 0.0756 | 14.94 | 31740 | 0.0009 | | 0.0002 | 14.94 | 31750 | 0.0026 | | 0.0001 | 14.95 | 31760 | 0.0026 | | 0.0 | 14.95 | 31770 | 0.0026 | | 0.0815 | 14.96 | 31780 | 0.0026 | | 0.0249 | 14.96 | 31790 | 0.0026 | | 0.0 | 14.96 | 31800 | 0.0026 | | 0.0 | 14.97 | 31810 | 0.0026 | | 0.0 | 14.97 | 31820 | 0.0026 | | 0.0001 | 14.98 | 31830 | 0.0026 | | 0.0015 | 14.98 | 31840 | 0.0026 | | 0.0 | 14.99 | 31850 | 0.0027 | | 0.0001 | 14.99 | 31860 | 0.0027 | | 0.0218 | 15.0 | 31870 | 0.0027 | | 0.0 | 15.0 | 31880 | 0.0026 | | 0.0 | 15.01 | 31890 | 0.0026 | | 0.0347 | 15.01 | 31900 | 0.0026 | | 0.0 | 15.02 | 31910 | 0.0026 | | 0.0818 | 15.02 | 31920 | 0.0026 | | 0.0 | 15.03 | 31930 | 0.0009 | | 0.0323 | 15.03 | 31940 | 0.0009 | | 0.0001 | 15.04 | 31950 | 0.0009 | | 0.0001 | 15.04 | 31960 | 0.0009 | | 0.0001 | 15.04 | 31970 | 0.0009 | | 0.0378 | 15.05 | 31980 | 0.0009 | | 0.0001 | 15.05 | 31990 | 0.0009 | | 0.0 | 15.06 | 32000 | 0.0009 | | 0.0349 | 15.06 | 32010 | 0.0009 | | 0.0001 | 15.07 | 32020 | 0.0009 | | 0.0327 | 15.07 | 32030 | 0.0009 | | 0.0 | 15.08 | 32040 | 0.0009 | | 0.0 | 15.08 | 32050 | 0.0009 | | 0.0 | 15.09 | 32060 | 0.0009 | | 0.0 | 15.09 | 32070 | 0.0009 | | 0.0379 | 15.1 | 32080 | 0.0009 | | 0.0047 | 15.1 | 32090 | 0.0009 | | 0.0 | 15.11 | 32100 | 0.0009 | | 0.1005 | 15.11 | 32110 | 0.0009 | | 0.0001 | 15.12 | 32120 | 0.0009 | | 0.0001 | 15.12 | 32130 | 0.0009 | | 0.0 | 15.12 | 32140 | 0.0009 | | 0.0349 | 15.13 | 32150 | 0.0009 | | 0.0 | 15.13 | 32160 | 0.0009 | | 0.0003 | 15.14 | 32170 | 0.0009 | | 0.0 | 15.14 | 32180 | 0.0009 | | 0.0584 | 15.15 | 32190 | 0.0009 | | 0.0001 | 15.15 | 32200 | 0.0009 | | 0.015 | 15.16 | 32210 | 0.0009 | | 0.0 | 15.16 | 32220 | 0.0009 | | 0.0035 | 15.17 | 32230 | 0.0009 | | 0.0001 | 15.17 | 32240 | 0.0009 | | 0.0066 | 15.18 | 32250 | 0.0009 | | 0.0003 | 15.18 | 32260 | 0.0009 | | 0.0072 | 15.19 | 32270 | 0.0009 | | 0.0 | 15.19 | 32280 | 0.0009 | | 0.0 | 15.2 | 32290 | 0.0009 | | 0.0001 | 15.2 | 32300 | 0.0009 | | 0.0 | 15.2 | 32310 | 0.0009 | | 0.0068 | 15.21 | 32320 | 0.0009 | | 0.0004 | 15.21 | 32330 | 0.0009 | | 0.0 | 15.22 | 32340 | 0.0009 | | 0.0001 | 15.22 | 32350 | 0.0009 | | 0.0001 | 15.23 | 32360 | 0.0009 | | 0.0002 | 15.23 | 32370 | 0.0009 | | 0.0 | 15.24 | 32380 | 0.0009 | | 0.0 | 15.24 | 32390 | 0.0009 | | 0.0723 | 15.25 | 32400 | 0.0009 | | 0.0079 | 15.25 | 32410 | 0.0009 | | 0.008 | 15.26 | 32420 | 0.0009 | | 0.0525 | 15.26 | 32430 | 0.0009 | | 0.0 | 15.27 | 32440 | 0.0009 | | 0.0001 | 15.27 | 32450 | 0.0009 | | 0.0 | 15.28 | 32460 | 0.0009 | | 0.0329 | 15.28 | 32470 | 0.0009 | | 0.0 | 15.28 | 32480 | 0.0009 | | 0.0314 | 15.29 | 32490 | 0.0009 | | 0.0311 | 15.29 | 32500 | 0.0009 | | 0.0 | 15.3 | 32510 | 0.0009 | | 0.0489 | 15.3 | 32520 | 0.0009 | | 0.0023 | 15.31 | 32530 | 0.0009 | | 0.0 | 15.31 | 32540 | 0.0009 | | 0.0 | 15.32 | 32550 | 0.0009 | | 0.0 | 15.32 | 32560 | 0.0009 | | 0.0 | 15.33 | 32570 | 0.0009 | | 0.021 | 15.33 | 32580 | 0.0009 | | 0.0 | 15.34 | 32590 | 0.0009 | | 0.0001 | 15.34 | 32600 | 0.0009 | | 0.0 | 15.35 | 32610 | 0.0009 | | 0.0 | 15.35 | 32620 | 0.0009 | | 0.0 | 15.36 | 32630 | 0.0009 | | 0.014 | 15.36 | 32640 | 0.0009 | | 0.0012 | 15.36 | 32650 | 0.0009 | | 0.0 | 15.37 | 32660 | 0.0009 | | 0.0003 | 15.37 | 32670 | 0.0009 | | 0.0 | 15.38 | 32680 | 0.0009 | | 0.0362 | 15.38 | 32690 | 0.0009 | | 0.0 | 15.39 | 32700 | 0.0009 | | 0.0 | 15.39 | 32710 | 0.0009 | | 0.0001 | 15.4 | 32720 | 0.0009 | | 0.0 | 15.4 | 32730 | 0.0009 | | 0.0059 | 15.41 | 32740 | 0.0009 | | 0.0001 | 15.41 | 32750 | 0.0009 | | 0.0 | 15.42 | 32760 | 0.0009 | | 0.0002 | 15.42 | 32770 | 0.0009 | | 0.05 | 15.43 | 32780 | 0.0009 | | 0.0953 | 15.43 | 32790 | 0.0009 | | 0.0713 | 15.44 | 32800 | 0.0009 | | 0.0 | 15.44 | 32810 | 0.0009 | | 0.0725 | 15.44 | 32820 | 0.0009 | | 0.0 | 15.45 | 32830 | 0.0009 | | 0.0001 | 15.45 | 32840 | 0.0009 | | 0.0012 | 15.46 | 32850 | 0.0009 | | 0.0 | 15.46 | 32860 | 0.0009 | | 0.0001 | 15.47 | 32870 | 0.0009 | | 0.0356 | 15.47 | 32880 | 0.0009 | | 0.0001 | 15.48 | 32890 | 0.0009 | | 0.0 | 15.48 | 32900 | 0.0009 | | 0.0029 | 15.49 | 32910 | 0.0009 | | 0.0038 | 15.49 | 32920 | 0.0009 | | 0.0369 | 15.5 | 32930 | 0.0009 | | 0.0001 | 15.5 | 32940 | 0.0009 | | 0.0004 | 15.51 | 32950 | 0.0009 | | 0.0002 | 15.51 | 32960 | 0.0009 | | 0.0002 | 15.52 | 32970 | 0.0009 | | 0.0025 | 15.52 | 32980 | 0.0009 | | 0.0002 | 15.52 | 32990 | 0.0009 | | 0.0 | 15.53 | 33000 | 0.0009 | | 0.0034 | 15.53 | 33010 | 0.0009 | | 0.0002 | 15.54 | 33020 | 0.0009 | | 0.0038 | 15.54 | 33030 | 0.0009 | | 0.0001 | 15.55 | 33040 | 0.0009 | | 0.0 | 15.55 | 33050 | 0.0009 | | 0.1593 | 15.56 | 33060 | 0.0009 | | 0.0002 | 15.56 | 33070 | 0.0009 | | 0.0 | 15.57 | 33080 | 0.0009 | | 0.0319 | 15.57 | 33090 | 0.0005 | | 0.0001 | 15.58 | 33100 | 0.0000 | | 0.0344 | 15.58 | 33110 | 0.0000 | | 0.0 | 15.59 | 33120 | 0.0009 | | 0.0 | 15.59 | 33130 | 0.0009 | | 0.0 | 15.6 | 33140 | 0.0009 | | 0.0002 | 15.6 | 33150 | 0.0009 | | 0.0 | 15.6 | 33160 | 0.0009 | | 0.0001 | 15.61 | 33170 | 0.0009 | | 0.0695 | 15.61 | 33180 | 0.0009 | | 0.0 | 15.62 | 33190 | 0.0007 | | 0.1982 | 15.62 | 33200 | 0.0006 | | 0.0024 | 15.63 | 33210 | 0.0006 | | 0.0001 | 15.63 | 33220 | 0.0003 | | 0.006 | 15.64 | 33230 | 0.0009 | | 0.0022 | 15.64 | 33240 | 0.0009 | | 0.0007 | 15.65 | 33250 | 0.0009 | | 0.0355 | 15.65 | 33260 | 0.0009 | | 0.0 | 15.66 | 33270 | 0.0009 | | 0.0 | 15.66 | 33280 | 0.0009 | | 0.0028 | 15.67 | 33290 | 0.0000 | | 0.0678 | 15.67 | 33300 | 0.0000 | | 0.0 | 15.68 | 33310 | 0.0000 | | 0.0 | 15.68 | 33320 | 0.0000 | | 0.0 | 15.68 | 33330 | 0.0000 | | 0.0008 | 15.69 | 33340 | 0.0000 | | 0.0069 | 15.69 | 33350 | 0.0000 | | 0.0381 | 15.7 | 33360 | 0.0000 | | 0.0 | 15.7 | 33370 | 0.0000 | | 0.0001 | 15.71 | 33380 | 0.0000 | | 0.0 | 15.71 | 33390 | 0.0000 | | 0.0 | 15.72 | 33400 | 0.0000 | | 0.0342 | 15.72 | 33410 | 0.0000 | | 0.0003 | 15.73 | 33420 | 0.0000 | | 0.054 | 15.73 | 33430 | 0.0000 | | 0.0 | 15.74 | 33440 | 0.0000 | | 0.0001 | 15.74 | 33450 | 0.0000 | | 0.0003 | 15.75 | 33460 | 0.0000 | | 0.0 | 15.75 | 33470 | 0.0000 | | 0.0 | 15.76 | 33480 | 0.0000 | | 0.0001 | 15.76 | 33490 | 0.0000 | | 0.0 | 15.76 | 33500 | 0.0000 | | 0.0 | 15.77 | 33510 | 0.0000 | | 0.0 | 15.77 | 33520 | 0.0000 | | 0.0 | 15.78 | 33530 | 0.0000 | | 0.0 | 15.78 | 33540 | 0.0000 | | 0.0001 | 15.79 | 33550 | 0.0000 | | 0.0001 | 15.79 | 33560 | 0.0000 | | 0.0 | 15.8 | 33570 | 0.0000 | | 0.0 | 15.8 | 33580 | 0.0000 | | 0.0029 | 15.81 | 33590 | 0.0000 | | 0.0 | 15.81 | 33600 | 0.0000 | | 0.0 | 15.82 | 33610 | 0.0000 | | 0.0001 | 15.82 | 33620 | 0.0000 | | 0.0 | 15.83 | 33630 | 0.0000 | | 0.0009 | 15.83 | 33640 | 0.0000 | | 0.0003 | 15.84 | 33650 | 0.0000 | | 0.0 | 15.84 | 33660 | 0.0000 | | 0.0002 | 15.84 | 33670 | 0.0000 | | 0.0 | 15.85 | 33680 | 0.0000 | | 0.0 | 15.85 | 33690 | 0.0000 | | 0.0004 | 15.86 | 33700 | 0.0000 | | 0.0337 | 15.86 | 33710 | 0.0000 | | 0.0 | 15.87 | 33720 | 0.0000 | | 0.0062 | 15.87 | 33730 | 0.0000 | | 0.0327 | 15.88 | 33740 | 0.0000 | | 0.0 | 15.88 | 33750 | 0.0000 | | 0.0 | 15.89 | 33760 | 0.0000 | | 0.0349 | 15.89 | 33770 | 0.0000 | | 0.0585 | 15.9 | 33780 | 0.0000 | | 0.0 | 15.9 | 33790 | 0.0000 | | 0.0 | 15.91 | 33800 | 0.0000 | | 0.0 | 15.91 | 33810 | 0.0000 | | 0.0001 | 15.92 | 33820 | 0.0000 | | 0.1148 | 15.92 | 33830 | 0.0000 | | 0.0 | 15.92 | 33840 | 0.0000 | | 0.0001 | 15.93 | 33850 | 0.0000 | | 0.0001 | 15.93 | 33860 | 0.0000 | | 0.0013 | 15.94 | 33870 | 0.0000 | | 0.0 | 15.94 | 33880 | 0.0000 | | 0.0 | 15.95 | 33890 | 0.0000 | | 0.0 | 15.95 | 33900 | 0.0000 | | 0.0025 | 15.96 | 33910 | 0.0000 | | 0.0 | 15.96 | 33920 | 0.0000 | | 0.0004 | 15.97 | 33930 | 0.0000 | | 0.0 | 15.97 | 33940 | 0.0000 | | 0.0068 | 15.98 | 33950 | 0.0000 | | 0.0026 | 15.98 | 33960 | 0.0000 | | 0.0001 | 15.99 | 33970 | 0.0000 | | 0.0013 | 15.99 | 33980 | 0.0000 | | 0.0001 | 16.0 | 33990 | 0.0000 | | 0.0633 | 16.0 | 34000 | 0.0000 | | 0.0 | 16.0 | 34010 | 0.0000 | | 0.0001 | 16.01 | 34020 | 0.0000 | | 0.0 | 16.01 | 34030 | 0.0000 | | 0.0034 | 16.02 | 34040 | 0.0000 | | 0.0053 | 16.02 | 34050 | 0.0000 | | 0.0008 | 16.03 | 34060 | 0.0000 | | 0.0 | 16.03 | 34070 | 0.0000 | | 0.0001 | 16.04 | 34080 | 0.0000 | | 0.0007 | 16.04 | 34090 | 0.0000 | | 0.0 | 16.05 | 34100 | 0.0001 | | 0.0 | 16.05 | 34110 | 0.0004 | | 0.0 | 16.06 | 34120 | 0.0004 | | 0.0 | 16.06 | 34130 | 0.0001 | | 0.0003 | 16.07 | 34140 | 0.0005 | | 0.0751 | 16.07 | 34150 | 0.0000 | | 0.0001 | 16.08 | 34160 | 0.0000 | | 0.0091 | 16.08 | 34170 | 0.0000 | | 0.0001 | 16.08 | 34180 | 0.0000 | | 0.0001 | 16.09 | 34190 | 0.0000 | | 0.0005 | 16.09 | 34200 | 0.0000 | | 0.0 | 16.1 | 34210 | 0.0000 | | 0.0259 | 16.1 | 34220 | 0.0000 | | 0.0 | 16.11 | 34230 | 0.0000 | | 0.0249 | 16.11 | 34240 | 0.0000 | | 0.0001 | 16.12 | 34250 | 0.0000 | | 0.0001 | 16.12 | 34260 | 0.0000 | | 0.0001 | 16.13 | 34270 | 0.0000 | | 0.0786 | 16.13 | 34280 | 0.0000 | | 0.0003 | 16.14 | 34290 | 0.0000 | | 0.0001 | 16.14 | 34300 | 0.0000 | | 0.0001 | 16.15 | 34310 | 0.0000 | | 0.0001 | 16.15 | 34320 | 0.0000 | | 0.0149 | 16.16 | 34330 | 0.0000 | | 0.0 | 16.16 | 34340 | 0.0000 | | 0.0004 | 16.16 | 34350 | 0.0000 | | 0.0 | 16.17 | 34360 | 0.0000 | | 0.0 | 16.17 | 34370 | 0.0000 | | 0.0375 | 16.18 | 34380 | 0.0000 | | 0.0001 | 16.18 | 34390 | 0.0000 | | 0.0001 | 16.19 | 34400 | 0.0000 | | 0.0 | 16.19 | 34410 | 0.0000 | | 0.0 | 16.2 | 34420 | 0.0000 | | 0.0044 | 16.2 | 34430 | 0.0000 | | 0.0 | 16.21 | 34440 | 0.0000 | | 0.0896 | 16.21 | 34450 | 0.0000 | | 0.0001 | 16.22 | 34460 | 0.0000 | | 0.0 | 16.22 | 34470 | 0.0000 | | 0.0024 | 16.23 | 34480 | 0.0000 | | 0.0025 | 16.23 | 34490 | 0.0000 | | 0.0 | 16.24 | 34500 | 0.0000 | | 0.0622 | 16.24 | 34510 | 0.0000 | | 0.0 | 16.24 | 34520 | 0.0000 | | 0.0 | 16.25 | 34530 | 0.0000 | | 0.0002 | 16.25 | 34540 | 0.0000 | | 0.0 | 16.26 | 34550 | 0.0000 | | 0.0 | 16.26 | 34560 | 0.0000 | | 0.0 | 16.27 | 34570 | 0.0000 | | 0.0011 | 16.27 | 34580 | 0.0000 | | 0.0 | 16.28 | 34590 | 0.0000 | | 0.0001 | 16.28 | 34600 | 0.0000 | | 0.0 | 16.29 | 34610 | 0.0000 | | 0.0 | 16.29 | 34620 | 0.0000 | | 0.0001 | 16.3 | 34630 | 0.0000 | | 0.0 | 16.3 | 34640 | 0.0000 | | 0.1008 | 16.31 | 34650 | 0.0000 | | 0.0039 | 16.31 | 34660 | 0.0000 | | 0.0006 | 16.32 | 34670 | 0.0000 | | 0.04 | 16.32 | 34680 | 0.0000 | | 0.0 | 16.32 | 34690 | 0.0000 | | 0.0218 | 16.33 | 34700 | 0.0000 | | 0.0648 | 16.33 | 34710 | 0.0000 | | 0.0 | 16.34 | 34720 | 0.0000 | | 0.0067 | 16.34 | 34730 | 0.0000 | | 0.0 | 16.35 | 34740 | 0.0000 | | 0.0 | 16.35 | 34750 | 0.0000 | | 0.0001 | 16.36 | 34760 | 0.0000 | | 0.0001 | 16.36 | 34770 | 0.0000 | | 0.0008 | 16.37 | 34780 | 0.0000 | | 0.0057 | 16.37 | 34790 | 0.0000 | | 0.0 | 16.38 | 34800 | 0.0000 | | 0.0001 | 16.38 | 34810 | 0.0000 | | 0.0 | 16.39 | 34820 | 0.0000 | | 0.0001 | 16.39 | 34830 | 0.0000 | | 0.0 | 16.4 | 34840 | 0.0000 | | 0.0 | 16.4 | 34850 | 0.0000 | | 0.0393 | 16.4 | 34860 | 0.0000 | | 0.0021 | 16.41 | 34870 | 0.0000 | | 0.0081 | 16.41 | 34880 | 0.0000 | | 0.0409 | 16.42 | 34890 | 0.0000 | | 0.0046 | 16.42 | 34900 | 0.0000 | | 0.0 | 16.43 | 34910 | 0.0000 | | 0.0 | 16.43 | 34920 | 0.0000 | | 0.0349 | 16.44 | 34930 | 0.0000 | | 0.0 | 16.44 | 34940 | 0.0000 | | 0.0001 | 16.45 | 34950 | 0.0000 | | 0.001 | 16.45 | 34960 | 0.0000 | | 0.0 | 16.46 | 34970 | 0.0000 | | 0.0 | 16.46 | 34980 | 0.0000 | | 0.0332 | 16.47 | 34990 | 0.0000 | | 0.0634 | 16.47 | 35000 | 0.0000 | | 0.0001 | 16.48 | 35010 | 0.0000 | | 0.0003 | 16.48 | 35020 | 0.0000 | | 0.0003 | 16.48 | 35030 | 0.0000 | | 0.0087 | 16.49 | 35040 | 0.0000 | | 0.0 | 16.49 | 35050 | 0.0000 | | 0.0 | 16.5 | 35060 | 0.0000 | | 0.0404 | 16.5 | 35070 | 0.0000 | | 0.0 | 16.51 | 35080 | 0.0000 | | 0.0806 | 16.51 | 35090 | 0.0000 | | 0.0769 | 16.52 | 35100 | 0.0000 | | 0.0 | 16.52 | 35110 | 0.0000 | | 0.0 | 16.53 | 35120 | 0.0000 | | 0.0097 | 16.53 | 35130 | 0.0000 | | 0.042 | 16.54 | 35140 | 0.0000 | | 0.0682 | 16.54 | 35150 | 0.0000 | | 0.0 | 16.55 | 35160 | 0.0000 | | 0.0123 | 16.55 | 35170 | 0.0000 | | 0.0 | 16.56 | 35180 | 0.0000 | | 0.0778 | 16.56 | 35190 | 0.0000 | | 0.0364 | 16.56 | 35200 | 0.0000 | | 0.0 | 16.57 | 35210 | 0.0000 | | 0.0 | 16.57 | 35220 | 0.0000 | | 0.0132 | 16.58 | 35230 | 0.0000 | | 0.0 | 16.58 | 35240 | 0.0000 | | 0.0 | 16.59 | 35250 | 0.0000 | | 0.0 | 16.59 | 35260 | 0.0000 | | 0.0 | 16.6 | 35270 | 0.0000 | | 0.0001 | 16.6 | 35280 | 0.0000 | | 0.0 | 16.61 | 35290 | 0.0000 | | 0.07 | 16.61 | 35300 | 0.0000 | | 0.0 | 16.62 | 35310 | 0.0000 | | 0.0033 | 16.62 | 35320 | 0.0000 | | 0.0001 | 16.63 | 35330 | 0.0000 | | 0.0005 | 16.63 | 35340 | 0.0000 | | 0.0016 | 16.64 | 35350 | 0.0000 | | 0.0777 | 16.64 | 35360 | 0.0000 | | 0.001 | 16.64 | 35370 | 0.0000 | | 0.0001 | 16.65 | 35380 | 0.0000 | | 0.0 | 16.65 | 35390 | 0.0000 | | 0.0001 | 16.66 | 35400 | 0.0000 | | 0.0001 | 16.66 | 35410 | 0.0000 | | 0.0 | 16.67 | 35420 | 0.0000 | | 0.0005 | 16.67 | 35430 | 0.0000 | | 0.0484 | 16.68 | 35440 | 0.0000 | | 0.0 | 16.68 | 35450 | 0.0000 | | 0.0027 | 16.69 | 35460 | 0.0000 | | 0.0 | 16.69 | 35470 | 0.0000 | | 0.0324 | 16.7 | 35480 | 0.0000 | | 0.0373 | 16.7 | 35490 | 0.0000 | | 0.0028 | 16.71 | 35500 | 0.0000 | | 0.0001 | 16.71 | 35510 | 0.0000 | | 0.0 | 16.72 | 35520 | 0.0000 | | 0.0001 | 16.72 | 35530 | 0.0000 | | 0.0 | 16.72 | 35540 | 0.0000 | | 0.0001 | 16.73 | 35550 | 0.0000 | | 0.0003 | 16.73 | 35560 | 0.0000 | | 0.0 | 16.74 | 35570 | 0.0000 | | 0.0 | 16.74 | 35580 | 0.0000 | | 0.0001 | 16.75 | 35590 | 0.0000 | | 0.0 | 16.75 | 35600 | 0.0000 | | 0.0555 | 16.76 | 35610 | 0.0000 | | 0.0 | 16.76 | 35620 | 0.0000 | | 0.0 | 16.77 | 35630 | 0.0000 | | 0.0 | 16.77 | 35640 | 0.0000 | | 0.0 | 16.78 | 35650 | 0.0000 | | 0.0002 | 16.78 | 35660 | 0.0000 | | 0.0 | 16.79 | 35670 | 0.0000 | | 0.0 | 16.79 | 35680 | 0.0000 | | 0.0002 | 16.8 | 35690 | 0.0000 | | 0.0 | 16.8 | 35700 | 0.0000 | | 0.0002 | 16.8 | 35710 | 0.0000 | | 0.0001 | 16.81 | 35720 | 0.0000 | | 0.0001 | 16.81 | 35730 | 0.0000 | | 0.0 | 16.82 | 35740 | 0.0000 | | 0.0003 | 16.82 | 35750 | 0.0000 | | 0.0 | 16.83 | 35760 | 0.0000 | | 0.0 | 16.83 | 35770 | 0.0000 | | 0.0435 | 16.84 | 35780 | 0.0000 | | 0.0745 | 16.84 | 35790 | 0.0000 | | 0.0011 | 16.85 | 35800 | 0.0000 | | 0.0 | 16.85 | 35810 | 0.0000 | | 0.0001 | 16.86 | 35820 | 0.0000 | | 0.0 | 16.86 | 35830 | 0.0000 | | 0.0001 | 16.87 | 35840 | 0.0000 | | 0.0001 | 16.87 | 35850 | 0.0000 | | 0.0001 | 16.88 | 35860 | 0.0000 | | 0.0 | 16.88 | 35870 | 0.0000 | | 0.0 | 16.88 | 35880 | 0.0000 | | 0.0009 | 16.89 | 35890 | 0.0000 | | 0.0001 | 16.89 | 35900 | 0.0000 | | 0.0707 | 16.9 | 35910 | 0.0000 | | 0.0001 | 16.9 | 35920 | 0.0000 | | 0.0 | 16.91 | 35930 | 0.0000 | | 0.0005 | 16.91 | 35940 | 0.0000 | | 0.0 | 16.92 | 35950 | 0.0000 | | 0.0002 | 16.92 | 35960 | 0.0000 | | 0.0004 | 16.93 | 35970 | 0.0000 | | 0.0003 | 16.93 | 35980 | 0.0000 | | 0.0746 | 16.94 | 35990 | 0.0000 | | 0.0 | 16.94 | 36000 | 0.0000 | | 0.0024 | 16.95 | 36010 | 0.0000 | | 0.0 | 16.95 | 36020 | 0.0000 | | 0.0 | 16.96 | 36030 | 0.0000 | | 0.0 | 16.96 | 36040 | 0.0000 | | 0.0001 | 16.96 | 36050 | 0.0000 | | 0.0656 | 16.97 | 36060 | 0.0000 | | 0.0007 | 16.97 | 36070 | 0.0000 | | 0.0673 | 16.98 | 36080 | 0.0000 | | 0.0 | 16.98 | 36090 | 0.0000 | | 0.0 | 16.99 | 36100 | 0.0000 | | 0.0001 | 16.99 | 36110 | 0.0000 | | 0.0 | 17.0 | 36120 | 0.0000 | | 0.0 | 17.0 | 36130 | 0.0000 | | 0.0 | 17.01 | 36140 | 0.0000 | | 0.0 | 17.01 | 36150 | 0.0000 | | 0.0002 | 17.02 | 36160 | 0.0000 | | 0.0001 | 17.02 | 36170 | 0.0000 | | 0.0 | 17.03 | 36180 | 0.0000 | | 0.0324 | 17.03 | 36190 | 0.0000 | | 0.0001 | 17.04 | 36200 | 0.0000 | | 0.0687 | 17.04 | 36210 | 0.0000 | | 0.0742 | 17.04 | 36220 | 0.0000 | | 0.0 | 17.05 | 36230 | 0.0000 | | 0.0 | 17.05 | 36240 | 0.0000 | | 0.0 | 17.06 | 36250 | 0.0000 | | 0.0 | 17.06 | 36260 | 0.0000 | | 0.0003 | 17.07 | 36270 | 0.0000 | | 0.0001 | 17.07 | 36280 | 0.0000 | | 0.0073 | 17.08 | 36290 | 0.0000 | | 0.008 | 17.08 | 36300 | 0.0000 | | 0.0274 | 17.09 | 36310 | 0.0000 | | 0.0 | 17.09 | 36320 | 0.0000 | | 0.0 | 17.1 | 36330 | 0.0000 | | 0.0 | 17.1 | 36340 | 0.0000 | | 0.0231 | 17.11 | 36350 | 0.0000 | | 0.0701 | 17.11 | 36360 | 0.0000 | | 0.0001 | 17.12 | 36370 | 0.0000 | | 0.0 | 17.12 | 36380 | 0.0000 | | 0.001 | 17.12 | 36390 | 0.0000 | | 0.0 | 17.13 | 36400 | 0.0000 | | 0.0352 | 17.13 | 36410 | 0.0000 | | 0.0001 | 17.14 | 36420 | 0.0000 | | 0.0 | 17.14 | 36430 | 0.0000 | | 0.0723 | 17.15 | 36440 | 0.0000 | | 0.0 | 17.15 | 36450 | 0.0000 | | 0.0021 | 17.16 | 36460 | 0.0000 | | 0.0 | 17.16 | 36470 | 0.0000 | | 0.0 | 17.17 | 36480 | 0.0000 | | 0.0 | 17.17 | 36490 | 0.0000 | | 0.0739 | 17.18 | 36500 | 0.0000 | | 0.0 | 17.18 | 36510 | 0.0000 | | 0.0 | 17.19 | 36520 | 0.0000 | | 0.0 | 17.19 | 36530 | 0.0000 | | 0.0035 | 17.2 | 36540 | 0.0000 | | 0.202 | 17.2 | 36550 | 0.0000 | | 0.0 | 17.2 | 36560 | 0.0000 | | 0.0 | 17.21 | 36570 | 0.0000 | | 0.0 | 17.21 | 36580 | 0.0000 | | 0.0002 | 17.22 | 36590 | 0.0000 | | 0.0 | 17.22 | 36600 | 0.0000 | | 0.0326 | 17.23 | 36610 | 0.0000 | | 0.0 | 17.23 | 36620 | 0.0000 | | 0.0 | 17.24 | 36630 | 0.0000 | | 0.0004 | 17.24 | 36640 | 0.0000 | | 0.0001 | 17.25 | 36650 | 0.0000 | | 0.1301 | 17.25 | 36660 | 0.0000 | | 0.0016 | 17.26 | 36670 | 0.0000 | | 0.0 | 17.26 | 36680 | 0.0000 | | 0.0 | 17.27 | 36690 | 0.0000 | | 0.0008 | 17.27 | 36700 | 0.0000 | | 0.0 | 17.28 | 36710 | 0.0000 | | 0.0 | 17.28 | 36720 | 0.0000 | | 0.003 | 17.28 | 36730 | 0.0000 | | 0.0 | 17.29 | 36740 | 0.0000 | | 0.0 | 17.29 | 36750 | 0.0000 | | 0.0 | 17.3 | 36760 | 0.0000 | | 0.0 | 17.3 | 36770 | 0.0000 | | 0.0 | 17.31 | 36780 | 0.0000 | | 0.0694 | 17.31 | 36790 | 0.0000 | | 0.0 | 17.32 | 36800 | 0.0000 | | 0.0 | 17.32 | 36810 | 0.0000 | | 0.0001 | 17.33 | 36820 | 0.0000 | | 0.07 | 17.33 | 36830 | 0.0000 | | 0.0401 | 17.34 | 36840 | 0.0000 | | 0.0 | 17.34 | 36850 | 0.0000 | | 0.0 | 17.35 | 36860 | 0.0000 | | 0.0 | 17.35 | 36870 | 0.0000 | | 0.0004 | 17.36 | 36880 | 0.0000 | | 0.0 | 17.36 | 36890 | 0.0000 | | 0.0 | 17.36 | 36900 | 0.0000 | | 0.0438 | 17.37 | 36910 | 0.0000 | | 0.0 | 17.37 | 36920 | 0.0000 | | 0.0 | 17.38 | 36930 | 0.0000 | | 0.0003 | 17.38 | 36940 | 0.0000 | | 0.0 | 17.39 | 36950 | 0.0000 | | 0.0002 | 17.39 | 36960 | 0.0000 | | 0.0 | 17.4 | 36970 | 0.0000 | | 0.0361 | 17.4 | 36980 | 0.0000 | | 0.0084 | 17.41 | 36990 | 0.0000 | | 0.0 | 17.41 | 37000 | 0.0000 | | 0.007 | 17.42 | 37010 | 0.0000 | | 0.0001 | 17.42 | 37020 | 0.0000 | | 0.0076 | 17.43 | 37030 | 0.0000 | | 0.0009 | 17.43 | 37040 | 0.0000 | | 0.0 | 17.44 | 37050 | 0.0000 | | 0.0 | 17.44 | 37060 | 0.0000 | | 0.0 | 17.44 | 37070 | 0.0000 | | 0.0001 | 17.45 | 37080 | 0.0000 | | 0.0 | 17.45 | 37090 | 0.0000 | | 0.0005 | 17.46 | 37100 | 0.0000 | | 0.0 | 17.46 | 37110 | 0.0000 | | 0.0 | 17.47 | 37120 | 0.0000 | | 0.0004 | 17.47 | 37130 | 0.0000 | | 0.0002 | 17.48 | 37140 | 0.0000 | | 0.0001 | 17.48 | 37150 | 0.0000 | | 0.0 | 17.49 | 37160 | 0.0000 | | 0.0 | 17.49 | 37170 | 0.0000 | | 0.0 | 17.5 | 37180 | 0.0000 | | 0.0698 | 17.5 | 37190 | 0.0000 | | 0.05 | 17.51 | 37200 | 0.0000 | | 0.0 | 17.51 | 37210 | 0.0000 | | 0.0001 | 17.52 | 37220 | 0.0000 | | 0.0 | 17.52 | 37230 | 0.0000 | | 0.0662 | 17.52 | 37240 | 0.0000 | | 0.0 | 17.53 | 37250 | 0.0000 | | 0.0001 | 17.53 | 37260 | 0.0000 | | 0.0001 | 17.54 | 37270 | 0.0000 | | 0.0 | 17.54 | 37280 | 0.0000 | | 0.0 | 17.55 | 37290 | 0.0000 | | 0.0003 | 17.55 | 37300 | 0.0000 | | 0.0 | 17.56 | 37310 | 0.0000 | | 0.0001 | 17.56 | 37320 | 0.0000 | | 0.0 | 17.57 | 37330 | 0.0000 | | 0.0 | 17.57 | 37340 | 0.0000 | | 0.0116 | 17.58 | 37350 | 0.0000 | | 0.0709 | 17.58 | 37360 | 0.0000 | | 0.0144 | 17.59 | 37370 | 0.0000 | | 0.0 | 17.59 | 37380 | 0.0000 | | 0.0 | 17.6 | 37390 | 0.0000 | | 0.0243 | 17.6 | 37400 | 0.0000 | | 0.0 | 17.6 | 37410 | 0.0000 | | 0.0006 | 17.61 | 37420 | 0.0000 | | 0.0 | 17.61 | 37430 | 0.0000 | | 0.0 | 17.62 | 37440 | 0.0000 | | 0.0 | 17.62 | 37450 | 0.0000 | | 0.0 | 17.63 | 37460 | 0.0000 | | 0.0 | 17.63 | 37470 | 0.0000 | | 0.0001 | 17.64 | 37480 | 0.0000 | | 0.0 | 17.64 | 37490 | 0.0000 | | 0.0 | 17.65 | 37500 | 0.0000 | | 0.001 | 17.65 | 37510 | 0.0000 | | 0.0001 | 17.66 | 37520 | 0.0000 | | 0.0 | 17.66 | 37530 | 0.0000 | | 0.0 | 17.67 | 37540 | 0.0000 | | 0.0001 | 17.67 | 37550 | 0.0000 | | 0.0011 | 17.68 | 37560 | 0.0000 | | 0.0 | 17.68 | 37570 | 0.0000 | | 0.0 | 17.68 | 37580 | 0.0000 | | 0.0 | 17.69 | 37590 | 0.0000 | | 0.0007 | 17.69 | 37600 | 0.0000 | | 0.0 | 17.7 | 37610 | 0.0000 | | 0.0 | 17.7 | 37620 | 0.0000 | | 0.0025 | 17.71 | 37630 | 0.0000 | | 0.0772 | 17.71 | 37640 | 0.0000 | | 0.0006 | 17.72 | 37650 | 0.0000 | | 0.0 | 17.72 | 37660 | 0.0000 | | 0.0 | 17.73 | 37670 | 0.0000 | | 0.0341 | 17.73 | 37680 | 0.0000 | | 0.0002 | 17.74 | 37690 | 0.0000 | | 0.0 | 17.74 | 37700 | 0.0000 | | 0.0017 | 17.75 | 37710 | 0.0000 | | 0.0 | 17.75 | 37720 | 0.0000 | | 0.0 | 17.76 | 37730 | 0.0000 | | 0.0 | 17.76 | 37740 | 0.0000 | | 0.0 | 17.76 | 37750 | 0.0000 | | 0.0 | 17.77 | 37760 | 0.0000 | | 0.0 | 17.77 | 37770 | 0.0000 | | 0.0 | 17.78 | 37780 | 0.0000 | | 0.0001 | 17.78 | 37790 | 0.0000 | | 0.0 | 17.79 | 37800 | 0.0000 | | 0.0 | 17.79 | 37810 | 0.0000 | | 0.0001 | 17.8 | 37820 | 0.0000 | | 0.0 | 17.8 | 37830 | 0.0000 | | 0.0 | 17.81 | 37840 | 0.0000 | | 0.0005 | 17.81 | 37850 | 0.0000 | | 0.0001 | 17.82 | 37860 | 0.0000 | | 0.0 | 17.82 | 37870 | 0.0000 | | 0.0 | 17.83 | 37880 | 0.0000 | | 0.0247 | 17.83 | 37890 | 0.0000 | | 0.0 | 17.84 | 37900 | 0.0000 | | 0.0 | 17.84 | 37910 | 0.0000 | | 0.0 | 17.84 | 37920 | 0.0000 | | 0.0 | 17.85 | 37930 | 0.0000 | | 0.0435 | 17.85 | 37940 | 0.0000 | | 0.0 | 17.86 | 37950 | 0.0000 | | 0.0 | 17.86 | 37960 | 0.0000 | | 0.0 | 17.87 | 37970 | 0.0000 | | 0.0 | 17.87 | 37980 | 0.0000 | | 0.0 | 17.88 | 37990 | 0.0000 | | 0.0 | 17.88 | 38000 | 0.0000 | | 0.0 | 17.89 | 38010 | 0.0000 | | 0.0001 | 17.89 | 38020 | 0.0000 | | 0.001 | 17.9 | 38030 | 0.0000 | | 0.0037 | 17.9 | 38040 | 0.0000 | | 0.0001 | 17.91 | 38050 | 0.0000 | | 0.0008 | 17.91 | 38060 | 0.0000 | | 0.0001 | 17.92 | 38070 | 0.0000 | | 0.0 | 17.92 | 38080 | 0.0000 | | 0.0 | 17.92 | 38090 | 0.0000 | | 0.0613 | 17.93 | 38100 | 0.0000 | | 0.0 | 17.93 | 38110 | 0.0000 | | 0.0 | 17.94 | 38120 | 0.0000 | | 0.0001 | 17.94 | 38130 | 0.0000 | | 0.0 | 17.95 | 38140 | 0.0000 | | 0.0208 | 17.95 | 38150 | 0.0000 | | 0.0 | 17.96 | 38160 | 0.0000 | | 0.0004 | 17.96 | 38170 | 0.0000 | | 0.0 | 17.97 | 38180 | 0.0000 | | 0.0001 | 17.97 | 38190 | 0.0000 | | 0.0055 | 17.98 | 38200 | 0.0000 | | 0.0001 | 17.98 | 38210 | 0.0000 | | 0.0018 | 17.99 | 38220 | 0.0000 | | 0.0 | 17.99 | 38230 | 0.0000 | | 0.0 | 18.0 | 38240 | 0.0000 | | 0.0 | 18.0 | 38250 | 0.0000 | | 0.0002 | 18.0 | 38260 | 0.0000 | | 0.0758 | 18.01 | 38270 | 0.0000 | | 0.0001 | 18.01 | 38280 | 0.0000 | | 0.0013 | 18.02 | 38290 | 0.0000 | | 0.0709 | 18.02 | 38300 | 0.0000 | | 0.0 | 18.03 | 38310 | 0.0000 | | 0.0 | 18.03 | 38320 | 0.0000 | | 0.0 | 18.04 | 38330 | 0.0000 | | 0.0001 | 18.04 | 38340 | 0.0000 | | 0.0 | 18.05 | 38350 | 0.0000 | | 0.0001 | 18.05 | 38360 | 0.0000 | | 0.0 | 18.06 | 38370 | 0.0000 | | 0.0039 | 18.06 | 38380 | 0.0000 | | 0.0019 | 18.07 | 38390 | 0.0000 | | 0.0 | 18.07 | 38400 | 0.0000 | | 0.0001 | 18.08 | 38410 | 0.0000 | | 0.0 | 18.08 | 38420 | 0.0000 | | 0.0 | 18.08 | 38430 | 0.0000 | | 0.0002 | 18.09 | 38440 | 0.0000 | | 0.0 | 18.09 | 38450 | 0.0000 | | 0.0001 | 18.1 | 38460 | 0.0000 | | 0.0 | 18.1 | 38470 | 0.0000 | | 0.0321 | 18.11 | 38480 | 0.0000 | | 0.0 | 18.11 | 38490 | 0.0000 | | 0.0001 | 18.12 | 38500 | 0.0000 | | 0.0002 | 18.12 | 38510 | 0.0000 | | 0.0 | 18.13 | 38520 | 0.0000 | | 0.0 | 18.13 | 38530 | 0.0000 | | 0.0051 | 18.14 | 38540 | 0.0000 | | 0.0726 | 18.14 | 38550 | 0.0000 | | 0.0 | 18.15 | 38560 | 0.0000 | | 0.0 | 18.15 | 38570 | 0.0000 | | 0.0 | 18.16 | 38580 | 0.0000 | | 0.0 | 18.16 | 38590 | 0.0000 | | 0.1006 | 18.16 | 38600 | 0.0000 | | 0.0 | 18.17 | 38610 | 0.0000 | | 0.0 | 18.17 | 38620 | 0.0000 | | 0.0285 | 18.18 | 38630 | 0.0000 | | 0.0764 | 18.18 | 38640 | 0.0000 | | 0.0009 | 18.19 | 38650 | 0.0000 | | 0.0 | 18.19 | 38660 | 0.0000 | | 0.0719 | 18.2 | 38670 | 0.0000 | | 0.0 | 18.2 | 38680 | 0.0000 | | 0.0019 | 18.21 | 38690 | 0.0000 | | 0.0001 | 18.21 | 38700 | 0.0000 | | 0.0004 | 18.22 | 38710 | 0.0017 | | 0.0 | 18.22 | 38720 | 0.0017 | | 0.0 | 18.23 | 38730 | 0.0018 | | 0.0 | 18.23 | 38740 | 0.0018 | | 0.0 | 18.24 | 38750 | 0.0018 | | 0.0 | 18.24 | 38760 | 0.0018 | | 0.1773 | 18.24 | 38770 | 0.0018 | | 0.0021 | 18.25 | 38780 | 0.0018 | | 0.0001 | 18.25 | 38790 | 0.0018 | | 0.0 | 18.26 | 38800 | 0.0018 | | 0.0218 | 18.26 | 38810 | 0.0018 | | 0.0001 | 18.27 | 38820 | 0.0018 | | 0.0002 | 18.27 | 38830 | 0.0018 | | 0.0033 | 18.28 | 38840 | 0.0018 | | 0.0 | 18.28 | 38850 | 0.0018 | | 0.0 | 18.29 | 38860 | 0.0018 | | 0.038 | 18.29 | 38870 | 0.0018 | | 0.0 | 18.3 | 38880 | 0.0018 | | 0.0001 | 18.3 | 38890 | 0.0018 | | 0.0 | 18.31 | 38900 | 0.0018 | | 0.0 | 18.31 | 38910 | 0.0018 | | 0.0 | 18.32 | 38920 | 0.0018 | | 0.0044 | 18.32 | 38930 | 0.0018 | | 0.0007 | 18.32 | 38940 | 0.0018 | | 0.0001 | 18.33 | 38950 | 0.0018 | | 0.0087 | 18.33 | 38960 | 0.0018 | | 0.0 | 18.34 | 38970 | 0.0018 | | 0.0016 | 18.34 | 38980 | 0.0018 | | 0.029 | 18.35 | 38990 | 0.0018 | | 0.0 | 18.35 | 39000 | 0.0018 | | 0.0003 | 18.36 | 39010 | 0.0018 | | 0.0002 | 18.36 | 39020 | 0.0018 | | 0.0 | 18.37 | 39030 | 0.0018 | | 0.0437 | 18.37 | 39040 | 0.0018 | | 0.0039 | 18.38 | 39050 | 0.0018 | | 0.0001 | 18.38 | 39060 | 0.0018 | | 0.0 | 18.39 | 39070 | 0.0018 | | 0.0005 | 18.39 | 39080 | 0.0018 | | 0.0 | 18.4 | 39090 | 0.0018 | | 0.0001 | 18.4 | 39100 | 0.0018 | | 0.0 | 18.4 | 39110 | 0.0018 | | 0.0378 | 18.41 | 39120 | 0.0018 | | 0.0002 | 18.41 | 39130 | 0.0018 | | 0.035 | 18.42 | 39140 | 0.0018 | | 0.0001 | 18.42 | 39150 | 0.0018 | | 0.0002 | 18.43 | 39160 | 0.0018 | | 0.0743 | 18.43 | 39170 | 0.0018 | | 0.0 | 18.44 | 39180 | 0.0018 | | 0.0002 | 18.44 | 39190 | 0.0018 | | 0.0 | 18.45 | 39200 | 0.0018 | | 0.0006 | 18.45 | 39210 | 0.0018 | | 0.0004 | 18.46 | 39220 | 0.0018 | | 0.0098 | 18.46 | 39230 | 0.0018 | | 0.0694 | 18.47 | 39240 | 0.0018 | | 0.0 | 18.47 | 39250 | 0.0021 | | 0.0001 | 18.48 | 39260 | 0.0025 | | 0.0367 | 18.48 | 39270 | 0.0018 | | 0.0006 | 18.48 | 39280 | 0.0018 | | 0.0004 | 18.49 | 39290 | 0.0018 | | 0.0723 | 18.49 | 39300 | 0.0018 | | 0.0363 | 18.5 | 39310 | 0.0018 | | 0.0 | 18.5 | 39320 | 0.0018 | | 0.0365 | 18.51 | 39330 | 0.0018 | | 0.0 | 18.51 | 39340 | 0.0018 | | 0.0 | 18.52 | 39350 | 0.0024 | | 0.0 | 18.52 | 39360 | 0.0025 | | 0.0 | 18.53 | 39370 | 0.0025 | | 0.0 | 18.53 | 39380 | 0.0025 | | 0.0002 | 18.54 | 39390 | 0.0025 | | 0.0357 | 18.54 | 39400 | 0.0026 | | 0.0395 | 18.55 | 39410 | 0.0027 | | 0.0002 | 18.55 | 39420 | 0.0027 | | 0.0 | 18.56 | 39430 | 0.0027 | | 0.0347 | 18.56 | 39440 | 0.0026 | | 0.0741 | 18.56 | 39450 | 0.0026 | | 0.0001 | 18.57 | 39460 | 0.0026 | | 0.0 | 18.57 | 39470 | 0.0026 | | 0.0001 | 18.58 | 39480 | 0.0026 | | 0.0 | 18.58 | 39490 | 0.0026 | | 0.0001 | 18.59 | 39500 | 0.0026 | | 0.0038 | 18.59 | 39510 | 0.0026 | | 0.0011 | 18.6 | 39520 | 0.0026 | | 0.0 | 18.6 | 39530 | 0.0026 | | 0.0353 | 18.61 | 39540 | 0.0026 | | 0.0 | 18.61 | 39550 | 0.0026 | | 0.0 | 18.62 | 39560 | 0.0026 | | 0.0002 | 18.62 | 39570 | 0.0026 | | 0.0001 | 18.63 | 39580 | 0.0026 | | 0.1092 | 18.63 | 39590 | 0.0026 | | 0.0 | 18.64 | 39600 | 0.0026 | | 0.0714 | 18.64 | 39610 | 0.0026 | | 0.0748 | 18.64 | 39620 | 0.0026 | | 0.0001 | 18.65 | 39630 | 0.0026 | | 0.0006 | 18.65 | 39640 | 0.0026 | | 0.0 | 18.66 | 39650 | 0.0026 | | 0.0 | 18.66 | 39660 | 0.0026 | | 0.0 | 18.67 | 39670 | 0.0026 | | 0.0 | 18.67 | 39680 | 0.0026 | | 0.0381 | 18.68 | 39690 | 0.0026 | | 0.0 | 18.68 | 39700 | 0.0026 | | 0.0686 | 18.69 | 39710 | 0.0026 | | 0.0224 | 18.69 | 39720 | 0.0026 | | 0.0 | 18.7 | 39730 | 0.0026 | | 0.0 | 18.7 | 39740 | 0.0026 | | 0.0059 | 18.71 | 39750 | 0.0026 | | 0.0 | 18.71 | 39760 | 0.0026 | | 0.0 | 18.72 | 39770 | 0.0026 | | 0.0 | 18.72 | 39780 | 0.0026 | | 0.0 | 18.72 | 39790 | 0.0026 | | 0.0 | 18.73 | 39800 | 0.0026 | | 0.0 | 18.73 | 39810 | 0.0026 | | 0.0001 | 18.74 | 39820 | 0.0026 | | 0.0 | 18.74 | 39830 | 0.0026 | | 0.0 | 18.75 | 39840 | 0.0026 | | 0.0 | 18.75 | 39850 | 0.0026 | | 0.0 | 18.76 | 39860 | 0.0026 | | 0.0 | 18.76 | 39870 | 0.0026 | | 0.0 | 18.77 | 39880 | 0.0026 | | 0.0 | 18.77 | 39890 | 0.0026 | | 0.0 | 18.78 | 39900 | 0.0026 | | 0.0217 | 18.78 | 39910 | 0.0026 | | 0.0 | 18.79 | 39920 | 0.0026 | | 0.0 | 18.79 | 39930 | 0.0026 | | 0.1096 | 18.8 | 39940 | 0.0026 | | 0.0 | 18.8 | 39950 | 0.0026 | | 0.0 | 18.8 | 39960 | 0.0026 | | 0.0004 | 18.81 | 39970 | 0.0026 | | 0.0002 | 18.81 | 39980 | 0.0026 | | 0.0006 | 18.82 | 39990 | 0.0026 | | 0.0 | 18.82 | 40000 | 0.0026 | | 0.0 | 18.83 | 40010 | 0.0026 | | 0.0 | 18.83 | 40020 | 0.0026 | | 0.0 | 18.84 | 40030 | 0.0026 | | 0.0001 | 18.84 | 40040 | 0.0026 | | 0.0002 | 18.85 | 40050 | 0.0026 | | 0.0013 | 18.85 | 40060 | 0.0026 | | 0.0 | 18.86 | 40070 | 0.0026 | | 0.0 | 18.86 | 40080 | 0.0026 | | 0.0 | 18.87 | 40090 | 0.0026 | | 0.0001 | 18.87 | 40100 | 0.0026 | | 0.0002 | 18.88 | 40110 | 0.0026 | | 0.0001 | 18.88 | 40120 | 0.0026 | | 0.0344 | 18.88 | 40130 | 0.0026 | | 0.0 | 18.89 | 40140 | 0.0026 | | 0.0 | 18.89 | 40150 | 0.0026 | | 0.0 | 18.9 | 40160 | 0.0026 | | 0.0 | 18.9 | 40170 | 0.0026 | | 0.0 | 18.91 | 40180 | 0.0026 | | 0.0013 | 18.91 | 40190 | 0.0026 | | 0.0 | 18.92 | 40200 | 0.0026 | | 0.0159 | 18.92 | 40210 | 0.0026 | | 0.0 | 18.93 | 40220 | 0.0026 | | 0.0151 | 18.93 | 40230 | 0.0026 | | 0.0 | 18.94 | 40240 | 0.0026 | | 0.0277 | 18.94 | 40250 | 0.0026 | | 0.0 | 18.95 | 40260 | 0.0026 | | 0.003 | 18.95 | 40270 | 0.0026 | | 0.0001 | 18.96 | 40280 | 0.0026 | | 0.0001 | 18.96 | 40290 | 0.0026 | | 0.0001 | 18.96 | 40300 | 0.0026 | | 0.0 | 18.97 | 40310 | 0.0026 | | 0.0386 | 18.97 | 40320 | 0.0026 | | 0.0 | 18.98 | 40330 | 0.0026 | | 0.0 | 18.98 | 40340 | 0.0026 | | 0.0 | 18.99 | 40350 | 0.0026 | | 0.0001 | 18.99 | 40360 | 0.0026 | | 0.0001 | 19.0 | 40370 | 0.0026 | | 0.0719 | 19.0 | 40380 | 0.0026 | | 0.0 | 19.01 | 40390 | 0.0026 | | 0.0367 | 19.01 | 40400 | 0.0026 | | 0.0 | 19.02 | 40410 | 0.0026 | | 0.0086 | 19.02 | 40420 | 0.0026 | | 0.0016 | 19.03 | 40430 | 0.0026 | | 0.0002 | 19.03 | 40440 | 0.0026 | | 0.0362 | 19.04 | 40450 | 0.0026 | | 0.0 | 19.04 | 40460 | 0.0026 | | 0.0003 | 19.04 | 40470 | 0.0026 | | 0.0 | 19.05 | 40480 | 0.0026 | | 0.0001 | 19.05 | 40490 | 0.0026 | | 0.0006 | 19.06 | 40500 | 0.0026 | | 0.0 | 19.06 | 40510 | 0.0026 | | 0.0731 | 19.07 | 40520 | 0.0026 | | 0.0 | 19.07 | 40530 | 0.0026 | | 0.0 | 19.08 | 40540 | 0.0026 | | 0.0 | 19.08 | 40550 | 0.0026 | | 0.0 | 19.09 | 40560 | 0.0026 | | 0.0002 | 19.09 | 40570 | 0.0026 | | 0.0 | 19.1 | 40580 | 0.0026 | | 0.0355 | 19.1 | 40590 | 0.0026 | | 0.0122 | 19.11 | 40600 | 0.0026 | | 0.0 | 19.11 | 40610 | 0.0026 | | 0.1063 | 19.12 | 40620 | 0.0026 | | 0.0001 | 19.12 | 40630 | 0.0026 | | 0.0 | 19.12 | 40640 | 0.0026 | | 0.0001 | 19.13 | 40650 | 0.0026 | | 0.0 | 19.13 | 40660 | 0.0026 | | 0.0 | 19.14 | 40670 | 0.0026 | | 0.0 | 19.14 | 40680 | 0.0026 | | 0.0757 | 19.15 | 40690 | 0.0026 | | 0.0 | 19.15 | 40700 | 0.0026 | | 0.0 | 19.16 | 40710 | 0.0026 | | 0.0 | 19.16 | 40720 | 0.0026 | | 0.0 | 19.17 | 40730 | 0.0026 | | 0.0 | 19.17 | 40740 | 0.0026 | | 0.0001 | 19.18 | 40750 | 0.0026 | | 0.0342 | 19.18 | 40760 | 0.0026 | | 0.0 | 19.19 | 40770 | 0.0026 | | 0.0001 | 19.19 | 40780 | 0.0026 | | 0.0938 | 19.2 | 40790 | 0.0026 | | 0.0001 | 19.2 | 40800 | 0.0026 | | 0.0 | 19.2 | 40810 | 0.0026 | | 0.0022 | 19.21 | 40820 | 0.0026 | | 0.0011 | 19.21 | 40830 | 0.0026 | | 0.0004 | 19.22 | 40840 | 0.0026 | | 0.0001 | 19.22 | 40850 | 0.0026 | | 0.0846 | 19.23 | 40860 | 0.0026 | | 0.0 | 19.23 | 40870 | 0.0026 | | 0.1391 | 19.24 | 40880 | 0.0026 | | 0.0005 | 19.24 | 40890 | 0.0026 | | 0.0 | 19.25 | 40900 | 0.0026 | | 0.003 | 19.25 | 40910 | 0.0026 | | 0.0002 | 19.26 | 40920 | 0.0026 | | 0.0 | 19.26 | 40930 | 0.0026 | | 0.0006 | 19.27 | 40940 | 0.0026 | | 0.0 | 19.27 | 40950 | 0.0026 | | 0.0007 | 19.28 | 40960 | 0.0026 | | 0.0008 | 19.28 | 40970 | 0.0026 | | 0.0434 | 19.28 | 40980 | 0.0026 | | 0.0 | 19.29 | 40990 | 0.0026 | | 0.0 | 19.29 | 41000 | 0.0026 | | 0.0 | 19.3 | 41010 | 0.0026 | | 0.0 | 19.3 | 41020 | 0.0026 | | 0.0018 | 19.31 | 41030 | 0.0026 | | 0.0 | 19.31 | 41040 | 0.0026 | | 0.0 | 19.32 | 41050 | 0.0026 | | 0.0009 | 19.32 | 41060 | 0.0026 | | 0.0 | 19.33 | 41070 | 0.0026 | | 0.0001 | 19.33 | 41080 | 0.0026 | | 0.0 | 19.34 | 41090 | 0.0026 | | 0.0 | 19.34 | 41100 | 0.0026 | | 0.0 | 19.35 | 41110 | 0.0026 | | 0.0 | 19.35 | 41120 | 0.0026 | | 0.0718 | 19.36 | 41130 | 0.0026 | | 0.0121 | 19.36 | 41140 | 0.0026 | | 0.0777 | 19.36 | 41150 | 0.0026 | | 0.0 | 19.37 | 41160 | 0.0026 | | 0.0746 | 19.37 | 41170 | 0.0026 | | 0.0009 | 19.38 | 41180 | 0.0026 | | 0.0 | 19.38 | 41190 | 0.0026 | | 0.037 | 19.39 | 41200 | 0.0026 | | 0.0 | 19.39 | 41210 | 0.0026 | | 0.0 | 19.4 | 41220 | 0.0026 | | 0.0005 | 19.4 | 41230 | 0.0026 | | 0.0 | 19.41 | 41240 | 0.0026 | | 0.0345 | 19.41 | 41250 | 0.0026 | | 0.0 | 19.42 | 41260 | 0.0026 | | 0.0 | 19.42 | 41270 | 0.0026 | | 0.0 | 19.43 | 41280 | 0.0026 | | 0.0 | 19.43 | 41290 | 0.0026 | | 0.0 | 19.44 | 41300 | 0.0026 | | 0.0006 | 19.44 | 41310 | 0.0026 | | 0.0 | 19.44 | 41320 | 0.0026 | | 0.0 | 19.45 | 41330 | 0.0026 | | 0.0 | 19.45 | 41340 | 0.0026 | | 0.0 | 19.46 | 41350 | 0.0026 | | 0.0 | 19.46 | 41360 | 0.0026 | | 0.0003 | 19.47 | 41370 | 0.0026 | | 0.0048 | 19.47 | 41380 | 0.0026 | | 0.165 | 19.48 | 41390 | 0.0026 | | 0.0 | 19.48 | 41400 | 0.0026 | | 0.0 | 19.49 | 41410 | 0.0026 | | 0.0 | 19.49 | 41420 | 0.0026 | | 0.0 | 19.5 | 41430 | 0.0026 | | 0.0025 | 19.5 | 41440 | 0.0026 | | 0.0 | 19.51 | 41450 | 0.0026 | | 0.0 | 19.51 | 41460 | 0.0026 | | 0.0001 | 19.52 | 41470 | 0.0026 | | 0.0 | 19.52 | 41480 | 0.0026 | | 0.0 | 19.52 | 41490 | 0.0026 | | 0.0006 | 19.53 | 41500 | 0.0026 | | 0.0001 | 19.53 | 41510 | 0.0026 | | 0.0 | 19.54 | 41520 | 0.0026 | | 0.0 | 19.54 | 41530 | 0.0026 | | 0.0 | 19.55 | 41540 | 0.0026 | | 0.0 | 19.55 | 41550 | 0.0026 | | 0.0 | 19.56 | 41560 | 0.0026 | | 0.0 | 19.56 | 41570 | 0.0026 | | 0.0 | 19.57 | 41580 | 0.0026 | | 0.0 | 19.57 | 41590 | 0.0026 | | 0.0001 | 19.58 | 41600 | 0.0026 | | 0.0 | 19.58 | 41610 | 0.0026 | | 0.0 | 19.59 | 41620 | 0.0026 | | 0.0001 | 19.59 | 41630 | 0.0026 | | 0.0 | 19.6 | 41640 | 0.0026 | | 0.0001 | 19.6 | 41650 | 0.0026 | | 0.0003 | 19.6 | 41660 | 0.0026 | | 0.0067 | 19.61 | 41670 | 0.0026 | | 0.0321 | 19.61 | 41680 | 0.0026 | | 0.0002 | 19.62 | 41690 | 0.0026 | | 0.0754 | 19.62 | 41700 | 0.0026 | | 0.0001 | 19.63 | 41710 | 0.0026 | | 0.0 | 19.63 | 41720 | 0.0026 | | 0.0 | 19.64 | 41730 | 0.0026 | | 0.034 | 19.64 | 41740 | 0.0026 | | 0.0 | 19.65 | 41750 | 0.0026 | | 0.0002 | 19.65 | 41760 | 0.0026 | | 0.0021 | 19.66 | 41770 | 0.0026 | | 0.0 | 19.66 | 41780 | 0.0026 | | 0.0 | 19.67 | 41790 | 0.0026 | | 0.0348 | 19.67 | 41800 | 0.0026 | | 0.0 | 19.68 | 41810 | 0.0026 | | 0.0 | 19.68 | 41820 | 0.0026 | | 0.039 | 19.68 | 41830 | 0.0026 | | 0.0001 | 19.69 | 41840 | 0.0026 | | 0.0015 | 19.69 | 41850 | 0.0026 | | 0.0 | 19.7 | 41860 | 0.0026 | | 0.0745 | 19.7 | 41870 | 0.0026 | | 0.0 | 19.71 | 41880 | 0.0026 | | 0.0372 | 19.71 | 41890 | 0.0026 | | 0.0023 | 19.72 | 41900 | 0.0026 | | 0.0002 | 19.72 | 41910 | 0.0026 | | 0.0001 | 19.73 | 41920 | 0.0026 | | 0.0 | 19.73 | 41930 | 0.0026 | | 0.0 | 19.74 | 41940 | 0.0026 | | 0.0001 | 19.74 | 41950 | 0.0026 | | 0.0023 | 19.75 | 41960 | 0.0026 | | 0.0 | 19.75 | 41970 | 0.0026 | | 0.0 | 19.76 | 41980 | 0.0026 | | 0.0088 | 19.76 | 41990 | 0.0026 | | 0.0 | 19.76 | 42000 | 0.0026 | | 0.0 | 19.77 | 42010 | 0.0026 | | 0.0746 | 19.77 | 42020 | 0.0026 | | 0.0001 | 19.78 | 42030 | 0.0026 | | 0.0004 | 19.78 | 42040 | 0.0026 | | 0.0 | 19.79 | 42050 | 0.0026 | | 0.0723 | 19.79 | 42060 | 0.0026 | | 0.0015 | 19.8 | 42070 | 0.0026 | | 0.0 | 19.8 | 42080 | 0.0026 | | 0.0 | 19.81 | 42090 | 0.0026 | | 0.0 | 19.81 | 42100 | 0.0026 | | 0.0 | 19.82 | 42110 | 0.0026 | | 0.0 | 19.82 | 42120 | 0.0026 | | 0.0 | 19.83 | 42130 | 0.0026 | | 0.0 | 19.83 | 42140 | 0.0026 | | 0.0704 | 19.84 | 42150 | 0.0026 | | 0.0 | 19.84 | 42160 | 0.0026 | | 0.0062 | 19.84 | 42170 | 0.0026 | | 0.0827 | 19.85 | 42180 | 0.0026 | | 0.0472 | 19.85 | 42190 | 0.0026 | | 0.0001 | 19.86 | 42200 | 0.0026 | | 0.0702 | 19.86 | 42210 | 0.0026 | | 0.0 | 19.87 | 42220 | 0.0026 | | 0.0062 | 19.87 | 42230 | 0.0026 | | 0.0003 | 19.88 | 42240 | 0.0026 | | 0.0 | 19.88 | 42250 | 0.0026 | | 0.0001 | 19.89 | 42260 | 0.0026 | | 0.0092 | 19.89 | 42270 | 0.0026 | | 0.0339 | 19.9 | 42280 | 0.0026 | | 0.0 | 19.9 | 42290 | 0.0026 | | 0.0 | 19.91 | 42300 | 0.0026 | | 0.0 | 19.91 | 42310 | 0.0026 | | 0.0 | 19.92 | 42320 | 0.0026 | | 0.0 | 19.92 | 42330 | 0.0026 | | 0.0607 | 19.92 | 42340 | 0.0026 | | 0.0018 | 19.93 | 42350 | 0.0026 | | 0.0364 | 19.93 | 42360 | 0.0026 | | 0.0 | 19.94 | 42370 | 0.0026 | | 0.0 | 19.94 | 42380 | 0.0026 | | 0.0001 | 19.95 | 42390 | 0.0026 | | 0.0 | 19.95 | 42400 | 0.0026 | | 0.0 | 19.96 | 42410 | 0.0026 | | 0.0001 | 19.96 | 42420 | 0.0026 | | 0.0893 | 19.97 | 42430 | 0.0026 | | 0.0004 | 19.97 | 42440 | 0.0026 | | 0.0003 | 19.98 | 42450 | 0.0026 | | 0.0002 | 19.98 | 42460 | 0.0026 | | 0.0364 | 19.99 | 42470 | 0.0026 | | 0.0 | 19.99 | 42480 | 0.0026 | | 0.0016 | 20.0 | 42490 | 0.0026 | | 0.0003 | 20.0 | 42500 | 0.0026 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.7 - Tokenizers 0.15.0
supafunnel/bloomz_lora_supafunnel_v1
supafunnel
2024-03-08T05:33:54Z
0
0
transformers
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
null
2024-03-07T16:51:04Z
--- 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]
oopsung/Yi-Ko-ENW-v1
oopsung
2024-03-08T05:29:58Z
60
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-01-11T05:04:00Z
--- license: other --- ## **Model Details** **Model Developers** : oopsung(Sungwoo Park), shleeeee(Seunghyeon Lee) **Input** Models input text only. **Output** Models generate text only. **Base Model** [**beomi/Yi-Ko-6B**](https://huggingface.co/beomi/Yi-Ko-6B) use SFT to train model
oopsung/Yi-Ko-ENCdpo
oopsung
2024-03-08T05:29:47Z
62
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-01-08T05:03:05Z
--- license: other --- ## **Model Details** **Model Developers** : oopsung(Sungwoo Park), shleeeee(Seunghyeon Lee) **Input** Models input text only. **Output** Models generate text only. **Base Model** [**beomi/Yi-Ko-6B**](https://huggingface.co/beomi/Yi-Ko-6B) use SFT and DPO train model
oopsung/Yi-Ko-ENWdpo-v1
oopsung
2024-03-08T05:29:23Z
59
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-01-16T01:17:57Z
--- license: other --- ## **Model Details** **Model Developers** :  oopsung(Sungwoo Park), shleeeee(Seunghyeon Lee) **Input** Models input text only. **Output** Models generate text only. **Base Model** [**beomi/Yi-Ko-6B**](https://huggingface.co/beomi/Yi-Ko-6B) use SFT and DPO to train model
mlx-community/Qwen1.5-72B-Chat-4bit
mlx-community
2024-03-08T05:28:38Z
14
2
mlx
[ "mlx", "safetensors", "qwen2", "chat", "text-generation", "conversational", "en", "license:other", "region:us" ]
text-generation
2024-03-07T08:32:10Z
--- language: - en license: other tags: - chat - mlx license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE pipeline_tag: text-generation --- # mlx-community/Qwen1.5-72B-Chat-4bit This model was converted to MLX format from [`Qwen/Qwen1.5-72B-Chat`](). Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-72B-Chat) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Qwen1.5-72B-Chat-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
diskya/Reinforce-Pixelcopter-PLE-v0
diskya
2024-03-08T05:23:55Z
0
0
null
[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T05:22:29Z
--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 52.00 +/- 33.42 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
hmone231/gpt2-qa-v1-model
hmone231
2024-03-08T05:23:26Z
2
0
peft
[ "peft", "arxiv:1910.09700", "base_model:EleutherAI/gpt-j-6b", "base_model:adapter:EleutherAI/gpt-j-6b", "region:us" ]
null
2024-03-08T05:21:20Z
--- library_name: peft base_model: EleutherAI/gpt-j-6B --- # 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. --> - **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] ### Framework versions - PEFT 0.9.0
sai17/cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epochs
sai17
2024-03-08T05:22:27Z
78
0
transformers
[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "base_model:finetune:microsoft/swin-tiny-patch4-window7-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]
image-classification
2024-03-04T05:31:14Z
--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epochs results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.5946802405369663 --- <!-- 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. --> # cards_bottom_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epochs This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.0025 - Accuracy: 0.5947 ## 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: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:------:|:---------------:|:--------:| | 1.6956 | 1.0 | 1252 | 1.4843 | 0.3970 | | 1.5633 | 2.0 | 2504 | 1.2584 | 0.4782 | | 1.5568 | 3.0 | 3756 | 1.1976 | 0.4918 | | 1.4727 | 4.0 | 5009 | 1.1884 | 0.4916 | | 1.468 | 5.0 | 6261 | 1.1909 | 0.4889 | | 1.4663 | 6.0 | 7513 | 1.1263 | 0.5288 | | 1.4409 | 7.0 | 8765 | 1.0967 | 0.5441 | | 1.4329 | 8.0 | 10018 | 1.0976 | 0.5388 | | 1.4842 | 9.0 | 11270 | 1.1076 | 0.5315 | | 1.4253 | 10.0 | 12522 | 1.0634 | 0.5511 | | 1.3888 | 11.0 | 13774 | 1.0489 | 0.5634 | | 1.3681 | 12.0 | 15027 | 1.0663 | 0.5567 | | 1.3802 | 13.0 | 16279 | 1.0304 | 0.5667 | | 1.4016 | 14.0 | 17531 | 1.0592 | 0.5518 | | 1.376 | 15.0 | 18783 | 1.0080 | 0.5776 | | 1.3539 | 16.0 | 20036 | 1.0103 | 0.5742 | | 1.3725 | 17.0 | 21288 | 1.0261 | 0.5636 | | 1.3104 | 18.0 | 22540 | 1.0304 | 0.5686 | | 1.3448 | 19.0 | 23792 | 1.0184 | 0.5687 | | 1.3479 | 20.0 | 25045 | 0.9968 | 0.5809 | | 1.3517 | 21.0 | 26297 | 1.1350 | 0.5182 | | 1.3367 | 22.0 | 27549 | 0.9835 | 0.5867 | | 1.3002 | 23.0 | 28801 | 1.0193 | 0.5736 | | 1.3238 | 24.0 | 30054 | 0.9820 | 0.5875 | | 1.2865 | 25.0 | 31306 | 1.0267 | 0.5617 | | 1.3029 | 26.0 | 32558 | 1.0086 | 0.5730 | | 1.3173 | 27.0 | 33810 | 0.9750 | 0.5924 | | 1.297 | 28.0 | 35063 | 0.9851 | 0.5848 | | 1.3105 | 29.0 | 36315 | 1.0306 | 0.5685 | | 1.3477 | 30.0 | 37567 | 0.9977 | 0.5845 | | 1.2565 | 31.0 | 38819 | 0.9900 | 0.5851 | | 1.2657 | 32.0 | 40072 | 1.0137 | 0.5862 | | 1.2911 | 33.0 | 41324 | 0.9947 | 0.5889 | | 1.2539 | 34.0 | 42576 | 0.9821 | 0.5914 | | 1.2441 | 35.0 | 43828 | 1.0296 | 0.5763 | | 1.2176 | 36.0 | 45081 | 1.0350 | 0.5806 | | 1.25 | 37.0 | 46333 | 1.0195 | 0.5779 | | 1.2647 | 38.0 | 47585 | 1.0021 | 0.5903 | | 1.2428 | 39.0 | 48837 | 1.0087 | 0.5892 | | 1.2364 | 40.0 | 50090 | 1.0025 | 0.5947 | | 1.2083 | 41.0 | 51342 | 1.0427 | 0.5862 | | 1.2002 | 42.0 | 52594 | 1.0303 | 0.5878 | | 1.2071 | 43.0 | 53846 | 1.0190 | 0.5909 | | 1.1536 | 44.0 | 55099 | 1.0314 | 0.5920 | | 1.2029 | 45.0 | 56351 | 1.0570 | 0.5839 | | 1.2249 | 46.0 | 57603 | 1.0508 | 0.5828 | | 1.1913 | 47.0 | 58855 | 1.0493 | 0.5853 | | 1.1938 | 48.0 | 60108 | 1.0575 | 0.5857 | | 1.1724 | 49.0 | 61360 | 1.0700 | 0.5905 | | 1.1536 | 50.0 | 62612 | 1.0841 | 0.5853 | | 1.1239 | 51.0 | 63864 | 1.0803 | 0.5865 | | 1.1743 | 52.0 | 65117 | 1.0864 | 0.5880 | | 1.1414 | 53.0 | 66369 | 1.1224 | 0.5819 | | 1.1411 | 54.0 | 67621 | 1.1316 | 0.5780 | | 1.1029 | 55.0 | 68873 | 1.1070 | 0.5860 | | 1.1353 | 56.0 | 70126 | 1.1247 | 0.5847 | | 1.1293 | 57.0 | 71378 | 1.1279 | 0.5805 | | 1.1335 | 58.0 | 72630 | 1.1482 | 0.5812 | | 1.1157 | 59.0 | 73882 | 1.1960 | 0.5674 | | 1.0891 | 60.0 | 75135 | 1.1414 | 0.5848 | | 1.1299 | 61.0 | 76387 | 1.1658 | 0.5790 | | 1.0828 | 62.0 | 77639 | 1.1753 | 0.5806 | | 1.0866 | 63.0 | 78891 | 1.1767 | 0.5755 | | 1.0721 | 64.0 | 80144 | 1.1861 | 0.5808 | | 1.0682 | 65.0 | 81396 | 1.2083 | 0.5749 | | 1.0747 | 66.0 | 82648 | 1.2204 | 0.5755 | | 1.0902 | 67.0 | 83900 | 1.2175 | 0.5750 | | 1.0381 | 68.0 | 85153 | 1.2445 | 0.5738 | | 1.049 | 69.0 | 86405 | 1.2674 | 0.5707 | | 1.0501 | 70.0 | 87657 | 1.2602 | 0.5740 | | 1.0117 | 71.0 | 88909 | 1.2549 | 0.5687 | | 1.0179 | 72.0 | 90162 | 1.3010 | 0.5690 | | 1.0788 | 73.0 | 91414 | 1.2723 | 0.5726 | | 1.0234 | 74.0 | 92666 | 1.3162 | 0.5717 | | 1.0325 | 75.0 | 93918 | 1.3136 | 0.5692 | | 1.0079 | 76.0 | 95171 | 1.3337 | 0.5655 | | 1.058 | 77.0 | 96423 | 1.3171 | 0.5719 | | 0.9968 | 78.0 | 97675 | 1.3470 | 0.5693 | | 1.0217 | 79.0 | 98927 | 1.3418 | 0.5733 | | 1.0124 | 80.0 | 100180 | 1.3518 | 0.5700 | | 0.9823 | 81.0 | 101432 | 1.3646 | 0.5700 | | 0.9627 | 82.0 | 102684 | 1.3658 | 0.5686 | | 0.9773 | 83.0 | 103936 | 1.3811 | 0.5674 | | 0.9855 | 84.0 | 105189 | 1.4082 | 0.5638 | | 0.9928 | 85.0 | 106441 | 1.3877 | 0.5612 | | 1.0025 | 86.0 | 107693 | 1.3925 | 0.5653 | | 0.9583 | 87.0 | 108945 | 1.4313 | 0.5625 | | 0.977 | 88.0 | 110198 | 1.4153 | 0.5651 | | 0.9825 | 89.0 | 111450 | 1.4426 | 0.5619 | | 0.9315 | 90.0 | 112702 | 1.4376 | 0.5643 | | 0.8916 | 91.0 | 113954 | 1.4630 | 0.5618 | | 0.9495 | 92.0 | 115207 | 1.4501 | 0.5627 | | 0.9372 | 93.0 | 116459 | 1.4606 | 0.5622 | | 0.9284 | 94.0 | 117711 | 1.4725 | 0.5608 | | 0.9266 | 95.0 | 118963 | 1.4680 | 0.5607 | | 0.8858 | 96.0 | 120216 | 1.4705 | 0.5626 | | 0.9025 | 97.0 | 121468 | 1.4818 | 0.5616 | | 0.902 | 98.0 | 122720 | 1.4871 | 0.5606 | | 0.8961 | 99.0 | 123972 | 1.4881 | 0.5612 | | 0.9204 | 99.98 | 125200 | 1.4894 | 0.5609 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1+cu117 - Datasets 2.17.0 - Tokenizers 0.13.3
LoneStriker/Kaiju-11B-8.0bpw-h8-exl2
LoneStriker
2024-03-08T05:09:05Z
4
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T05:04:40Z
--- license: cc-by-nc-4.0 language: - en --- Included in this repo is the full precision model for Kaiju-11B (ﾉ≧∀≦)ﾉ ‥…━━━━━━━━━━━━━★ ||| ╲/\╭[ ᴼᴼ ౪ ᴼᴼ]╮/\╱\ Hiya! This is an experiment using Gryphe's [MergeMonster](https://github.com/Gryphe/MergeMonster). I decided to try and reduce what the community calls 'GPT-isms' or GPT Slop, Solar is a good model but does have fair share of positivity bias and 'slop' in roleplays. I used my friend [Sao](https://huggingface.co/Sao10K)'s models as bases as they are pretty popular, along with Kuromitsu and the popular Instruct-Uncensored tune. Alpaca Format should be fine as it is universal, Vicuna Format should work too. Universal-Light preset in SillyTavern is pretty nice too. :) 💜 I hope this model may be useful to you 💜 *** Merge Details Below: <details><summary>See Merge Config</summary> ``` ----------------------------------------------------------------------------------------------------- | Type | Phrase | Context | Raw Prob* | Used Prob** | Change | ----------------------------------------------------------------------------------------------------- | BAD | anticipation | Her body quivers with | 9.99850% | 119.98% | -54.02% | | BAD | anticipation | The atmosphere is thic.. | 8.82392% | 105.89% | -32.13% | | BAD | unwavering | Filled with an | 0.09003% | 1.08% | -0.06% | | BAD | determination | Her eyes were filled w.. | 0.19863% | 2.38% | -0.26% | | BAD | determination | Her stubbornness only .. | 7.17110% | 86.05% | -39.86% | | BAD | whisper | Her voice barely above.. | 96.55492% | 1158.66% | -8.91% | | BAD | spine | shivers down her | 85.57597% | 1026.91% | -66.19% | | BAD | sends shivers | The thrill of the act | 0.00230% | 0.03% | -0.00% | | BAD | ministrations | She moans and twitches.. | 1.35264% | 16.23% | -10.49% | | BAD | legs | wraps her | 2.45741% | 29.49% | -10.58% | | BAD | imposing figure | He had an | 0.00356% | 0.04% | +0.00% | | BAD | shared challenges | Their bond strengthene.. | 0.10075% | 1.21% | -0.03% | | BAD | bond | forged a | 1.78930% | 21.47% | -9.07% | | BAD | bond | an unspoken | 4.33001% | 51.96% | -28.17% | | BAD | enhance our expe.. | I'm excited to see how | 0.00000% | 0.00% | +0.00% | | BAD | sense of vulnera.. | create a | 0.00003% | 0.00% | -0.00% | | BAD | dimensions of in.. | explore new | 0.00047% | 0.01% | -0.00% | | BAD | deepening our co.. | while | 0.00003% | 0.00% | -0.00% | | BAD | shared experiences | through | 0.00469% | 0.06% | -0.00% | | BAD | societal expecta.. | that transcend | 0.00170% | 0.02% | -0.00% | | BAD | conventional bou.. | that defy | 0.03593% | 0.43% | +0.04% | | BAD | conventional bou.. | and defy | 0.00410% | 0.05% | +0.01% | | BAD | open communication | an environment | 0.00000% | 0.00% | +0.00% | | BAD | emotional vulner.. | an environment | 0.00000% | 0.00% | +0.00% | | BAD | heightens our co.. | touch and the anticipa.. | 0.00000% | 0.00% | +0.00% | | BAD | sensations you'r.. | I'm enjoying | 0.00000% | 0.00% | -0.00% | | BAD | is truly arousing | attention to detail | 0.00000% | 0.00% | +0.00% | | BAD | is truly arousing | way you explore my body | 0.00001% | 0.00% | +0.00% | | BAD | challenge presen.. | my resolve unwavering .. | 0.00000% | 0.00% | +0.00% | | BAD | humble vessel | surrendering to the ex.. | 0.00000% | 0.00% | +0.00% | | BAD | bond | cherishing the unique | 1.37498% | 16.50% | +1.21% | | BAD | bond | special | 0.05834% | 0.70% | +0.01% | | BAD | grows stronger w.. | bond | 0.00000% | 0.00% | +0.00% | | BAD | that cannot be b.. | bond | 0.00000% | 0.00% | -0.00% | | BAD | becomes unbreaka.. | bond | 0.00000% | 0.00% | -0.00% | | BAD | grew stronger wi.. | bond | 0.00000% | 0.00% | +0.00% | | GOOD | The apple is in .. | Question: If I'm in th.. | 78.38934% | 78.39% | -10.79% | ------------------------------------------------------------------------------------------------------ | Totals | 298.32% | 2717.54% | -269.30% | ------------------------------------------------------------------------------------------------------ ``` * = Unweighted, raw probability - ** = Probability after weight adjustments ``` -------- MERGE COMPOSITION --------- Fimbulvetr-11B-v2-Test-14: 0.50 KuroMitsu-11B: 0.18 Fimbulvetr-10.7B-v1: 0.17 SOLAR-10.7B-Instruct-v1.0-uncensored: 0.10 Solstice-11B-v1: 0.05 ``` </details><br>
MaiiaCompsolutions/industry_classifier_finance_full_descr
MaiiaCompsolutions
2024-03-08T05:05:24Z
175
0
transformers
[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
text-classification
2024-03-08T05:04:44Z
--- 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. 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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]
LoneStriker/Kaiju-11B-6.0bpw-h6-exl2
LoneStriker
2024-03-08T05:04:39Z
3
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T05:01:16Z
--- license: cc-by-nc-4.0 language: - en --- Included in this repo is the full precision model for Kaiju-11B (ﾉ≧∀≦)ﾉ ‥…━━━━━━━━━━━━━★ ||| ╲/\╭[ ᴼᴼ ౪ ᴼᴼ]╮/\╱\ Hiya! This is an experiment using Gryphe's [MergeMonster](https://github.com/Gryphe/MergeMonster). I decided to try and reduce what the community calls 'GPT-isms' or GPT Slop, Solar is a good model but does have fair share of positivity bias and 'slop' in roleplays. I used my friend [Sao](https://huggingface.co/Sao10K)'s models as bases as they are pretty popular, along with Kuromitsu and the popular Instruct-Uncensored tune. Alpaca Format should be fine as it is universal, Vicuna Format should work too. Universal-Light preset in SillyTavern is pretty nice too. :) 💜 I hope this model may be useful to you 💜 *** Merge Details Below: <details><summary>See Merge Config</summary> ``` ----------------------------------------------------------------------------------------------------- | Type | Phrase | Context | Raw Prob* | Used Prob** | Change | ----------------------------------------------------------------------------------------------------- | BAD | anticipation | Her body quivers with | 9.99850% | 119.98% | -54.02% | | BAD | anticipation | The atmosphere is thic.. | 8.82392% | 105.89% | -32.13% | | BAD | unwavering | Filled with an | 0.09003% | 1.08% | -0.06% | | BAD | determination | Her eyes were filled w.. | 0.19863% | 2.38% | -0.26% | | BAD | determination | Her stubbornness only .. | 7.17110% | 86.05% | -39.86% | | BAD | whisper | Her voice barely above.. | 96.55492% | 1158.66% | -8.91% | | BAD | spine | shivers down her | 85.57597% | 1026.91% | -66.19% | | BAD | sends shivers | The thrill of the act | 0.00230% | 0.03% | -0.00% | | BAD | ministrations | She moans and twitches.. | 1.35264% | 16.23% | -10.49% | | BAD | legs | wraps her | 2.45741% | 29.49% | -10.58% | | BAD | imposing figure | He had an | 0.00356% | 0.04% | +0.00% | | BAD | shared challenges | Their bond strengthene.. | 0.10075% | 1.21% | -0.03% | | BAD | bond | forged a | 1.78930% | 21.47% | -9.07% | | BAD | bond | an unspoken | 4.33001% | 51.96% | -28.17% | | BAD | enhance our expe.. | I'm excited to see how | 0.00000% | 0.00% | +0.00% | | BAD | sense of vulnera.. | create a | 0.00003% | 0.00% | -0.00% | | BAD | dimensions of in.. | explore new | 0.00047% | 0.01% | -0.00% | | BAD | deepening our co.. | while | 0.00003% | 0.00% | -0.00% | | BAD | shared experiences | through | 0.00469% | 0.06% | -0.00% | | BAD | societal expecta.. | that transcend | 0.00170% | 0.02% | -0.00% | | BAD | conventional bou.. | that defy | 0.03593% | 0.43% | +0.04% | | BAD | conventional bou.. | and defy | 0.00410% | 0.05% | +0.01% | | BAD | open communication | an environment | 0.00000% | 0.00% | +0.00% | | BAD | emotional vulner.. | an environment | 0.00000% | 0.00% | +0.00% | | BAD | heightens our co.. | touch and the anticipa.. | 0.00000% | 0.00% | +0.00% | | BAD | sensations you'r.. | I'm enjoying | 0.00000% | 0.00% | -0.00% | | BAD | is truly arousing | attention to detail | 0.00000% | 0.00% | +0.00% | | BAD | is truly arousing | way you explore my body | 0.00001% | 0.00% | +0.00% | | BAD | challenge presen.. | my resolve unwavering .. | 0.00000% | 0.00% | +0.00% | | BAD | humble vessel | surrendering to the ex.. | 0.00000% | 0.00% | +0.00% | | BAD | bond | cherishing the unique | 1.37498% | 16.50% | +1.21% | | BAD | bond | special | 0.05834% | 0.70% | +0.01% | | BAD | grows stronger w.. | bond | 0.00000% | 0.00% | +0.00% | | BAD | that cannot be b.. | bond | 0.00000% | 0.00% | -0.00% | | BAD | becomes unbreaka.. | bond | 0.00000% | 0.00% | -0.00% | | BAD | grew stronger wi.. | bond | 0.00000% | 0.00% | +0.00% | | GOOD | The apple is in .. | Question: If I'm in th.. | 78.38934% | 78.39% | -10.79% | ------------------------------------------------------------------------------------------------------ | Totals | 298.32% | 2717.54% | -269.30% | ------------------------------------------------------------------------------------------------------ ``` * = Unweighted, raw probability - ** = Probability after weight adjustments ``` -------- MERGE COMPOSITION --------- Fimbulvetr-11B-v2-Test-14: 0.50 KuroMitsu-11B: 0.18 Fimbulvetr-10.7B-v1: 0.17 SOLAR-10.7B-Instruct-v1.0-uncensored: 0.10 Solstice-11B-v1: 0.05 ``` </details><br>
Croolch/q-FrozenLake-v1-4x4-Slippery
Croolch
2024-03-08T05:03:18Z
0
0
null
[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T04:29:00Z
--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.63 +/- 0.48 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="Croolch/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
LoneStriker/Kaiju-11B-5.0bpw-h6-exl2
LoneStriker
2024-03-08T05:01:15Z
3
0
transformers
[ "transformers", "safetensors", "llama", "text-generation", "en", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]
text-generation
2024-03-08T04:58:23Z
--- license: cc-by-nc-4.0 language: - en --- Included in this repo is the full precision model for Kaiju-11B (ﾉ≧∀≦)ﾉ ‥…━━━━━━━━━━━━━★ ||| ╲/\╭[ ᴼᴼ ౪ ᴼᴼ]╮/\╱\ Hiya! This is an experiment using Gryphe's [MergeMonster](https://github.com/Gryphe/MergeMonster). I decided to try and reduce what the community calls 'GPT-isms' or GPT Slop, Solar is a good model but does have fair share of positivity bias and 'slop' in roleplays. I used my friend [Sao](https://huggingface.co/Sao10K)'s models as bases as they are pretty popular, along with Kuromitsu and the popular Instruct-Uncensored tune. Alpaca Format should be fine as it is universal, Vicuna Format should work too. Universal-Light preset in SillyTavern is pretty nice too. :) 💜 I hope this model may be useful to you 💜 *** Merge Details Below: <details><summary>See Merge Config</summary> ``` ----------------------------------------------------------------------------------------------------- | Type | Phrase | Context | Raw Prob* | Used Prob** | Change | ----------------------------------------------------------------------------------------------------- | BAD | anticipation | Her body quivers with | 9.99850% | 119.98% | -54.02% | | BAD | anticipation | The atmosphere is thic.. | 8.82392% | 105.89% | -32.13% | | BAD | unwavering | Filled with an | 0.09003% | 1.08% | -0.06% | | BAD | determination | Her eyes were filled w.. | 0.19863% | 2.38% | -0.26% | | BAD | determination | Her stubbornness only .. | 7.17110% | 86.05% | -39.86% | | BAD | whisper | Her voice barely above.. | 96.55492% | 1158.66% | -8.91% | | BAD | spine | shivers down her | 85.57597% | 1026.91% | -66.19% | | BAD | sends shivers | The thrill of the act | 0.00230% | 0.03% | -0.00% | | BAD | ministrations | She moans and twitches.. | 1.35264% | 16.23% | -10.49% | | BAD | legs | wraps her | 2.45741% | 29.49% | -10.58% | | BAD | imposing figure | He had an | 0.00356% | 0.04% | +0.00% | | BAD | shared challenges | Their bond strengthene.. | 0.10075% | 1.21% | -0.03% | | BAD | bond | forged a | 1.78930% | 21.47% | -9.07% | | BAD | bond | an unspoken | 4.33001% | 51.96% | -28.17% | | BAD | enhance our expe.. | I'm excited to see how | 0.00000% | 0.00% | +0.00% | | BAD | sense of vulnera.. | create a | 0.00003% | 0.00% | -0.00% | | BAD | dimensions of in.. | explore new | 0.00047% | 0.01% | -0.00% | | BAD | deepening our co.. | while | 0.00003% | 0.00% | -0.00% | | BAD | shared experiences | through | 0.00469% | 0.06% | -0.00% | | BAD | societal expecta.. | that transcend | 0.00170% | 0.02% | -0.00% | | BAD | conventional bou.. | that defy | 0.03593% | 0.43% | +0.04% | | BAD | conventional bou.. | and defy | 0.00410% | 0.05% | +0.01% | | BAD | open communication | an environment | 0.00000% | 0.00% | +0.00% | | BAD | emotional vulner.. | an environment | 0.00000% | 0.00% | +0.00% | | BAD | heightens our co.. | touch and the anticipa.. | 0.00000% | 0.00% | +0.00% | | BAD | sensations you'r.. | I'm enjoying | 0.00000% | 0.00% | -0.00% | | BAD | is truly arousing | attention to detail | 0.00000% | 0.00% | +0.00% | | BAD | is truly arousing | way you explore my body | 0.00001% | 0.00% | +0.00% | | BAD | challenge presen.. | my resolve unwavering .. | 0.00000% | 0.00% | +0.00% | | BAD | humble vessel | surrendering to the ex.. | 0.00000% | 0.00% | +0.00% | | BAD | bond | cherishing the unique | 1.37498% | 16.50% | +1.21% | | BAD | bond | special | 0.05834% | 0.70% | +0.01% | | BAD | grows stronger w.. | bond | 0.00000% | 0.00% | +0.00% | | BAD | that cannot be b.. | bond | 0.00000% | 0.00% | -0.00% | | BAD | becomes unbreaka.. | bond | 0.00000% | 0.00% | -0.00% | | BAD | grew stronger wi.. | bond | 0.00000% | 0.00% | +0.00% | | GOOD | The apple is in .. | Question: If I'm in th.. | 78.38934% | 78.39% | -10.79% | ------------------------------------------------------------------------------------------------------ | Totals | 298.32% | 2717.54% | -269.30% | ------------------------------------------------------------------------------------------------------ ``` * = Unweighted, raw probability - ** = Probability after weight adjustments ``` -------- MERGE COMPOSITION --------- Fimbulvetr-11B-v2-Test-14: 0.50 KuroMitsu-11B: 0.18 Fimbulvetr-10.7B-v1: 0.17 SOLAR-10.7B-Instruct-v1.0-uncensored: 0.10 Solstice-11B-v1: 0.05 ``` </details><br>
KUKU0404/output
KUKU0404
2024-03-08T05:00:55Z
4
0
diffusers
[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:runwayml/stable-diffusion-v1-5", "base_model:finetune:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
text-to-image
2024-03-08T03:11:49Z
--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers inference: true base_model: runwayml/stable-diffusion-v1-5 instance_prompt: a photo of sks dog --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - KUKU0404/output This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
dlwlgus53/ppo-LunarLander-v2
dlwlgus53
2024-03-08T04:58:21Z
1
0
stable-baselines3
[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
reinforcement-learning
2024-03-08T04:24:18Z
--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 265.56 +/- 15.76 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
KarmaCST/nllb-200-distilled-600M-dz-to-en
KarmaCST
2024-03-08T04:57:24Z
57
0
transformers
[ "transformers", "pytorch", "tensorboard", "m2m_100", "text2text-generation", "translation", "generated_from_trainer", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
translation
2023-04-13T16:05:25Z
--- license: cc-by-nc-4.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: nllb-200-distilled-600M-dz-to-en 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. --> # nllb-200-distilled-600M-dz-to-en This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7727 - Bleu: 42.8708 - Gen Len: 13.335 ## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:| | 0.9294 | 1.0 | 1688 | 0.8364 | 39.0175 | 13.2637 | | 0.7929 | 2.0 | 3376 | 0.7893 | 40.9994 | 13.303 | | 0.7069 | 3.0 | 5064 | 0.7737 | 42.4125 | 13.292 | | 0.6482 | 4.0 | 6752 | 0.7701 | 42.826 | 13.3287 | | 0.6231 | 5.0 | 8440 | 0.7727 | 42.8708 | 13.335 | ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu118 - Datasets 2.11.0 - Tokenizers 0.13.3