librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-13 12:28:20	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 518 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-13 12:26:25	card stringlengths 11 1.01M
Mouwiya/image-model-demo	Mouwiya	2024-04-19T15:02:55Z	9	0	transformers	[ "transformers", "safetensors", "blip", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-04-19T14:55:51Z	--- 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]
tawreck-hasaballah/whisper-small-eg	tawreck-hasaballah	2024-04-19T14:57:19Z	17	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "eg", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-25T00:14:31Z	--- language: - eg license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Small Eg - Tariq Hasaballah 100330 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Eg - Tariq Hasaballah 100330 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ASR-EGARBCSC: AN EGYPTIAN ARABIC CONVERSATIONAL SPEECH CORPUS dataset. It achieves the following results on the evaluation set: - Loss: 0.5626 - Wer: 47.4960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.7309 \| 0.7267 \| 125 \| 0.5984 \| 52.4512 \| \| 0.3608 \| 1.4535 \| 250 \| 0.5488 \| 48.6031 \| \| 0.1789 \| 2.1802 \| 375 \| 0.5537 \| 46.5999 \| \| 0.1844 \| 2.9070 \| 500 \| 0.5626 \| 47.4960 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
carloswbarros/QAmembertTest	carloswbarros	2024-04-19T14:56:07Z	5	0	transformers	[ "transformers", "safetensors", "camembert", "question-answering", "generated_from_trainer", "dataset:generator", "base_model:CATIE-AQ/QAmembert", "base_model:finetune:CATIE-AQ/QAmembert", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]	question-answering	2024-04-19T09:11:05Z	--- license: cc-by-4.0 base_model: CATIE-AQ/QAmembert tags: - generated_from_trainer datasets: - generator model-index: - name: QAmembertTest 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. --> # QAmembertTest This model is a fine-tuned version of [CATIE-AQ/QAmembert](https://huggingface.co/CATIE-AQ/QAmembert) 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 0 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
ipurwadi/swin-tiny-patch4-window7-224-finetuned-eurosat	ipurwadi	2024-04-19T14:54:24Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "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	2024-04-18T05:55:57Z	--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9093137254901961 --- <!-- 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. --> # swin-tiny-patch4-window7-224-finetuned-eurosat 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: 0.2963 - Accuracy: 0.9093 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4987 \| 1.0 \| 86 \| 0.4083 \| 0.8693 \| \| 0.3837 \| 2.0 \| 172 \| 0.4003 \| 0.8611 \| \| 0.3595 \| 3.0 \| 258 \| 0.2963 \| 0.9093 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2 - Datasets 2.19.0 - Tokenizers 0.19.1
Liya009/code-search-net-tokenizer	Liya009	2024-04-19T14:53:21Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-19T14:52:24Z	--- 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]
cstr/Spaetzle-v69-7b	cstr	2024-04-19T14:51:43Z	48	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "de", "en", "base_model:DRXD1000/Phoenix-7B", "base_model:merge:DRXD1000/Phoenix-7B", "base_model:DiscoResearch/DiscoLM_German_7b_v1", "base_model:merge:DiscoResearch/DiscoLM_German_7b_v1", "base_model:LeoLM/leo-mistral-hessianai-7b", "base_model:merge:LeoLM/leo-mistral-hessianai-7b", "base_model:OpenPipe/mistral-ft-optimized-1227", "base_model:merge:OpenPipe/mistral-ft-optimized-1227", "base_model:PetroGPT/WestSeverus-7B-DPO-v2", "base_model:merge:PetroGPT/WestSeverus-7B-DPO-v2", "base_model:ResplendentAI/Flora_DPO_7B", "base_model:merge:ResplendentAI/Flora_DPO_7B", "base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral", "base_model:merge:VAGOsolutions/SauerkrautLM-7b-v1-mistral", "base_model:abideen/AlphaMonarch-dora", "base_model:merge:abideen/AlphaMonarch-dora", "base_model:cognitivecomputations/openchat-3.5-0106-laser", "base_model:merge:cognitivecomputations/openchat-3.5-0106-laser", "base_model:flemmingmiguel/NeuDist-Ro-7B", "base_model:merge:flemmingmiguel/NeuDist-Ro-7B", "base_model:malteos/hermeo-7b", "base_model:merge:malteos/hermeo-7b", "base_model:mayflowergmbh/Wiedervereinigung-7b-dpo", "base_model:merge:mayflowergmbh/Wiedervereinigung-7b-dpo", "base_model:occiglot/occiglot-7b-de-en-instruct", "base_model:merge:occiglot/occiglot-7b-de-en-instruct", "base_model:yleo/EmertonMonarch-7B", "base_model:merge:yleo/EmertonMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-17T03:40:34Z	--- tags: - merge - mergekit - lazymergekit language: - de - en base_model: - abideen/AlphaMonarch-dora - mayflowergmbh/Wiedervereinigung-7b-dpo - flemmingmiguel/NeuDist-Ro-7B - ResplendentAI/Flora_DPO_7B - yleo/EmertonMonarch-7B - occiglot/occiglot-7b-de-en-instruct - OpenPipe/mistral-ft-optimized-1227 - DiscoResearch/DiscoLM_German_7b_v1 - LeoLM/leo-mistral-hessianai-7b - DRXD1000/Phoenix - VAGOsolutions/SauerkrautLM-7b-v1-mistral - malteos/hermeo-7b - FelixChao/WestSeverus-7B-DPO-v2 - cognitivecomputations/openchat-3.5-0106-laser license: cc-by-nc-4.0 --- # Spaetzle-v69-7b This is a progressive (mostly dare-ties, but also slerp) merge with the intention of a suitable compromise for English and German local tasks. There is also a 4q_k_m quantized [GGUF](https://huggingface.co/cstr/Spaetzle-v69-7b-GGUF). It should work sufficiently well with ChatML prompt template (for all merged models should have seen ChatML prompts at least in DPO stage). ## Evaluation Benchmark scores are not the possible optimum, as the model attempts a compromise with a number of parameters, like German language performance, instruction following, reasoning capabilities, robustness (so far, i did not encounter inserted tokens, e.g.), model licensing, and other criteria. Nevertheless, they are not too bad: It achieves (running quantized) in - German EQ Bench: Score (v2_de): 62.59 (Parseable: 171.0). - English EQ Bench: Score (v2): 76.43 (Parseable: 171.0). [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_cstr__Spaetzle-v69-7b) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|72.87\| \|AI2 Reasoning Challenge (25-Shot)\|69.54\| \|HellaSwag (10-Shot) \|86.77\| \|MMLU (5-Shot) \|64.63\| \|TruthfulQA (0-shot) \|65.61\| \|Winogrande (5-shot) \|81.93\| \|GSM8k (5-shot) \|68.76\| Nous benchmark results: \| Model \|AGIEval\|GPT4All\|TruthfulQA\|Bigbench\|Average\| \|--------------------------------------------------------------\|------:\|------:\|---------:\|-------:\|------:\| \|[Spaetzle-v69-7b](https://huggingface.co/cstr/Spaetzle-v69-7b)\| 44.48\| 75.84\| 66.15\| 46.59\| 58.27\| ### AGIEval \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------\|------:\|--------\|----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|25.98\|± \| 2.76\| \| \| \|acc_norm\|23.62\|± \| 2.67\| \|agieval_logiqa_en \| 0\|acc \|39.78\|± \| 1.92\| \| \| \|acc_norm\|39.48\|± \| 1.92\| \|agieval_lsat_ar \| 0\|acc \|23.48\|± \| 2.80\| \| \| \|acc_norm\|23.91\|± \| 2.82\| \|agieval_lsat_lr \| 0\|acc \|50.00\|± \| 2.22\| \| \| \|acc_norm\|51.76\|± \| 2.21\| \|agieval_lsat_rc \| 0\|acc \|63.94\|± \| 2.93\| \| \| \|acc_norm\|64.31\|± \| 2.93\| \|agieval_sat_en \| 0\|acc \|76.70\|± \| 2.95\| \| \| \|acc_norm\|77.67\|± \| 2.91\| \|agieval_sat_en_without_passage\| 0\|acc \|46.12\|± \| 3.48\| \| \| \|acc_norm\|44.17\|± \| 3.47\| \|agieval_sat_math \| 0\|acc \|34.09\|± \| 3.20\| \| \| \|acc_norm\|30.91\|± \| 3.12\| Average: 44.48% ### GPT4All \| Task \|Version\| Metric \|Value\| \|Stderr\| \|-------------\|------:\|--------\|----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|63.23\|± \| 1.41\| \| \| \|acc_norm\|64.16\|± \| 1.40\| \|arc_easy \| 0\|acc \|85.90\|± \| 0.71\| \| \| \|acc_norm\|82.49\|± \| 0.78\| \|boolq \| 1\|acc \|87.80\|± \| 0.57\| \|hellaswag \| 0\|acc \|67.05\|± \| 0.47\| \| \| \|acc_norm\|85.19\|± \| 0.35\| \|openbookqa \| 0\|acc \|38.40\|± \| 2.18\| \| \| \|acc_norm\|48.40\|± \| 2.24\| \|piqa \| 0\|acc \|82.75\|± \| 0.88\| \| \| \|acc_norm\|84.28\|± \| 0.85\| \|winogrande \| 0\|acc \|78.53\|± \| 1.15\| Average: 75.84% ### TruthfulQA \| Task \|Version\|Metric\|Value\| \|Stderr\| \|-------------\|------:\|------\|----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|50.67\|± \| 1.75\| \| \| \|mc2 \|66.15\|± \| 1.48\| Average: 66.15% ### Bigbench \| Task \|Version\| Metric \|Value\| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|56.84\|± \| 3.60\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|66.67\|± \| 2.46\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|40.70\|± \| 3.06\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|24.79\|± \| 2.28\| \| \| \|exact_str_match \|10.58\|± \| 1.63\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|31.00\|± \| 2.07\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|23.00\|± \| 1.59\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|58.00\|± \| 2.85\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|45.80\|± \| 2.23\| \|bigbench_navigate \| 0\|multiple_choice_grade\|52.10\|± \| 1.58\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|69.55\|± \| 1.03\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|48.88\|± \| 2.36\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|30.96\|± \| 1.46\| \|bigbench_snarks \| 0\|multiple_choice_grade\|73.48\|± \| 3.29\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|74.14\|± \| 1.40\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|42.70\|± \| 1.56\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|23.60\|± \| 1.20\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|18.40\|± \| 0.93\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|58.00\|± \| 2.85\| Average: 46.59% Average score: 58.27% ## 🧩 Merge Configuration Spaetzle-v69-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora) * [cstr/Spaetzle-v68-7b](https://huggingface.co/cstr/Spaetzle-v68-7b) The merge tree in total involves the following original models: - [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora) - [mayflowergmbh/Wiedervereinigung-7b-dpo](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo) - [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B) - [ResplendentAI/Flora_DPO_7B](https://huggingface.co/ResplendentAI/Flora_DPO_7B) - [yleo/EmertonMonarch-7B](https://huggingface.co/yleo/EmertonMonarch-7B) - [occiglot/occiglot-7b-de-en-instruct](https://huggingface.co/occiglot/occiglot-7b-de-en-instruct) - [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227) - [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1) - [LeoLM/leo-mistral-hessianai-7b](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b) - [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix) - [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral) - [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b) - [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2) - [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser) For this last merge: ```yaml models: - model: cstr/Spaetzle-v68-7b # no parameters necessary for base model - model: abideen/AlphaMonarch-dora parameters: density: 0.60 weight: 0.30 merge_method: dare_ties base_model: cstr/Spaetzle-v68-7b parameters: int8_mask: true dtype: bfloat16 random_seed: 0 tokenizer_source: base ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "cstr/Spaetzle-v69-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
abhishek/autotrain-llama3-8b-open-hermes-sft	abhishek	2024-04-19T14:46:07Z	7	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T11:38:35Z	--- tags: - autotrain - text-generation-inference - text-generation library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? 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) ```
Sagicc/whisper-base-sr-yodas	Sagicc	2024-04-19T14:45:09Z	10	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "sr", "dataset:espnet/yodas", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-04-19T12:37:48Z	--- language: - sr license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - espnet/yodas metrics: - wer model-index: - name: Whisper Small Sr Yodas results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Yodas type: espnet/yodas config: sr split: test args: sr metrics: - name: Wer type: wer value: 0.24497708847373986 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Sr Yodas This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Yodas dataset. It achieves the following results on the evaluation set: - Loss: 0.2688 - Wer Ortho: 0.3334 - Wer: 0.2450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\| \| 1.0469 \| 0.24 \| 500 \| 0.4020 \| 0.5071 \| 0.4270 \| \| 0.9924 \| 0.49 \| 1000 \| 0.3401 \| 0.4082 \| 0.3183 \| \| 0.865 \| 0.73 \| 1500 \| 0.3047 \| 0.3644 \| 0.2776 \| \| 0.8443 \| 0.98 \| 2000 \| 0.2893 \| 0.3623 \| 0.2735 \| \| 0.7377 \| 1.22 \| 2500 \| 0.2817 \| 0.3472 \| 0.2591 \| \| 0.6851 \| 1.46 \| 3000 \| 0.2728 \| 0.3348 \| 0.2466 \| \| 0.7286 \| 1.71 \| 3500 \| 0.2702 \| 0.3325 \| 0.2444 \| \| 0.7215 \| 1.95 \| 4000 \| 0.2688 \| 0.3334 \| 0.2450 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1+cu117 - Datasets 2.18.0 - Tokenizers 0.15.1
blockblockblock/Meta-Llama-3-70B-Instruct-hf-bpw3	blockblockblock	2024-04-19T14:44:36Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]	text-generation	2024-04-19T14:40:33Z	--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: other license_name: llama3 license_link: LICENSE extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Meta Llama 3" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof). 2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Meta Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-70B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/" --local-dir Meta-Llama-3-70B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
DUAL-GPO-2/phi-2-ipo-renew1	DUAL-GPO-2	2024-04-19T14:44:01Z	10	0	peft	[ "peft", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]	null	2024-04-19T02:43:27Z	--- license: mit library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo base_model: microsoft/phi-2 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-ipo-renew1 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. --> # phi-2-ipo-renew1 This model is a fine-tuned version of [lole25/phi-2-sft-ultrachat-lora](https://huggingface.co/lole25/phi-2-sft-ultrachat-lora) on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 2028.0933 - Rewards/chosen: -0.1243 - Rewards/rejected: -0.2158 - Rewards/accuracies: 0.6900 - Rewards/margins: 0.0915 - Logps/rejected: -255.1287 - Logps/chosen: -269.0499 - Logits/rejected: 0.5909 - Logits/chosen: 0.5352 ## 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 2496.843 \| 0.05 \| 100 \| 2502.2668 \| -0.0003 \| -0.0002 \| 0.5005 \| -0.0002 \| -233.5649 \| -256.6506 \| 0.8888 \| 0.8318 \| \| 2499.2807 \| 0.1 \| 200 \| 2494.8354 \| 0.0001 \| -0.0005 \| 0.5190 \| 0.0006 \| -233.5995 \| -256.6106 \| 0.8882 \| 0.8310 \| \| 2477.7609 \| 0.16 \| 300 \| 2481.5015 \| -0.0011 \| -0.0031 \| 0.5595 \| 0.0019 \| -233.8548 \| -256.7285 \| 0.8892 \| 0.8319 \| \| 2428.4195 \| 0.21 \| 400 \| 2419.1045 \| -0.0068 \| -0.0156 \| 0.6495 \| 0.0089 \| -235.1127 \| -257.2951 \| 0.8983 \| 0.8404 \| \| 2296.8842 \| 0.26 \| 500 \| 2349.4358 \| -0.0240 \| -0.0419 \| 0.6565 \| 0.0179 \| -237.7379 \| -259.0124 \| 0.8806 \| 0.8214 \| \| 2254.5846 \| 0.31 \| 600 \| 2273.4993 \| -0.0525 \| -0.0829 \| 0.6570 \| 0.0304 \| -241.8383 \| -261.8659 \| 0.8478 \| 0.7868 \| \| 2330.7787 \| 0.37 \| 700 \| 2224.3350 \| -0.0819 \| -0.1221 \| 0.6630 \| 0.0402 \| -245.7631 \| -264.8093 \| 0.8128 \| 0.7517 \| \| 2223.6863 \| 0.42 \| 800 \| 2196.0991 \| -0.1009 \| -0.1487 \| 0.6675 \| 0.0478 \| -248.4222 \| -266.7057 \| 0.7611 \| 0.6992 \| \| 2066.7418 \| 0.47 \| 900 \| 2166.0732 \| -0.1112 \| -0.1658 \| 0.6700 \| 0.0546 \| -250.1319 \| -267.7397 \| 0.7518 \| 0.6917 \| \| 2119.2691 \| 0.52 \| 1000 \| 2138.9312 \| -0.1215 \| -0.1821 \| 0.6715 \| 0.0606 \| -251.7610 \| -268.7693 \| 0.7213 \| 0.6619 \| \| 2191.7109 \| 0.58 \| 1100 \| 2121.8115 \| -0.1257 \| -0.1906 \| 0.6695 \| 0.0648 \| -252.6059 \| -269.1910 \| 0.7176 \| 0.6584 \| \| 2308.1883 \| 0.63 \| 1200 \| 2110.3069 \| -0.1409 \| -0.2123 \| 0.6665 \| 0.0715 \| -254.7812 \| -270.7044 \| 0.6920 \| 0.6330 \| \| 1996.7178 \| 0.68 \| 1300 \| 2095.3130 \| -0.1314 \| -0.2042 \| 0.6755 \| 0.0728 \| -253.9726 \| -269.7621 \| 0.6722 \| 0.6141 \| \| 2038.3844 \| 0.73 \| 1400 \| 2085.0852 \| -0.1383 \| -0.2140 \| 0.6800 \| 0.0756 \| -254.9441 \| -270.4488 \| 0.6513 \| 0.5933 \| \| 2094.2182 \| 0.79 \| 1500 \| 2076.3042 \| -0.1390 \| -0.2166 \| 0.6790 \| 0.0777 \| -255.2133 \| -270.5129 \| 0.6474 \| 0.5898 \| \| 2171.3457 \| 0.84 \| 1600 \| 2069.3757 \| -0.1374 \| -0.2166 \| 0.6810 \| 0.0792 \| -255.2130 \| -270.3595 \| 0.6392 \| 0.5818 \| \| 2189.3863 \| 0.89 \| 1700 \| 2062.1995 \| -0.1386 \| -0.2192 \| 0.6780 \| 0.0806 \| -255.4675 \| -270.4739 \| 0.6291 \| 0.5723 \| \| 2292.8938 \| 0.94 \| 1800 \| 2053.1299 \| -0.1196 \| -0.2005 \| 0.6830 \| 0.0809 \| -253.6025 \| -268.5789 \| 0.6275 \| 0.5703 \| \| 2085.5805 \| 0.99 \| 1900 \| 2052.3237 \| -0.1086 \| -0.1906 \| 0.6900 \| 0.0821 \| -252.6131 \| -267.4730 \| 0.6319 \| 0.5747 \| \| 1847.759 \| 1.05 \| 2000 \| 2050.4177 \| -0.1118 \| -0.1953 \| 0.6850 \| 0.0836 \| -253.0827 \| -267.7950 \| 0.6333 \| 0.5763 \| \| 2024.9559 \| 1.1 \| 2100 \| 2046.7593 \| -0.1219 \| -0.2083 \| 0.6900 \| 0.0864 \| -254.3799 \| -268.8073 \| 0.6157 \| 0.5590 \| \| 2038.6354 \| 1.15 \| 2200 \| 2043.5728 \| -0.1205 \| -0.2072 \| 0.6880 \| 0.0867 \| -254.2731 \| -268.6722 \| 0.6083 \| 0.5518 \| \| 2022.9617 \| 1.2 \| 2300 \| 2035.5857 \| -0.1173 \| -0.2041 \| 0.6895 \| 0.0868 \| -253.9597 \| -268.3491 \| 0.6101 \| 0.5535 \| \| 1871.641 \| 1.26 \| 2400 \| 2036.3373 \| -0.1190 \| -0.2073 \| 0.6895 \| 0.0884 \| -254.2831 \| -268.5161 \| 0.6046 \| 0.5482 \| \| 1907.3463 \| 1.31 \| 2500 \| 2034.7010 \| -0.1216 \| -0.2108 \| 0.6880 \| 0.0892 \| -254.6297 \| -268.7765 \| 0.6022 \| 0.5460 \| \| 1884.6086 \| 1.36 \| 2600 \| 2033.7977 \| -0.1215 \| -0.2105 \| 0.6910 \| 0.0890 \| -254.6014 \| -268.7708 \| 0.6013 \| 0.5451 \| \| 2034.9129 \| 1.41 \| 2700 \| 2032.5447 \| -0.1235 \| -0.2140 \| 0.6900 \| 0.0905 \| -254.9471 \| -268.9633 \| 0.5987 \| 0.5426 \| \| 2068.2822 \| 1.47 \| 2800 \| 2030.8698 \| -0.1251 \| -0.2162 \| 0.6900 \| 0.0911 \| -255.1671 \| -269.1270 \| 0.5943 \| 0.5383 \| \| 1977.4029 \| 1.52 \| 2900 \| 2030.6033 \| -0.1251 \| -0.2162 \| 0.6895 \| 0.0911 \| -255.1690 \| -269.1252 \| 0.5941 \| 0.5381 \| \| 2110.2887 \| 1.57 \| 3000 \| 2030.5707 \| -0.1259 \| -0.2173 \| 0.6905 \| 0.0915 \| -255.2821 \| -269.2050 \| 0.5908 \| 0.5348 \| \| 2068.2863 \| 1.62 \| 3100 \| 2029.4174 \| -0.1242 \| -0.2156 \| 0.6935 \| 0.0914 \| -255.1087 \| -269.0390 \| 0.5913 \| 0.5357 \| \| 1977.8852 \| 1.67 \| 3200 \| 2026.1289 \| -0.1249 \| -0.2165 \| 0.6960 \| 0.0916 \| -255.2016 \| -269.1071 \| 0.5920 \| 0.5364 \| \| 2123.3787 \| 1.73 \| 3300 \| 2027.3552 \| -0.1248 \| -0.2162 \| 0.6930 \| 0.0914 \| -255.1666 \| -269.0933 \| 0.5926 \| 0.5370 \| \| 1945.4934 \| 1.78 \| 3400 \| 2025.7804 \| -0.1248 \| -0.2164 \| 0.6935 \| 0.0916 \| -255.1899 \| -269.1010 \| 0.5909 \| 0.5353 \| \| 1937.2627 \| 1.83 \| 3500 \| 2027.8240 \| -0.1247 \| -0.2163 \| 0.6930 \| 0.0916 \| -255.1750 \| -269.0878 \| 0.5903 \| 0.5347 \| \| 2007.2062 \| 1.88 \| 3600 \| 2025.3228 \| -0.1244 \| -0.2164 \| 0.6895 \| 0.0919 \| -255.1843 \| -269.0623 \| 0.5910 \| 0.5352 \| \| 2076.715 \| 1.94 \| 3700 \| 2027.4857 \| -0.1243 \| -0.2159 \| 0.6920 \| 0.0916 \| -255.1383 \| -269.0487 \| 0.5913 \| 0.5358 \| \| 2055.2201 \| 1.99 \| 3800 \| 2027.8082 \| -0.1244 \| -0.2160 \| 0.6920 \| 0.0916 \| -255.1455 \| -269.0543 \| 0.5902 \| 0.5347 \| ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.14.6 - Tokenizers 0.15.2
clam004/Qwen-Qwen1.5-0.5B-Chat	clam004	2024-04-19T14:43:35Z	7	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-06T18:19:10Z	--- 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]
ntvcie/Gemma2bVinhntV7_16bit	ntvcie	2024-04-19T14:41:11Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T14:41:10Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-2b-bnb-4bit --- # Uploaded model - Developed by: ntvcie - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
wendy41/llama-2-koen-user111-100	wendy41	2024-04-19T14:41:01Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-19T14:40:59Z	--- 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]
qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5	qubvel-hf	2024-04-19T14:39:26Z	6	0	transformers	[ "transformers", "safetensors", "conditional_detr", "object-detection", "vision", "generated_from_trainer", "base_model:microsoft/conditional-detr-resnet-50", "base_model:finetune:microsoft/conditional-detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2024-04-19T12:48:17Z	--- license: apache-2.0 base_model: microsoft/conditional-detr-resnet-50 tags: - object-detection - vision - generated_from_trainer model-index: - name: microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5 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. --> # microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.3007 - Map: 0.3131 - Map 50: 0.6268 - Map 75: 0.2698 - Map Small: 0.2089 - Map Medium: 0.2237 - Map Large: 0.5358 - Mar 1: 0.3352 - Mar 10: 0.4586 - Mar 100: 0.4682 - Mar Small: 0.2773 - Mar Medium: 0.3748 - Mar Large: 0.6417 - Map Coverall: 0.5362 - Mar 100 Coverall: 0.6844 - Map Face Shield: 0.3199 - Mar 100 Face Shield: 0.4635 - Map Gloves: 0.1628 - Mar 100 Gloves: 0.3176 - Map Goggles: 0.2427 - Mar 100 Goggles: 0.4475 - Map Mask: 0.3038 - Mar 100 Mask: 0.4278 ## 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: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Map \| Map 50 \| Map 75 \| Map Small \| Map Medium \| Map Large \| Mar 1 \| Mar 10 \| Mar 100 \| Mar Small \| Mar Medium \| Mar Large \| Map Coverall \| Mar 100 Coverall \| Map Face Shield \| Mar 100 Face Shield \| Map Gloves \| Mar 100 Gloves \| Map Goggles \| Mar 100 Goggles \| Map Mask \| Mar 100 Mask \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:----------:\|:---------:\|:------:\|:------:\|:-------:\|:---------:\|:----------:\|:---------:\|:------------:\|:----------------:\|:---------------:\|:-------------------:\|:----------:\|:--------------:\|:-----------:\|:---------------:\|:--------:\|:------------:\| \| 13.6734 \| 1.0 \| 107 \| 3.0782 \| 0.0022 \| 0.009 \| 0.0004 \| 0.0002 \| 0.0002 \| 0.0027 \| 0.0054 \| 0.0291 \| 0.0632 \| 0.0028 \| 0.0108 \| 0.0955 \| 0.0105 \| 0.2595 \| 0.0 \| 0.0 \| 0.0 \| 0.0126 \| 0.0 \| 0.0 \| 0.0005 \| 0.0438 \| \| 2.9712 \| 2.0 \| 214 \| 2.7340 \| 0.0089 \| 0.0267 \| 0.0034 \| 0.0036 \| 0.0017 \| 0.0114 \| 0.0186 \| 0.0749 \| 0.1162 \| 0.0082 \| 0.0358 \| 0.1266 \| 0.0374 \| 0.4457 \| 0.0 \| 0.0 \| 0.0004 \| 0.0101 \| 0.0 \| 0.0 \| 0.0069 \| 0.1253 \| \| 2.6599 \| 3.0 \| 321 \| 2.6057 \| 0.0122 \| 0.0432 \| 0.0034 \| 0.0011 \| 0.003 \| 0.0138 \| 0.0281 \| 0.1003 \| 0.1355 \| 0.0324 \| 0.0542 \| 0.1526 \| 0.0538 \| 0.4428 \| 0.0004 \| 0.0288 \| 0.0003 \| 0.0322 \| 0.0 \| 0.0049 \| 0.0064 \| 0.1686 \| \| 2.53 \| 4.0 \| 428 \| 2.3681 \| 0.0187 \| 0.0542 \| 0.0099 \| 0.0061 \| 0.0057 \| 0.0372 \| 0.0494 \| 0.1502 \| 0.1831 \| 0.0272 \| 0.1134 \| 0.1631 \| 0.0737 \| 0.5191 \| 0.0032 \| 0.1 \| 0.0012 \| 0.0487 \| 0.0058 \| 0.0656 \| 0.0097 \| 0.182 \| \| 2.3495 \| 5.0 \| 535 \| 2.2947 \| 0.0282 \| 0.0843 \| 0.0163 \| 0.0163 \| 0.0175 \| 0.0387 \| 0.0796 \| 0.194 \| 0.2282 \| 0.0407 \| 0.1538 \| 0.2516 \| 0.0928 \| 0.5798 \| 0.015 \| 0.1577 \| 0.0009 \| 0.0563 \| 0.0131 \| 0.123 \| 0.0193 \| 0.2242 \| \| 2.2478 \| 6.0 \| 642 \| 2.2016 \| 0.0346 \| 0.0914 \| 0.0186 \| 0.0079 \| 0.0116 \| 0.0668 \| 0.0938 \| 0.2406 \| 0.2729 \| 0.0362 \| 0.1578 \| 0.4043 \| 0.1208 \| 0.6121 \| 0.0178 \| 0.2577 \| 0.0021 \| 0.0925 \| 0.0152 \| 0.2033 \| 0.017 \| 0.199 \| \| 2.1515 \| 7.0 \| 749 \| 2.1026 \| 0.0424 \| 0.1121 \| 0.0226 \| 0.0162 \| 0.0197 \| 0.0747 \| 0.1058 \| 0.2613 \| 0.2973 \| 0.0996 \| 0.2269 \| 0.3484 \| 0.1382 \| 0.5769 \| 0.0236 \| 0.3 \| 0.0052 \| 0.1216 \| 0.0125 \| 0.2246 \| 0.0327 \| 0.2634 \| \| 2.0571 \| 8.0 \| 856 \| 2.1115 \| 0.0485 \| 0.1183 \| 0.0346 \| 0.0078 \| 0.0287 \| 0.0922 \| 0.1332 \| 0.2587 \| 0.2995 \| 0.0573 \| 0.1973 \| 0.424 \| 0.1446 \| 0.6318 \| 0.0442 \| 0.2808 \| 0.0027 \| 0.1211 \| 0.0182 \| 0.1836 \| 0.0327 \| 0.2804 \| \| 1.9981 \| 9.0 \| 963 \| 2.0429 \| 0.0433 \| 0.1161 \| 0.0242 \| 0.0239 \| 0.0216 \| 0.0831 \| 0.1163 \| 0.2543 \| 0.2985 \| 0.1208 \| 0.1892 \| 0.4359 \| 0.1403 \| 0.6358 \| 0.0251 \| 0.2365 \| 0.0059 \| 0.1673 \| 0.0187 \| 0.1754 \| 0.0265 \| 0.2773 \| \| 1.9338 \| 10.0 \| 1070 \| 1.9025 \| 0.079 \| 0.1694 \| 0.061 \| 0.0175 \| 0.0481 \| 0.1245 \| 0.1621 \| 0.313 \| 0.3524 \| 0.1106 \| 0.2565 \| 0.5211 \| 0.2324 \| 0.6809 \| 0.0753 \| 0.3269 \| 0.0094 \| 0.198 \| 0.0112 \| 0.2328 \| 0.0667 \| 0.3232 \| \| 1.8745 \| 11.0 \| 1177 \| 1.8462 \| 0.0938 \| 0.2008 \| 0.0772 \| 0.0191 \| 0.0666 \| 0.1644 \| 0.1632 \| 0.3078 \| 0.3485 \| 0.1358 \| 0.2725 \| 0.483 \| 0.3093 \| 0.6618 \| 0.0471 \| 0.3442 \| 0.0104 \| 0.2005 \| 0.0122 \| 0.2033 \| 0.0899 \| 0.3325 \| \| 1.7669 \| 12.0 \| 1284 \| 1.7660 \| 0.12 \| 0.271 \| 0.0939 \| 0.0298 \| 0.0714 \| 0.2035 \| 0.1928 \| 0.3391 \| 0.366 \| 0.1539 \| 0.2852 \| 0.5395 \| 0.368 \| 0.6566 \| 0.0741 \| 0.3327 \| 0.0236 \| 0.2442 \| 0.0363 \| 0.2459 \| 0.0981 \| 0.3505 \| \| 1.7122 \| 13.0 \| 1391 \| 1.7355 \| 0.1376 \| 0.2863 \| 0.1139 \| 0.027 \| 0.0899 \| 0.2379 \| 0.2151 \| 0.3567 \| 0.3878 \| 0.1285 \| 0.2998 \| 0.5798 \| 0.3958 \| 0.637 \| 0.0969 \| 0.3712 \| 0.0389 \| 0.2548 \| 0.0271 \| 0.3115 \| 0.1294 \| 0.3644 \| \| 1.6336 \| 14.0 \| 1498 \| 1.6695 \| 0.1609 \| 0.3374 \| 0.1317 \| 0.0196 \| 0.107 \| 0.2757 \| 0.2211 \| 0.3673 \| 0.3964 \| 0.1449 \| 0.3237 \| 0.5801 \| 0.4353 \| 0.6595 \| 0.146 \| 0.3923 \| 0.034 \| 0.2482 \| 0.0437 \| 0.3361 \| 0.1455 \| 0.3459 \| \| 1.5696 \| 15.0 \| 1605 \| 1.6275 \| 0.1601 \| 0.3509 \| 0.127 \| 0.0231 \| 0.102 \| 0.2989 \| 0.2181 \| 0.3813 \| 0.4083 \| 0.1688 \| 0.3313 \| 0.5781 \| 0.4323 \| 0.6549 \| 0.1061 \| 0.4038 \| 0.0618 \| 0.3005 \| 0.029 \| 0.3246 \| 0.1714 \| 0.3577 \| \| 1.5149 \| 16.0 \| 1712 \| 1.5810 \| 0.1862 \| 0.3984 \| 0.1463 \| 0.0622 \| 0.1143 \| 0.3439 \| 0.2268 \| 0.3819 \| 0.4019 \| 0.1881 \| 0.3158 \| 0.6013 \| 0.4779 \| 0.6624 \| 0.1141 \| 0.4 \| 0.0694 \| 0.2668 \| 0.0704 \| 0.3262 \| 0.1991 \| 0.3541 \| \| 1.4732 \| 17.0 \| 1819 \| 1.5458 \| 0.1905 \| 0.4135 \| 0.1472 \| 0.0514 \| 0.1196 \| 0.3237 \| 0.2289 \| 0.3868 \| 0.4171 \| 0.167 \| 0.3378 \| 0.5905 \| 0.4746 \| 0.6538 \| 0.1547 \| 0.4519 \| 0.0932 \| 0.3055 \| 0.0488 \| 0.3279 \| 0.1812 \| 0.3464 \| \| 1.4327 \| 18.0 \| 1926 \| 1.5617 \| 0.1887 \| 0.4038 \| 0.142 \| 0.0323 \| 0.1178 \| 0.3574 \| 0.2395 \| 0.3855 \| 0.4114 \| 0.1902 \| 0.3236 \| 0.589 \| 0.4801 \| 0.6595 \| 0.1518 \| 0.4462 \| 0.0866 \| 0.2829 \| 0.0389 \| 0.3197 \| 0.186 \| 0.349 \| \| 1.3925 \| 19.0 \| 2033 \| 1.5032 \| 0.2162 \| 0.4531 \| 0.1701 \| 0.0303 \| 0.1292 \| 0.3854 \| 0.2572 \| 0.4077 \| 0.4328 \| 0.1759 \| 0.3377 \| 0.6233 \| 0.5043 \| 0.6734 \| 0.169 \| 0.4442 \| 0.1341 \| 0.3221 \| 0.0725 \| 0.3754 \| 0.201 \| 0.349 \| \| 1.3582 \| 20.0 \| 2140 \| 1.4728 \| 0.2077 \| 0.4357 \| 0.1744 \| 0.0412 \| 0.1414 \| 0.3759 \| 0.2516 \| 0.4017 \| 0.4251 \| 0.1842 \| 0.3402 \| 0.6106 \| 0.4879 \| 0.6607 \| 0.1537 \| 0.4404 \| 0.1159 \| 0.2955 \| 0.0633 \| 0.359 \| 0.2178 \| 0.3701 \| \| 1.329 \| 21.0 \| 2247 \| 1.4826 \| 0.2062 \| 0.4509 \| 0.1642 \| 0.0325 \| 0.1256 \| 0.4185 \| 0.2559 \| 0.3974 \| 0.4214 \| 0.1371 \| 0.3201 \| 0.619 \| 0.4879 \| 0.6584 \| 0.146 \| 0.4212 \| 0.1136 \| 0.2698 \| 0.0884 \| 0.4164 \| 0.195 \| 0.3412 \| \| 1.3458 \| 22.0 \| 2354 \| 1.4653 \| 0.2244 \| 0.4636 \| 0.1859 \| 0.074 \| 0.1501 \| 0.4031 \| 0.272 \| 0.4102 \| 0.4276 \| 0.1735 \| 0.3346 \| 0.6213 \| 0.5139 \| 0.6711 \| 0.1848 \| 0.4712 \| 0.1083 \| 0.2769 \| 0.0732 \| 0.323 \| 0.2419 \| 0.3959 \| \| 1.3028 \| 23.0 \| 2461 \| 1.4139 \| 0.2289 \| 0.4906 \| 0.1886 \| 0.0572 \| 0.1524 \| 0.4043 \| 0.2721 \| 0.4191 \| 0.4353 \| 0.1771 \| 0.3517 \| 0.5964 \| 0.4964 \| 0.6653 \| 0.1865 \| 0.4481 \| 0.1575 \| 0.3211 \| 0.0746 \| 0.3738 \| 0.2293 \| 0.368 \| \| 1.2704 \| 24.0 \| 2568 \| 1.4173 \| 0.2371 \| 0.4973 \| 0.199 \| 0.0605 \| 0.1634 \| 0.419 \| 0.2752 \| 0.4172 \| 0.4401 \| 0.1776 \| 0.3515 \| 0.6027 \| 0.5033 \| 0.6636 \| 0.2403 \| 0.4596 \| 0.1149 \| 0.3005 \| 0.1009 \| 0.4066 \| 0.2261 \| 0.3701 \| \| 1.2379 \| 25.0 \| 2675 \| 1.3933 \| 0.2608 \| 0.5303 \| 0.2271 \| 0.0541 \| 0.188 \| 0.4582 \| 0.2849 \| 0.4189 \| 0.4396 \| 0.158 \| 0.3455 \| 0.6343 \| 0.5376 \| 0.6913 \| 0.2509 \| 0.4635 \| 0.1194 \| 0.3065 \| 0.1534 \| 0.3623 \| 0.2428 \| 0.3742 \| \| 1.2257 \| 26.0 \| 2782 \| 1.4209 \| 0.2467 \| 0.5049 \| 0.2078 \| 0.0557 \| 0.1592 \| 0.4446 \| 0.2708 \| 0.4135 \| 0.4292 \| 0.1526 \| 0.3298 \| 0.6245 \| 0.5239 \| 0.6636 \| 0.2282 \| 0.4288 \| 0.134 \| 0.294 \| 0.1079 \| 0.3902 \| 0.2394 \| 0.3696 \| \| 1.1706 \| 27.0 \| 2889 \| 1.3593 \| 0.2631 \| 0.5273 \| 0.2353 \| 0.0955 \| 0.1891 \| 0.4521 \| 0.2912 \| 0.4196 \| 0.4422 \| 0.1554 \| 0.3475 \| 0.6437 \| 0.5374 \| 0.685 \| 0.261 \| 0.4692 \| 0.1276 \| 0.2925 \| 0.1269 \| 0.3803 \| 0.2624 \| 0.384 \| \| 1.1512 \| 28.0 \| 2996 \| 1.3320 \| 0.27 \| 0.5371 \| 0.2171 \| 0.0727 \| 0.192 \| 0.456 \| 0.2981 \| 0.4398 \| 0.4609 \| 0.1954 \| 0.3663 \| 0.6427 \| 0.5406 \| 0.6971 \| 0.2264 \| 0.5 \| 0.1629 \| 0.3131 \| 0.1617 \| 0.4066 \| 0.2584 \| 0.3876 \| \| 1.1511 \| 29.0 \| 3103 \| 1.3592 \| 0.2641 \| 0.526 \| 0.2346 \| 0.0526 \| 0.1701 \| 0.498 \| 0.2977 \| 0.4249 \| 0.4395 \| 0.1836 \| 0.3263 \| 0.6367 \| 0.5376 \| 0.6717 \| 0.2129 \| 0.4154 \| 0.1414 \| 0.291 \| 0.1589 \| 0.418 \| 0.2697 \| 0.4015 \| \| 1.1312 \| 30.0 \| 3210 \| 1.3863 \| 0.2582 \| 0.5328 \| 0.2115 \| 0.0667 \| 0.1747 \| 0.4443 \| 0.283 \| 0.4188 \| 0.4418 \| 0.2191 \| 0.332 \| 0.6478 \| 0.5086 \| 0.6526 \| 0.2387 \| 0.4808 \| 0.1249 \| 0.295 \| 0.1609 \| 0.3787 \| 0.2579 \| 0.4021 \| \| 1.1207 \| 31.0 \| 3317 \| 1.3294 \| 0.2813 \| 0.5581 \| 0.252 \| 0.1121 \| 0.1988 \| 0.4749 \| 0.296 \| 0.4362 \| 0.4568 \| 0.2423 \| 0.3532 \| 0.6468 \| 0.5314 \| 0.6775 \| 0.2837 \| 0.4788 \| 0.1526 \| 0.3131 \| 0.1613 \| 0.4033 \| 0.2777 \| 0.4113 \| \| 1.0856 \| 32.0 \| 3424 \| 1.3426 \| 0.274 \| 0.565 \| 0.2282 \| 0.1612 \| 0.2023 \| 0.4789 \| 0.2989 \| 0.4375 \| 0.4541 \| 0.2492 \| 0.3497 \| 0.6661 \| 0.5195 \| 0.6844 \| 0.2952 \| 0.4731 \| 0.149 \| 0.298 \| 0.1551 \| 0.4393 \| 0.2516 \| 0.3758 \| \| 1.0909 \| 33.0 \| 3531 \| 1.3174 \| 0.2868 \| 0.5799 \| 0.2445 \| 0.0956 \| 0.2097 \| 0.4865 \| 0.3119 \| 0.4466 \| 0.4652 \| 0.2553 \| 0.3672 \| 0.653 \| 0.536 \| 0.704 \| 0.3096 \| 0.4788 \| 0.1576 \| 0.3211 \| 0.1758 \| 0.441 \| 0.2552 \| 0.3809 \| \| 1.0726 \| 34.0 \| 3638 \| 1.3767 \| 0.2751 \| 0.561 \| 0.2242 \| 0.0993 \| 0.1951 \| 0.4811 \| 0.2831 \| 0.4241 \| 0.452 \| 0.2247 \| 0.3529 \| 0.6578 \| 0.5375 \| 0.6919 \| 0.2638 \| 0.4692 \| 0.1562 \| 0.3075 \| 0.1456 \| 0.3885 \| 0.2724 \| 0.4026 \| \| 1.0799 \| 35.0 \| 3745 \| 1.3316 \| 0.2691 \| 0.5569 \| 0.2126 \| 0.1108 \| 0.1876 \| 0.4522 \| 0.2913 \| 0.4243 \| 0.4443 \| 0.2217 \| 0.3366 \| 0.6247 \| 0.5257 \| 0.6879 \| 0.2661 \| 0.4712 \| 0.1575 \| 0.3055 \| 0.1659 \| 0.3869 \| 0.2301 \| 0.3701 \| \| 1.0402 \| 36.0 \| 3852 \| 1.3501 \| 0.2735 \| 0.5757 \| 0.207 \| 0.1217 \| 0.2057 \| 0.4564 \| 0.284 \| 0.4377 \| 0.4568 \| 0.2358 \| 0.3814 \| 0.6465 \| 0.5171 \| 0.685 \| 0.2325 \| 0.4481 \| 0.1429 \| 0.3035 \| 0.2158 \| 0.4656 \| 0.2594 \| 0.382 \| \| 1.0531 \| 37.0 \| 3959 \| 1.3324 \| 0.2701 \| 0.5336 \| 0.2233 \| 0.0834 \| 0.1907 \| 0.4826 \| 0.2852 \| 0.4439 \| 0.4644 \| 0.2142 \| 0.3754 \| 0.6658 \| 0.5318 \| 0.7052 \| 0.2218 \| 0.4596 \| 0.1431 \| 0.2995 \| 0.1909 \| 0.4639 \| 0.2632 \| 0.3938 \| \| 1.028 \| 38.0 \| 4066 \| 1.3593 \| 0.2637 \| 0.5412 \| 0.2147 \| 0.0638 \| 0.1752 \| 0.4642 \| 0.2853 \| 0.4231 \| 0.4438 \| 0.1997 \| 0.3274 \| 0.6417 \| 0.5256 \| 0.6705 \| 0.2526 \| 0.4327 \| 0.1312 \| 0.2854 \| 0.1766 \| 0.4344 \| 0.2324 \| 0.3959 \| \| 1.0211 \| 39.0 \| 4173 \| 1.3359 \| 0.2728 \| 0.5553 \| 0.2294 \| 0.1086 \| 0.1991 \| 0.4663 \| 0.3018 \| 0.4335 \| 0.4525 \| 0.2277 \| 0.3625 \| 0.6463 \| 0.5408 \| 0.6821 \| 0.2664 \| 0.4635 \| 0.1253 \| 0.2995 \| 0.1883 \| 0.4377 \| 0.2433 \| 0.3799 \| \| 0.9986 \| 40.0 \| 4280 \| 1.2900 \| 0.2921 \| 0.5961 \| 0.2449 \| 0.1617 \| 0.2199 \| 0.4851 \| 0.3028 \| 0.4506 \| 0.4659 \| 0.2422 \| 0.3836 \| 0.6408 \| 0.546 \| 0.696 \| 0.2649 \| 0.4538 \| 0.1729 \| 0.3111 \| 0.2041 \| 0.4574 \| 0.2725 \| 0.4113 \| \| 0.9951 \| 41.0 \| 4387 \| 1.3541 \| 0.2695 \| 0.5709 \| 0.2055 \| 0.1418 \| 0.1857 \| 0.4392 \| 0.2921 \| 0.4326 \| 0.4456 \| 0.2248 \| 0.3602 \| 0.602 \| 0.5355 \| 0.6855 \| 0.2612 \| 0.4365 \| 0.137 \| 0.2879 \| 0.1907 \| 0.4344 \| 0.2233 \| 0.3835 \| \| 0.9838 \| 42.0 \| 4494 \| 1.2978 \| 0.2868 \| 0.5888 \| 0.2326 \| 0.1262 \| 0.209 \| 0.5085 \| 0.3099 \| 0.4478 \| 0.463 \| 0.2469 \| 0.3642 \| 0.6518 \| 0.5266 \| 0.6867 \| 0.2904 \| 0.4692 \| 0.1673 \| 0.3291 \| 0.2 \| 0.4295 \| 0.2497 \| 0.4005 \| \| 0.9637 \| 43.0 \| 4601 \| 1.2950 \| 0.285 \| 0.5769 \| 0.2351 \| 0.1588 \| 0.2034 \| 0.4954 \| 0.3066 \| 0.4479 \| 0.463 \| 0.2504 \| 0.3566 \| 0.658 \| 0.5326 \| 0.689 \| 0.2793 \| 0.4615 \| 0.1678 \| 0.3171 \| 0.1861 \| 0.4262 \| 0.2593 \| 0.4211 \| \| 0.9408 \| 44.0 \| 4708 \| 1.3063 \| 0.2795 \| 0.5601 \| 0.2375 \| 0.1689 \| 0.1905 \| 0.5034 \| 0.3126 \| 0.4407 \| 0.4552 \| 0.2524 \| 0.3763 \| 0.6449 \| 0.5461 \| 0.6884 \| 0.2866 \| 0.4635 \| 0.1291 \| 0.2935 \| 0.1614 \| 0.4262 \| 0.2741 \| 0.4046 \| \| 0.9457 \| 45.0 \| 4815 \| 1.2866 \| 0.2851 \| 0.5794 \| 0.2471 \| 0.1712 \| 0.2011 \| 0.5017 \| 0.3168 \| 0.448 \| 0.4642 \| 0.2719 \| 0.3629 \| 0.6464 \| 0.544 \| 0.6896 \| 0.2772 \| 0.4692 \| 0.1691 \| 0.3176 \| 0.1561 \| 0.4328 \| 0.279 \| 0.4119 \| \| 0.9476 \| 46.0 \| 4922 \| 1.3047 \| 0.2737 \| 0.5779 \| 0.2219 \| 0.1539 \| 0.1932 \| 0.4666 \| 0.3059 \| 0.4356 \| 0.449 \| 0.2126 \| 0.3626 \| 0.6348 \| 0.5344 \| 0.6896 \| 0.2537 \| 0.4615 \| 0.1547 \| 0.3055 \| 0.1925 \| 0.4393 \| 0.233 \| 0.349 \| \| 0.9206 \| 47.0 \| 5029 \| 1.2955 \| 0.283 \| 0.579 \| 0.2365 \| 0.1437 \| 0.1993 \| 0.5024 \| 0.3048 \| 0.4388 \| 0.4501 \| 0.2412 \| 0.3643 \| 0.6283 \| 0.5501 \| 0.6913 \| 0.2723 \| 0.4096 \| 0.1598 \| 0.302 \| 0.1965 \| 0.4689 \| 0.2364 \| 0.3789 \| \| 0.9431 \| 48.0 \| 5136 \| 1.2809 \| 0.2955 \| 0.6039 \| 0.2482 \| 0.0914 \| 0.2308 \| 0.5222 \| 0.3096 \| 0.4442 \| 0.4548 \| 0.2012 \| 0.3786 \| 0.6271 \| 0.5476 \| 0.6855 \| 0.3006 \| 0.4154 \| 0.1713 \| 0.3246 \| 0.2015 \| 0.441 \| 0.2567 \| 0.4072 \| \| 0.8965 \| 49.0 \| 5243 \| 1.2968 \| 0.2935 \| 0.6015 \| 0.2566 \| 0.1579 \| 0.2276 \| 0.4831 \| 0.3112 \| 0.4451 \| 0.4623 \| 0.2279 \| 0.3816 \| 0.6389 \| 0.5336 \| 0.6844 \| 0.2907 \| 0.4558 \| 0.1725 \| 0.3317 \| 0.1938 \| 0.4344 \| 0.2771 \| 0.4052 \| \| 0.8636 \| 50.0 \| 5350 \| 1.2918 \| 0.2933 \| 0.611 \| 0.2363 \| 0.1728 \| 0.2087 \| 0.5035 \| 0.3236 \| 0.4496 \| 0.468 \| 0.2535 \| 0.3745 \| 0.6468 \| 0.5353 \| 0.6786 \| 0.2819 \| 0.4769 \| 0.1849 \| 0.3432 \| 0.2064 \| 0.4475 \| 0.2583 \| 0.3938 \| \| 0.8769 \| 51.0 \| 5457 \| 1.2744 \| 0.2959 \| 0.6161 \| 0.2408 \| 0.1599 \| 0.2291 \| 0.4925 \| 0.3212 \| 0.4461 \| 0.4567 \| 0.2249 \| 0.3782 \| 0.6405 \| 0.5475 \| 0.6942 \| 0.289 \| 0.4365 \| 0.1629 \| 0.3191 \| 0.2061 \| 0.4361 \| 0.2737 \| 0.3974 \| \| 0.8591 \| 52.0 \| 5564 \| 1.3174 \| 0.2949 \| 0.6031 \| 0.254 \| 0.1589 \| 0.2241 \| 0.5068 \| 0.3164 \| 0.4543 \| 0.4668 \| 0.2307 \| 0.3872 \| 0.6637 \| 0.5382 \| 0.6884 \| 0.2941 \| 0.4596 \| 0.1565 \| 0.3106 \| 0.2212 \| 0.4689 \| 0.2645 \| 0.4067 \| \| 0.8347 \| 53.0 \| 5671 \| 1.2626 \| 0.3071 \| 0.6126 \| 0.2618 \| 0.1107 \| 0.228 \| 0.5266 \| 0.3237 \| 0.4523 \| 0.4672 \| 0.2187 \| 0.3786 \| 0.6472 \| 0.5428 \| 0.6809 \| 0.3195 \| 0.4673 \| 0.1497 \| 0.3156 \| 0.2318 \| 0.4377 \| 0.2917 \| 0.4345 \| \| 0.8474 \| 54.0 \| 5778 \| 1.2730 \| 0.3094 \| 0.6174 \| 0.2591 \| 0.1729 \| 0.2387 \| 0.5153 \| 0.3268 \| 0.4591 \| 0.4714 \| 0.2482 \| 0.3975 \| 0.645 \| 0.5352 \| 0.6925 \| 0.3235 \| 0.4615 \| 0.1627 \| 0.3151 \| 0.2273 \| 0.459 \| 0.2983 \| 0.4289 \| \| 0.8326 \| 55.0 \| 5885 \| 1.3201 \| 0.2977 \| 0.6056 \| 0.259 \| 0.1635 \| 0.2296 \| 0.4971 \| 0.3159 \| 0.4505 \| 0.4619 \| 0.2297 \| 0.3789 \| 0.6458 \| 0.5208 \| 0.6769 \| 0.3094 \| 0.4462 \| 0.1661 \| 0.3075 \| 0.1961 \| 0.4508 \| 0.2963 \| 0.4284 \| \| 0.8263 \| 56.0 \| 5992 \| 1.2886 \| 0.3029 \| 0.6093 \| 0.2592 \| 0.1656 \| 0.2243 \| 0.5254 \| 0.3256 \| 0.4529 \| 0.471 \| 0.2464 \| 0.3875 \| 0.6623 \| 0.5289 \| 0.6838 \| 0.3448 \| 0.4808 \| 0.1479 \| 0.3095 \| 0.2026 \| 0.4574 \| 0.2906 \| 0.4237 \| \| 0.8222 \| 57.0 \| 6099 \| 1.2601 \| 0.3086 \| 0.6085 \| 0.2789 \| 0.1746 \| 0.2259 \| 0.5201 \| 0.3328 \| 0.4596 \| 0.4718 \| 0.2474 \| 0.3766 \| 0.6718 \| 0.5455 \| 0.6931 \| 0.3148 \| 0.4769 \| 0.1575 \| 0.3181 \| 0.2356 \| 0.4525 \| 0.2895 \| 0.4186 \| \| 0.8328 \| 58.0 \| 6206 \| 1.2603 \| 0.3137 \| 0.6183 \| 0.2922 \| 0.1647 \| 0.2253 \| 0.5418 \| 0.3307 \| 0.4605 \| 0.4712 \| 0.243 \| 0.3802 \| 0.6654 \| 0.5519 \| 0.6936 \| 0.3174 \| 0.4615 \| 0.1559 \| 0.3276 \| 0.2486 \| 0.4492 \| 0.2948 \| 0.4242 \| \| 0.8005 \| 59.0 \| 6313 \| 1.2536 \| 0.3103 \| 0.6141 \| 0.266 \| 0.173 \| 0.2226 \| 0.5386 \| 0.3322 \| 0.4597 \| 0.4703 \| 0.2512 \| 0.3798 \| 0.6546 \| 0.5517 \| 0.6867 \| 0.3166 \| 0.4673 \| 0.1487 \| 0.309 \| 0.2489 \| 0.4623 \| 0.2857 \| 0.4263 \| \| 0.7786 \| 60.0 \| 6420 \| 1.2642 \| 0.309 \| 0.6275 \| 0.2553 \| 0.1445 \| 0.2286 \| 0.5254 \| 0.3205 \| 0.4492 \| 0.4578 \| 0.223 \| 0.3713 \| 0.6501 \| 0.5394 \| 0.6786 \| 0.3146 \| 0.4346 \| 0.1573 \| 0.3126 \| 0.2571 \| 0.4623 \| 0.2764 \| 0.401 \| \| 0.8077 \| 61.0 \| 6527 \| 1.2750 \| 0.2959 \| 0.6106 \| 0.236 \| 0.1838 \| 0.2134 \| 0.5175 \| 0.3227 \| 0.4463 \| 0.4581 \| 0.2573 \| 0.3555 \| 0.662 \| 0.5316 \| 0.6884 \| 0.3034 \| 0.4308 \| 0.1394 \| 0.302 \| 0.2304 \| 0.459 \| 0.2748 \| 0.4103 \| \| 0.7867 \| 62.0 \| 6634 \| 1.2521 \| 0.3135 \| 0.6255 \| 0.2768 \| 0.1781 \| 0.2267 \| 0.5261 \| 0.3347 \| 0.4584 \| 0.4696 \| 0.2595 \| 0.3671 \| 0.6675 \| 0.5386 \| 0.6925 \| 0.3145 \| 0.4635 \| 0.1656 \| 0.3211 \| 0.258 \| 0.4639 \| 0.2908 \| 0.4072 \| \| 0.772 \| 63.0 \| 6741 \| 1.2546 \| 0.3072 \| 0.6313 \| 0.2524 \| 0.1802 \| 0.2297 \| 0.5233 \| 0.3295 \| 0.4587 \| 0.4696 \| 0.2715 \| 0.3667 \| 0.6625 \| 0.5381 \| 0.6913 \| 0.277 \| 0.4346 \| 0.1792 \| 0.3327 \| 0.2497 \| 0.4787 \| 0.2918 \| 0.4108 \| \| 0.7736 \| 64.0 \| 6848 \| 1.2673 \| 0.3021 \| 0.614 \| 0.244 \| 0.1813 \| 0.2327 \| 0.4996 \| 0.3305 \| 0.4561 \| 0.4622 \| 0.2509 \| 0.3738 \| 0.6488 \| 0.5308 \| 0.6803 \| 0.3057 \| 0.4423 \| 0.1597 \| 0.306 \| 0.2247 \| 0.459 \| 0.2895 \| 0.4232 \| \| 0.7561 \| 65.0 \| 6955 \| 1.3063 \| 0.2936 \| 0.6089 \| 0.2321 \| 0.1759 \| 0.2158 \| 0.5179 \| 0.3244 \| 0.4481 \| 0.4604 \| 0.2518 \| 0.3706 \| 0.6391 \| 0.519 \| 0.6769 \| 0.3054 \| 0.4558 \| 0.1472 \| 0.308 \| 0.2309 \| 0.459 \| 0.2653 \| 0.4021 \| \| 0.7316 \| 66.0 \| 7062 \| 1.2575 \| 0.315 \| 0.6168 \| 0.2804 \| 0.1995 \| 0.2251 \| 0.5303 \| 0.3363 \| 0.464 \| 0.4719 \| 0.2563 \| 0.3883 \| 0.6547 \| 0.5418 \| 0.6913 \| 0.3304 \| 0.4577 \| 0.1497 \| 0.3141 \| 0.2533 \| 0.4738 \| 0.3001 \| 0.4227 \| \| 0.746 \| 67.0 \| 7169 \| 1.2713 \| 0.3118 \| 0.6229 \| 0.2625 \| 0.1695 \| 0.2338 \| 0.5371 \| 0.3323 \| 0.4597 \| 0.4707 \| 0.2419 \| 0.3916 \| 0.6594 \| 0.5428 \| 0.6896 \| 0.3193 \| 0.4538 \| 0.1503 \| 0.3166 \| 0.2676 \| 0.4787 \| 0.2791 \| 0.4149 \| \| 0.7539 \| 68.0 \| 7276 \| 1.2705 \| 0.3161 \| 0.6353 \| 0.2591 \| 0.1718 \| 0.2332 \| 0.5451 \| 0.3343 \| 0.4568 \| 0.4702 \| 0.2554 \| 0.3826 \| 0.6569 \| 0.5358 \| 0.6803 \| 0.3318 \| 0.4577 \| 0.1494 \| 0.3146 \| 0.2715 \| 0.4918 \| 0.292 \| 0.4067 \| \| 0.7219 \| 69.0 \| 7383 \| 1.2644 \| 0.3175 \| 0.6321 \| 0.2758 \| 0.1924 \| 0.2341 \| 0.5379 \| 0.3404 \| 0.4653 \| 0.4744 \| 0.2457 \| 0.3925 \| 0.6648 \| 0.5442 \| 0.6988 \| 0.3151 \| 0.4712 \| 0.1662 \| 0.3176 \| 0.2727 \| 0.4672 \| 0.2895 \| 0.417 \| \| 0.7202 \| 70.0 \| 7490 \| 1.2718 \| 0.3124 \| 0.6233 \| 0.2622 \| 0.1758 \| 0.2246 \| 0.5305 \| 0.334 \| 0.4534 \| 0.4602 \| 0.2437 \| 0.3718 \| 0.6419 \| 0.547 \| 0.6954 \| 0.3204 \| 0.4327 \| 0.16 \| 0.3251 \| 0.2487 \| 0.4361 \| 0.2858 \| 0.4119 \| \| 0.7019 \| 71.0 \| 7597 \| 1.2819 \| 0.3073 \| 0.6104 \| 0.2737 \| 0.1777 \| 0.2295 \| 0.5232 \| 0.3287 \| 0.4558 \| 0.4679 \| 0.2492 \| 0.3812 \| 0.6526 \| 0.548 \| 0.6913 \| 0.3097 \| 0.4269 \| 0.1571 \| 0.3176 \| 0.2321 \| 0.4951 \| 0.2894 \| 0.4088 \| \| 0.6951 \| 72.0 \| 7704 \| 1.2853 \| 0.3099 \| 0.6174 \| 0.2613 \| 0.1872 \| 0.2375 \| 0.5177 \| 0.3358 \| 0.463 \| 0.4711 \| 0.2495 \| 0.3905 \| 0.6495 \| 0.5493 \| 0.6954 \| 0.3007 \| 0.4462 \| 0.1597 \| 0.3312 \| 0.2477 \| 0.4689 \| 0.2921 \| 0.4139 \| \| 0.6821 \| 73.0 \| 7811 \| 1.2949 \| 0.304 \| 0.6157 \| 0.2543 \| 0.1795 \| 0.2194 \| 0.5134 \| 0.3303 \| 0.4579 \| 0.4682 \| 0.2485 \| 0.3667 \| 0.6627 \| 0.5392 \| 0.6884 \| 0.3043 \| 0.4596 \| 0.1457 \| 0.3106 \| 0.2493 \| 0.4803 \| 0.2817 \| 0.4021 \| \| 0.6943 \| 74.0 \| 7918 \| 1.2790 \| 0.3101 \| 0.6194 \| 0.2567 \| 0.1863 \| 0.2366 \| 0.52 \| 0.3337 \| 0.4559 \| 0.4647 \| 0.2422 \| 0.3708 \| 0.6517 \| 0.5416 \| 0.6844 \| 0.3093 \| 0.4442 \| 0.1441 \| 0.3005 \| 0.2547 \| 0.4754 \| 0.3007 \| 0.4191 \| \| 0.6786 \| 75.0 \| 8025 \| 1.2823 \| 0.3121 \| 0.6285 \| 0.2576 \| 0.1497 \| 0.2309 \| 0.5135 \| 0.3348 \| 0.4575 \| 0.4686 \| 0.2583 \| 0.3764 \| 0.6485 \| 0.5449 \| 0.6838 \| 0.3174 \| 0.4635 \| 0.1517 \| 0.3111 \| 0.2466 \| 0.4639 \| 0.2997 \| 0.4206 \| \| 0.6606 \| 76.0 \| 8132 \| 1.2780 \| 0.3191 \| 0.628 \| 0.2754 \| 0.1958 \| 0.2363 \| 0.5414 \| 0.3399 \| 0.4597 \| 0.4708 \| 0.2592 \| 0.3744 \| 0.6633 \| 0.5547 \| 0.7 \| 0.316 \| 0.4442 \| 0.1575 \| 0.3111 \| 0.2601 \| 0.4672 \| 0.3071 \| 0.4314 \| \| 0.6585 \| 77.0 \| 8239 \| 1.2696 \| 0.3162 \| 0.6344 \| 0.2645 \| 0.1962 \| 0.2277 \| 0.5346 \| 0.3365 \| 0.4586 \| 0.4682 \| 0.2466 \| 0.3861 \| 0.6505 \| 0.544 \| 0.6931 \| 0.3221 \| 0.4462 \| 0.1604 \| 0.3231 \| 0.243 \| 0.4541 \| 0.3117 \| 0.4247 \| \| 0.6592 \| 78.0 \| 8346 \| 1.2684 \| 0.3196 \| 0.6245 \| 0.2718 \| 0.2043 \| 0.2306 \| 0.5384 \| 0.3429 \| 0.4657 \| 0.4755 \| 0.2704 \| 0.3934 \| 0.6572 \| 0.5458 \| 0.6902 \| 0.3302 \| 0.4577 \| 0.1748 \| 0.3377 \| 0.2455 \| 0.4672 \| 0.3014 \| 0.4247 \| \| 0.6585 \| 79.0 \| 8453 \| 1.2727 \| 0.3178 \| 0.6234 \| 0.2755 \| 0.2032 \| 0.2309 \| 0.5422 \| 0.337 \| 0.4627 \| 0.4718 \| 0.2685 \| 0.3828 \| 0.6604 \| 0.5462 \| 0.6879 \| 0.334 \| 0.4538 \| 0.1643 \| 0.3337 \| 0.2447 \| 0.4738 \| 0.2997 \| 0.4098 \| \| 0.6463 \| 80.0 \| 8560 \| 1.2868 \| 0.3123 \| 0.6186 \| 0.2752 \| 0.1921 \| 0.2364 \| 0.5315 \| 0.3377 \| 0.4535 \| 0.465 \| 0.2578 \| 0.3804 \| 0.6535 \| 0.5375 \| 0.6879 \| 0.333 \| 0.4519 \| 0.1566 \| 0.3201 \| 0.2372 \| 0.4525 \| 0.2973 \| 0.4129 \| \| 0.6458 \| 81.0 \| 8667 \| 1.2962 \| 0.3167 \| 0.6227 \| 0.277 \| 0.1767 \| 0.2339 \| 0.5406 \| 0.3376 \| 0.4549 \| 0.4652 \| 0.244 \| 0.3766 \| 0.6626 \| 0.5454 \| 0.6948 \| 0.3445 \| 0.4692 \| 0.1444 \| 0.3146 \| 0.2609 \| 0.441 \| 0.2885 \| 0.4062 \| \| 0.6207 \| 82.0 \| 8774 \| 1.2957 \| 0.3139 \| 0.6258 \| 0.2759 \| 0.202 \| 0.2299 \| 0.5281 \| 0.3389 \| 0.4568 \| 0.47 \| 0.2643 \| 0.3793 \| 0.6634 \| 0.5374 \| 0.6896 \| 0.3243 \| 0.4692 \| 0.151 \| 0.3246 \| 0.258 \| 0.4443 \| 0.2987 \| 0.4222 \| \| 0.6356 \| 83.0 \| 8881 \| 1.3102 \| 0.31 \| 0.618 \| 0.2765 \| 0.1833 \| 0.229 \| 0.5134 \| 0.3317 \| 0.4583 \| 0.4686 \| 0.2588 \| 0.394 \| 0.642 \| 0.5361 \| 0.689 \| 0.3412 \| 0.4769 \| 0.1453 \| 0.3241 \| 0.241 \| 0.4443 \| 0.2865 \| 0.4088 \| \| 0.6286 \| 84.0 \| 8988 \| 1.2985 \| 0.3066 \| 0.6197 \| 0.2678 \| 0.1879 \| 0.2187 \| 0.524 \| 0.3336 \| 0.4478 \| 0.4549 \| 0.2533 \| 0.3669 \| 0.6431 \| 0.5354 \| 0.6844 \| 0.3222 \| 0.4423 \| 0.1468 \| 0.306 \| 0.2344 \| 0.4344 \| 0.294 \| 0.4072 \| \| 0.6269 \| 85.0 \| 9095 \| 1.2769 \| 0.3174 \| 0.6259 \| 0.2708 \| 0.2034 \| 0.2266 \| 0.5397 \| 0.3425 \| 0.4593 \| 0.4669 \| 0.2736 \| 0.3741 \| 0.6577 \| 0.5429 \| 0.6919 \| 0.3334 \| 0.4596 \| 0.1592 \| 0.3211 \| 0.2454 \| 0.441 \| 0.3061 \| 0.4211 \| \| 0.5998 \| 86.0 \| 9202 \| 1.2841 \| 0.317 \| 0.616 \| 0.2823 \| 0.1881 \| 0.2347 \| 0.538 \| 0.3419 \| 0.4606 \| 0.4711 \| 0.2701 \| 0.3843 \| 0.6488 \| 0.5414 \| 0.6896 \| 0.3243 \| 0.4692 \| 0.1586 \| 0.3256 \| 0.2489 \| 0.4443 \| 0.3116 \| 0.4268 \| \| 0.6054 \| 87.0 \| 9309 \| 1.2951 \| 0.3163 \| 0.6223 \| 0.2731 \| 0.1937 \| 0.2287 \| 0.541 \| 0.3368 \| 0.4578 \| 0.4648 \| 0.2732 \| 0.3682 \| 0.6534 \| 0.5436 \| 0.6832 \| 0.3263 \| 0.4577 \| 0.1617 \| 0.3196 \| 0.251 \| 0.4377 \| 0.2988 \| 0.4258 \| \| 0.6138 \| 88.0 \| 9416 \| 1.2934 \| 0.3144 \| 0.6109 \| 0.2739 \| 0.1932 \| 0.2315 \| 0.5285 \| 0.3451 \| 0.4584 \| 0.4683 \| 0.264 \| 0.3767 \| 0.6479 \| 0.5384 \| 0.6844 \| 0.3286 \| 0.4712 \| 0.1513 \| 0.3131 \| 0.2518 \| 0.4492 \| 0.3019 \| 0.4237 \| \| 0.5887 \| 89.0 \| 9523 \| 1.2940 \| 0.3171 \| 0.6147 \| 0.279 \| 0.2023 \| 0.2306 \| 0.5411 \| 0.3377 \| 0.4613 \| 0.4698 \| 0.2773 \| 0.3725 \| 0.6505 \| 0.5397 \| 0.689 \| 0.336 \| 0.4673 \| 0.161 \| 0.3161 \| 0.248 \| 0.4541 \| 0.3008 \| 0.4227 \| \| 0.6026 \| 90.0 \| 9630 \| 1.3081 \| 0.3106 \| 0.6154 \| 0.2657 \| 0.2063 \| 0.2143 \| 0.5398 \| 0.3342 \| 0.4543 \| 0.4616 \| 0.2846 \| 0.3601 \| 0.6397 \| 0.5287 \| 0.6769 \| 0.3076 \| 0.4442 \| 0.1675 \| 0.3146 \| 0.2498 \| 0.4426 \| 0.2996 \| 0.4299 \| \| 0.6116 \| 91.0 \| 9737 \| 1.3193 \| 0.3046 \| 0.5974 \| 0.2664 \| 0.215 \| 0.2096 \| 0.5344 \| 0.3349 \| 0.4514 \| 0.4594 \| 0.2806 \| 0.3671 \| 0.6362 \| 0.5371 \| 0.6844 \| 0.2908 \| 0.4327 \| 0.1537 \| 0.3121 \| 0.2481 \| 0.4475 \| 0.2931 \| 0.4201 \| \| 0.5874 \| 92.0 \| 9844 \| 1.3148 \| 0.307 \| 0.5997 \| 0.2706 \| 0.207 \| 0.2168 \| 0.5303 \| 0.3364 \| 0.457 \| 0.4653 \| 0.2759 \| 0.38 \| 0.6418 \| 0.5305 \| 0.6827 \| 0.3038 \| 0.4519 \| 0.1554 \| 0.3176 \| 0.2488 \| 0.4541 \| 0.2965 \| 0.4201 \| \| 0.5765 \| 93.0 \| 9951 \| 1.3114 \| 0.31 \| 0.6063 \| 0.2757 \| 0.2056 \| 0.2172 \| 0.5388 \| 0.3349 \| 0.4549 \| 0.4625 \| 0.275 \| 0.369 \| 0.6406 \| 0.5308 \| 0.6827 \| 0.3106 \| 0.4538 \| 0.1567 \| 0.309 \| 0.2532 \| 0.4459 \| 0.2988 \| 0.4211 \| \| 0.5799 \| 94.0 \| 10058 \| 1.3074 \| 0.3101 \| 0.6134 \| 0.2681 \| 0.2042 \| 0.2135 \| 0.5519 \| 0.3365 \| 0.454 \| 0.4634 \| 0.272 \| 0.3674 \| 0.6508 \| 0.53 \| 0.6821 \| 0.3105 \| 0.4596 \| 0.1525 \| 0.3161 \| 0.2582 \| 0.4377 \| 0.2993 \| 0.4216 \| \| 0.5678 \| 95.0 \| 10165 \| 1.3073 \| 0.3102 \| 0.6184 \| 0.2637 \| 0.2103 \| 0.2233 \| 0.537 \| 0.3327 \| 0.4527 \| 0.461 \| 0.2775 \| 0.3668 \| 0.6309 \| 0.537 \| 0.6884 \| 0.3217 \| 0.4404 \| 0.1558 \| 0.3106 \| 0.2354 \| 0.4459 \| 0.3011 \| 0.4196 \| \| 0.5776 \| 96.0 \| 10272 \| 1.3083 \| 0.3146 \| 0.6177 \| 0.2751 \| 0.2083 \| 0.2193 \| 0.5465 \| 0.3364 \| 0.4548 \| 0.4627 \| 0.2731 \| 0.3681 \| 0.6399 \| 0.5359 \| 0.6861 \| 0.3306 \| 0.4538 \| 0.1566 \| 0.3176 \| 0.2484 \| 0.4361 \| 0.3012 \| 0.4201 \| \| 0.5768 \| 97.0 \| 10379 \| 1.3027 \| 0.3134 \| 0.6204 \| 0.2681 \| 0.2056 \| 0.2203 \| 0.5453 \| 0.3365 \| 0.4545 \| 0.4622 \| 0.2774 \| 0.3652 \| 0.6417 \| 0.5329 \| 0.6832 \| 0.3282 \| 0.4538 \| 0.1591 \| 0.3181 \| 0.2445 \| 0.4328 \| 0.3021 \| 0.4232 \| \| 0.5708 \| 98.0 \| 10486 \| 1.3045 \| 0.3094 \| 0.6223 \| 0.2705 \| 0.2077 \| 0.2196 \| 0.5411 \| 0.3325 \| 0.4544 \| 0.4623 \| 0.2754 \| 0.3668 \| 0.641 \| 0.5342 \| 0.6838 \| 0.3201 \| 0.4519 \| 0.1588 \| 0.3181 \| 0.2347 \| 0.4361 \| 0.299 \| 0.4216 \| \| 0.5779 \| 99.0 \| 10593 \| 1.3050 \| 0.3104 \| 0.6231 \| 0.2616 \| 0.2049 \| 0.2159 \| 0.5424 \| 0.3324 \| 0.455 \| 0.4645 \| 0.274 \| 0.3682 \| 0.6457 \| 0.536 \| 0.6838 \| 0.3162 \| 0.4615 \| 0.1593 \| 0.3176 \| 0.2376 \| 0.4377 \| 0.303 \| 0.4216 \| \| 0.5571 \| 100.0 \| 10700 \| 1.3007 \| 0.3131 \| 0.6268 \| 0.2698 \| 0.2089 \| 0.2237 \| 0.5358 \| 0.3352 \| 0.4586 \| 0.4682 \| 0.2773 \| 0.3748 \| 0.6417 \| 0.5362 \| 0.6844 \| 0.3199 \| 0.4635 \| 0.1628 \| 0.3176 \| 0.2427 \| 0.4475 \| 0.3038 \| 0.4278 \| ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0
mychen76/invoice-and-receipts_donut_v1	mychen76	2024-04-19T14:39:23Z	1,608	45	transformers	[ "transformers", "pytorch", "safetensors", "vision-encoder-decoder", "image-text-to-text", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2023-09-23T00:34:40Z	--- license: apache-2.0 --- Model Architecture: The mychen76/invoice-and-receipts_donut_v1 (LLM) is a finetuned for convert Invoice or Receipt Image to XML or Json data strucutre task. this experimental model is based on Donut model. Motivation: Remove OCR engine, use only LLM model to convert an invoice or receipt json object could reduce the conversion step and reduce resource utilization and deployment dependencies. Result, better performance. Model Usage: Take following an invoice receipt image and get an output Json or xml like this: *JSON OUTPUT* ```json { 'header': { 'invoice_no': '13194726', 'invoice_date': '05/29/2021', 'seller': 'Hopkins and Sons 62283 Flores Tunnel North Luis, IA 69983', 'client': 'Sims PLC USS Kramer FPO AA 81651', 'seller_tax_id': '952-73-7223', 'client_tax_id': '995-88-9495', 'iban': 'GB31LZX520242755934691' }, 'items': [ { 'item_desc': 'Beach Lunch Lounge Striped Shirt Dress Large Navy Blue White Long Sleeve Casual', 'item_qty': '1,00', 'item_net_price': '16,99', 'item_net_worth': '16,99', 'item_vat': '10%', 'item_gross_worth': '18,69' }, { 'item_desc': 'Jams World Hawaiian 0 Dress Rayon SZ.L', 'item_qty': '5,00', 'item_net_price': '65,00', 'item_net_worth': '325,00', 'item_vat': '10%', 'item_gross_worth': '357,50' }, { 'item_desc': 'LuLaRoe Nicole Dress Size Large 26', 'item_qty': '2,00', 'item_net_price': '1,99', 'item_net_worth': '3,98', 'item_vat': '10%', 'item_gross_worth': '4,38' }, { 'item_desc': 'phynny Was Medium Linen Wrap Dress Dessert Rose Embroidered Bohemian', 'item_qty': '2,00', 'item_net_price': '89,99', 'item_net_worth': '179,98', 'item_vat': '10%', 'item_gross_worth': '197,98' }, { 'item_desc': "Eileen Fisher Women's Long Sleeve Fleece Lined Front Pockets Dress XS Gray", 'item_qty': '2,00', 'item_net_price': '15,99', 'item_net_worth': '31,98', 'item_vat': '10%', 'item_gross_worth': '35,18' }, { 'item_desc': "Hanna Anderson Women's L Large Coral Short Sleeve Casual Fall Tee Shirt Dress", 'item_qty': '1,00', 'item_net_price': '24,00', 'item_net_worth': '24,00', 'item_vat': '10%', 'item_gross_worth': '26,40' } ], 'summary': {'total_net_worth': '$581,93', 'total_vat': '$58,19', 'total_gross_worth': '$ 640,12'} } ``` *XML OUTPUT* ```xml <s_header> <s_invoice_no> 13194726</s_invoice_no> <s_invoice_date> 05/29/2021</s_invoice_date> <s_seller> Hopkins and Sons 62283 Flores Tunnel North Luis, IA 69983</s_seller> <s_client> Sims PLC USS Kramer FPO AA 81651</s_client> <s_seller_tax_id> 952-73-7223</s_seller_tax_id> <s_client_tax_id> 995-88-9495</s_client_tax_id> <s_iban> GB31LZX520242755934691</s_iban> </s_header> <s_items> <s_item_desc> Beach Lunch Lounge Striped Shirt Dress Large Navy Blue White Long Sleeve Casual</s_item_desc> <s_item_qty> 1,00</s_item_qty> <s_item_net_price> 16,99</s_item_net_price> <s_item_net_worth> 16,99</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 18,69</s_item_gross_worth> <sep/> <s_item_desc> Jams World Hawaiian 0 Dress Rayon SZ.L</s_item_desc> <s_item_qty> 5,00</s_item_qty> <s_item_net_price> 65,00</s_item_net_price> <s_item_net_worth> 325,00</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 357,50</s_item_gross_worth> <sep/> <s_item_desc> LuLaRoe Nicole Dress Size Large 26</s_item_desc> <s_item_qty> 2,00</s_item_qty> <s_item_net_price> 1,99</s_item_net_price> <s_item_net_worth> 3,98</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 4,38</s_item_gross_worth> <sep/> <s_item_desc> phynny Was Medium Linen Wrap Dress Dessert Rose Embroidered Bohemian</s_item_desc> <s_item_qty> 2,00</s_item_qty> <s_item_net_price> 89,99</s_item_net_price> <s_item_net_worth> 179,98</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 197,98</s_item_gross_worth> <sep/> <s_item_desc> Eileen Fisher Women's Long Sleeve Fleece Lined Front Pockets Dress XS Gray</s_item_desc> <s_item_qty> 2,00</s_item_qty> <s_item_net_price> 15,99</s_item_net_price> <s_item_net_worth> 31,98</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 35,18</s_item_gross_worth> <sep/> <s_item_desc> Hanna Anderson Women's L Large Coral Short Sleeve Casual Fall Tee Shirt Dress</s_item_desc> <s_item_qty> 1,00</s_item_qty> <s_item_net_price> 24,00</s_item_net_price> <s_item_net_worth> 24,00</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 26,40</s_item_gross_worth> </s_items> <s_summary> <s_total_net_worth> $581,93</s_total_net_worth> <s_total_vat> $58,19</s_total_vat> <s_total_gross_worth> $ 640,12</s_total_gross_worth> </s_summary> ```
ali9132/CostumData_new	ali9132	2024-04-19T14:34:00Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "fa", "dataset:mozilla-foundation/CostumData_new", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-04-19T12:18:38Z	--- language: - fa license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/CostumData_new model-index: - name: Whisper Small CostumData_new results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small CostumData_new This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the CostumData_new dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2603 - eval_wer: 28.9730 - eval_runtime: 3297.5897 - eval_samples_per_second: 1.931 - eval_steps_per_second: 0.241 - epoch: 2.5126 - step: 1000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
AlignmentResearch/robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10	AlignmentResearch	2024-04-19T14:32:55Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m", "base_model:finetune:EleutherAI/pythia-160m", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-04-19T14:32:35Z	--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m model-index: - name: robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10 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. --> # robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10 This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2
Resi/layfi-v1-docvqa	Resi	2024-04-19T14:29:51Z	5	0	transformers	[ "transformers", "safetensors", "layoutlmv2", "document-question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	document-question-answering	2024-04-19T14:29:17Z	--- 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]
Raul569/lora_outfit_recommender_model	Raul569	2024-04-19T14:26:21Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T14:26:09Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Raul569 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
MR-Eder/llama3-1000-steps-wiki-de-conversation-lora	MR-Eder	2024-04-19T14:23:18Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T14:22:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: MR-Eder - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Sif10/summarization	Sif10	2024-04-19T14:22:38Z	22	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	2024-04-19T10:30:44Z	--- license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: summarization 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. --> # summarization This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2491 - Rouge1: 0.3279 - Rouge2: 0.2271 - Rougel: 0.3003 - Rougelsum: 0.3005 - Gen Len: 18.9811 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.372 \| 1.0 \| 4189 \| 0.2643 \| 0.3326 \| 0.2341 \| 0.3055 \| 0.3053 \| 18.9784 \| \| 0.3303 \| 2.0 \| 8378 \| 0.2558 \| 0.3379 \| 0.2401 \| 0.3112 \| 0.3112 \| 18.9808 \| \| 0.3069 \| 3.0 \| 12567 \| 0.2482 \| 0.34 \| 0.241 \| 0.3129 \| 0.313 \| 18.9815 \| \| 0.3057 \| 4.0 \| 16756 \| 0.2491 \| 0.3279 \| 0.2271 \| 0.3003 \| 0.3005 \| 18.9811 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
coralexbadea/llama3-sql-16bit	coralexbadea	2024-04-19T14:21:23Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T13:30:07Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: coralexbadea - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dylanebert/mvdream	dylanebert	2024-04-19T14:17:31Z	27	5	diffusers	[ "diffusers", "safetensors", "text-to-3d", "arxiv:2308.16512", "license:openrail", "diffusers:MVDreamPipeline", "region:us" ]	text-to-3d	2024-04-19T14:17:10Z	--- license: openrail pipeline_tag: text-to-3d --- This is a copy of [ashawkey/mvdream-sd2.1-diffusers](https://huggingface.co/ashawkey/mvdream-sd2.1-diffusers). It is hosted here for persistence throughout the ML for 3D course. # MVDream-diffusers Model Card This is a port of https://huggingface.co/MVDream/MVDream into diffusers. For usage, please check: https://github.com/ashawkey/mvdream_diffusers ## Citation ``` @article{shi2023MVDream, author = {Shi, Yichun and Wang, Peng and Ye, Jianglong and Mai, Long and Li, Kejie and Yang, Xiao}, title = {MVDream: Multi-view Diffusion for 3D Generation}, journal = {arXiv:2308.16512}, year = {2023}, } ``` ## Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
brugmark/all-MiniLM-L6-v2-personal-project-default-2024-04-19	brugmark	2024-04-19T14:14:31Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-04-19T13:12:36Z	--- license: apache-2.0 base_model: sentence-transformers/all-MiniLM-L6-v2 tags: - generated_from_trainer model-index: - name: all-MiniLM-L6-v2-personal-project-default-2024-04-19 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. --> # all-MiniLM-L6-v2-personal-project-default-2024-04-19 This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 10.7201 - eval_runtime: 3708.4666 - eval_samples_per_second: 8.168 - eval_steps_per_second: 0.255 - step: 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2
MR-Eder/llama3-1000-steps-wiki-de-conversation-merged-16bit-GGUF	MR-Eder	2024-04-19T14:10:52Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T14:04:35Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: MR-Eder - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
idoru/jetmoe-8b-MyRus-kto	idoru	2024-04-19T14:01:19Z	8	0	transformers	[ "transformers", "safetensors", "jetmoe", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T13:57:01Z	--- 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]
OneGate/Llama3-OGSQL-FT-8B	OneGate	2024-04-19T14:00:49Z	43	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "Text-to-sql", "conversational", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T11:45:16Z	--- license: cc-by-4.0 tags: - Text-to-sql library_name: transformers --- ### Llama3-OGSQL-8B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657ad4e2583493c1d1efb05b/YzOlyYJEeD4HWAIhcdHGB.png) ### Model Description Llama3-OGSQL-8B was fine-tuned on the most recent and state of the art models (LLAMA 3) for the task of converting natural language text into SQL queries. The model has been trained on more than 270 million tokens, ensuring robust performance and high accuracy in SQL generation tasks. - Model type: Auto-regressive language model - Language(s) (NLP): SQL (target language for generation) - Finetuned from model: Llama3-8B ## Use Case OGSQL-7B is designed to facilitate the conversion of natural language queries into structured SQL commands, aiding in database querying without the need for manual SQL knowledge. ## How to Get Started with the Model ```python # Example code to load and use the model from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model_name = "Llama3-OGSQL-8B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) def generate_sql(query): inputs = tokenizer.encode(query, return_tensors="pt") outputs = model.generate(inputs) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example use query = """ using this context: -- Create Customers Table CREATE TABLE Customers ( customer_id INTEGER PRIMARY KEY, name TEXT NOT NULL, email TEXT, join_date DATE ); -- Create Products Table CREATE TABLE Products ( product_id INTEGER PRIMARY KEY, name TEXT NOT NULL, price DECIMAL(10, 2) ); -- Create Orders Table CREATE TABLE Orders ( order_id INTEGER PRIMARY KEY, customer_id INTEGER, product_id INTEGER, order_date DATE, quantity INTEGER, total_price DECIMAL(10, 2), FOREIGN KEY (customer_id) REFERENCES Customers(customer_id), FOREIGN KEY (product_id) REFERENCES Products(product_id) ); show me all the orders from last month , sort by date """ print(generate_sql(query)) ``` ## alternatively you can use this notebook: [![Colab notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pQuIuCdoFMG76AH3BNZzep8PgRaZkkYS?usp=sharing)
erlend123/emotion-analysis	erlend123	2024-04-19T13:59:55Z	6	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:dair-ai/emotion", "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	2024-04-09T10:11:19Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: emotion-analysis-3000 results: [] datasets: - dair-ai/emotion language: - en --- <!-- 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. --> # emotion-analysis-3000 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: 0.3205 - Accuracy: 0.9015 - F1: 0.9014 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 233 \| 0.2691 \| 0.9070 \| 0.9068 \| \| No log \| 2.0 \| 466 \| 0.2963 \| 0.8928 \| 0.8922 \| \| 0.2332 \| 3.0 \| 699 \| 0.3205 \| 0.9015 \| 0.9014 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
dylanebert/imagedream	dylanebert	2024-04-19T13:57:32Z	8	0	diffusers	[ "diffusers", "safetensors", "image-to-3d", "arxiv:2312.02201", "license:openrail", "diffusers:MVDreamPipeline", "region:us" ]	image-to-3d	2024-04-19T13:57:16Z	--- license: openrail pipeline_tag: image-to-3d --- This is a copy of [ashawkey/imagedream-ipmv-diffusers](https://huggingface.co/ashawkey/imagedream-ipmv-diffusers). It is hosted here for persistence throughout the ML for 3D course. # MVDream-diffusers Model Card This is a port of https://huggingface.co/Peng-Wang/ImageDream into diffusers. For usage, please check: https://github.com/ashawkey/mvdream_diffusers ## Citation ``` @article{wang2023imagedream, title={ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation}, author={Wang, Peng and Shi, Yichun}, journal={arXiv preprint arXiv:2312.02201}, year={2023} } ``` ## Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx	sosoai	2024-04-19T13:56:07Z	7	0	mlx	[ "mlx", "safetensors", "llama", "region:us" ]	null	2024-04-19T13:40:45Z	--- tags: - mlx --- # sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx This model was converted to MLX format from [`sosoai/Hansoldeco-llama3-8b-unsloth-v0.1`]() using mlx-lm version 0.9.0. Refer to the [original model card](https://huggingface.co/sosoai/Hansoldeco-llama3-8b-unsloth-v0.1) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```
mgoin/Meta-Llama-3-8B-Instruct-Marlin	mgoin	2024-04-19T13:52:09Z	112	0	transformers	[ "transformers", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "marlin", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-04-18T19:03:30Z	--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - marlin license: other license_name: llama3 license_link: LICENSE --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/" --local-dir Meta-Llama-3-8B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
Augusto777/swinv2-tiny-patch4-window8-256-dmae-va-U5-42B	Augusto777	2024-04-19T13:43:20Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "swinv2", "image-classification", "generated_from_trainer", "base_model:microsoft/swinv2-tiny-patch4-window8-256", "base_model:finetune:microsoft/swinv2-tiny-patch4-window8-256", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-19T04:23:12Z	--- license: apache-2.0 base_model: microsoft/swinv2-tiny-patch4-window8-256 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swinv2-tiny-patch4-window8-256-dmae-va-U5-42B 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. --> # swinv2-tiny-patch4-window8-256-dmae-va-U5-42B This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9637 - Accuracy: 0.6667 ## 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: 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: 42 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.9 \| 7 \| 7.8663 \| 0.1167 \| \| 6.936 \| 1.94 \| 15 \| 7.7572 \| 0.1167 \| \| 6.936 \| 2.97 \| 23 \| 7.1790 \| 0.1167 \| \| 6.7016 \| 4.0 \| 31 \| 5.9033 \| 0.1167 \| \| 5.5439 \| 4.9 \| 38 \| 4.6116 \| 0.1167 \| \| 5.5439 \| 5.94 \| 46 \| 3.2830 \| 0.1167 \| \| 3.6477 \| 6.97 \| 54 \| 2.2014 \| 0.1167 \| \| 2.2506 \| 8.0 \| 62 \| 1.5647 \| 0.45 \| \| 2.2506 \| 8.9 \| 69 \| 1.3160 \| 0.45 \| \| 1.5088 \| 9.94 \| 77 \| 1.3676 \| 0.3333 \| \| 1.3868 \| 10.97 \| 85 \| 1.3390 \| 0.45 \| \| 1.3868 \| 12.0 \| 93 \| 1.3223 \| 0.3833 \| \| 1.351 \| 12.9 \| 100 \| 1.3156 \| 0.45 \| \| 1.3271 \| 13.94 \| 108 \| 1.3485 \| 0.4833 \| \| 1.3271 \| 14.97 \| 116 \| 1.2646 \| 0.4833 \| \| 1.2322 \| 16.0 \| 124 \| 1.2308 \| 0.4833 \| \| 1.2322 \| 16.9 \| 131 \| 1.2160 \| 0.5 \| \| 1.22 \| 17.94 \| 139 \| 1.2015 \| 0.5 \| \| 1.1899 \| 18.97 \| 147 \| 1.2008 \| 0.5 \| \| 1.1899 \| 20.0 \| 155 \| 1.1606 \| 0.5 \| \| 1.109 \| 20.9 \| 162 \| 1.1182 \| 0.5667 \| \| 1.0603 \| 21.94 \| 170 \| 1.0855 \| 0.5333 \| \| 1.0603 \| 22.97 \| 178 \| 1.0763 \| 0.5667 \| \| 1.0264 \| 24.0 \| 186 \| 1.1153 \| 0.5833 \| \| 1.0086 \| 24.9 \| 193 \| 1.0770 \| 0.65 \| \| 1.0086 \| 25.94 \| 201 \| 1.0041 \| 0.6167 \| \| 0.9301 \| 26.97 \| 209 \| 0.9637 \| 0.6667 \| \| 0.9077 \| 28.0 \| 217 \| 0.9824 \| 0.5833 \| \| 0.9077 \| 28.9 \| 224 \| 0.9485 \| 0.6 \| \| 0.8725 \| 29.94 \| 232 \| 0.9294 \| 0.6167 \| \| 0.8203 \| 30.97 \| 240 \| 0.9348 \| 0.6167 \| \| 0.8203 \| 32.0 \| 248 \| 0.9295 \| 0.6 \| \| 0.8211 \| 32.9 \| 255 \| 0.9167 \| 0.6 \| \| 0.8211 \| 33.94 \| 263 \| 0.9281 \| 0.5833 \| \| 0.7916 \| 34.97 \| 271 \| 0.8803 \| 0.6333 \| \| 0.7822 \| 36.0 \| 279 \| 0.8785 \| 0.6333 \| \| 0.7822 \| 36.9 \| 286 \| 0.8906 \| 0.6 \| \| 0.7937 \| 37.94 \| 294 \| 0.8899 \| 0.6 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0
xshubhamx/tiny-llama-lora	xshubhamx	2024-04-19T13:42:28Z	0	0	null	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-04-16T15:23:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tiny-llama 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. --> # tiny-llama This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6966 - Accuracy: 0.8195 - Precision: 0.8222 - Recall: 0.8195 - Precision Macro: 0.7955 - Recall Macro: 0.7536 - Macro Fpr: 0.0148 - Weighted Fpr: 0.0141 - Weighted Specificity: 0.9765 - Macro Specificity: 0.9873 - Weighted Sensitivity: 0.8327 - Macro Sensitivity: 0.7536 - F1 Micro: 0.8327 - F1 Macro: 0.7609 - F1 Weighted: 0.8291 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| Precision Macro \| Recall Macro \| Macro Fpr \| Weighted Fpr \| Weighted Specificity \| Macro Specificity \| Weighted Sensitivity \| Macro Sensitivity \| F1 Micro \| F1 Macro \| F1 Weighted \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:---------------:\|:------------:\|:---------:\|:------------:\|:--------------------:\|:-----------------:\|:--------------------:\|:-----------------:\|:--------:\|:--------:\|:-----------:\| \| 1.0444 \| 1.0 \| 642 \| 0.5968 \| 0.8056 \| 0.8050 \| 0.8056 \| 0.7122 \| 0.6995 \| 0.0175 \| 0.0169 \| 0.9730 \| 0.9852 \| 0.8056 \| 0.6995 \| 0.8056 \| 0.6986 \| 0.8014 \| \| 0.4788 \| 2.0 \| 1284 \| 0.6966 \| 0.8195 \| 0.8222 \| 0.8195 \| 0.8092 \| 0.7825 \| 0.0161 \| 0.0155 \| 0.9755 \| 0.9863 \| 0.8195 \| 0.7825 \| 0.8195 \| 0.7849 \| 0.8172 \| \| 0.3354 \| 3.0 \| 1926 \| 0.8046 \| 0.8327 \| 0.8276 \| 0.8327 \| 0.8058 \| 0.7582 \| 0.0148 \| 0.0141 \| 0.9758 \| 0.9872 \| 0.8327 \| 0.7582 \| 0.8327 \| 0.7742 \| 0.8282 \| \| 0.0571 \| 4.0 \| 2569 \| 1.1143 \| 0.8265 \| 0.8312 \| 0.8265 \| 0.7904 \| 0.7763 \| 0.0152 \| 0.0148 \| 0.9772 \| 0.9869 \| 0.8265 \| 0.7763 \| 0.8265 \| 0.7690 \| 0.8262 \| \| 0.0187 \| 5.0 \| 3211 \| 1.1104 \| 0.8319 \| 0.8316 \| 0.8319 \| 0.7745 \| 0.7724 \| 0.0149 \| 0.0142 \| 0.9770 \| 0.9873 \| 0.8319 \| 0.7724 \| 0.8319 \| 0.7638 \| 0.8303 \| \| 0.0071 \| 6.0 \| 3853 \| 1.1445 \| 0.8242 \| 0.8210 \| 0.8242 \| 0.7684 \| 0.7384 \| 0.0157 \| 0.0150 \| 0.9755 \| 0.9866 \| 0.8242 \| 0.7384 \| 0.8242 \| 0.7451 \| 0.8209 \| \| 0.0002 \| 7.0 \| 4495 \| 1.2032 \| 0.8327 \| 0.8302 \| 0.8327 \| 0.7985 \| 0.7529 \| 0.0148 \| 0.0141 \| 0.9765 \| 0.9873 \| 0.8327 \| 0.7529 \| 0.8327 \| 0.7617 \| 0.8293 \| \| 0.0028 \| 8.0 \| 5138 \| 1.1918 \| 0.8257 \| 0.8226 \| 0.8257 \| 0.7738 \| 0.7493 \| 0.0155 \| 0.0149 \| 0.9756 \| 0.9868 \| 0.8257 \| 0.7493 \| 0.8257 \| 0.7552 \| 0.8229 \| \| 0.0 \| 9.0 \| 5780 \| 1.2181 \| 0.8311 \| 0.8286 \| 0.8311 \| 0.7935 \| 0.7522 \| 0.0150 \| 0.0143 \| 0.9764 \| 0.9872 \| 0.8311 \| 0.7522 \| 0.8311 \| 0.7592 \| 0.8276 \| \| 0.0018 \| 10.0 \| 6420 \| 1.2265 \| 0.8327 \| 0.8301 \| 0.8327 \| 0.7955 \| 0.7536 \| 0.0148 \| 0.0141 \| 0.9765 \| 0.9873 \| 0.8327 \| 0.7536 \| 0.8327 \| 0.7609 \| 0.8291 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1
Raelina/RaemuMix	Raelina	2024-04-19T13:39:47Z	0	4	null	[ "anime", "stable-diffusion-1.5", "license:creativeml-openrail-m", "region:us" ]	null	2023-07-21T09:46:53Z	--- license: creativeml-openrail-m tags: - anime - stable-diffusion-1.5 --- Instruction and example images go here https://civitai.com/models/113362/raemumix
michaelw37/sc40	michaelw37	2024-04-19T13:39:33Z	5	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T13:38:03Z	--- 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]
GladiusTn/llama3_ocr_to_xml_A1	GladiusTn	2024-04-19T13:36:42Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T13:30:52Z	--- 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]
paloalma/ECE-TW3-JRGL-V1	paloalma	2024-04-19T13:32:48Z	4,222	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "ShinojiResearch/Senku-70B-Full", "152334H/miqu-1-70b-sf", "conversational", "arxiv:2312.06281", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-03T20:54:20Z	--- license: apache-2.0 tags: - merge - mergekit - ShinojiResearch/Senku-70B-Full - 152334H/miqu-1-70b-sf --- # ECE-TW3-JRGL-V1 ## This model has been produced by : - [Louis Garcia](https://www.linkedin.com/in/louis-garcia-profil/), engineering student at [French Engineering School ECE](https://www.ece.fr/en/) - [Matthieu Jollard](https://www.linkedin.com/in/matthieu-jollard/), engineering student at [French Engineering School ECE](https://www.ece.fr/en/) ## Under the supervision of : - [Andre-Louis Rochet](https://www.linkedin.com/in/andrelouisrochet/), Lecturer at ECE & Co-Founder of [TW3 Partners](https://tw3partners.fr/) - [Paul Lemaistre](https://www.linkedin.com/in/paul-lemaistre/), CTO of [TW3 Partners](https://tw3partners.fr/) ## With the contribution of : - ECE engineering school as sponsor and financial contributor - RunPod as financial contributor ## About ECE >_ECE, a multi-program, multi-campus, and multi-sector engineering school specializing in digital engineering, > trains engineers and technology experts for the 21st century, capable of meeting the challenges of the dual digital and sustainable development revolutions. >[French Engineering School ECE](https://www.ece.fr/en/)_ ## Description ECE-TW3-JRGL-V1 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [ShinojiResearch/Senku-70B-Full](https://huggingface.co/ShinojiResearch/Senku-70B-Full) * [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) ```yaml slices: - sources: - model: ShinojiResearch/Senku-70B-Full layer_range: [0, 80] - model: 152334H/miqu-1-70b-sf layer_range: [0, 80] merge_method: slerp base_model: 152334H/miqu-1-70b-sf parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ``` ## Results - ECE-TW3-JRGL-v1 scores 83.07 on [EQ-Bench V2](https://eqbench.com/index.html) --- @misc{paech2023eqbench, title={EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models}, author={Samuel J. Paech}, year={2023}, eprint={2312.06281}, archivePrefix={arXiv}, primaryClass={cs.CL} } ---
sosoai/hansoldeco-llama3-8b-unsloth-lora-v0.1	sosoai	2024-04-19T13:32:29Z	3	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:adapter:unsloth/llama-3-8b-bnb-4bit", "region:us" ]	null	2024-04-19T13:31:55Z	--- library_name: peft base_model: unsloth/llama-3-8b-bnb-4bit --- # 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 0.10.0
LoneStriker/opus-v1.2-llama-3-8b-6.0bpw-h6-exl2	LoneStriker	2024-04-19T13:28:44Z	7	1	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "unsloth", "axolotl", "conversational", "en", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-04-19T13:25:56Z	--- language: - en pipeline_tag: text-generation tags: - unsloth - axolotl license: cc-by-nc-nd-4.0 --- # Llama 3 DreamGen Opus V1 <div style="display: flex; flex-direction: row; align-items: center;"> <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style=" border-radius: 12px; margin-right: 12px; margin-top: 0px; margin-bottom: 0px; max-width: 100px; height: auto; "/> Models for (steerable) story-writing and role-playing. <br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31). </div> ## Resources - [Opus V1 prompting guide](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy. - [Google Colab](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`. - [Python code](example/prompt/format.py) to format the prompt correctly. - Join the community on [Discord](https://dreamgen.com/discord) to get early access to new models. <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style=" padding: 12px; border-radius: 12px; border: 2px solid #f9a8d4; background: rgb(9, 9, 11); "/> ## Prompting <details> <summary>The models use an extended version of ChatML.</summary> ``` <\|im_start\|>system (Story description in the right format here) (Typically consists of plot description, style description and characters)<\|im_end\|> <\|im_start\|>user (Your instruction on how the story should continue)<\|im_end\|> <\|im_start\|>text names= Alice (Continuation of the story from the Alice character)<\|im_end\|> <\|im_start\|>text (Continuation of the story from no character in particular (pure narration))<\|im_end\|> <\|im_start\|>user (Your instruction on how the story should continue)<\|im_end\|> <\|im_start\|>text names= Bob (Continuation of the story from the Bob character)<\|im_end\|> ``` The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names. Pay attention to the following: - The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play. - There can be multiple subsequent message with a `text` role, especially if names are involved. - There can be multiple names attached to a message. - The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names. </details> While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance. Here's how you can prompt the model for the following tasks ### Steerable Story-writing and Role-playing: - [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing) - [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing) - Input: - System prompt: You provide story / role-play description, which consists of: - Plot description - Style description - Characters and their descriptions - Conversation turns: - Text / message turn: This represents part of the story or role play - Instruction: This tells the model what should happen next - Output: Continuation of the story / role-play. ### Story plot summarization - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description) - Input: A story, or a few chapters of a story. - Output: A description of the story or chapters. #### Examples: <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary> <blockquote> Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic. </blockquote> </details> <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary> <blockquote> The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth. One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic. As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary> <blockquote> The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary> <blockquote> A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses. </blockquote> </details> ### Story character description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description) - Input: A story, or a few chapters of a story, set of characters. - Output: A description of the characters. #### Examples: <details> <summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary> <blockquote> Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui. </blockquote> </details> <details> <summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary> <blockquote> The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision. </blockquote> </details> ### Story style description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description) - Input: A story, or a few chapters of a story. - Output: A description the style of the story. <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary> <blockquote> The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose. </blockquote> </details> <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary> <blockquote> The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory. </blockquote> </details> ### Story description to chapters - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions) - Input: A brief plot description and the desired number of chapters. - Output: A description for each chapter. ### And more... ## Sampling params For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`. You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures. ## Dataset The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long. All story-writing and role-playing examples were based on human-written text. ![token count distribution](images/token_count_cum__token_bucket.png) ## Running the model The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization. I recommend using these model versions: - 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b) - 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq) - 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq) ### Running on DreamGen.com (free) You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api). ### Running Locally - Make sure your prompt is as close as possible to the Opus V1 - Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly. - [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1) - [Read the prompt formatting code](example/prompt/format.py) - Make sure `<\|im_start\|>` and `<\|im_end\|>` are tokenized correctly - vLLM - [Google Colab](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU. - [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario. - SillyTavern - [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models - SillyTavern (staging) comes with built-in DreamGen preset for RP - Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti - Make sure to unselect `Skip special tokens`, otherwise it won't work - This is just an attempt at approximating the Opus V1 prompt, it won't be perfect - Character cards specifically rewritten for the built-in DreamGen preset: - [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card) - [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony) - LM Studio - [Config](configs/lmstudio/preset.json) - Just like ChatML, just changed "assistant" to "text" role. - There's a bug in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280). - HuggingFace - [Chat template](tokenizer_config.json#L51) - Just like ChatML, just changed "assistant" to "text" role. ## Known Issues - 34B repetition: - The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes. - GGUF: - The tokenization might be messed up. Some users reported that `<\|im_start\|>` and `<\|im_end\|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer). ## License - This model is intended for personal use only, other use is not permitted.
0x0mom/sl25	0x0mom	2024-04-19T13:28:15Z	7	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T09:39:38Z	--- 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]
Alefiah/UrduSum6	Alefiah	2024-04-19T13:26:44Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-19T13:22:17Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: UrduSum6 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. --> # UrduSum6 This model is a fine-tuned version of [ahmed0189/mT5-Arabic-text-summarization](https://huggingface.co/ahmed0189/mT5-Arabic-text-summarization) on the None 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: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 147 \| 3.6410 \| 4.6547 \| 1.7375 \| 4.8048 \| 4.8048 \| 31.8514 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3
Raul569/oufit_recommender_19_Apr_2024_v2	Raul569	2024-04-19T13:26:08Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-04-19T13:25:14Z	--- library_name: transformers tags: - trl - sft --- # 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/opus-v1.2-llama-3-8b-5.0bpw-h6-exl2	LoneStriker	2024-04-19T13:25:53Z	8	2	transformers	[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "unsloth", "axolotl", "conversational", "en", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]	text-generation	2024-04-19T13:23:27Z	--- language: - en pipeline_tag: text-generation tags: - unsloth - axolotl license: cc-by-nc-nd-4.0 --- # Llama 3 DreamGen Opus V1 <div style="display: flex; flex-direction: row; align-items: center;"> <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style=" border-radius: 12px; margin-right: 12px; margin-top: 0px; margin-bottom: 0px; max-width: 100px; height: auto; "/> Models for (steerable) story-writing and role-playing. <br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31). </div> ## Resources - [Opus V1 prompting guide](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy. - [Google Colab](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`. - [Python code](example/prompt/format.py) to format the prompt correctly. - Join the community on [Discord](https://dreamgen.com/discord) to get early access to new models. <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style=" padding: 12px; border-radius: 12px; border: 2px solid #f9a8d4; background: rgb(9, 9, 11); "/> ## Prompting <details> <summary>The models use an extended version of ChatML.</summary> ``` <\|im_start\|>system (Story description in the right format here) (Typically consists of plot description, style description and characters)<\|im_end\|> <\|im_start\|>user (Your instruction on how the story should continue)<\|im_end\|> <\|im_start\|>text names= Alice (Continuation of the story from the Alice character)<\|im_end\|> <\|im_start\|>text (Continuation of the story from no character in particular (pure narration))<\|im_end\|> <\|im_start\|>user (Your instruction on how the story should continue)<\|im_end\|> <\|im_start\|>text names= Bob (Continuation of the story from the Bob character)<\|im_end\|> ``` The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names. Pay attention to the following: - The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play. - There can be multiple subsequent message with a `text` role, especially if names are involved. - There can be multiple names attached to a message. - The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names. </details> While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance. Here's how you can prompt the model for the following tasks ### Steerable Story-writing and Role-playing: - [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing) - [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing) - Input: - System prompt: You provide story / role-play description, which consists of: - Plot description - Style description - Characters and their descriptions - Conversation turns: - Text / message turn: This represents part of the story or role play - Instruction: This tells the model what should happen next - Output: Continuation of the story / role-play. ### Story plot summarization - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description) - Input: A story, or a few chapters of a story. - Output: A description of the story or chapters. #### Examples: <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary> <blockquote> Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic. </blockquote> </details> <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary> <blockquote> The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth. One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic. As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary> <blockquote> The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary> <blockquote> A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses. </blockquote> </details> ### Story character description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description) - Input: A story, or a few chapters of a story, set of characters. - Output: A description of the characters. #### Examples: <details> <summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary> <blockquote> Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui. </blockquote> </details> <details> <summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary> <blockquote> The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision. </blockquote> </details> ### Story style description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description) - Input: A story, or a few chapters of a story. - Output: A description the style of the story. <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary> <blockquote> The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose. </blockquote> </details> <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary> <blockquote> The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory. </blockquote> </details> ### Story description to chapters - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions) - Input: A brief plot description and the desired number of chapters. - Output: A description for each chapter. ### And more... ## Sampling params For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`. You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures. ## Dataset The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long. All story-writing and role-playing examples were based on human-written text. ![token count distribution](images/token_count_cum__token_bucket.png) ## Running the model The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization. I recommend using these model versions: - 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b) - 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq) - 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq) ### Running on DreamGen.com (free) You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api). ### Running Locally - Make sure your prompt is as close as possible to the Opus V1 - Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly. - [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1) - [Read the prompt formatting code](example/prompt/format.py) - Make sure `<\|im_start\|>` and `<\|im_end\|>` are tokenized correctly - vLLM - [Google Colab](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU. - [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario. - SillyTavern - [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models - SillyTavern (staging) comes with built-in DreamGen preset for RP - Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti - Make sure to unselect `Skip special tokens`, otherwise it won't work - This is just an attempt at approximating the Opus V1 prompt, it won't be perfect - Character cards specifically rewritten for the built-in DreamGen preset: - [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card) - [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony) - LM Studio - [Config](configs/lmstudio/preset.json) - Just like ChatML, just changed "assistant" to "text" role. - There's a bug in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280). - HuggingFace - [Chat template](tokenizer_config.json#L51) - Just like ChatML, just changed "assistant" to "text" role. ## Known Issues - 34B repetition: - The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes. - GGUF: - The tokenization might be messed up. Some users reported that `<\|im_start\|>` and `<\|im_end\|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer). ## License - This model is intended for personal use only, other use is not permitted.
yujiepan/chatglm3-tiny-random	yujiepan	2024-04-19T13:22:22Z	5	0	transformers	[ "transformers", "safetensors", "chatglm", "feature-extraction", "text-generation", "conversational", "custom_code", "region:us" ]	text-generation	2024-03-30T15:46:40Z	--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This model is randomly initialized, using the config from [THUDM/chatglm3-6b-128k](https://huggingface.co/THUDM/chatglm3-6b-128k/blob/main/config.json) but with smaller size. Note the model is in float16. Codes: ```python import transformers import torch import os from huggingface_hub import create_repo, upload_folder source_model_id = 'THUDM/chatglm3-6b-128k' tiny_random_name = 'chatglm3-tiny-random' save_path = f'/tmp/yujiepan/{tiny_random_name}' repo_id = f'yujiepan/{tiny_random_name}' config = transformers.AutoConfig.from_pretrained( source_model_id, trust_remote_code=True) config.hidden_size = 4 config.ffn_hidden_size = 6 config.num_attention_heads = 4 config.kv_channels = 2 config.num_layers = 2 config.torch_dtype = torch.float16 model = transformers.AutoModelForCausalLM.from_config( config, trust_remote_code=True, torch_dtype=torch.float16) model = model.half() tokenizer = transformers.AutoTokenizer.from_pretrained( source_model_id, trust_remote_code=True) # result = transformers.pipelines.pipeline( # 'text-generation', # model=model, tokenizer=tokenizer, # device=0, # max_new_tokens=16, # )('Hello') # print(result) model = model.cuda() response, history = model.chat(tokenizer, "Hi", history=[], max_length=32) print(response) model.save_pretrained(save_path) tokenizer.save_pretrained(save_path) os.system(f'ls -alh {save_path}') create_repo(repo_id, exist_ok=True) upload_folder(repo_id=repo_id, folder_path=save_path) ```
yujiepan/llama-2-tiny-3layers-random	yujiepan	2024-04-19T13:22:12Z	139	1	transformers	[ "transformers", "pytorch", "safetensors", "openvino", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-31T09:30:50Z	--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- # yujiepan/llama-2-tiny-3layers-random This model is randomly initialized, using the config from [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/yujiepan/llama-2-tiny-3layers-random/blob/main/config.json) but with the following modifications: ```json { "hidden_size": 8, "intermediate_size": 32, "num_attention_heads": 2, "num_hidden_layers": 3, "num_key_value_heads": 2, } ```
yujiepan/llama-2-tiny-random	yujiepan	2024-04-19T13:22:10Z	2,541	1	transformers	[ "transformers", "pytorch", "safetensors", "openvino", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-08-21T05:31:01Z	--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- # yujiepan/llama-2-tiny-random This model is randomly initialized, using the config from [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/yujiepan/llama-2-tiny-random/blob/main/config.json) but with the following modifications: ```json { "hidden_size": 8, "intermediate_size": 32, "num_attention_heads": 2, "num_hidden_layers": 1, "num_key_value_heads": 2, } ```
yujiepan/mistral-tiny-random	yujiepan	2024-04-19T13:22:05Z	33	0	transformers	[ "transformers", "safetensors", "openvino", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-12T14:20:47Z	--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This model is randomly initialized, using the config from [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) but with smaller size. Codes: ```python from optimum.intel.openvino import OVModelForCausalLM from transformers import pipeline from huggingface_hub import create_repo, upload_folder import torch import transformers import os model_id = 'mistralai/Mistral-7B-v0.1' save_path = '/tmp/yujiepan/mistral-tiny-random' repo_id = 'yujiepan/mistral-tiny-random' config = transformers.AutoConfig.from_pretrained(model_id) config.hidden_size = 8 config.intermediate_size = 32 config.num_attention_heads = 4 config.num_hidden_layers = 2 config.num_key_value_heads = 2 print(config) tokenizer = transformers.AutoTokenizer.from_pretrained(model_id) tokenizer.save_pretrained(save_path) model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16) model = model.half() pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False, device='cuda') print(pipe('Hello World!')) model.save_pretrained(save_path) ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True) ovmodel = ovmodel.half() ovmodel.save_pretrained(save_path) os.system(f'ls -alh {save_path}') create_repo(repo_id, exist_ok=True) upload_folder(repo_id=repo_id, folder_path=save_path) ```
yujiepan/gemma-tiny-random	yujiepan	2024-04-19T13:22:04Z	481	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-21T18:43:38Z	--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This model is randomly initialized, using the config from [https://huggingface.co/google/gemma-7b-it] but with smaller size. Note the model is in float16. Codes: ```python from transformers import pipeline from huggingface_hub import create_repo, upload_folder import torch import transformers import os model_id = 'google/gemma-7b-it' save_path = '/tmp/yujiepan/gemma-tiny-random' repo_id = 'yujiepan/gemma-tiny-random' config = transformers.AutoConfig.from_pretrained(model_id) config.hidden_size = 8 config.head_dim = 2 config.intermediate_size = 16 config.num_attention_heads = 4 config.num_hidden_layers = 2 config.num_key_value_heads = 2 print(config) tokenizer = transformers.AutoTokenizer.from_pretrained(model_id) tokenizer.save_pretrained(save_path) model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16) model = model.half() pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False, device='cuda') print(pipe('Hello World!')) model.save_pretrained(save_path) # ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True) # ovmodel = ovmodel.half() # ovmodel.save_pretrained(save_path) os.system(f'ls -alh {save_path}') create_repo(repo_id, exist_ok=True) upload_folder(repo_id=repo_id, folder_path=save_path) ```
NatLibFi/HogwartsSortingHat-fastText	NatLibFi	2024-04-19T13:21:31Z	0	0	null	[ "glam", "lam", "subject indexing", "annif", "hogwarts", "text-classification", "en", "license:cc-by-4.0", "region:us" ]	text-classification	2024-04-19T13:06:41Z	--- license: cc-by-4.0 language: - en pipeline_tag: text-classification tags: - glam - lam - subject indexing - annif - hogwarts --- # Hogwarts Sorting Hat using Annif and its fastText backend The model is the output of [this Annif tutorial exercise](https://github.com/NatLibFi/Annif-tutorial/blob/master/exercises/OPT_hogwarts.md). > The original Sorting Hat reads the thoughts of the student, but Annif generally does not have access to that kind of information, so we will simply use the name of the student as input. We will train a fastText model on the names of characters from the Harry Potter novels whose house is known. To make it possible to generalize the model to new, unseen names, we will use character n-grams to split all names into chunks of 1 to 4 characters - for example harry becomes [h, ha, har, harr, a, ar, arr, arry ...]. fastText can do this when given the minn and maxn parameters, which set the minimum and maximum length of character n-grams to generate from input text.
spietari/Reinforce-Pixelcopter-PLE-v0	spietari	2024-04-19T13:20:05Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-04-18T15:31:11Z	--- 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: 103.20 +/- 55.03 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
t1msan/swin-base-patch4-window7-224-in22k-Kontur-competition-1.3K	t1msan	2024-04-19T13:17:28Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-base-patch4-window7-224-in22k", "base_model:finetune:microsoft/swin-base-patch4-window7-224-in22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-18T19:48:58Z	--- license: apache-2.0 base_model: microsoft/swin-base-patch4-window7-224-in22k tags: - generated_from_trainer datasets: - imagefolder model-index: - name: swin-base-patch4-window7-224-in22k-Kontur-competition-1.3K 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. --> # swin-base-patch4-window7-224-in22k-Kontur-competition-1.3K This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0008 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0593 \| 0.99 \| 55 \| 0.0294 \| \| 0.0098 \| 1.99 \| 111 \| 0.0315 \| \| 0.0066 \| 3.0 \| 167 \| 0.0322 \| \| 0.0179 \| 4.0 \| 223 \| 0.0068 \| \| 0.0078 \| 4.99 \| 278 \| 0.0033 \| \| 0.0015 \| 5.99 \| 334 \| 0.0008 \| \| 0.0017 \| 7.0 \| 390 \| 0.0078 \| \| 0.0008 \| 8.0 \| 446 \| 0.0027 \| \| 0.0019 \| 8.99 \| 501 \| 0.0011 \| \| 0.0014 \| 9.87 \| 550 \| 0.0036 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
birdy654/CHECK_P_MISTRAL	birdy654	2024-04-19T13:16:37Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	null	2024-04-19T13:13:15Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # 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 0.10.0
OwOOwO/dumbo-krillin103	OwOOwO	2024-04-19T13:11:45Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T13:08:42Z	--- 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]
DandinPower/deberta-v3-base-cotat	DandinPower	2024-04-19T13:11:34Z	12	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "nycu-112-2-datamining-hw2", "generated_from_trainer", "en", "dataset:DandinPower/review_cleanonlytitleandtext", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-19T12:42:34Z	--- language: - en license: mit base_model: microsoft/deberta-v3-base tags: - nycu-112-2-datamining-hw2 - generated_from_trainer datasets: - DandinPower/review_cleanonlytitleandtext metrics: - accuracy model-index: - name: deberta-v3-base-cotat results: - task: name: Text Classification type: text-classification dataset: name: DandinPower/review_cleanonlytitleandtext type: DandinPower/review_cleanonlytitleandtext metrics: - name: Accuracy type: accuracy value: 0.623 --- <!-- 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. --> # deberta-v3-base-cotat This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_cleanonlytitleandtext dataset. It achieves the following results on the evaluation set: - Loss: 1.4985 - Accuracy: 0.623 - Macro F1: 0.6247 ## 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: 4.5e-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: 1500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Macro F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:--------:\| \| 1.0223 \| 0.14 \| 500 \| 0.9610 \| 0.592 \| 0.5971 \| \| 1.0108 \| 0.29 \| 1000 \| 0.9378 \| 0.6044 \| 0.6083 \| \| 0.9323 \| 0.43 \| 1500 \| 0.9605 \| 0.589 \| 0.5652 \| \| 0.9651 \| 0.57 \| 2000 \| 0.9845 \| 0.5797 \| 0.5687 \| \| 0.928 \| 0.71 \| 2500 \| 0.9521 \| 0.5907 \| 0.5656 \| \| 0.9205 \| 0.86 \| 3000 \| 0.9073 \| 0.603 \| 0.5740 \| \| 0.9243 \| 1.0 \| 3500 \| 0.8876 \| 0.616 \| 0.6113 \| \| 0.8545 \| 1.14 \| 4000 \| 0.8631 \| 0.6267 \| 0.6290 \| \| 0.8267 \| 1.29 \| 4500 \| 0.8908 \| 0.624 \| 0.6185 \| \| 0.8175 \| 1.43 \| 5000 \| 0.8771 \| 0.6173 \| 0.6222 \| \| 0.8613 \| 1.57 \| 5500 \| 0.9564 \| 0.6209 \| 0.6081 \| \| 0.8138 \| 1.71 \| 6000 \| 0.9246 \| 0.6089 \| 0.6063 \| \| 0.7314 \| 1.86 \| 6500 \| 0.9030 \| 0.6329 \| 0.6313 \| \| 0.8287 \| 2.0 \| 7000 \| 0.8753 \| 0.6211 \| 0.6235 \| \| 0.6963 \| 2.14 \| 7500 \| 0.9700 \| 0.6247 \| 0.6257 \| \| 0.7034 \| 2.29 \| 8000 \| 0.9592 \| 0.6234 \| 0.6220 \| \| 0.679 \| 2.43 \| 8500 \| 0.8994 \| 0.6233 \| 0.6272 \| \| 0.7207 \| 2.57 \| 9000 \| 1.0013 \| 0.6236 \| 0.6183 \| \| 0.6992 \| 2.71 \| 9500 \| 0.9385 \| 0.6169 \| 0.6219 \| \| 0.7032 \| 2.86 \| 10000 \| 0.9247 \| 0.6366 \| 0.6364 \| \| 0.6949 \| 3.0 \| 10500 \| 0.9615 \| 0.6239 \| 0.6281 \| \| 0.5581 \| 3.14 \| 11000 \| 1.0439 \| 0.6217 \| 0.6267 \| \| 0.55 \| 3.29 \| 11500 \| 1.1205 \| 0.6259 \| 0.6232 \| \| 0.5496 \| 3.43 \| 12000 \| 1.1122 \| 0.6226 \| 0.6267 \| \| 0.5462 \| 3.57 \| 12500 \| 1.0692 \| 0.6251 \| 0.6263 \| \| 0.5121 \| 3.71 \| 13000 \| 1.1563 \| 0.6197 \| 0.6214 \| \| 0.531 \| 3.86 \| 13500 \| 1.1123 \| 0.6261 \| 0.6256 \| \| 0.5256 \| 4.0 \| 14000 \| 1.1194 \| 0.6247 \| 0.6264 \| \| 0.3908 \| 4.14 \| 14500 \| 1.3631 \| 0.6204 \| 0.6210 \| \| 0.4439 \| 4.29 \| 15000 \| 1.4810 \| 0.6204 \| 0.6211 \| \| 0.4252 \| 4.43 \| 15500 \| 1.4454 \| 0.6211 \| 0.6217 \| \| 0.3721 \| 4.57 \| 16000 \| 1.5315 \| 0.6204 \| 0.6231 \| \| 0.369 \| 4.71 \| 16500 \| 1.4797 \| 0.6184 \| 0.6190 \| \| 0.3907 \| 4.86 \| 17000 \| 1.4857 \| 0.6219 \| 0.6234 \| \| 0.4022 \| 5.0 \| 17500 \| 1.4985 \| 0.623 \| 0.6247 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
LoneStriker/opus-v1.2-llama-3-8b-GGUF	LoneStriker	2024-04-19T13:11:11Z	86	14	null	[ "gguf", "unsloth", "axolotl", "text-generation", "en", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-04-19T12:59:08Z	--- language: - en pipeline_tag: text-generation tags: - unsloth - axolotl license: cc-by-nc-nd-4.0 --- # Llama 3 DreamGen Opus V1 <div style="display: flex; flex-direction: row; align-items: center;"> <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style=" border-radius: 12px; margin-right: 12px; margin-top: 0px; margin-bottom: 0px; max-width: 100px; height: auto; "/> Models for (steerable) story-writing and role-playing. <br/>[All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31). </div> ## Resources - [Opus V1 prompting guide](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy. - [Google Colab](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`. - [Python code](example/prompt/format.py) to format the prompt correctly. - Join the community on [Discord](https://dreamgen.com/discord) to get early access to new models. <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style=" padding: 12px; border-radius: 12px; border: 2px solid #f9a8d4; background: rgb(9, 9, 11); "/> ## Prompting <details> <summary>The models use an extended version of ChatML.</summary> ``` <\|im_start\|>system (Story description in the right format here) (Typically consists of plot description, style description and characters)<\|im_end\|> <\|im_start\|>user (Your instruction on how the story should continue)<\|im_end\|> <\|im_start\|>text names= Alice (Continuation of the story from the Alice character)<\|im_end\|> <\|im_start\|>text (Continuation of the story from no character in particular (pure narration))<\|im_end\|> <\|im_start\|>user (Your instruction on how the story should continue)<\|im_end\|> <\|im_start\|>text names= Bob (Continuation of the story from the Bob character)<\|im_end\|> ``` The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names. Pay attention to the following: - The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play. - There can be multiple subsequent message with a `text` role, especially if names are involved. - There can be multiple names attached to a message. - The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names. </details> While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance. Here's how you can prompt the model for the following tasks ### Steerable Story-writing and Role-playing: - [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing) - [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing) - Input: - System prompt: You provide story / role-play description, which consists of: - Plot description - Style description - Characters and their descriptions - Conversation turns: - Text / message turn: This represents part of the story or role play - Instruction: This tells the model what should happen next - Output: Continuation of the story / role-play. ### Story plot summarization - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description) - Input: A story, or a few chapters of a story. - Output: A description of the story or chapters. #### Examples: <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary> <blockquote> Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic. </blockquote> </details> <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary> <blockquote> The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth. One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic. As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary> <blockquote> The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary> <blockquote> A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses. </blockquote> </details> ### Story character description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description) - Input: A story, or a few chapters of a story, set of characters. - Output: A description of the characters. #### Examples: <details> <summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary> <blockquote> Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui. </blockquote> </details> <details> <summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary> <blockquote> The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision. </blockquote> </details> ### Story style description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description) - Input: A story, or a few chapters of a story. - Output: A description the style of the story. <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary> <blockquote> The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose. </blockquote> </details> <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary> <blockquote> The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory. </blockquote> </details> ### Story description to chapters - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions) - Input: A brief plot description and the desired number of chapters. - Output: A description for each chapter. ### And more... ## Sampling params For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`. You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures. ## Dataset The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long. All story-writing and role-playing examples were based on human-written text. ![token count distribution](images/token_count_cum__token_bucket.png) ## Running the model The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization. I recommend using these model versions: - 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b) - 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq) - 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq) ### Running on DreamGen.com (free) You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api). ### Running Locally - Make sure your prompt is as close as possible to the Opus V1 - Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly. - [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1) - [Read the prompt formatting code](example/prompt/format.py) - Make sure `<\|im_start\|>` and `<\|im_end\|>` are tokenized correctly - vLLM - [Google Colab](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU. - [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario. - SillyTavern - [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models - SillyTavern (staging) comes with built-in DreamGen preset for RP - Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti - Make sure to unselect `Skip special tokens`, otherwise it won't work - This is just an attempt at approximating the Opus V1 prompt, it won't be perfect - Character cards specifically rewritten for the built-in DreamGen preset: - [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card) - [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony) - LM Studio - [Config](configs/lmstudio/preset.json) - Just like ChatML, just changed "assistant" to "text" role. - There's a bug in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280). - HuggingFace - [Chat template](tokenizer_config.json#L51) - Just like ChatML, just changed "assistant" to "text" role. ## Known Issues - 34B repetition: - The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes. - GGUF: - The tokenization might be messed up. Some users reported that `<\|im_start\|>` and `<\|im_end\|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer). ## License - This model is intended for personal use only, other use is not permitted.
numen-tech/Meta-Llama-3-8B-Instruct-w4a16g128asym	numen-tech	2024-04-19T13:08:47Z	0	1	null	[ "arxiv:2308.13137", "license:other", "region:us" ]	null	2024-04-19T13:06:04Z	--- license: other license_name: llama3 license_link: LICENSE --- 4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).
NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-GGUF	NikolayKozloff	2024-04-19T13:05:04Z	4	1	null	[ "gguf", "alignment-handbook", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "dataset:maxidl/distilabel-capybara-dpo-7k-binarized_en_de", "base_model:mistralai/Mistral-7B-v0.1", "base_model:quantized:mistralai/Mistral-7B-v0.1", "endpoints_compatible", "region:us", "conversational" ]	null	2024-04-19T13:04:37Z	--- tags: - alignment-handbook - generated_from_trainer - llama-cpp - gguf-my-repo base_model: mistralai/Mistral-7B-v0.1 datasets: - maxidl/distilabel-capybara-dpo-7k-binarized_en_de model-index: - name: Mistral-7B-v0.1-capybara-orpo-en-de results: [] --- # NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF This model was converted to GGUF format from [`maxidl/Mistral-7B-v0.1-capybara-orpo-en-de`](https://huggingface.co/maxidl/Mistral-7B-v0.1-capybara-orpo-en-de) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maxidl/Mistral-7B-v0.1-capybara-orpo-en-de) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF --model mistral-7b-v0.1-capybara-orpo-en-de.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF --model mistral-7b-v0.1-capybara-orpo-en-de.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-7b-v0.1-capybara-orpo-en-de.Q8_0.gguf -n 128 ```
himanshue2e/gemma-2b-g	himanshue2e	2024-04-19T13:03:26Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]	null	2024-04-18T13:00:38Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b model-index: - name: gemma-2b-g 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. --> # gemma-2b-g This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9563 ## 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: 2.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.016 \| 2 \| 0.9410 \| \| No log \| 0.032 \| 4 \| 0.9443 \| \| No log \| 0.048 \| 6 \| 0.9413 \| \| No log \| 0.064 \| 8 \| 0.9398 \| \| No log \| 0.08 \| 10 \| 0.9401 \| \| No log \| 0.096 \| 12 \| 0.9406 \| \| No log \| 0.112 \| 14 \| 0.9404 \| \| No log \| 0.128 \| 16 \| 0.9409 \| \| No log \| 0.144 \| 18 \| 0.9412 \| \| No log \| 0.16 \| 20 \| 0.9412 \| \| No log \| 0.176 \| 22 \| 0.9411 \| \| No log \| 0.192 \| 24 \| 0.9408 \| \| No log \| 0.208 \| 26 \| 0.9412 \| \| No log \| 0.224 \| 28 \| 0.9411 \| \| No log \| 0.24 \| 30 \| 0.9408 \| \| No log \| 0.256 \| 32 \| 0.9406 \| \| No log \| 0.272 \| 34 \| 0.9404 \| \| No log \| 0.288 \| 36 \| 0.9406 \| \| No log \| 0.304 \| 38 \| 0.9409 \| \| No log \| 0.32 \| 40 \| 0.9414 \| \| No log \| 0.336 \| 42 \| 0.9419 \| \| No log \| 0.352 \| 44 \| 0.9425 \| \| No log \| 0.368 \| 46 \| 0.9425 \| \| No log \| 0.384 \| 48 \| 0.9416 \| \| No log \| 0.4 \| 50 \| 0.9408 \| \| No log \| 0.416 \| 52 \| 0.9403 \| \| No log \| 0.432 \| 54 \| 0.9398 \| \| No log \| 0.448 \| 56 \| 0.9393 \| \| No log \| 0.464 \| 58 \| 0.9385 \| \| No log \| 0.48 \| 60 \| 0.9390 \| \| No log \| 0.496 \| 62 \| 0.9394 \| \| No log \| 0.512 \| 64 \| 0.9392 \| \| No log \| 0.528 \| 66 \| 0.9386 \| \| No log \| 0.544 \| 68 \| 0.9385 \| \| No log \| 0.56 \| 70 \| 0.9380 \| \| No log \| 0.576 \| 72 \| 0.9373 \| \| No log \| 0.592 \| 74 \| 0.9369 \| \| No log \| 0.608 \| 76 \| 0.9367 \| \| No log \| 0.624 \| 78 \| 0.9369 \| \| No log \| 0.64 \| 80 \| 0.9370 \| \| No log \| 0.656 \| 82 \| 0.9371 \| \| No log \| 0.672 \| 84 \| 0.9366 \| \| No log \| 0.688 \| 86 \| 0.9361 \| \| No log \| 0.704 \| 88 \| 0.9361 \| \| No log \| 0.72 \| 90 \| 0.9354 \| \| No log \| 0.736 \| 92 \| 0.9352 \| \| No log \| 0.752 \| 94 \| 0.9354 \| \| No log \| 0.768 \| 96 \| 0.9352 \| \| No log \| 0.784 \| 98 \| 0.9350 \| \| No log \| 0.8 \| 100 \| 0.9349 \| \| No log \| 0.816 \| 102 \| 0.9353 \| \| No log \| 0.832 \| 104 \| 0.9349 \| \| No log \| 0.848 \| 106 \| 0.9346 \| \| No log \| 0.864 \| 108 \| 0.9341 \| \| No log \| 0.88 \| 110 \| 0.9335 \| \| No log \| 0.896 \| 112 \| 0.9327 \| \| No log \| 0.912 \| 114 \| 0.9321 \| \| No log \| 0.928 \| 116 \| 0.9323 \| \| No log \| 0.944 \| 118 \| 0.9327 \| \| No log \| 0.96 \| 120 \| 0.9325 \| \| No log \| 0.976 \| 122 \| 0.9318 \| \| No log \| 0.992 \| 124 \| 0.9316 \| \| No log \| 1.008 \| 126 \| 0.9321 \| \| No log \| 1.024 \| 128 \| 0.9332 \| \| No log \| 1.04 \| 130 \| 0.9351 \| \| No log \| 1.056 \| 132 \| 0.9370 \| \| No log \| 1.072 \| 134 \| 0.9383 \| \| No log \| 1.088 \| 136 \| 0.9390 \| \| No log \| 1.104 \| 138 \| 0.9386 \| \| No log \| 1.12 \| 140 \| 0.9378 \| \| No log \| 1.1360 \| 142 \| 0.9375 \| \| No log \| 1.152 \| 144 \| 0.9380 \| \| No log \| 1.168 \| 146 \| 0.9380 \| \| No log \| 1.184 \| 148 \| 0.9376 \| \| No log \| 1.2 \| 150 \| 0.9381 \| \| No log \| 1.216 \| 152 \| 0.9390 \| \| No log \| 1.232 \| 154 \| 0.9400 \| \| No log \| 1.248 \| 156 \| 0.9410 \| \| No log \| 1.264 \| 158 \| 0.9411 \| \| No log \| 1.28 \| 160 \| 0.9405 \| \| No log \| 1.296 \| 162 \| 0.9402 \| \| No log \| 1.312 \| 164 \| 0.9400 \| \| No log \| 1.328 \| 166 \| 0.9399 \| \| No log \| 1.3440 \| 168 \| 0.9397 \| \| No log \| 1.3600 \| 170 \| 0.9398 \| \| No log \| 1.376 \| 172 \| 0.9403 \| \| No log \| 1.392 \| 174 \| 0.9412 \| \| No log \| 1.408 \| 176 \| 0.9424 \| \| No log \| 1.424 \| 178 \| 0.9432 \| \| No log \| 1.44 \| 180 \| 0.9417 \| \| No log \| 1.456 \| 182 \| 0.9403 \| \| No log \| 1.472 \| 184 \| 0.9397 \| \| No log \| 1.488 \| 186 \| 0.9393 \| \| No log \| 1.504 \| 188 \| 0.9391 \| \| No log \| 1.52 \| 190 \| 0.9385 \| \| No log \| 1.536 \| 192 \| 0.9385 \| \| No log \| 1.552 \| 194 \| 0.9387 \| \| No log \| 1.568 \| 196 \| 0.9393 \| \| No log \| 1.584 \| 198 \| 0.9402 \| \| No log \| 1.6 \| 200 \| 0.9410 \| \| No log \| 1.616 \| 202 \| 0.9410 \| \| No log \| 1.6320 \| 204 \| 0.9417 \| \| No log \| 1.6480 \| 206 \| 0.9414 \| \| No log \| 1.6640 \| 208 \| 0.9410 \| \| No log \| 1.6800 \| 210 \| 0.9402 \| \| No log \| 1.696 \| 212 \| 0.9400 \| \| No log \| 1.712 \| 214 \| 0.9398 \| \| No log \| 1.728 \| 216 \| 0.9397 \| \| No log \| 1.744 \| 218 \| 0.9395 \| \| No log \| 1.76 \| 220 \| 0.9398 \| \| No log \| 1.776 \| 222 \| 0.9400 \| \| No log \| 1.792 \| 224 \| 0.9403 \| \| No log \| 1.808 \| 226 \| 0.9403 \| \| No log \| 1.8240 \| 228 \| 0.9399 \| \| No log \| 1.8400 \| 230 \| 0.9392 \| \| No log \| 1.8560 \| 232 \| 0.9385 \| \| No log \| 1.8720 \| 234 \| 0.9385 \| \| No log \| 1.888 \| 236 \| 0.9390 \| \| No log \| 1.904 \| 238 \| 0.9394 \| \| No log \| 1.92 \| 240 \| 0.9395 \| \| No log \| 1.936 \| 242 \| 0.9392 \| \| No log \| 1.952 \| 244 \| 0.9391 \| \| No log \| 1.968 \| 246 \| 0.9390 \| \| No log \| 1.984 \| 248 \| 0.9386 \| \| No log \| 2.0 \| 250 \| 0.9380 \| \| No log \| 2.016 \| 252 \| 0.9381 \| \| No log \| 2.032 \| 254 \| 0.9401 \| \| No log \| 2.048 \| 256 \| 0.9431 \| \| No log \| 2.064 \| 258 \| 0.9469 \| \| No log \| 2.08 \| 260 \| 0.9507 \| \| No log \| 2.096 \| 262 \| 0.9529 \| \| No log \| 2.112 \| 264 \| 0.9524 \| \| No log \| 2.128 \| 266 \| 0.9501 \| \| No log \| 2.144 \| 268 \| 0.9478 \| \| No log \| 2.16 \| 270 \| 0.9466 \| \| No log \| 2.176 \| 272 \| 0.9463 \| \| No log \| 2.192 \| 274 \| 0.9458 \| \| No log \| 2.208 \| 276 \| 0.9454 \| \| No log \| 2.224 \| 278 \| 0.9451 \| \| No log \| 2.24 \| 280 \| 0.9456 \| \| No log \| 2.2560 \| 282 \| 0.9468 \| \| No log \| 2.2720 \| 284 \| 0.9477 \| \| No log \| 2.288 \| 286 \| 0.9484 \| \| No log \| 2.304 \| 288 \| 0.9486 \| \| No log \| 2.32 \| 290 \| 0.9479 \| \| No log \| 2.336 \| 292 \| 0.9473 \| \| No log \| 2.352 \| 294 \| 0.9473 \| \| No log \| 2.368 \| 296 \| 0.9473 \| \| No log \| 2.384 \| 298 \| 0.9475 \| \| No log \| 2.4 \| 300 \| 0.9479 \| \| No log \| 2.416 \| 302 \| 0.9490 \| \| No log \| 2.432 \| 304 \| 0.9499 \| \| No log \| 2.448 \| 306 \| 0.9501 \| \| No log \| 2.464 \| 308 \| 0.9498 \| \| No log \| 2.48 \| 310 \| 0.9491 \| \| No log \| 2.496 \| 312 \| 0.9489 \| \| No log \| 2.512 \| 314 \| 0.9490 \| \| No log \| 2.528 \| 316 \| 0.9487 \| \| No log \| 2.544 \| 318 \| 0.9483 \| \| No log \| 2.56 \| 320 \| 0.9483 \| \| No log \| 2.576 \| 322 \| 0.9483 \| \| No log \| 2.592 \| 324 \| 0.9485 \| \| No log \| 2.608 \| 326 \| 0.9487 \| \| No log \| 2.624 \| 328 \| 0.9492 \| \| No log \| 2.64 \| 330 \| 0.9493 \| \| No log \| 2.656 \| 332 \| 0.9488 \| \| No log \| 2.672 \| 334 \| 0.9487 \| \| No log \| 2.6880 \| 336 \| 0.9486 \| \| No log \| 2.7040 \| 338 \| 0.9485 \| \| No log \| 2.7200 \| 340 \| 0.9481 \| \| No log \| 2.7360 \| 342 \| 0.9477 \| \| No log \| 2.752 \| 344 \| 0.9478 \| \| No log \| 2.768 \| 346 \| 0.9482 \| \| No log \| 2.784 \| 348 \| 0.9487 \| \| No log \| 2.8 \| 350 \| 0.9483 \| \| No log \| 2.816 \| 352 \| 0.9481 \| \| No log \| 2.832 \| 354 \| 0.9480 \| \| No log \| 2.848 \| 356 \| 0.9480 \| \| No log \| 2.864 \| 358 \| 0.9479 \| \| No log \| 2.88 \| 360 \| 0.9481 \| \| No log \| 2.896 \| 362 \| 0.9484 \| \| No log \| 2.912 \| 364 \| 0.9488 \| \| No log \| 2.928 \| 366 \| 0.9490 \| \| No log \| 2.944 \| 368 \| 0.9489 \| \| No log \| 2.96 \| 370 \| 0.9487 \| \| No log \| 2.976 \| 372 \| 0.9484 \| \| No log \| 2.992 \| 374 \| 0.9476 \| \| No log \| 3.008 \| 376 \| 0.9468 \| \| No log \| 3.024 \| 378 \| 0.9471 \| \| No log \| 3.04 \| 380 \| 0.9481 \| \| No log \| 3.056 \| 382 \| 0.9499 \| \| No log \| 3.072 \| 384 \| 0.9521 \| \| No log \| 3.088 \| 386 \| 0.9543 \| \| No log \| 3.104 \| 388 \| 0.9562 \| \| No log \| 3.12 \| 390 \| 0.9572 \| \| No log \| 3.136 \| 392 \| 0.9577 \| \| No log \| 3.152 \| 394 \| 0.9577 \| \| No log \| 3.168 \| 396 \| 0.9577 \| \| No log \| 3.184 \| 398 \| 0.9574 \| \| No log \| 3.2 \| 400 \| 0.9570 \| \| No log \| 3.216 \| 402 \| 0.9569 \| \| No log \| 3.232 \| 404 \| 0.9567 \| \| No log \| 3.248 \| 406 \| 0.9565 \| \| No log \| 3.2640 \| 408 \| 0.9564 \| \| No log \| 3.2800 \| 410 \| 0.9562 \| \| No log \| 3.296 \| 412 \| 0.9561 \| \| No log \| 3.312 \| 414 \| 0.9561 \| \| No log \| 3.328 \| 416 \| 0.9562 \| \| No log \| 3.344 \| 418 \| 0.9565 \| \| No log \| 3.36 \| 420 \| 0.9568 \| \| No log \| 3.376 \| 422 \| 0.9570 \| \| No log \| 3.392 \| 424 \| 0.9572 \| \| No log \| 3.408 \| 426 \| 0.9573 \| \| No log \| 3.424 \| 428 \| 0.9572 \| \| No log \| 3.44 \| 430 \| 0.9569 \| \| No log \| 3.456 \| 432 \| 0.9570 \| \| No log \| 3.472 \| 434 \| 0.9572 \| \| No log \| 3.488 \| 436 \| 0.9574 \| \| No log \| 3.504 \| 438 \| 0.9575 \| \| No log \| 3.52 \| 440 \| 0.9577 \| \| No log \| 3.536 \| 442 \| 0.9577 \| \| No log \| 3.552 \| 444 \| 0.9578 \| \| No log \| 3.568 \| 446 \| 0.9579 \| \| No log \| 3.584 \| 448 \| 0.9577 \| \| No log \| 3.6 \| 450 \| 0.9575 \| \| No log \| 3.616 \| 452 \| 0.9575 \| \| No log \| 3.632 \| 454 \| 0.9575 \| \| No log \| 3.648 \| 456 \| 0.9576 \| \| No log \| 3.664 \| 458 \| 0.9576 \| \| No log \| 3.68 \| 460 \| 0.9574 \| \| No log \| 3.6960 \| 462 \| 0.9573 \| \| No log \| 3.7120 \| 464 \| 0.9571 \| \| No log \| 3.7280 \| 466 \| 0.9569 \| \| No log \| 3.7440 \| 468 \| 0.9567 \| \| No log \| 3.76 \| 470 \| 0.9565 \| \| No log \| 3.776 \| 472 \| 0.9563 \| \| No log \| 3.792 \| 474 \| 0.9563 \| \| No log \| 3.808 \| 476 \| 0.9563 \| \| No log \| 3.824 \| 478 \| 0.9564 \| \| No log \| 3.84 \| 480 \| 0.9565 \| \| No log \| 3.856 \| 482 \| 0.9565 \| \| No log \| 3.872 \| 484 \| 0.9566 \| \| No log \| 3.888 \| 486 \| 0.9566 \| \| No log \| 3.904 \| 488 \| 0.9565 \| \| No log \| 3.92 \| 490 \| 0.9565 \| \| No log \| 3.936 \| 492 \| 0.9565 \| \| No log \| 3.952 \| 494 \| 0.9564 \| \| No log \| 3.968 \| 496 \| 0.9564 \| \| No log \| 3.984 \| 498 \| 0.9564 \| \| 0.814 \| 4.0 \| 500 \| 0.9563 \| ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
ws11yrin/Huggy-ppo-mlagents	ws11yrin	2024-04-19T13:01:30Z	4	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-04-19T13:00:46Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: u8208306949/mlagents-ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Jacque008/unsloth-llama-3-8b-instruct_4396_ori_refer_fwd_epoch2_merge	Jacque008	2024-04-19T13:01:30Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct", "base_model:finetune:unsloth/llama-3-8b-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T13:01:02Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct --- # Uploaded model - Developed by: Jacque008 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
indiehackers/gemma-telugu-instruct-test1	indiehackers	2024-04-19T12:58:47Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T12:58:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: unsloth/gemma-7b-it-bnb-4bit --- # Uploaded model - Developed by: indiehackers - License: apache-2.0 - Finetuned from model : unsloth/gemma-7b-it-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
larrydai/peft-starcoder-lora-a100	larrydai	2024-04-19T12:58:09Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:bigcode/starcoderbase-1b", "base_model:adapter:bigcode/starcoderbase-1b", "license:bigcode-openrail-m", "region:us" ]	null	2024-04-19T09:29:44Z	--- license: bigcode-openrail-m library_name: peft tags: - generated_from_trainer base_model: bigcode/starcoderbase-1b model-index: - name: peft-starcoder-lora-a100 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. --> # peft-starcoder-lora-a100 This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9744 ## 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: 0.0005 - 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: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.7228 \| 0.5 \| 100 \| 1.0017 \| \| 0.8952 \| 1.0 \| 200 \| 0.9744 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
Raul569/oufit_recommender_19_Apr_2024_v1	Raul569	2024-04-19T12:51:21Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-19T12:51:20Z	--- 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]
Raul569/SFTOpenLM-Dolly15k	Raul569	2024-04-19T12:51:07Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:openlm-research/open_llama_3b", "base_model:adapter:openlm-research/open_llama_3b", "license:apache-2.0", "region:us" ]	null	2024-04-19T12:50:45Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: openlm-research/open_llama_3b model-index: - name: SFTOpenLM-Dolly15k 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. --> # SFTOpenLM-Dolly15k This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_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: 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: linear - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
choprahetarth/starcoder2	choprahetarth	2024-04-19T12:49:08Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:bigcode/starcoder2-3b", "base_model:adapter:bigcode/starcoder2-3b", "license:bigcode-openrail-m", "region:us" ]	null	2024-04-19T12:41:24Z	--- license: bigcode-openrail-m library_name: peft tags: - trl - sft - generated_from_trainer base_model: bigcode/starcoder2-3b model-index: - name: starcoder2 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. --> # starcoder2 This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) 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: 0.0002 - train_batch_size: 10 - eval_batch_size: 8 - seed: 1234 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 160 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 343 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
thusinh1969/LLaMA-2-finetune-100k-CP45600-19APRIL2024	thusinh1969	2024-04-19T12:46:43Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T12:44:08Z	--- 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]
jgrc3/unipelt_adapter_classification_trained_lr0_0001	jgrc3	2024-04-19T12:43:41Z	0	0	adapter-transformers	[ "adapter-transformers", "roberta", "dataset:BigTMiami/amazon_helpfulness", "region:us" ]	null	2024-04-19T12:43:38Z	--- tags: - adapter-transformers - roberta datasets: - BigTMiami/amazon_helpfulness --- # Adapter `jgrc3/unipelt_adapter_classification_trained_lr0_0001` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification. This adapter was created for usage with the [Adapters](https://github.com/Adapter-Hub/adapters) library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("jgrc3/unipelt_adapter_classification_trained_lr0_0001", source="hf", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->
erlend123/emotion-analysis-binary-trans	erlend123	2024-04-19T12:43:04Z	7	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-19T11:42:45Z	--- tags: - generated_from_trainer model-index: - name: emotion-analysis-ntnu 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. --> # emotion-analysis-ntnu This model was trained from scratch 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
DeepMount00/OCR_corrector	DeepMount00	2024-04-19T12:41:51Z	151	15	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "it", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-10T05:50:46Z	--- language: - it license: apache-2.0 library_name: transformers --- # Italian OCR Error Correction Sequence-to-Sequence Model ## Model Details This model represents the first version of an experimental sequence-to-sequence architecture designed specifically for the Italian language. It aims to correct approximately 93% of the errors generated by low-quality Optical Character Recognition (OCR) systems, which tend to perform poorly on Italian text. By taking raw, OCR-scanned text as input, the model outputs the corrected version of the text, significantly reducing errors and improving readability and accuracy. ## Intended Use - Primary Use: This model is intended for use in processing and correcting Italian text that has been digitized using OCR technology. It is particularly useful for texts scanned at low quality, where the OCR's error rate is noticeably high. - Users: It is designed for developers, researchers, and archivists working with Italian historical documents, books, and any digitized material where OCR errors are prevalent. ## Limitations - While the model corrects approximately 93% of OCR errors, there may be certain types of errors or specific contexts where its performance could be lower. - The model is specifically trained on Italian text and may not perform well on texts in other languages or texts that include significant amounts of non-Italian languages. ## How to Use ```python import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM device = "cuda" if torch.cuda.is_available() else "cpu" MODEL_NAME = "DeepMount00/OCR_corrector" model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME).eval() tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model.to(device) my_text = "In un'epca lontnaa, un re goernava le sue tere con saggez2a e giustiia. Sotot il suo regno, il rgeno prosperava e la getne era flice. Ma un gionro, un drgoa feroce attcò il regno, semniando ditruzione e paurra tra i suoi abtanti." inputs = tokenizer(my_text, return_tensors="pt").to(device) outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], num_beams=2, max_length=1050, top_k=10) clean_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(clean_text) ```
namkyeong/facebook_wav2vec2-xls-r-300m_meet_tr_p_10_30h	namkyeong	2024-04-19T12:35:04Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-04-17T05:49:21Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: facebook_wav2vec2-xls-r-300m_meet_tr_p_10_30h 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. --> # facebook_wav2vec2-xls-r-300m_meet_tr_p_10_30h This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5611 - Cer: 0.1334 ## 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: 7 - eval_batch_size: 56 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 6.2651 \| 0.21 \| 500 \| 5.9159 \| 1.0 \| \| 2.8965 \| 0.43 \| 1000 \| 2.0493 \| 0.4184 \| \| 1.3122 \| 0.64 \| 1500 \| 1.0627 \| 0.2433 \| \| 1.0682 \| 0.85 \| 2000 \| 0.8674 \| 0.2083 \| \| 0.9281 \| 1.07 \| 2500 \| 0.7413 \| 0.1749 \| \| 0.8103 \| 1.28 \| 3000 \| 0.7032 \| 0.1652 \| \| 0.7823 \| 1.5 \| 3500 \| 0.6806 \| 0.1616 \| \| 0.7429 \| 1.71 \| 4000 \| 0.6430 \| 0.1547 \| \| 0.7375 \| 1.92 \| 4500 \| 0.6253 \| 0.1533 \| \| 0.6299 \| 2.14 \| 5000 \| 0.6375 \| 0.1423 \| \| 0.5801 \| 2.35 \| 5500 \| 0.6086 \| 0.1398 \| \| 0.5735 \| 2.56 \| 6000 \| 0.5808 \| 0.1394 \| \| 0.5448 \| 2.78 \| 6500 \| 0.5736 \| 0.1351 \| \| 0.5555 \| 2.99 \| 7000 \| 0.5611 \| 0.1334 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.15.2
fangzhaoz/hellaswag_dora_llama_merged	fangzhaoz	2024-04-19T12:34:25Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T12:31:30Z	--- 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]
aamonten/gemma-2b-danish-chatml	aamonten	2024-04-19T12:31:01Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-19T12:27:44Z	--- 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. 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]
tb2pi-persistent/Llama-2-7b-chat-hf-tb2pi-merged-v7	tb2pi-persistent	2024-04-19T12:27:33Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-18T12:55:19Z	--- 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]
madelineoliver/ToolsBaer-EML-to-Office-365-Importer	madelineoliver	2024-04-19T12:14:55Z	0	0	null	[ "region:us" ]	null	2024-04-19T12:14:25Z	Users can export EML files into Office 365 formats using the ToolsBaer EML to Office 365 Migrator Software. Programs that convert EML to Office 365 can easily handle EML files of any size or quality. Throughout the conversion process, there is no loss of data or damage to the original files. This program can be utilized without the need for previous technological expertise because of its user-friendly interface. The program has two distinct filtration feature types, both of which are safe to use. The File Mode lets users export individual EML files from local folders, while the Folder Mode lets users upload the data of certain folders that store EML files. It has undergone many tests, all of which have shown it to be a secure utility. The email attributes that are exported by the program include name, CC, BCC, to, from, hyperlinks, photos, and attachments. EML files that are imported to an Office 365 account can be utilized with Windows 11, Windows 8.1, Windows 8, Windows 7, XP, Vista, and other operating systems. Download the free trial version of the program and install it on your Windows system. Read More:- http://www.toolsbaer.com/eml-to-office-365-conversion/
amine-01/Pyramids	amine-01	2024-04-19T12:13:54Z	0	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2024-04-19T11:01:14Z	--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: amine-01/Pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
Woutervdbos/ppo-LunarLander-v2	Woutervdbos	2024-04-19T12:11:16Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-04-19T12:10:55Z	--- 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: 269.86 +/- 10.81 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 ... ```
manishiitg/open-aditi-v6-gemma	manishiitg	2024-04-19T12:08:14Z	0	0	peft	[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "license:gemma", "region:us" ]	null	2024-04-07T15:24:55Z	--- license: gemma library_name: peft tags: - axolotl - generated_from_trainer base_model: google/gemma-7B model-index: - name: open-aditi-chat-hi-1.25-gemma results: [] --- Preview of dataset trained on: https://huggingface.co/datasets/manishiitg/aditi-syn-v2 The synthetic dataset (https://huggingface.co/datasets/manishiitg/aditi-syn-v2) and the full data creation pipeline (https://github.com/manishiitg/aditi_dataset) have been open-sourced, enabling transparency and fostering further research in this domain. The dataset is a rich tapestry of Hinglish (a blend of Hindi and English) data, as well as a diverse array of tasks spanning tools, retrieval-augmented generation (RAG), mathematics, and reasoning – all in the Hindi language. LMJudge Eval ============ https://github.com/manishiitg/IndicLMJudge #### LLM Judge Language: hi \| Model \| Language \| Score \| No# Questions \| \| --- \| --- \| --- \| --- \| \| mistralai/Mixtral-8x7B-Instruct-v0.1 \| hi \| 8.7148 \| 554 \| \| Qwen/Qwen1.5-72B-Chat-AWQ \| hi \| 8.3695 \| 554 \| \| manishiitg/open-aditi-v6-llama3 \| hi \| 8.2659 \| 551 \| \| Qwen/Qwen1.5-14B-Chat \| hi \| 8.2404 \| 554 \| \| google/gemma-7b-it \| hi \| 7.9152 \| 554 \| \| manishiitg/open-aditi-v6-gemma \| hi \| 7.8634 \| 549 \| \| Qwen/Qwen1.5-7B-Chat \| hi \| 7.8587 \| 554 \| \| manishiitg/open-aditi-hi-v3 \| hi \| 7.7644 \| 554 \| \| manishiitg/open-aditi-hi-v4 \| hi \| 7.6150 \| 554 \| \| manishiitg/open-aditi-hi-v2 \| hi \| 7.2518 \| 554 \| \| teknium/OpenHermes-2.5-Mistral-7B \| hi \| 7.2489 \| 554 \| \| ai4bharat/Airavata \| hi \| 6.9468 \| 554 \| \| 01-ai/Yi-34B-Chat \| hi \| 6.5801 \| 554 \| \| manishiitg/open-aditi-hi-v1 \| hi \| 4.7022 \| 554 \| \| sarvamai/OpenHathi-7B-Hi-v0.1-Base \| hi \| 4.2834 \| 598 \| \| Qwen/Qwen1.5-4B-Chat \| hi \| 4.1101 \| 554 \| #### LLM Judge Language: en \| Model \| Language \| Score \| No# Questions \| \| --- \| --- \| --- \| --- \| \| Qwen/Qwen1.5-14B-Chat \| en \| 9.1947 \| 356 \| \| Qwen/Qwen1.5-72B-Chat-AWQ \| en \| 9.1618 \| 356 \| \| Qwen/Qwen1.5-7B-Chat \| en \| 9.1570 \| 356 \| \| 01-ai/Yi-34B-Chat \| en \| 9.1368 \| 356 \| \| mistralai/Mixtral-8x7B-Instruct-v0.1 \| en \| 9.1306 \| 356 \| \| manishiitg/open-aditi-v6-gemma \| en \| 9.1003 \| 356 \| \| teknium/OpenHermes-2.5-Mistral-7B \| en \| 9.0230 \| 356 \| \| manishiitg/open-aditi-v6-llama3 \| en \| 9.0197 \| 356 \| \| manishiitg/open-aditi-hi-v3 \| en \| 8.9615 \| 356 \| \| manishiitg/open-aditi-hi-v4 \| en \| 8.9188 \| 356 \| \| google/gemma-7b-it \| en \| 8.8191 \| 356 \| \| Qwen/Qwen1.5-4B-Chat \| en \| 8.7500 \| 356 \| \| google/gemma-2b-it \| en \| 8.4671 \| 356 \| \| manishiitg/open-aditi-hi-v2 \| en \| 8.4584 \| 356 \| \| ai4bharat/Airavata \| en \| 7.3834 \| 356 \| \| manishiitg/open-aditi-hi-v1 \| en \| 6.6559 \| 356 \| \| sarvamai/OpenHathi-7B-Hi-v0.1-Base \| en \| 5.9567 \| 312 \| DHARMA TINY EVAL ============ #### Language Hi \| Model \| ARC-Easy \| bigbench \| truthful_qa \| BoolQ \| winogrande \| agieval \| ARC-Challenge \| MMLU \| openbookqa \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| open-aditi-hi-v2 \| 0.6245 \| 0.4959 \| 0.3866 \| 0.7192 \| 0.5353 \| 0.2945 \| 0.4828 \| 0.3457 \| 0.5279 \| \| open-aditi-hi-v3 \| 0.6803 \| 0.4553 \| 0.2788 \| 0.7385 \| 0.5390 \| 0.2178 \| 0.4914 \| 0.3346 \| 0.5688 \| \| open-aditi-hi-v4 \| 0.6989 \| 0.4526 \| 0.2714 \| 0.7231 \| 0.5167 \| 0.2331 \| 0.5302 \| 0.3123 \| 0.5316 \| \| open-aditi-v6-gemma \| 0.7212 \| 0.4146 \| 0.3234 \| 0.6923 \| 0.4870 \| 0.2638 \| 0.4957 \| 0.3680 \| 0.4349 \| \| open-aditi-v6-llama3 \| 0.5688 \| 0.4119 \| 0.2268 \| 0.6500 \| 0.4498 \| 0.2331 \| 0.4310 \| 0.3420 \| 0.3792 \| \| open-aditi-hi-v1 \| 0.4572 \| 0.3767 \| 0.2230 \| 0.6346 \| 0.4647 \| 0.1840 \| 0.3405 \| 0.3271 \| 0.3532 \| \| OpenHermes-2.5-Mistral-7B \| 0.3309 \| 0.4201 \| 0.3197 \| 0.6077 \| 0.4981 \| 0.2331 \| 0.3276 \| 0.3086 \| 0.3086 \| \| OpenHathi-7B-Hi-v0.1-Base \| 0.2862 \| 0.3333 \| 0.5130 \| 0.6077 \| 0.4907 \| 0.2301 \| 0.3017 \| 0.2677 \| 0.1933 \| \| Airavata \| 0.2751 \| 0.1274 \| 0.2268 \| 0.0615 \| 0.3866 \| 0.1104 \| 0.2845 \| 0.1450 \| 0.3383 \| \| gemma-7b-it \| 0.1227 \| 0.0786 \| 0.0743 \| 0.1808 \| 0.1561 \| 0.0491 \| 0.1078 \| 0.0818 \| 0.0855 \| #### Language En \| Model \| ARC-Easy \| bigbench \| truthful_qa \| BoolQ \| winogrande \| agieval \| ARC-Challenge \| MMLU \| openbookqa \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| OpenHermes-2.5-Mistral-7B \| 0.8922 \| 0.5745 \| 0.3197 \| 0.8346 \| 0.6989 \| 0.4908 \| 0.7802 \| 0.5911 \| 0.7621 \| \| open-aditi-hi-v2 \| 0.8625 \| 0.5149 \| 0.3532 \| 0.8192 \| 0.6877 \| 0.4571 \| 0.7500 \| 0.5613 \| 0.7732 \| \| open-aditi-hi-v4 \| 0.8959 \| 0.5041 \| 0.2862 \| 0.8423 \| 0.6914 \| 0.4571 \| 0.7716 \| 0.5651 \| 0.7138 \| \| open-aditi-hi-v3 \| 0.8773 \| 0.4986 \| 0.3048 \| 0.8385 \| 0.6766 \| 0.4663 \| 0.7371 \| 0.5613 \| 0.7249 \| \| Qwen1.5-7B-Chat \| 0.8922 \| 0.5122 \| 0.2007 \| 0.8000 \| 0.6654 \| 0.4294 \| 0.7759 \| 0.5799 \| 0.7621 \| \| open-aditi-v6-gemma \| 0.8699 \| 0.4959 \| 0.2602 \| 0.7385 \| 0.5465 \| 0.4540 \| 0.7371 \| 0.5167 \| 0.6654 \| \| open-aditi-v6-llama3 \| 0.8810 \| 0.4634 \| 0.1822 \| 0.7577 \| 0.5353 \| 0.4110 \| 0.7457 \| 0.5688 \| 0.6506 \| \| open-aditi-hi-v1 \| 0.8104 \| 0.3902 \| 0.2491 \| 0.6962 \| 0.5539 \| 0.3681 \| 0.6379 \| 0.5056 \| 0.5911 \| \| Airavata \| 0.7026 \| 0.4282 \| 0.3123 \| 0.7192 \| 0.5651 \| 0.3313 \| 0.5172 \| 0.3792 \| 0.5093 \| \| OpenHathi-7B-Hi-v0.1-Base \| 0.4684 \| 0.3062 \| 0.4758 \| 0.6346 \| 0.5167 \| 0.2577 \| 0.3017 \| 0.2788 \| 0.2714 \| Task: BoolQ Metric: score Task: ARC-Easy Metric: score Task: openbookqa Metric: score Task: winogrande Metric: score Task: ARC-Challenge Metric: score Task: truthful_qa Metric: score Task: bigbench Metric: score Task: MMLU Metric: score Task: agieval Metric: score <!-- 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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: google/gemma-7B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer tokenizer_config: philschmid/gemma-tokenizer-chatml tokenizer_use_fast: true load_in_8bit: false load_in_4bit: true strict: false datasets: - path: manishiitg/aditi-syn-train-small-v3 type: completion # 25 has only sythentic data, and has judge removed data hub_model_id: manishiitg/open-aditi-chat-hi-1.25-gemma hf_use_auth_token: true wandb_project: open-aditi-chat-hi-1.25-gemma dataset_prepared_path: manishiitg push_dataset_to_hub: manishiitg val_set_size: .1 output_dir: /sky-notebook/manishiitg/open-aditi-chat-hi-1.25-gemma adapter: qlora lora_model_dir: save_safetensors: true sequence_len: 2048 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true wandb_entity: wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 4 num_epochs: 1 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 adam_beta2: 0.95 adam_epsilon: 0.00001 max_grad_norm: 1.0 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: auto_resume_from_checkpoints: true ## manage check point resume from here local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 2 eval_table_size: eval_table_max_new_tokens: 128 save_steps: 20 ## increase based on your dataset save_strategy: steps debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: ``` </details><br> # open-aditi-chat-hi-1.25-gemma This model is a fine-tuned version of [google/gemma-7B](https://huggingface.co/google/gemma-7B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0992 ## 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: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.8213 \| 0.0 \| 1 \| 8.4429 \| \| 0.9759 \| 0.5 \| 121 \| 2.0992 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.40.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0
jorgefg03/deberta-v3-base-autext	jorgefg03	2024-04-19T12:05:43Z	5	0	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-19T11:18:04Z	--- 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]
leliuga/gemma-7b-it-bnb-4bit	leliuga	2024-04-19T12:04:18Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "conversational", "base_model:google/gemma-7b-it", "base_model:quantized:google/gemma-7b-it", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	2024-03-18T20:56:54Z	--- base_model: google/gemma-7b-it license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false model_creator: Google model_name: gemma-7b-it quantized_by: Leliuga pipeline_tag: text-generation tags: - gemma - arxiv:1910.09700 --- # gemma-7b-it - bnb 4bit - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-7b-it](https://huggingface.co/google/gemma-7b-it) ## Description This model is 4bit quantized version of [gemma-7b-it](https://huggingface.co/google/gemma-7b-it) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<pad>".
leliuga/gemma-7b-bnb-4bit	leliuga	2024-04-19T12:04:01Z	6	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "base_model:google/gemma-7b", "base_model:quantized:google/gemma-7b", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	2024-03-18T20:39:10Z	--- base_model: google/gemma-7b license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false model_creator: Google model_name: gemma-7b quantized_by: Leliuga pipeline_tag: text-generation tags: - gemma - arxiv:1910.09700 --- # gemma-7b - bnb 4bit - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-7b](https://huggingface.co/google/gemma-7b) ## Description This model is 4bit quantized version of [gemma-7b](https://huggingface.co/google/gemma-7b) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as <<pad>>.
leliuga/gemma-2b-it-bnb-4bit	leliuga	2024-04-19T12:03:37Z	9	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "conversational", "base_model:google/gemma-2b-it", "base_model:quantized:google/gemma-2b-it", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	2024-03-18T20:34:10Z	--- base_model: google/gemma-2b-it license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false model_creator: Google model_name: gemma-2b-it quantized_by: Leliuga pipeline_tag: text-generation tags: - gemma - arxiv:1910.09700 --- # gemma-2b-it - bnb 4bit - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) ## Description This model is 4bit quantized version of [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<pad>".
kiroloskhela/Sentiment-Bert	kiroloskhela	2024-04-19T12:03:25Z	18	1	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-05T07:19:13Z	# Emotion Analysis Model (Arabic) This repository contains a model for emotion analysis in Arabic text. The model predicts the emotion associated with a given text input. It was developed by Kirolos Amgad Khela and trained on the Emotone dataset and the SetFit/Emotion dataset after transcribing text to Arabic. ## Model Details - Model type: Text classification - Language(s): Arabic - Finetuned from model: MarBert ## Usage ### Direct Use The model can be directly used for emotion analysis. It takes text inputs in Arabic and predicts the corresponding emotion associated with the text. ## Training Details ### Training Data The model was trained using the Emotone dataset and the SetFit/Emotion dataset after translate text to Arabic. ### Training Procedure #### Preprocessing The training data was preprocessed by cleaning the text from English and removing punctuations. #### Training Hyperparameters - Output Directory: "train" - Logging Directory: "logs" - Evaluation Strategy: "epoch" - Per Device Train Batch Size: 32 - Per Device Eval Batch Size: 32 - Gradient Accumulation Steps: 1 - Number of Train Epochs: 3 - Learning Rate: 3e-5 - Warmup Ratio: 0.1 - Weight Decay: 0.01 - Adam Beta1: 0.9 - Adam Beta2: 0.999 - Adam Epsilon: 1e-8 - FP16: True - Save Strategy: "epoch" - Save Total Limit: 3 - Load Best Model at End: True - Metric for Best Model: "macro_f1" - Greater is Better: True ## Dataset The Emotone dataset and the SetFit/Emotion dataset are available for training. These datasets contain Arabic text annotated with emotion labels. ## Fine-tuning in Colab To fine-tune the model on a custom dataset, you can use the provided Colab notebook. Follow the steps outlined in the notebook to upload your dataset, configure the training parameters, and start the fine-tuning process. ## Fine-tuned Model and Dataset The fine-tuned model files and dataset are available in this [Google Drive folder](https://drive.google.com/drive/folders/1UWcBal3Myn4SipHhIX9bNpmbkQsO0Yow?usp=sharing). You can download the necessary files from this folder. ## Accuracy The model achieved an accuracy of 82% on the evaluation dataset. ## Authors - Kirolos Amgad Khela ## Contact For any inquiries about the model, please contact Kirolos Amgad Khela at [email protected].
leliuga/gemma-2b-bnb-4bit	leliuga	2024-04-19T12:03:22Z	18	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "base_model:google/gemma-2b", "base_model:quantized:google/gemma-2b", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	2024-03-18T20:19:24Z	--- base_model: google/gemma-2b license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false model_creator: Google model_name: gemma-2b quantized_by: Leliuga pipeline_tag: text-generation tags: - gemma - arxiv:1910.09700 --- # gemma-2b - bnb 4bit - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-2b](https://huggingface.co/google/gemma-2b) ## Description This model is 4bit quantized version of [gemma-2b](https://huggingface.co/google/gemma-2b) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<pad>".
leliuga/mistral-7b-instruct-v0.1-bnb-4bit	leliuga	2024-04-19T12:01:31Z	75	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:2310.06825", "arxiv:1910.09700", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	2024-03-17T21:35:43Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.1 license: apache-2.0 inference: false model_creator: Mistral AI_ model_name: Mistral-7B-Instruct-v0.1 quantized_by: Leliuga pipeline_tag: text-generation tags: - mistral - arxiv:2310.06825 - arxiv:1910.09700 --- # Mistral-7B-Instruct-v0.1 - bnb 4bit - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) ## Description This model is 4bit quantized version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.
leliuga/Mistral-7B-v0.1-bnb-4bit	leliuga	2024-04-19T12:01:12Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:2310.06825", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:quantized:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	2024-03-18T21:51:32Z	--- base_model: mistralai/Mistral-7B-v0.1 license: apache-2.0 inference: false model_creator: Mistral AI_ model_name: Mistral-7B-v0.1 quantized_by: Leliuga pipeline_tag: text-generation tags: - mistral - arxiv:2310.06825 - arxiv:1910.09700 --- # Mistral-7B-v0.1 - bnb 4bit - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ## Description This model is 4bit quantized version of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.
leliuga/Llama-2-13b-chat-hf-bnb-4bit	leliuga	2024-04-19T11:59:53Z	75	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-2", "arxiv:2307.09288", "arxiv:1910.09700", "conversational", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:quantized:meta-llama/Llama-2-13b-chat-hf", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	2024-03-17T22:28:16Z	--- base_model: meta-llama/Llama-2-13b-chat-hf license: apache-2.0 inference: false model_creator: Meta model_name: Llama-2-13b-chat-hf quantized_by: Leliuga pipeline_tag: text-generation tags: - llama-2 - arxiv:2307.09288 - arxiv:1910.09700 --- # Llama-2-13b-chat-hf - bnb 4bit - Model creator: [Meta](https://huggingface.co/meta-llama) - Original model: [Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) ## Description This model is 4bit quantized version of [Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.
leliuga/Llama-2-13b-hf-bnb-4bit	leliuga	2024-04-19T11:59:30Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-2", "arxiv:2307.09288", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-13b-hf", "base_model:quantized:meta-llama/Llama-2-13b-hf", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]	text-generation	2024-03-17T22:59:01Z	--- base_model: meta-llama/Llama-2-13b-hf license: apache-2.0 inference: false model_creator: Meta model_name: Llama-2-13b-hf quantized_by: Leliuga pipeline_tag: text-generation tags: - llama-2 - arxiv:2307.09288 - arxiv:1910.09700 --- # Llama-2-13b-hf - bnb 4bit - Model creator: [Meta](https://huggingface.co/meta-llama) - Original model: [Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) ## Description This model is 4bit quantized version of [Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.
AdinaM/hackathon_datacrowd	AdinaM	2024-04-19T11:42:23Z	4	0	transformers	[ "transformers", "safetensors", "llama", "moe", "fr", "it", "de", "es", "en", "license:apache-2.0", "model-index", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2024-04-18T14:33:05Z	--- language: - fr - it - de - es - en license: apache-2.0 tags: - moe model-index: - name: Mixtral-8x22B-v0.1 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: 70.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 88.73 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 77.81 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 51.08 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 84.53 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 74.15 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 name: Open LLM Leaderboard --- # Mixtral-8x22B > [!TIP] > MistralAI has uploaded weights to their organization at [mistralai/Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) and [mistralai/Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1) too. > [!TIP] > Kudos to [@v2ray](https://huggingface.co/v2ray) for converting the checkpoints and uploading them in `transformers` compatible format. Go give them a follow! Converted to HuggingFace Transformers format using the script [here](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1/blob/main/convert.py). The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistral-community/Mixtral-8x22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem: ### In half-precision Note `float16` precision only works on GPU devices <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistral-community/Mixtral-8x22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Lower precision using (8-bit & 4-bit) using `bitsandbytes` <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistral-community/Mixtral-8x22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Load the model with Flash Attention 2 <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistral-community/Mixtral-8x22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ## Notice Mixtral-8x22B-v0.1 is a pretrained base model and therefore does not have any moderation mechanisms. # The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall. # [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_mistral-community__Mixtral-8x22B-v0.1) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|74.46\| \|AI2 Reasoning Challenge (25-Shot)\|70.48\| \|HellaSwag (10-Shot) \|88.73\| \|MMLU (5-Shot) \|77.81\| \|TruthfulQA (0-shot) \|51.08\| \|Winogrande (5-shot) \|84.53\| \|GSM8k (5-shot) \|74.15\|
Yakonrus/SDXL_Controlnet_Tile_Realistic_v2	Yakonrus	2024-04-19T11:33:29Z	303	1	diffusers	[ "diffusers", "Controlnet", "Tile", "stable diffustion", "image-feature-extraction", "license:openrail", "region:us" ]	image-feature-extraction	2024-04-19T11:26:55Z	--- library_name: diffusers pipeline_tag: image-feature-extraction tags: - Controlnet - Tile - stable diffustion license: openrail --- This is just a renamed copy of the original controlnet https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic (V2) that works out of the box with diffusers in fp16 mode. ```py controlnet = ControlNetModel.from_pretrained( "Yakonrus/SDXL_Controlnet_Tile_Realistic_v2", torch_dtype=torch.float16, variant="fp16" ) ```
adammoss/gpt-pretrain-lm-w1	adammoss	2024-04-19T11:30:20Z	5	0	transformers	[ "transformers", "safetensors", "gptmodel", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-18T14:42:22Z	--- 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]
pavi156/my_awesome_qa_model	pavi156	2024-04-19T11:28:38Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-04-18T13:28:56Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: my_awesome_qa_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_qa_model 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: 1.9998 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 2.7540 \| \| 2.9386 \| 2.0 \| 500 \| 2.1089 \| \| 2.9386 \| 3.0 \| 750 \| 1.9998 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2
jorgefg03/bert-base-uncased-autext	jorgefg03	2024-04-19T11:22:39Z	5	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-19T09:45:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
automerger/Multiverseex26Meliodaspercival_01_experiment26t3q-7B	automerger	2024-04-19T11:20:12Z	0	0	null	[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:MaziyarPanahi/MeliodasPercival_01_Experiment26T3q", "base_model:merge:MaziyarPanahi/MeliodasPercival_01_Experiment26T3q", "base_model:allknowingroger/MultiverseEx26-7B-slerp", "base_model:merge:allknowingroger/MultiverseEx26-7B-slerp", "license:apache-2.0", "region:us" ]	null	2024-04-19T11:20:11Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - allknowingroger/MultiverseEx26-7B-slerp - MaziyarPanahi/MeliodasPercival_01_Experiment26T3q --- # Multiverseex26Meliodaspercival_01_experiment26t3q-7B Multiverseex26Meliodaspercival_01_experiment26t3q-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [allknowingroger/MultiverseEx26-7B-slerp](https://huggingface.co/allknowingroger/MultiverseEx26-7B-slerp) * [MaziyarPanahi/MeliodasPercival_01_Experiment26T3q](https://huggingface.co/MaziyarPanahi/MeliodasPercival_01_Experiment26T3q) ## 🧩 Configuration ```yaml slices: - sources: - model: allknowingroger/MultiverseEx26-7B-slerp layer_range: [0, 32] - model: MaziyarPanahi/MeliodasPercival_01_Experiment26T3q layer_range: [0, 32] merge_method: slerp base_model: allknowingroger/MultiverseEx26-7B-slerp parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Multiverseex26Meliodaspercival_01_experiment26t3q-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
MelanieKoe/w2v2-base-pretrained_lr5e-5_at1_da1-p4	MelanieKoe	2024-04-19T11:16:41Z	6	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-04-19T05:35:32Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - wer model-index: - name: w2v2-base-pretrained_lr5e-5_at1_da1-p4 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. --> # w2v2-base-pretrained_lr5e-5_at1_da1-p4 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6341 - Wer: 0.1039 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 18.6256 \| 5.21 \| 250 \| 4.2622 \| 1.0 \| \| 3.3901 \| 10.42 \| 500 \| 3.2209 \| 1.0 \| \| 3.0963 \| 15.62 \| 750 \| 3.1175 \| 1.0 \| \| 2.0992 \| 20.83 \| 1000 \| 0.5962 \| 0.4402 \| \| 0.2069 \| 26.04 \| 1250 \| 0.4456 \| 0.1310 \| \| 0.0849 \| 31.25 \| 1500 \| 0.4902 \| 0.1200 \| \| 0.0596 \| 36.46 \| 1750 \| 0.5079 \| 0.1176 \| \| 0.0437 \| 41.67 \| 2000 \| 0.5362 \| 0.1136 \| \| 0.0355 \| 46.88 \| 2250 \| 0.5433 \| 0.1156 \| \| 0.0281 \| 52.08 \| 2500 \| 0.5994 \| 0.1136 \| \| 0.0238 \| 57.29 \| 2750 \| 0.6018 \| 0.1112 \| \| 0.02 \| 62.5 \| 3000 \| 0.5970 \| 0.1120 \| \| 0.0181 \| 67.71 \| 3250 \| 0.6282 \| 0.1083 \| \| 0.0167 \| 72.92 \| 3500 \| 0.6120 \| 0.1075 \| \| 0.0145 \| 78.12 \| 3750 \| 0.6404 \| 0.1047 \| \| 0.014 \| 83.33 \| 4000 \| 0.6341 \| 0.1039 \| ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.14.6 - Tokenizers 0.14.1
reecursion/stress-RoBERTa	reecursion	2024-04-19T11:16:38Z	5	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-04-19T11:15:55Z	--- license: mit base_model: FacebookAI/roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-chatbot-stress 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. --> # roberta-chatbot-stress This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9676 - Accuracy: 0.8275 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4295 \| 1.0 \| 249 \| 0.4018 \| 0.8157 \| \| 0.4911 \| 2.0 \| 498 \| 0.4656 \| 0.8039 \| \| 0.472 \| 3.0 \| 747 \| 0.6054 \| 0.8431 \| \| 0.1464 \| 4.0 \| 996 \| 0.9441 \| 0.8157 \| \| 0.08 \| 5.0 \| 1245 \| 0.9676 \| 0.8275 \| ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1
RJ3vans/CMV1spanTagger	RJ3vans	2024-04-19T11:13:24Z	105	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	This model identifies compound verb phrases (including conjoins and coordinators) in an input sentence. Try the test sentence: John kicked the ball [and] chased after it. Please note that it is necessary for you to highlight the verb phrase coordinator "and" using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger. The model should tag the tokens in the sentence with information about whether or not they are contained within a compound verb phrase. If you find the model useful, please cite my thesis which presents the dataset used for finetuning: Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf) There you will find more information about the tagging scheme. The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton.
RJ3vans/CMN1spanTagger	RJ3vans	2024-04-19T11:11:21Z	8	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:04Z	This model identifies compound noun phrases in an input sentence. Try the test sentence: The inquiry, which continues, will recall John Smith [and] Peter Montgomery next month for further questioning. Please note that it is necessary for you to highlight the noun phrase coordinator "and" using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger. The model should tag the tokens in the sentence with information about whether or not they are contained within a compound noun phrase. If you find the model useful, please cite my thesis which presents the dataset used for finetuning: Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf) There you will find more information about the tagging scheme. The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton.
RJ3vans/SSCCVspanTagger	RJ3vans	2024-04-19T11:03:19Z	53	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-05-17T14:59:38Z	Try the test sentences: My name is Sarah and I live in London[, which] is the largest city in the UK. John thought [that] that was a strange idea. It was on Tuesdays [when] Peter took Tess for a walk. John was so large [that] he had to crouch to fit through the front door. Please note that it is necessary for you to highlight the left clause boundary using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger. The model should tag the tokens in the sentence with information about whether or not they are contained within particular types of syntactic constituents. If you find the model useful, please cite my thesis which presents the dataset used for finetuning: Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf) There you will find more information about the tagging scheme.
DavidAU/UNA-TheBeagle-7b-v1-Q6_K-GGUF	DavidAU	2024-04-19T11:00:32Z	3	0	transformers	[ "transformers", "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "dataset:jondurbin/bagel-v0.3", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us" ]	null	2024-04-19T11:00:16Z	--- license: cc-by-nc-nd-4.0 library_name: transformers tags: - generated_from_trainer - llama-cpp - gguf-my-repo datasets: - jondurbin/bagel-v0.3 model-index: - name: UNA-TheBeagle-7b-v1 results: [] --- # DavidAU/UNA-TheBeagle-7b-v1-Q6_K-GGUF This model was converted to GGUF format from [`fblgit/UNA-TheBeagle-7b-v1`](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/UNA-TheBeagle-7b-v1-Q6_K-GGUF --model una-thebeagle-7b-v1.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/UNA-TheBeagle-7b-v1-Q6_K-GGUF --model una-thebeagle-7b-v1.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m una-thebeagle-7b-v1.Q6_K.gguf -n 128 ```
RJ3vans/DeBERTaSSCCVspanTagger	RJ3vans	2024-04-19T10:58:13Z	106	0	transformers	[ "transformers", "pytorch", "deberta-v2", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-07-14T15:08:04Z	Try the test sentences: My name is Sarah and I live in London[, which] is the largest city in the UK. John thought [that] that was a strange idea. It was on Tuesdays [when] Peter took Tess for a walk. John was so large [that] he had to crouch to fit through the front door. Please note that it is necessary for you to highlight the left clause boundary using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger. The model should tag the tokens in the sentence with information about whether or not they are contained within particular types of syntactic constituents. If you find the model useful, please cite my thesis which presents the dataset used for finetuning: Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf) There you will find more information about the tagging scheme.

Mouwiya/image-model-demo

Mouwiya

2024-04-19T15:02:55Z

9

0

transformers

[ "transformers", "safetensors", "blip", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

image-text-to-text

2024-04-19T14:55:51Z

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

tawreck-hasaballah/whisper-small-eg

tawreck-hasaballah

2024-04-19T14:57:19Z

17

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "eg", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-03-25T00:14:31Z

--- language: - eg license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer metrics: - wer model-index: - name: Whisper Small Eg - Tariq Hasaballah 100330 results: [] ---  # Whisper Small Eg - Tariq Hasaballah 100330 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ASR-EGARBCSC: AN EGYPTIAN ARABIC CONVERSATIONAL SPEECH CORPUS dataset. It achieves the following results on the evaluation set: - Loss: 0.5626 - Wer: 47.4960 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.7309 | 0.7267 | 125 | 0.5984 | 52.4512 | | 0.3608 | 1.4535 | 250 | 0.5488 | 48.6031 | | 0.1789 | 2.1802 | 375 | 0.5537 | 46.5999 | | 0.1844 | 2.9070 | 500 | 0.5626 | 47.4960 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

carloswbarros/QAmembertTest

carloswbarros

2024-04-19T14:56:07Z

5

0

transformers

[ "transformers", "safetensors", "camembert", "question-answering", "generated_from_trainer", "dataset:generator", "base_model:CATIE-AQ/QAmembert", "base_model:finetune:CATIE-AQ/QAmembert", "license:cc-by-4.0", "endpoints_compatible", "region:us" ]

question-answering

2024-04-19T09:11:05Z

--- license: cc-by-4.0 base_model: CATIE-AQ/QAmembert tags: - generated_from_trainer datasets: - generator model-index: - name: QAmembertTest results: [] ---  # QAmembertTest This model is a fine-tuned version of [CATIE-AQ/QAmembert](https://huggingface.co/CATIE-AQ/QAmembert) 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: 2e-05 - train_batch_size: 8 - eval_batch_size: 0 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

ipurwadi/swin-tiny-patch4-window7-224-finetuned-eurosat

ipurwadi

2024-04-19T14:54:24Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "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

2024-04-18T05:55:57Z

--- license: apache-2.0 base_model: microsoft/swin-tiny-patch4-window7-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9093137254901961 ---  # swin-tiny-patch4-window7-224-finetuned-eurosat 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: 0.2963 - Accuracy: 0.9093 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4987 | 1.0 | 86 | 0.4083 | 0.8693 | | 0.3837 | 2.0 | 172 | 0.4003 | 0.8611 | | 0.3595 | 3.0 | 258 | 0.2963 | 0.9093 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.2 - Datasets 2.19.0 - Tokenizers 0.19.1

Liya009/code-search-net-tokenizer

Liya009

2024-04-19T14:53:21Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-04-19T14:52:24Z

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

cstr/Spaetzle-v69-7b

cstr

2024-04-19T14:51:43Z

48

1

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "conversational", "de", "en", "base_model:DRXD1000/Phoenix-7B", "base_model:merge:DRXD1000/Phoenix-7B", "base_model:DiscoResearch/DiscoLM_German_7b_v1", "base_model:merge:DiscoResearch/DiscoLM_German_7b_v1", "base_model:LeoLM/leo-mistral-hessianai-7b", "base_model:merge:LeoLM/leo-mistral-hessianai-7b", "base_model:OpenPipe/mistral-ft-optimized-1227", "base_model:merge:OpenPipe/mistral-ft-optimized-1227", "base_model:PetroGPT/WestSeverus-7B-DPO-v2", "base_model:merge:PetroGPT/WestSeverus-7B-DPO-v2", "base_model:ResplendentAI/Flora_DPO_7B", "base_model:merge:ResplendentAI/Flora_DPO_7B", "base_model:VAGOsolutions/SauerkrautLM-7b-v1-mistral", "base_model:merge:VAGOsolutions/SauerkrautLM-7b-v1-mistral", "base_model:abideen/AlphaMonarch-dora", "base_model:merge:abideen/AlphaMonarch-dora", "base_model:cognitivecomputations/openchat-3.5-0106-laser", "base_model:merge:cognitivecomputations/openchat-3.5-0106-laser", "base_model:flemmingmiguel/NeuDist-Ro-7B", "base_model:merge:flemmingmiguel/NeuDist-Ro-7B", "base_model:malteos/hermeo-7b", "base_model:merge:malteos/hermeo-7b", "base_model:mayflowergmbh/Wiedervereinigung-7b-dpo", "base_model:merge:mayflowergmbh/Wiedervereinigung-7b-dpo", "base_model:occiglot/occiglot-7b-de-en-instruct", "base_model:merge:occiglot/occiglot-7b-de-en-instruct", "base_model:yleo/EmertonMonarch-7B", "base_model:merge:yleo/EmertonMonarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-04-17T03:40:34Z

--- tags: - merge - mergekit - lazymergekit language: - de - en base_model: - abideen/AlphaMonarch-dora - mayflowergmbh/Wiedervereinigung-7b-dpo - flemmingmiguel/NeuDist-Ro-7B - ResplendentAI/Flora_DPO_7B - yleo/EmertonMonarch-7B - occiglot/occiglot-7b-de-en-instruct - OpenPipe/mistral-ft-optimized-1227 - DiscoResearch/DiscoLM_German_7b_v1 - LeoLM/leo-mistral-hessianai-7b - DRXD1000/Phoenix - VAGOsolutions/SauerkrautLM-7b-v1-mistral - malteos/hermeo-7b - FelixChao/WestSeverus-7B-DPO-v2 - cognitivecomputations/openchat-3.5-0106-laser license: cc-by-nc-4.0 --- # Spaetzle-v69-7b This is a progressive (mostly dare-ties, but also slerp) merge with the intention of a suitable compromise for English and German local tasks. There is also a 4q_k_m quantized [GGUF](https://huggingface.co/cstr/Spaetzle-v69-7b-GGUF). It should work sufficiently well with ChatML prompt template (for all merged models should have seen ChatML prompts at least in DPO stage). ## Evaluation Benchmark scores are not the possible optimum, as the model attempts a compromise with a number of parameters, like German language performance, instruction following, reasoning capabilities, robustness (so far, i did not encounter inserted tokens, e.g.), model licensing, and other criteria. Nevertheless, they are not too bad: It achieves (running quantized) in - German EQ Bench: Score (v2_de): 62.59 (Parseable: 171.0). - English EQ Bench: Score (v2): 76.43 (Parseable: 171.0). [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_cstr__Spaetzle-v69-7b) | Metric |Value| |---------------------------------|----:| |Avg. |72.87| |AI2 Reasoning Challenge (25-Shot)|69.54| |HellaSwag (10-Shot) |86.77| |MMLU (5-Shot) |64.63| |TruthfulQA (0-shot) |65.61| |Winogrande (5-shot) |81.93| |GSM8k (5-shot) |68.76| Nous benchmark results: | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |--------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[Spaetzle-v69-7b](https://huggingface.co/cstr/Spaetzle-v69-7b)| 44.48| 75.84| 66.15| 46.59| 58.27| ### AGIEval | Task |Version| Metric |Value| |Stderr| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |25.98|± | 2.76| | | |acc_norm|23.62|± | 2.67| |agieval_logiqa_en | 0|acc |39.78|± | 1.92| | | |acc_norm|39.48|± | 1.92| |agieval_lsat_ar | 0|acc |23.48|± | 2.80| | | |acc_norm|23.91|± | 2.82| |agieval_lsat_lr | 0|acc |50.00|± | 2.22| | | |acc_norm|51.76|± | 2.21| |agieval_lsat_rc | 0|acc |63.94|± | 2.93| | | |acc_norm|64.31|± | 2.93| |agieval_sat_en | 0|acc |76.70|± | 2.95| | | |acc_norm|77.67|± | 2.91| |agieval_sat_en_without_passage| 0|acc |46.12|± | 3.48| | | |acc_norm|44.17|± | 3.47| |agieval_sat_math | 0|acc |34.09|± | 3.20| | | |acc_norm|30.91|± | 3.12| Average: 44.48% ### GPT4All | Task |Version| Metric |Value| |Stderr| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |63.23|± | 1.41| | | |acc_norm|64.16|± | 1.40| |arc_easy | 0|acc |85.90|± | 0.71| | | |acc_norm|82.49|± | 0.78| |boolq | 1|acc |87.80|± | 0.57| |hellaswag | 0|acc |67.05|± | 0.47| | | |acc_norm|85.19|± | 0.35| |openbookqa | 0|acc |38.40|± | 2.18| | | |acc_norm|48.40|± | 2.24| |piqa | 0|acc |82.75|± | 0.88| | | |acc_norm|84.28|± | 0.85| |winogrande | 0|acc |78.53|± | 1.15| Average: 75.84% ### TruthfulQA | Task |Version|Metric|Value| |Stderr| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |50.67|± | 1.75| | | |mc2 |66.15|± | 1.48| Average: 66.15% ### Bigbench | Task |Version| Metric |Value| |Stderr| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|56.84|± | 3.60| |bigbench_date_understanding | 0|multiple_choice_grade|66.67|± | 2.46| |bigbench_disambiguation_qa | 0|multiple_choice_grade|40.70|± | 3.06| |bigbench_geometric_shapes | 0|multiple_choice_grade|24.79|± | 2.28| | | |exact_str_match |10.58|± | 1.63| |bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|31.00|± | 2.07| |bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|23.00|± | 1.59| |bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|58.00|± | 2.85| |bigbench_movie_recommendation | 0|multiple_choice_grade|45.80|± | 2.23| |bigbench_navigate | 0|multiple_choice_grade|52.10|± | 1.58| |bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|69.55|± | 1.03| |bigbench_ruin_names | 0|multiple_choice_grade|48.88|± | 2.36| |bigbench_salient_translation_error_detection | 0|multiple_choice_grade|30.96|± | 1.46| |bigbench_snarks | 0|multiple_choice_grade|73.48|± | 3.29| |bigbench_sports_understanding | 0|multiple_choice_grade|74.14|± | 1.40| |bigbench_temporal_sequences | 0|multiple_choice_grade|42.70|± | 1.56| |bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|23.60|± | 1.20| |bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|18.40|± | 0.93| |bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|58.00|± | 2.85| Average: 46.59% Average score: 58.27% ## 🧩 Merge Configuration Spaetzle-v69-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora) * [cstr/Spaetzle-v68-7b](https://huggingface.co/cstr/Spaetzle-v68-7b) The merge tree in total involves the following original models: - [abideen/AlphaMonarch-dora](https://huggingface.co/abideen/AlphaMonarch-dora) - [mayflowergmbh/Wiedervereinigung-7b-dpo](https://huggingface.co/mayflowergmbh/Wiedervereinigung-7b-dpo) - [flemmingmiguel/NeuDist-Ro-7B](https://huggingface.co/flemmingmiguel/NeuDist-Ro-7B) - [ResplendentAI/Flora_DPO_7B](https://huggingface.co/ResplendentAI/Flora_DPO_7B) - [yleo/EmertonMonarch-7B](https://huggingface.co/yleo/EmertonMonarch-7B) - [occiglot/occiglot-7b-de-en-instruct](https://huggingface.co/occiglot/occiglot-7b-de-en-instruct) - [OpenPipe/mistral-ft-optimized-1227](https://huggingface.co/OpenPipe/mistral-ft-optimized-1227) - [DiscoResearch/DiscoLM_German_7b_v1](https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1) - [LeoLM/leo-mistral-hessianai-7b](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b) - [DRXD1000/Phoenix](https://huggingface.co/DRXD1000/Phoenix) - [VAGOsolutions/SauerkrautLM-7b-v1-mistral](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-v1-mistral) - [malteos/hermeo-7b](https://huggingface.co/malteos/hermeo-7b) - [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2) - [cognitivecomputations/openchat-3.5-0106-laser](https://huggingface.co/cognitivecomputations/openchat-3.5-0106-laser) For this last merge: ```yaml models: - model: cstr/Spaetzle-v68-7b # no parameters necessary for base model - model: abideen/AlphaMonarch-dora parameters: density: 0.60 weight: 0.30 merge_method: dare_ties base_model: cstr/Spaetzle-v68-7b parameters: int8_mask: true dtype: bfloat16 random_seed: 0 tokenizer_source: base ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "cstr/Spaetzle-v69-7b" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

abhishek/autotrain-llama3-8b-open-hermes-sft

abhishek

2024-04-19T14:46:07Z

7

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-04-19T11:38:35Z

--- tags: - autotrain - text-generation-inference - text-generation library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? 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) ```

Sagicc/whisper-base-sr-yodas

Sagicc

2024-04-19T14:45:09Z

10

0

transformers

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

automatic-speech-recognition

2024-04-19T12:37:48Z

--- language: - sr license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - espnet/yodas metrics: - wer model-index: - name: Whisper Small Sr Yodas results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Yodas type: espnet/yodas config: sr split: test args: sr metrics: - name: Wer type: wer value: 0.24497708847373986 ---  # Whisper Small Sr Yodas This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Yodas dataset. It achieves the following results on the evaluation set: - Loss: 0.2688 - Wer Ortho: 0.3334 - Wer: 0.2450 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:| | 1.0469 | 0.24 | 500 | 0.4020 | 0.5071 | 0.4270 | | 0.9924 | 0.49 | 1000 | 0.3401 | 0.4082 | 0.3183 | | 0.865 | 0.73 | 1500 | 0.3047 | 0.3644 | 0.2776 | | 0.8443 | 0.98 | 2000 | 0.2893 | 0.3623 | 0.2735 | | 0.7377 | 1.22 | 2500 | 0.2817 | 0.3472 | 0.2591 | | 0.6851 | 1.46 | 3000 | 0.2728 | 0.3348 | 0.2466 | | 0.7286 | 1.71 | 3500 | 0.2702 | 0.3325 | 0.2444 | | 0.7215 | 1.95 | 4000 | 0.2688 | 0.3334 | 0.2450 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.0.1+cu117 - Datasets 2.18.0 - Tokenizers 0.15.1

blockblockblock/Meta-Llama-3-70B-Instruct-hf-bpw3

blockblockblock

2024-04-19T14:44:36Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "conversational", "en", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "3-bit", "exl2", "region:us" ]

text-generation

2024-04-19T14:40:33Z

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: other license_name: llama3 license_link: LICENSE extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Meta Llama 3" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof). 2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Meta Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected] extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td>Training Data </td> <td>Params </td> <td>Context length </td> <td>GQA </td> <td>Token count </td> <td>Knowledge cutoff </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-70B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td>Time (GPU hours) </td> <td>Power Consumption (W) </td> <td>Carbon Emitted(tCO2eq) </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td>Category </td> <td>Benchmark </td> <td>Llama 3 8B </td> <td>Llama2 7B </td> <td>Llama2 13B </td> <td>Llama 3 70B </td> <td>Llama2 70B </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td>Benchmark </td> <td>Llama 3 8B </td> <td>Llama 2 7B </td> <td>Llama 2 13B </td> <td>Llama 3 70B </td> <td>Llama 2 70B </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. Safety For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. Refusals In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks CBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### Cyber Security We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### Child Safety Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

DUAL-GPO-2/phi-2-ipo-renew1

DUAL-GPO-2

2024-04-19T14:44:01Z

10

0

peft

[ "peft", "safetensors", "phi", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "custom_code", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:microsoft/phi-2", "base_model:adapter:microsoft/phi-2", "license:mit", "region:us" ]

null

2024-04-19T02:43:27Z

--- license: mit library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo base_model: microsoft/phi-2 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: phi-2-ipo-renew1 results: [] ---  # phi-2-ipo-renew1 This model is a fine-tuned version of [lole25/phi-2-sft-ultrachat-lora](https://huggingface.co/lole25/phi-2-sft-ultrachat-lora) on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 2028.0933 - Rewards/chosen: -0.1243 - Rewards/rejected: -0.2158 - Rewards/accuracies: 0.6900 - Rewards/margins: 0.0915 - Logps/rejected: -255.1287 - Logps/chosen: -269.0499 - Logits/rejected: 0.5909 - Logits/chosen: 0.5352 ## 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-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 4 - 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | 2496.843 | 0.05 | 100 | 2502.2668 | -0.0003 | -0.0002 | 0.5005 | -0.0002 | -233.5649 | -256.6506 | 0.8888 | 0.8318 | | 2499.2807 | 0.1 | 200 | 2494.8354 | 0.0001 | -0.0005 | 0.5190 | 0.0006 | -233.5995 | -256.6106 | 0.8882 | 0.8310 | | 2477.7609 | 0.16 | 300 | 2481.5015 | -0.0011 | -0.0031 | 0.5595 | 0.0019 | -233.8548 | -256.7285 | 0.8892 | 0.8319 | | 2428.4195 | 0.21 | 400 | 2419.1045 | -0.0068 | -0.0156 | 0.6495 | 0.0089 | -235.1127 | -257.2951 | 0.8983 | 0.8404 | | 2296.8842 | 0.26 | 500 | 2349.4358 | -0.0240 | -0.0419 | 0.6565 | 0.0179 | -237.7379 | -259.0124 | 0.8806 | 0.8214 | | 2254.5846 | 0.31 | 600 | 2273.4993 | -0.0525 | -0.0829 | 0.6570 | 0.0304 | -241.8383 | -261.8659 | 0.8478 | 0.7868 | | 2330.7787 | 0.37 | 700 | 2224.3350 | -0.0819 | -0.1221 | 0.6630 | 0.0402 | -245.7631 | -264.8093 | 0.8128 | 0.7517 | | 2223.6863 | 0.42 | 800 | 2196.0991 | -0.1009 | -0.1487 | 0.6675 | 0.0478 | -248.4222 | -266.7057 | 0.7611 | 0.6992 | | 2066.7418 | 0.47 | 900 | 2166.0732 | -0.1112 | -0.1658 | 0.6700 | 0.0546 | -250.1319 | -267.7397 | 0.7518 | 0.6917 | | 2119.2691 | 0.52 | 1000 | 2138.9312 | -0.1215 | -0.1821 | 0.6715 | 0.0606 | -251.7610 | -268.7693 | 0.7213 | 0.6619 | | 2191.7109 | 0.58 | 1100 | 2121.8115 | -0.1257 | -0.1906 | 0.6695 | 0.0648 | -252.6059 | -269.1910 | 0.7176 | 0.6584 | | 2308.1883 | 0.63 | 1200 | 2110.3069 | -0.1409 | -0.2123 | 0.6665 | 0.0715 | -254.7812 | -270.7044 | 0.6920 | 0.6330 | | 1996.7178 | 0.68 | 1300 | 2095.3130 | -0.1314 | -0.2042 | 0.6755 | 0.0728 | -253.9726 | -269.7621 | 0.6722 | 0.6141 | | 2038.3844 | 0.73 | 1400 | 2085.0852 | -0.1383 | -0.2140 | 0.6800 | 0.0756 | -254.9441 | -270.4488 | 0.6513 | 0.5933 | | 2094.2182 | 0.79 | 1500 | 2076.3042 | -0.1390 | -0.2166 | 0.6790 | 0.0777 | -255.2133 | -270.5129 | 0.6474 | 0.5898 | | 2171.3457 | 0.84 | 1600 | 2069.3757 | -0.1374 | -0.2166 | 0.6810 | 0.0792 | -255.2130 | -270.3595 | 0.6392 | 0.5818 | | 2189.3863 | 0.89 | 1700 | 2062.1995 | -0.1386 | -0.2192 | 0.6780 | 0.0806 | -255.4675 | -270.4739 | 0.6291 | 0.5723 | | 2292.8938 | 0.94 | 1800 | 2053.1299 | -0.1196 | -0.2005 | 0.6830 | 0.0809 | -253.6025 | -268.5789 | 0.6275 | 0.5703 | | 2085.5805 | 0.99 | 1900 | 2052.3237 | -0.1086 | -0.1906 | 0.6900 | 0.0821 | -252.6131 | -267.4730 | 0.6319 | 0.5747 | | 1847.759 | 1.05 | 2000 | 2050.4177 | -0.1118 | -0.1953 | 0.6850 | 0.0836 | -253.0827 | -267.7950 | 0.6333 | 0.5763 | | 2024.9559 | 1.1 | 2100 | 2046.7593 | -0.1219 | -0.2083 | 0.6900 | 0.0864 | -254.3799 | -268.8073 | 0.6157 | 0.5590 | | 2038.6354 | 1.15 | 2200 | 2043.5728 | -0.1205 | -0.2072 | 0.6880 | 0.0867 | -254.2731 | -268.6722 | 0.6083 | 0.5518 | | 2022.9617 | 1.2 | 2300 | 2035.5857 | -0.1173 | -0.2041 | 0.6895 | 0.0868 | -253.9597 | -268.3491 | 0.6101 | 0.5535 | | 1871.641 | 1.26 | 2400 | 2036.3373 | -0.1190 | -0.2073 | 0.6895 | 0.0884 | -254.2831 | -268.5161 | 0.6046 | 0.5482 | | 1907.3463 | 1.31 | 2500 | 2034.7010 | -0.1216 | -0.2108 | 0.6880 | 0.0892 | -254.6297 | -268.7765 | 0.6022 | 0.5460 | | 1884.6086 | 1.36 | 2600 | 2033.7977 | -0.1215 | -0.2105 | 0.6910 | 0.0890 | -254.6014 | -268.7708 | 0.6013 | 0.5451 | | 2034.9129 | 1.41 | 2700 | 2032.5447 | -0.1235 | -0.2140 | 0.6900 | 0.0905 | -254.9471 | -268.9633 | 0.5987 | 0.5426 | | 2068.2822 | 1.47 | 2800 | 2030.8698 | -0.1251 | -0.2162 | 0.6900 | 0.0911 | -255.1671 | -269.1270 | 0.5943 | 0.5383 | | 1977.4029 | 1.52 | 2900 | 2030.6033 | -0.1251 | -0.2162 | 0.6895 | 0.0911 | -255.1690 | -269.1252 | 0.5941 | 0.5381 | | 2110.2887 | 1.57 | 3000 | 2030.5707 | -0.1259 | -0.2173 | 0.6905 | 0.0915 | -255.2821 | -269.2050 | 0.5908 | 0.5348 | | 2068.2863 | 1.62 | 3100 | 2029.4174 | -0.1242 | -0.2156 | 0.6935 | 0.0914 | -255.1087 | -269.0390 | 0.5913 | 0.5357 | | 1977.8852 | 1.67 | 3200 | 2026.1289 | -0.1249 | -0.2165 | 0.6960 | 0.0916 | -255.2016 | -269.1071 | 0.5920 | 0.5364 | | 2123.3787 | 1.73 | 3300 | 2027.3552 | -0.1248 | -0.2162 | 0.6930 | 0.0914 | -255.1666 | -269.0933 | 0.5926 | 0.5370 | | 1945.4934 | 1.78 | 3400 | 2025.7804 | -0.1248 | -0.2164 | 0.6935 | 0.0916 | -255.1899 | -269.1010 | 0.5909 | 0.5353 | | 1937.2627 | 1.83 | 3500 | 2027.8240 | -0.1247 | -0.2163 | 0.6930 | 0.0916 | -255.1750 | -269.0878 | 0.5903 | 0.5347 | | 2007.2062 | 1.88 | 3600 | 2025.3228 | -0.1244 | -0.2164 | 0.6895 | 0.0919 | -255.1843 | -269.0623 | 0.5910 | 0.5352 | | 2076.715 | 1.94 | 3700 | 2027.4857 | -0.1243 | -0.2159 | 0.6920 | 0.0916 | -255.1383 | -269.0487 | 0.5913 | 0.5358 | | 2055.2201 | 1.99 | 3800 | 2027.8082 | -0.1244 | -0.2160 | 0.6920 | 0.0916 | -255.1455 | -269.0543 | 0.5902 | 0.5347 | ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2 - Datasets 2.14.6 - Tokenizers 0.15.2

clam004/Qwen-Qwen1.5-0.5B-Chat

clam004

2024-04-19T14:43:35Z

7

0

transformers

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

text-generation

2024-04-06T18:19:10Z

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

ntvcie/Gemma2bVinhntV7_16bit

ntvcie

2024-04-19T14:41:11Z

0

transformers

[ "transformers", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-2b-bnb-4bit", "base_model:finetune:unsloth/gemma-2b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-04-19T14:41:10Z

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

wendy41/llama-2-koen-user111-100

wendy41

2024-04-19T14:41:01Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-04-19T14:40:59Z

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

qubvel-hf/microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5

qubvel-hf

2024-04-19T14:39:26Z

6

0

transformers

[ "transformers", "safetensors", "conditional_detr", "object-detection", "vision", "generated_from_trainer", "base_model:microsoft/conditional-detr-resnet-50", "base_model:finetune:microsoft/conditional-detr-resnet-50", "license:apache-2.0", "endpoints_compatible", "region:us" ]

object-detection

2024-04-19T12:48:17Z

--- license: apache-2.0 base_model: microsoft/conditional-detr-resnet-50 tags: - object-detection - vision - generated_from_trainer model-index: - name: microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5 results: [] ---  # microsoft-conditional-detr-resnet-50-finetuned-10k-cppe5 This model is a fine-tuned version of [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) on the cppe-5 dataset. It achieves the following results on the evaluation set: - Loss: 1.3007 - Map: 0.3131 - Map 50: 0.6268 - Map 75: 0.2698 - Map Small: 0.2089 - Map Medium: 0.2237 - Map Large: 0.5358 - Mar 1: 0.3352 - Mar 10: 0.4586 - Mar 100: 0.4682 - Mar Small: 0.2773 - Mar Medium: 0.3748 - Mar Large: 0.6417 - Map Coverall: 0.5362 - Mar 100 Coverall: 0.6844 - Map Face Shield: 0.3199 - Mar 100 Face Shield: 0.4635 - Map Gloves: 0.1628 - Mar 100 Gloves: 0.3176 - Map Goggles: 0.2427 - Mar 100 Goggles: 0.4475 - Map Mask: 0.3038 - Mar 100 Mask: 0.4278 ## 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: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:| | 13.6734 | 1.0 | 107 | 3.0782 | 0.0022 | 0.009 | 0.0004 | 0.0002 | 0.0002 | 0.0027 | 0.0054 | 0.0291 | 0.0632 | 0.0028 | 0.0108 | 0.0955 | 0.0105 | 0.2595 | 0.0 | 0.0 | 0.0 | 0.0126 | 0.0 | 0.0 | 0.0005 | 0.0438 | | 2.9712 | 2.0 | 214 | 2.7340 | 0.0089 | 0.0267 | 0.0034 | 0.0036 | 0.0017 | 0.0114 | 0.0186 | 0.0749 | 0.1162 | 0.0082 | 0.0358 | 0.1266 | 0.0374 | 0.4457 | 0.0 | 0.0 | 0.0004 | 0.0101 | 0.0 | 0.0 | 0.0069 | 0.1253 | | 2.6599 | 3.0 | 321 | 2.6057 | 0.0122 | 0.0432 | 0.0034 | 0.0011 | 0.003 | 0.0138 | 0.0281 | 0.1003 | 0.1355 | 0.0324 | 0.0542 | 0.1526 | 0.0538 | 0.4428 | 0.0004 | 0.0288 | 0.0003 | 0.0322 | 0.0 | 0.0049 | 0.0064 | 0.1686 | | 2.53 | 4.0 | 428 | 2.3681 | 0.0187 | 0.0542 | 0.0099 | 0.0061 | 0.0057 | 0.0372 | 0.0494 | 0.1502 | 0.1831 | 0.0272 | 0.1134 | 0.1631 | 0.0737 | 0.5191 | 0.0032 | 0.1 | 0.0012 | 0.0487 | 0.0058 | 0.0656 | 0.0097 | 0.182 | | 2.3495 | 5.0 | 535 | 2.2947 | 0.0282 | 0.0843 | 0.0163 | 0.0163 | 0.0175 | 0.0387 | 0.0796 | 0.194 | 0.2282 | 0.0407 | 0.1538 | 0.2516 | 0.0928 | 0.5798 | 0.015 | 0.1577 | 0.0009 | 0.0563 | 0.0131 | 0.123 | 0.0193 | 0.2242 | | 2.2478 | 6.0 | 642 | 2.2016 | 0.0346 | 0.0914 | 0.0186 | 0.0079 | 0.0116 | 0.0668 | 0.0938 | 0.2406 | 0.2729 | 0.0362 | 0.1578 | 0.4043 | 0.1208 | 0.6121 | 0.0178 | 0.2577 | 0.0021 | 0.0925 | 0.0152 | 0.2033 | 0.017 | 0.199 | | 2.1515 | 7.0 | 749 | 2.1026 | 0.0424 | 0.1121 | 0.0226 | 0.0162 | 0.0197 | 0.0747 | 0.1058 | 0.2613 | 0.2973 | 0.0996 | 0.2269 | 0.3484 | 0.1382 | 0.5769 | 0.0236 | 0.3 | 0.0052 | 0.1216 | 0.0125 | 0.2246 | 0.0327 | 0.2634 | | 2.0571 | 8.0 | 856 | 2.1115 | 0.0485 | 0.1183 | 0.0346 | 0.0078 | 0.0287 | 0.0922 | 0.1332 | 0.2587 | 0.2995 | 0.0573 | 0.1973 | 0.424 | 0.1446 | 0.6318 | 0.0442 | 0.2808 | 0.0027 | 0.1211 | 0.0182 | 0.1836 | 0.0327 | 0.2804 | | 1.9981 | 9.0 | 963 | 2.0429 | 0.0433 | 0.1161 | 0.0242 | 0.0239 | 0.0216 | 0.0831 | 0.1163 | 0.2543 | 0.2985 | 0.1208 | 0.1892 | 0.4359 | 0.1403 | 0.6358 | 0.0251 | 0.2365 | 0.0059 | 0.1673 | 0.0187 | 0.1754 | 0.0265 | 0.2773 | | 1.9338 | 10.0 | 1070 | 1.9025 | 0.079 | 0.1694 | 0.061 | 0.0175 | 0.0481 | 0.1245 | 0.1621 | 0.313 | 0.3524 | 0.1106 | 0.2565 | 0.5211 | 0.2324 | 0.6809 | 0.0753 | 0.3269 | 0.0094 | 0.198 | 0.0112 | 0.2328 | 0.0667 | 0.3232 | | 1.8745 | 11.0 | 1177 | 1.8462 | 0.0938 | 0.2008 | 0.0772 | 0.0191 | 0.0666 | 0.1644 | 0.1632 | 0.3078 | 0.3485 | 0.1358 | 0.2725 | 0.483 | 0.3093 | 0.6618 | 0.0471 | 0.3442 | 0.0104 | 0.2005 | 0.0122 | 0.2033 | 0.0899 | 0.3325 | | 1.7669 | 12.0 | 1284 | 1.7660 | 0.12 | 0.271 | 0.0939 | 0.0298 | 0.0714 | 0.2035 | 0.1928 | 0.3391 | 0.366 | 0.1539 | 0.2852 | 0.5395 | 0.368 | 0.6566 | 0.0741 | 0.3327 | 0.0236 | 0.2442 | 0.0363 | 0.2459 | 0.0981 | 0.3505 | | 1.7122 | 13.0 | 1391 | 1.7355 | 0.1376 | 0.2863 | 0.1139 | 0.027 | 0.0899 | 0.2379 | 0.2151 | 0.3567 | 0.3878 | 0.1285 | 0.2998 | 0.5798 | 0.3958 | 0.637 | 0.0969 | 0.3712 | 0.0389 | 0.2548 | 0.0271 | 0.3115 | 0.1294 | 0.3644 | | 1.6336 | 14.0 | 1498 | 1.6695 | 0.1609 | 0.3374 | 0.1317 | 0.0196 | 0.107 | 0.2757 | 0.2211 | 0.3673 | 0.3964 | 0.1449 | 0.3237 | 0.5801 | 0.4353 | 0.6595 | 0.146 | 0.3923 | 0.034 | 0.2482 | 0.0437 | 0.3361 | 0.1455 | 0.3459 | | 1.5696 | 15.0 | 1605 | 1.6275 | 0.1601 | 0.3509 | 0.127 | 0.0231 | 0.102 | 0.2989 | 0.2181 | 0.3813 | 0.4083 | 0.1688 | 0.3313 | 0.5781 | 0.4323 | 0.6549 | 0.1061 | 0.4038 | 0.0618 | 0.3005 | 0.029 | 0.3246 | 0.1714 | 0.3577 | | 1.5149 | 16.0 | 1712 | 1.5810 | 0.1862 | 0.3984 | 0.1463 | 0.0622 | 0.1143 | 0.3439 | 0.2268 | 0.3819 | 0.4019 | 0.1881 | 0.3158 | 0.6013 | 0.4779 | 0.6624 | 0.1141 | 0.4 | 0.0694 | 0.2668 | 0.0704 | 0.3262 | 0.1991 | 0.3541 | | 1.4732 | 17.0 | 1819 | 1.5458 | 0.1905 | 0.4135 | 0.1472 | 0.0514 | 0.1196 | 0.3237 | 0.2289 | 0.3868 | 0.4171 | 0.167 | 0.3378 | 0.5905 | 0.4746 | 0.6538 | 0.1547 | 0.4519 | 0.0932 | 0.3055 | 0.0488 | 0.3279 | 0.1812 | 0.3464 | | 1.4327 | 18.0 | 1926 | 1.5617 | 0.1887 | 0.4038 | 0.142 | 0.0323 | 0.1178 | 0.3574 | 0.2395 | 0.3855 | 0.4114 | 0.1902 | 0.3236 | 0.589 | 0.4801 | 0.6595 | 0.1518 | 0.4462 | 0.0866 | 0.2829 | 0.0389 | 0.3197 | 0.186 | 0.349 | | 1.3925 | 19.0 | 2033 | 1.5032 | 0.2162 | 0.4531 | 0.1701 | 0.0303 | 0.1292 | 0.3854 | 0.2572 | 0.4077 | 0.4328 | 0.1759 | 0.3377 | 0.6233 | 0.5043 | 0.6734 | 0.169 | 0.4442 | 0.1341 | 0.3221 | 0.0725 | 0.3754 | 0.201 | 0.349 | | 1.3582 | 20.0 | 2140 | 1.4728 | 0.2077 | 0.4357 | 0.1744 | 0.0412 | 0.1414 | 0.3759 | 0.2516 | 0.4017 | 0.4251 | 0.1842 | 0.3402 | 0.6106 | 0.4879 | 0.6607 | 0.1537 | 0.4404 | 0.1159 | 0.2955 | 0.0633 | 0.359 | 0.2178 | 0.3701 | | 1.329 | 21.0 | 2247 | 1.4826 | 0.2062 | 0.4509 | 0.1642 | 0.0325 | 0.1256 | 0.4185 | 0.2559 | 0.3974 | 0.4214 | 0.1371 | 0.3201 | 0.619 | 0.4879 | 0.6584 | 0.146 | 0.4212 | 0.1136 | 0.2698 | 0.0884 | 0.4164 | 0.195 | 0.3412 | | 1.3458 | 22.0 | 2354 | 1.4653 | 0.2244 | 0.4636 | 0.1859 | 0.074 | 0.1501 | 0.4031 | 0.272 | 0.4102 | 0.4276 | 0.1735 | 0.3346 | 0.6213 | 0.5139 | 0.6711 | 0.1848 | 0.4712 | 0.1083 | 0.2769 | 0.0732 | 0.323 | 0.2419 | 0.3959 | | 1.3028 | 23.0 | 2461 | 1.4139 | 0.2289 | 0.4906 | 0.1886 | 0.0572 | 0.1524 | 0.4043 | 0.2721 | 0.4191 | 0.4353 | 0.1771 | 0.3517 | 0.5964 | 0.4964 | 0.6653 | 0.1865 | 0.4481 | 0.1575 | 0.3211 | 0.0746 | 0.3738 | 0.2293 | 0.368 | | 1.2704 | 24.0 | 2568 | 1.4173 | 0.2371 | 0.4973 | 0.199 | 0.0605 | 0.1634 | 0.419 | 0.2752 | 0.4172 | 0.4401 | 0.1776 | 0.3515 | 0.6027 | 0.5033 | 0.6636 | 0.2403 | 0.4596 | 0.1149 | 0.3005 | 0.1009 | 0.4066 | 0.2261 | 0.3701 | | 1.2379 | 25.0 | 2675 | 1.3933 | 0.2608 | 0.5303 | 0.2271 | 0.0541 | 0.188 | 0.4582 | 0.2849 | 0.4189 | 0.4396 | 0.158 | 0.3455 | 0.6343 | 0.5376 | 0.6913 | 0.2509 | 0.4635 | 0.1194 | 0.3065 | 0.1534 | 0.3623 | 0.2428 | 0.3742 | | 1.2257 | 26.0 | 2782 | 1.4209 | 0.2467 | 0.5049 | 0.2078 | 0.0557 | 0.1592 | 0.4446 | 0.2708 | 0.4135 | 0.4292 | 0.1526 | 0.3298 | 0.6245 | 0.5239 | 0.6636 | 0.2282 | 0.4288 | 0.134 | 0.294 | 0.1079 | 0.3902 | 0.2394 | 0.3696 | | 1.1706 | 27.0 | 2889 | 1.3593 | 0.2631 | 0.5273 | 0.2353 | 0.0955 | 0.1891 | 0.4521 | 0.2912 | 0.4196 | 0.4422 | 0.1554 | 0.3475 | 0.6437 | 0.5374 | 0.685 | 0.261 | 0.4692 | 0.1276 | 0.2925 | 0.1269 | 0.3803 | 0.2624 | 0.384 | | 1.1512 | 28.0 | 2996 | 1.3320 | 0.27 | 0.5371 | 0.2171 | 0.0727 | 0.192 | 0.456 | 0.2981 | 0.4398 | 0.4609 | 0.1954 | 0.3663 | 0.6427 | 0.5406 | 0.6971 | 0.2264 | 0.5 | 0.1629 | 0.3131 | 0.1617 | 0.4066 | 0.2584 | 0.3876 | | 1.1511 | 29.0 | 3103 | 1.3592 | 0.2641 | 0.526 | 0.2346 | 0.0526 | 0.1701 | 0.498 | 0.2977 | 0.4249 | 0.4395 | 0.1836 | 0.3263 | 0.6367 | 0.5376 | 0.6717 | 0.2129 | 0.4154 | 0.1414 | 0.291 | 0.1589 | 0.418 | 0.2697 | 0.4015 | | 1.1312 | 30.0 | 3210 | 1.3863 | 0.2582 | 0.5328 | 0.2115 | 0.0667 | 0.1747 | 0.4443 | 0.283 | 0.4188 | 0.4418 | 0.2191 | 0.332 | 0.6478 | 0.5086 | 0.6526 | 0.2387 | 0.4808 | 0.1249 | 0.295 | 0.1609 | 0.3787 | 0.2579 | 0.4021 | | 1.1207 | 31.0 | 3317 | 1.3294 | 0.2813 | 0.5581 | 0.252 | 0.1121 | 0.1988 | 0.4749 | 0.296 | 0.4362 | 0.4568 | 0.2423 | 0.3532 | 0.6468 | 0.5314 | 0.6775 | 0.2837 | 0.4788 | 0.1526 | 0.3131 | 0.1613 | 0.4033 | 0.2777 | 0.4113 | | 1.0856 | 32.0 | 3424 | 1.3426 | 0.274 | 0.565 | 0.2282 | 0.1612 | 0.2023 | 0.4789 | 0.2989 | 0.4375 | 0.4541 | 0.2492 | 0.3497 | 0.6661 | 0.5195 | 0.6844 | 0.2952 | 0.4731 | 0.149 | 0.298 | 0.1551 | 0.4393 | 0.2516 | 0.3758 | | 1.0909 | 33.0 | 3531 | 1.3174 | 0.2868 | 0.5799 | 0.2445 | 0.0956 | 0.2097 | 0.4865 | 0.3119 | 0.4466 | 0.4652 | 0.2553 | 0.3672 | 0.653 | 0.536 | 0.704 | 0.3096 | 0.4788 | 0.1576 | 0.3211 | 0.1758 | 0.441 | 0.2552 | 0.3809 | | 1.0726 | 34.0 | 3638 | 1.3767 | 0.2751 | 0.561 | 0.2242 | 0.0993 | 0.1951 | 0.4811 | 0.2831 | 0.4241 | 0.452 | 0.2247 | 0.3529 | 0.6578 | 0.5375 | 0.6919 | 0.2638 | 0.4692 | 0.1562 | 0.3075 | 0.1456 | 0.3885 | 0.2724 | 0.4026 | | 1.0799 | 35.0 | 3745 | 1.3316 | 0.2691 | 0.5569 | 0.2126 | 0.1108 | 0.1876 | 0.4522 | 0.2913 | 0.4243 | 0.4443 | 0.2217 | 0.3366 | 0.6247 | 0.5257 | 0.6879 | 0.2661 | 0.4712 | 0.1575 | 0.3055 | 0.1659 | 0.3869 | 0.2301 | 0.3701 | | 1.0402 | 36.0 | 3852 | 1.3501 | 0.2735 | 0.5757 | 0.207 | 0.1217 | 0.2057 | 0.4564 | 0.284 | 0.4377 | 0.4568 | 0.2358 | 0.3814 | 0.6465 | 0.5171 | 0.685 | 0.2325 | 0.4481 | 0.1429 | 0.3035 | 0.2158 | 0.4656 | 0.2594 | 0.382 | | 1.0531 | 37.0 | 3959 | 1.3324 | 0.2701 | 0.5336 | 0.2233 | 0.0834 | 0.1907 | 0.4826 | 0.2852 | 0.4439 | 0.4644 | 0.2142 | 0.3754 | 0.6658 | 0.5318 | 0.7052 | 0.2218 | 0.4596 | 0.1431 | 0.2995 | 0.1909 | 0.4639 | 0.2632 | 0.3938 | | 1.028 | 38.0 | 4066 | 1.3593 | 0.2637 | 0.5412 | 0.2147 | 0.0638 | 0.1752 | 0.4642 | 0.2853 | 0.4231 | 0.4438 | 0.1997 | 0.3274 | 0.6417 | 0.5256 | 0.6705 | 0.2526 | 0.4327 | 0.1312 | 0.2854 | 0.1766 | 0.4344 | 0.2324 | 0.3959 | | 1.0211 | 39.0 | 4173 | 1.3359 | 0.2728 | 0.5553 | 0.2294 | 0.1086 | 0.1991 | 0.4663 | 0.3018 | 0.4335 | 0.4525 | 0.2277 | 0.3625 | 0.6463 | 0.5408 | 0.6821 | 0.2664 | 0.4635 | 0.1253 | 0.2995 | 0.1883 | 0.4377 | 0.2433 | 0.3799 | | 0.9986 | 40.0 | 4280 | 1.2900 | 0.2921 | 0.5961 | 0.2449 | 0.1617 | 0.2199 | 0.4851 | 0.3028 | 0.4506 | 0.4659 | 0.2422 | 0.3836 | 0.6408 | 0.546 | 0.696 | 0.2649 | 0.4538 | 0.1729 | 0.3111 | 0.2041 | 0.4574 | 0.2725 | 0.4113 | | 0.9951 | 41.0 | 4387 | 1.3541 | 0.2695 | 0.5709 | 0.2055 | 0.1418 | 0.1857 | 0.4392 | 0.2921 | 0.4326 | 0.4456 | 0.2248 | 0.3602 | 0.602 | 0.5355 | 0.6855 | 0.2612 | 0.4365 | 0.137 | 0.2879 | 0.1907 | 0.4344 | 0.2233 | 0.3835 | | 0.9838 | 42.0 | 4494 | 1.2978 | 0.2868 | 0.5888 | 0.2326 | 0.1262 | 0.209 | 0.5085 | 0.3099 | 0.4478 | 0.463 | 0.2469 | 0.3642 | 0.6518 | 0.5266 | 0.6867 | 0.2904 | 0.4692 | 0.1673 | 0.3291 | 0.2 | 0.4295 | 0.2497 | 0.4005 | | 0.9637 | 43.0 | 4601 | 1.2950 | 0.285 | 0.5769 | 0.2351 | 0.1588 | 0.2034 | 0.4954 | 0.3066 | 0.4479 | 0.463 | 0.2504 | 0.3566 | 0.658 | 0.5326 | 0.689 | 0.2793 | 0.4615 | 0.1678 | 0.3171 | 0.1861 | 0.4262 | 0.2593 | 0.4211 | | 0.9408 | 44.0 | 4708 | 1.3063 | 0.2795 | 0.5601 | 0.2375 | 0.1689 | 0.1905 | 0.5034 | 0.3126 | 0.4407 | 0.4552 | 0.2524 | 0.3763 | 0.6449 | 0.5461 | 0.6884 | 0.2866 | 0.4635 | 0.1291 | 0.2935 | 0.1614 | 0.4262 | 0.2741 | 0.4046 | | 0.9457 | 45.0 | 4815 | 1.2866 | 0.2851 | 0.5794 | 0.2471 | 0.1712 | 0.2011 | 0.5017 | 0.3168 | 0.448 | 0.4642 | 0.2719 | 0.3629 | 0.6464 | 0.544 | 0.6896 | 0.2772 | 0.4692 | 0.1691 | 0.3176 | 0.1561 | 0.4328 | 0.279 | 0.4119 | | 0.9476 | 46.0 | 4922 | 1.3047 | 0.2737 | 0.5779 | 0.2219 | 0.1539 | 0.1932 | 0.4666 | 0.3059 | 0.4356 | 0.449 | 0.2126 | 0.3626 | 0.6348 | 0.5344 | 0.6896 | 0.2537 | 0.4615 | 0.1547 | 0.3055 | 0.1925 | 0.4393 | 0.233 | 0.349 | | 0.9206 | 47.0 | 5029 | 1.2955 | 0.283 | 0.579 | 0.2365 | 0.1437 | 0.1993 | 0.5024 | 0.3048 | 0.4388 | 0.4501 | 0.2412 | 0.3643 | 0.6283 | 0.5501 | 0.6913 | 0.2723 | 0.4096 | 0.1598 | 0.302 | 0.1965 | 0.4689 | 0.2364 | 0.3789 | | 0.9431 | 48.0 | 5136 | 1.2809 | 0.2955 | 0.6039 | 0.2482 | 0.0914 | 0.2308 | 0.5222 | 0.3096 | 0.4442 | 0.4548 | 0.2012 | 0.3786 | 0.6271 | 0.5476 | 0.6855 | 0.3006 | 0.4154 | 0.1713 | 0.3246 | 0.2015 | 0.441 | 0.2567 | 0.4072 | | 0.8965 | 49.0 | 5243 | 1.2968 | 0.2935 | 0.6015 | 0.2566 | 0.1579 | 0.2276 | 0.4831 | 0.3112 | 0.4451 | 0.4623 | 0.2279 | 0.3816 | 0.6389 | 0.5336 | 0.6844 | 0.2907 | 0.4558 | 0.1725 | 0.3317 | 0.1938 | 0.4344 | 0.2771 | 0.4052 | | 0.8636 | 50.0 | 5350 | 1.2918 | 0.2933 | 0.611 | 0.2363 | 0.1728 | 0.2087 | 0.5035 | 0.3236 | 0.4496 | 0.468 | 0.2535 | 0.3745 | 0.6468 | 0.5353 | 0.6786 | 0.2819 | 0.4769 | 0.1849 | 0.3432 | 0.2064 | 0.4475 | 0.2583 | 0.3938 | | 0.8769 | 51.0 | 5457 | 1.2744 | 0.2959 | 0.6161 | 0.2408 | 0.1599 | 0.2291 | 0.4925 | 0.3212 | 0.4461 | 0.4567 | 0.2249 | 0.3782 | 0.6405 | 0.5475 | 0.6942 | 0.289 | 0.4365 | 0.1629 | 0.3191 | 0.2061 | 0.4361 | 0.2737 | 0.3974 | | 0.8591 | 52.0 | 5564 | 1.3174 | 0.2949 | 0.6031 | 0.254 | 0.1589 | 0.2241 | 0.5068 | 0.3164 | 0.4543 | 0.4668 | 0.2307 | 0.3872 | 0.6637 | 0.5382 | 0.6884 | 0.2941 | 0.4596 | 0.1565 | 0.3106 | 0.2212 | 0.4689 | 0.2645 | 0.4067 | | 0.8347 | 53.0 | 5671 | 1.2626 | 0.3071 | 0.6126 | 0.2618 | 0.1107 | 0.228 | 0.5266 | 0.3237 | 0.4523 | 0.4672 | 0.2187 | 0.3786 | 0.6472 | 0.5428 | 0.6809 | 0.3195 | 0.4673 | 0.1497 | 0.3156 | 0.2318 | 0.4377 | 0.2917 | 0.4345 | | 0.8474 | 54.0 | 5778 | 1.2730 | 0.3094 | 0.6174 | 0.2591 | 0.1729 | 0.2387 | 0.5153 | 0.3268 | 0.4591 | 0.4714 | 0.2482 | 0.3975 | 0.645 | 0.5352 | 0.6925 | 0.3235 | 0.4615 | 0.1627 | 0.3151 | 0.2273 | 0.459 | 0.2983 | 0.4289 | | 0.8326 | 55.0 | 5885 | 1.3201 | 0.2977 | 0.6056 | 0.259 | 0.1635 | 0.2296 | 0.4971 | 0.3159 | 0.4505 | 0.4619 | 0.2297 | 0.3789 | 0.6458 | 0.5208 | 0.6769 | 0.3094 | 0.4462 | 0.1661 | 0.3075 | 0.1961 | 0.4508 | 0.2963 | 0.4284 | | 0.8263 | 56.0 | 5992 | 1.2886 | 0.3029 | 0.6093 | 0.2592 | 0.1656 | 0.2243 | 0.5254 | 0.3256 | 0.4529 | 0.471 | 0.2464 | 0.3875 | 0.6623 | 0.5289 | 0.6838 | 0.3448 | 0.4808 | 0.1479 | 0.3095 | 0.2026 | 0.4574 | 0.2906 | 0.4237 | | 0.8222 | 57.0 | 6099 | 1.2601 | 0.3086 | 0.6085 | 0.2789 | 0.1746 | 0.2259 | 0.5201 | 0.3328 | 0.4596 | 0.4718 | 0.2474 | 0.3766 | 0.6718 | 0.5455 | 0.6931 | 0.3148 | 0.4769 | 0.1575 | 0.3181 | 0.2356 | 0.4525 | 0.2895 | 0.4186 | | 0.8328 | 58.0 | 6206 | 1.2603 | 0.3137 | 0.6183 | 0.2922 | 0.1647 | 0.2253 | 0.5418 | 0.3307 | 0.4605 | 0.4712 | 0.243 | 0.3802 | 0.6654 | 0.5519 | 0.6936 | 0.3174 | 0.4615 | 0.1559 | 0.3276 | 0.2486 | 0.4492 | 0.2948 | 0.4242 | | 0.8005 | 59.0 | 6313 | 1.2536 | 0.3103 | 0.6141 | 0.266 | 0.173 | 0.2226 | 0.5386 | 0.3322 | 0.4597 | 0.4703 | 0.2512 | 0.3798 | 0.6546 | 0.5517 | 0.6867 | 0.3166 | 0.4673 | 0.1487 | 0.309 | 0.2489 | 0.4623 | 0.2857 | 0.4263 | | 0.7786 | 60.0 | 6420 | 1.2642 | 0.309 | 0.6275 | 0.2553 | 0.1445 | 0.2286 | 0.5254 | 0.3205 | 0.4492 | 0.4578 | 0.223 | 0.3713 | 0.6501 | 0.5394 | 0.6786 | 0.3146 | 0.4346 | 0.1573 | 0.3126 | 0.2571 | 0.4623 | 0.2764 | 0.401 | | 0.8077 | 61.0 | 6527 | 1.2750 | 0.2959 | 0.6106 | 0.236 | 0.1838 | 0.2134 | 0.5175 | 0.3227 | 0.4463 | 0.4581 | 0.2573 | 0.3555 | 0.662 | 0.5316 | 0.6884 | 0.3034 | 0.4308 | 0.1394 | 0.302 | 0.2304 | 0.459 | 0.2748 | 0.4103 | | 0.7867 | 62.0 | 6634 | 1.2521 | 0.3135 | 0.6255 | 0.2768 | 0.1781 | 0.2267 | 0.5261 | 0.3347 | 0.4584 | 0.4696 | 0.2595 | 0.3671 | 0.6675 | 0.5386 | 0.6925 | 0.3145 | 0.4635 | 0.1656 | 0.3211 | 0.258 | 0.4639 | 0.2908 | 0.4072 | | 0.772 | 63.0 | 6741 | 1.2546 | 0.3072 | 0.6313 | 0.2524 | 0.1802 | 0.2297 | 0.5233 | 0.3295 | 0.4587 | 0.4696 | 0.2715 | 0.3667 | 0.6625 | 0.5381 | 0.6913 | 0.277 | 0.4346 | 0.1792 | 0.3327 | 0.2497 | 0.4787 | 0.2918 | 0.4108 | | 0.7736 | 64.0 | 6848 | 1.2673 | 0.3021 | 0.614 | 0.244 | 0.1813 | 0.2327 | 0.4996 | 0.3305 | 0.4561 | 0.4622 | 0.2509 | 0.3738 | 0.6488 | 0.5308 | 0.6803 | 0.3057 | 0.4423 | 0.1597 | 0.306 | 0.2247 | 0.459 | 0.2895 | 0.4232 | | 0.7561 | 65.0 | 6955 | 1.3063 | 0.2936 | 0.6089 | 0.2321 | 0.1759 | 0.2158 | 0.5179 | 0.3244 | 0.4481 | 0.4604 | 0.2518 | 0.3706 | 0.6391 | 0.519 | 0.6769 | 0.3054 | 0.4558 | 0.1472 | 0.308 | 0.2309 | 0.459 | 0.2653 | 0.4021 | | 0.7316 | 66.0 | 7062 | 1.2575 | 0.315 | 0.6168 | 0.2804 | 0.1995 | 0.2251 | 0.5303 | 0.3363 | 0.464 | 0.4719 | 0.2563 | 0.3883 | 0.6547 | 0.5418 | 0.6913 | 0.3304 | 0.4577 | 0.1497 | 0.3141 | 0.2533 | 0.4738 | 0.3001 | 0.4227 | | 0.746 | 67.0 | 7169 | 1.2713 | 0.3118 | 0.6229 | 0.2625 | 0.1695 | 0.2338 | 0.5371 | 0.3323 | 0.4597 | 0.4707 | 0.2419 | 0.3916 | 0.6594 | 0.5428 | 0.6896 | 0.3193 | 0.4538 | 0.1503 | 0.3166 | 0.2676 | 0.4787 | 0.2791 | 0.4149 | | 0.7539 | 68.0 | 7276 | 1.2705 | 0.3161 | 0.6353 | 0.2591 | 0.1718 | 0.2332 | 0.5451 | 0.3343 | 0.4568 | 0.4702 | 0.2554 | 0.3826 | 0.6569 | 0.5358 | 0.6803 | 0.3318 | 0.4577 | 0.1494 | 0.3146 | 0.2715 | 0.4918 | 0.292 | 0.4067 | | 0.7219 | 69.0 | 7383 | 1.2644 | 0.3175 | 0.6321 | 0.2758 | 0.1924 | 0.2341 | 0.5379 | 0.3404 | 0.4653 | 0.4744 | 0.2457 | 0.3925 | 0.6648 | 0.5442 | 0.6988 | 0.3151 | 0.4712 | 0.1662 | 0.3176 | 0.2727 | 0.4672 | 0.2895 | 0.417 | | 0.7202 | 70.0 | 7490 | 1.2718 | 0.3124 | 0.6233 | 0.2622 | 0.1758 | 0.2246 | 0.5305 | 0.334 | 0.4534 | 0.4602 | 0.2437 | 0.3718 | 0.6419 | 0.547 | 0.6954 | 0.3204 | 0.4327 | 0.16 | 0.3251 | 0.2487 | 0.4361 | 0.2858 | 0.4119 | | 0.7019 | 71.0 | 7597 | 1.2819 | 0.3073 | 0.6104 | 0.2737 | 0.1777 | 0.2295 | 0.5232 | 0.3287 | 0.4558 | 0.4679 | 0.2492 | 0.3812 | 0.6526 | 0.548 | 0.6913 | 0.3097 | 0.4269 | 0.1571 | 0.3176 | 0.2321 | 0.4951 | 0.2894 | 0.4088 | | 0.6951 | 72.0 | 7704 | 1.2853 | 0.3099 | 0.6174 | 0.2613 | 0.1872 | 0.2375 | 0.5177 | 0.3358 | 0.463 | 0.4711 | 0.2495 | 0.3905 | 0.6495 | 0.5493 | 0.6954 | 0.3007 | 0.4462 | 0.1597 | 0.3312 | 0.2477 | 0.4689 | 0.2921 | 0.4139 | | 0.6821 | 73.0 | 7811 | 1.2949 | 0.304 | 0.6157 | 0.2543 | 0.1795 | 0.2194 | 0.5134 | 0.3303 | 0.4579 | 0.4682 | 0.2485 | 0.3667 | 0.6627 | 0.5392 | 0.6884 | 0.3043 | 0.4596 | 0.1457 | 0.3106 | 0.2493 | 0.4803 | 0.2817 | 0.4021 | | 0.6943 | 74.0 | 7918 | 1.2790 | 0.3101 | 0.6194 | 0.2567 | 0.1863 | 0.2366 | 0.52 | 0.3337 | 0.4559 | 0.4647 | 0.2422 | 0.3708 | 0.6517 | 0.5416 | 0.6844 | 0.3093 | 0.4442 | 0.1441 | 0.3005 | 0.2547 | 0.4754 | 0.3007 | 0.4191 | | 0.6786 | 75.0 | 8025 | 1.2823 | 0.3121 | 0.6285 | 0.2576 | 0.1497 | 0.2309 | 0.5135 | 0.3348 | 0.4575 | 0.4686 | 0.2583 | 0.3764 | 0.6485 | 0.5449 | 0.6838 | 0.3174 | 0.4635 | 0.1517 | 0.3111 | 0.2466 | 0.4639 | 0.2997 | 0.4206 | | 0.6606 | 76.0 | 8132 | 1.2780 | 0.3191 | 0.628 | 0.2754 | 0.1958 | 0.2363 | 0.5414 | 0.3399 | 0.4597 | 0.4708 | 0.2592 | 0.3744 | 0.6633 | 0.5547 | 0.7 | 0.316 | 0.4442 | 0.1575 | 0.3111 | 0.2601 | 0.4672 | 0.3071 | 0.4314 | | 0.6585 | 77.0 | 8239 | 1.2696 | 0.3162 | 0.6344 | 0.2645 | 0.1962 | 0.2277 | 0.5346 | 0.3365 | 0.4586 | 0.4682 | 0.2466 | 0.3861 | 0.6505 | 0.544 | 0.6931 | 0.3221 | 0.4462 | 0.1604 | 0.3231 | 0.243 | 0.4541 | 0.3117 | 0.4247 | | 0.6592 | 78.0 | 8346 | 1.2684 | 0.3196 | 0.6245 | 0.2718 | 0.2043 | 0.2306 | 0.5384 | 0.3429 | 0.4657 | 0.4755 | 0.2704 | 0.3934 | 0.6572 | 0.5458 | 0.6902 | 0.3302 | 0.4577 | 0.1748 | 0.3377 | 0.2455 | 0.4672 | 0.3014 | 0.4247 | | 0.6585 | 79.0 | 8453 | 1.2727 | 0.3178 | 0.6234 | 0.2755 | 0.2032 | 0.2309 | 0.5422 | 0.337 | 0.4627 | 0.4718 | 0.2685 | 0.3828 | 0.6604 | 0.5462 | 0.6879 | 0.334 | 0.4538 | 0.1643 | 0.3337 | 0.2447 | 0.4738 | 0.2997 | 0.4098 | | 0.6463 | 80.0 | 8560 | 1.2868 | 0.3123 | 0.6186 | 0.2752 | 0.1921 | 0.2364 | 0.5315 | 0.3377 | 0.4535 | 0.465 | 0.2578 | 0.3804 | 0.6535 | 0.5375 | 0.6879 | 0.333 | 0.4519 | 0.1566 | 0.3201 | 0.2372 | 0.4525 | 0.2973 | 0.4129 | | 0.6458 | 81.0 | 8667 | 1.2962 | 0.3167 | 0.6227 | 0.277 | 0.1767 | 0.2339 | 0.5406 | 0.3376 | 0.4549 | 0.4652 | 0.244 | 0.3766 | 0.6626 | 0.5454 | 0.6948 | 0.3445 | 0.4692 | 0.1444 | 0.3146 | 0.2609 | 0.441 | 0.2885 | 0.4062 | | 0.6207 | 82.0 | 8774 | 1.2957 | 0.3139 | 0.6258 | 0.2759 | 0.202 | 0.2299 | 0.5281 | 0.3389 | 0.4568 | 0.47 | 0.2643 | 0.3793 | 0.6634 | 0.5374 | 0.6896 | 0.3243 | 0.4692 | 0.151 | 0.3246 | 0.258 | 0.4443 | 0.2987 | 0.4222 | | 0.6356 | 83.0 | 8881 | 1.3102 | 0.31 | 0.618 | 0.2765 | 0.1833 | 0.229 | 0.5134 | 0.3317 | 0.4583 | 0.4686 | 0.2588 | 0.394 | 0.642 | 0.5361 | 0.689 | 0.3412 | 0.4769 | 0.1453 | 0.3241 | 0.241 | 0.4443 | 0.2865 | 0.4088 | | 0.6286 | 84.0 | 8988 | 1.2985 | 0.3066 | 0.6197 | 0.2678 | 0.1879 | 0.2187 | 0.524 | 0.3336 | 0.4478 | 0.4549 | 0.2533 | 0.3669 | 0.6431 | 0.5354 | 0.6844 | 0.3222 | 0.4423 | 0.1468 | 0.306 | 0.2344 | 0.4344 | 0.294 | 0.4072 | | 0.6269 | 85.0 | 9095 | 1.2769 | 0.3174 | 0.6259 | 0.2708 | 0.2034 | 0.2266 | 0.5397 | 0.3425 | 0.4593 | 0.4669 | 0.2736 | 0.3741 | 0.6577 | 0.5429 | 0.6919 | 0.3334 | 0.4596 | 0.1592 | 0.3211 | 0.2454 | 0.441 | 0.3061 | 0.4211 | | 0.5998 | 86.0 | 9202 | 1.2841 | 0.317 | 0.616 | 0.2823 | 0.1881 | 0.2347 | 0.538 | 0.3419 | 0.4606 | 0.4711 | 0.2701 | 0.3843 | 0.6488 | 0.5414 | 0.6896 | 0.3243 | 0.4692 | 0.1586 | 0.3256 | 0.2489 | 0.4443 | 0.3116 | 0.4268 | | 0.6054 | 87.0 | 9309 | 1.2951 | 0.3163 | 0.6223 | 0.2731 | 0.1937 | 0.2287 | 0.541 | 0.3368 | 0.4578 | 0.4648 | 0.2732 | 0.3682 | 0.6534 | 0.5436 | 0.6832 | 0.3263 | 0.4577 | 0.1617 | 0.3196 | 0.251 | 0.4377 | 0.2988 | 0.4258 | | 0.6138 | 88.0 | 9416 | 1.2934 | 0.3144 | 0.6109 | 0.2739 | 0.1932 | 0.2315 | 0.5285 | 0.3451 | 0.4584 | 0.4683 | 0.264 | 0.3767 | 0.6479 | 0.5384 | 0.6844 | 0.3286 | 0.4712 | 0.1513 | 0.3131 | 0.2518 | 0.4492 | 0.3019 | 0.4237 | | 0.5887 | 89.0 | 9523 | 1.2940 | 0.3171 | 0.6147 | 0.279 | 0.2023 | 0.2306 | 0.5411 | 0.3377 | 0.4613 | 0.4698 | 0.2773 | 0.3725 | 0.6505 | 0.5397 | 0.689 | 0.336 | 0.4673 | 0.161 | 0.3161 | 0.248 | 0.4541 | 0.3008 | 0.4227 | | 0.6026 | 90.0 | 9630 | 1.3081 | 0.3106 | 0.6154 | 0.2657 | 0.2063 | 0.2143 | 0.5398 | 0.3342 | 0.4543 | 0.4616 | 0.2846 | 0.3601 | 0.6397 | 0.5287 | 0.6769 | 0.3076 | 0.4442 | 0.1675 | 0.3146 | 0.2498 | 0.4426 | 0.2996 | 0.4299 | | 0.6116 | 91.0 | 9737 | 1.3193 | 0.3046 | 0.5974 | 0.2664 | 0.215 | 0.2096 | 0.5344 | 0.3349 | 0.4514 | 0.4594 | 0.2806 | 0.3671 | 0.6362 | 0.5371 | 0.6844 | 0.2908 | 0.4327 | 0.1537 | 0.3121 | 0.2481 | 0.4475 | 0.2931 | 0.4201 | | 0.5874 | 92.0 | 9844 | 1.3148 | 0.307 | 0.5997 | 0.2706 | 0.207 | 0.2168 | 0.5303 | 0.3364 | 0.457 | 0.4653 | 0.2759 | 0.38 | 0.6418 | 0.5305 | 0.6827 | 0.3038 | 0.4519 | 0.1554 | 0.3176 | 0.2488 | 0.4541 | 0.2965 | 0.4201 | | 0.5765 | 93.0 | 9951 | 1.3114 | 0.31 | 0.6063 | 0.2757 | 0.2056 | 0.2172 | 0.5388 | 0.3349 | 0.4549 | 0.4625 | 0.275 | 0.369 | 0.6406 | 0.5308 | 0.6827 | 0.3106 | 0.4538 | 0.1567 | 0.309 | 0.2532 | 0.4459 | 0.2988 | 0.4211 | | 0.5799 | 94.0 | 10058 | 1.3074 | 0.3101 | 0.6134 | 0.2681 | 0.2042 | 0.2135 | 0.5519 | 0.3365 | 0.454 | 0.4634 | 0.272 | 0.3674 | 0.6508 | 0.53 | 0.6821 | 0.3105 | 0.4596 | 0.1525 | 0.3161 | 0.2582 | 0.4377 | 0.2993 | 0.4216 | | 0.5678 | 95.0 | 10165 | 1.3073 | 0.3102 | 0.6184 | 0.2637 | 0.2103 | 0.2233 | 0.537 | 0.3327 | 0.4527 | 0.461 | 0.2775 | 0.3668 | 0.6309 | 0.537 | 0.6884 | 0.3217 | 0.4404 | 0.1558 | 0.3106 | 0.2354 | 0.4459 | 0.3011 | 0.4196 | | 0.5776 | 96.0 | 10272 | 1.3083 | 0.3146 | 0.6177 | 0.2751 | 0.2083 | 0.2193 | 0.5465 | 0.3364 | 0.4548 | 0.4627 | 0.2731 | 0.3681 | 0.6399 | 0.5359 | 0.6861 | 0.3306 | 0.4538 | 0.1566 | 0.3176 | 0.2484 | 0.4361 | 0.3012 | 0.4201 | | 0.5768 | 97.0 | 10379 | 1.3027 | 0.3134 | 0.6204 | 0.2681 | 0.2056 | 0.2203 | 0.5453 | 0.3365 | 0.4545 | 0.4622 | 0.2774 | 0.3652 | 0.6417 | 0.5329 | 0.6832 | 0.3282 | 0.4538 | 0.1591 | 0.3181 | 0.2445 | 0.4328 | 0.3021 | 0.4232 | | 0.5708 | 98.0 | 10486 | 1.3045 | 0.3094 | 0.6223 | 0.2705 | 0.2077 | 0.2196 | 0.5411 | 0.3325 | 0.4544 | 0.4623 | 0.2754 | 0.3668 | 0.641 | 0.5342 | 0.6838 | 0.3201 | 0.4519 | 0.1588 | 0.3181 | 0.2347 | 0.4361 | 0.299 | 0.4216 | | 0.5779 | 99.0 | 10593 | 1.3050 | 0.3104 | 0.6231 | 0.2616 | 0.2049 | 0.2159 | 0.5424 | 0.3324 | 0.455 | 0.4645 | 0.274 | 0.3682 | 0.6457 | 0.536 | 0.6838 | 0.3162 | 0.4615 | 0.1593 | 0.3176 | 0.2376 | 0.4377 | 0.303 | 0.4216 | | 0.5571 | 100.0 | 10700 | 1.3007 | 0.3131 | 0.6268 | 0.2698 | 0.2089 | 0.2237 | 0.5358 | 0.3352 | 0.4586 | 0.4682 | 0.2773 | 0.3748 | 0.6417 | 0.5362 | 0.6844 | 0.3199 | 0.4635 | 0.1628 | 0.3176 | 0.2427 | 0.4475 | 0.3038 | 0.4278 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 1.13.0+cu117 - Datasets 2.18.0 - Tokenizers 0.19.0

mychen76/invoice-and-receipts_donut_v1

mychen76

2024-04-19T14:39:23Z

1,608

45

transformers

[ "transformers", "pytorch", "safetensors", "vision-encoder-decoder", "image-text-to-text", "license:apache-2.0", "endpoints_compatible", "region:us" ]

image-text-to-text

2023-09-23T00:34:40Z

--- license: apache-2.0 --- Model Architecture: The mychen76/invoice-and-receipts_donut_v1 (LLM) is a finetuned for convert Invoice or Receipt Image to XML or Json data strucutre task. this experimental model is based on Donut model. Motivation: Remove OCR engine, use only LLM model to convert an invoice or receipt json object could reduce the conversion step and reduce resource utilization and deployment dependencies. Result, better performance. Model Usage: Take following an invoice receipt image and get an output Json or xml like this: ***JSON OUTPUT*** ```json { 'header': { 'invoice_no': '13194726', 'invoice_date': '05/29/2021', 'seller': 'Hopkins and Sons 62283 Flores Tunnel North Luis, IA 69983', 'client': 'Sims PLC USS Kramer FPO AA 81651', 'seller_tax_id': '952-73-7223', 'client_tax_id': '995-88-9495', 'iban': 'GB31LZX520242755934691' }, 'items': [ { 'item_desc': 'Beach Lunch Lounge Striped Shirt Dress Large Navy Blue White Long Sleeve Casual', 'item_qty': '1,00', 'item_net_price': '16,99', 'item_net_worth': '16,99', 'item_vat': '10%', 'item_gross_worth': '18,69' }, { 'item_desc': 'Jams World Hawaiian 0 Dress Rayon SZ.L', 'item_qty': '5,00', 'item_net_price': '65,00', 'item_net_worth': '325,00', 'item_vat': '10%', 'item_gross_worth': '357,50' }, { 'item_desc': 'LuLaRoe Nicole Dress Size Large 26', 'item_qty': '2,00', 'item_net_price': '1,99', 'item_net_worth': '3,98', 'item_vat': '10%', 'item_gross_worth': '4,38' }, { 'item_desc': 'phynny Was Medium Linen Wrap Dress Dessert Rose Embroidered Bohemian', 'item_qty': '2,00', 'item_net_price': '89,99', 'item_net_worth': '179,98', 'item_vat': '10%', 'item_gross_worth': '197,98' }, { 'item_desc': "Eileen Fisher Women's Long Sleeve Fleece Lined Front Pockets Dress XS Gray", 'item_qty': '2,00', 'item_net_price': '15,99', 'item_net_worth': '31,98', 'item_vat': '10%', 'item_gross_worth': '35,18' }, { 'item_desc': "Hanna Anderson Women's L Large Coral Short Sleeve Casual Fall Tee Shirt Dress", 'item_qty': '1,00', 'item_net_price': '24,00', 'item_net_worth': '24,00', 'item_vat': '10%', 'item_gross_worth': '26,40' } ], 'summary': {'total_net_worth': '$581,93', 'total_vat': '$58,19', 'total_gross_worth': '$ 640,12'} } ``` ***XML OUTPUT*** ```xml <s_header> <s_invoice_no> 13194726</s_invoice_no> <s_invoice_date> 05/29/2021</s_invoice_date> <s_seller> Hopkins and Sons 62283 Flores Tunnel North Luis, IA 69983</s_seller> <s_client> Sims PLC USS Kramer FPO AA 81651</s_client> <s_seller_tax_id> 952-73-7223</s_seller_tax_id> <s_client_tax_id> 995-88-9495</s_client_tax_id> <s_iban> GB31LZX520242755934691</s_iban> </s_header> <s_items> <s_item_desc> Beach Lunch Lounge Striped Shirt Dress Large Navy Blue White Long Sleeve Casual</s_item_desc> <s_item_qty> 1,00</s_item_qty> <s_item_net_price> 16,99</s_item_net_price> <s_item_net_worth> 16,99</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 18,69</s_item_gross_worth> <sep/> <s_item_desc> Jams World Hawaiian 0 Dress Rayon SZ.L</s_item_desc> <s_item_qty> 5,00</s_item_qty> <s_item_net_price> 65,00</s_item_net_price> <s_item_net_worth> 325,00</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 357,50</s_item_gross_worth> <sep/> <s_item_desc> LuLaRoe Nicole Dress Size Large 26</s_item_desc> <s_item_qty> 2,00</s_item_qty> <s_item_net_price> 1,99</s_item_net_price> <s_item_net_worth> 3,98</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 4,38</s_item_gross_worth> <sep/> <s_item_desc> phynny Was Medium Linen Wrap Dress Dessert Rose Embroidered Bohemian</s_item_desc> <s_item_qty> 2,00</s_item_qty> <s_item_net_price> 89,99</s_item_net_price> <s_item_net_worth> 179,98</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 197,98</s_item_gross_worth> <sep/> <s_item_desc> Eileen Fisher Women's Long Sleeve Fleece Lined Front Pockets Dress XS Gray</s_item_desc> <s_item_qty> 2,00</s_item_qty> <s_item_net_price> 15,99</s_item_net_price> <s_item_net_worth> 31,98</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 35,18</s_item_gross_worth> <sep/> <s_item_desc> Hanna Anderson Women's L Large Coral Short Sleeve Casual Fall Tee Shirt Dress</s_item_desc> <s_item_qty> 1,00</s_item_qty> <s_item_net_price> 24,00</s_item_net_price> <s_item_net_worth> 24,00</s_item_net_worth> <s_item_vat> 10%</s_item_vat> <s_item_gross_worth> 26,40</s_item_gross_worth> </s_items> <s_summary> <s_total_net_worth> $581,93</s_total_net_worth> <s_total_vat> $58,19</s_total_vat> <s_total_gross_worth> $ 640,12</s_total_gross_worth> </s_summary> ```

ali9132/CostumData_new

ali9132

2024-04-19T14:34:00Z

7

0

transformers

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

automatic-speech-recognition

2024-04-19T12:18:38Z

--- language: - fa license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/CostumData_new model-index: - name: Whisper Small CostumData_new results: [] ---  # Whisper Small CostumData_new This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the CostumData_new dataset. It achieves the following results on the evaluation set: - eval_loss: 0.2603 - eval_wer: 28.9730 - eval_runtime: 3297.5897 - eval_samples_per_second: 1.931 - eval_steps_per_second: 0.241 - epoch: 2.5126 - step: 1000 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

AlignmentResearch/robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10

AlignmentResearch

2024-04-19T14:32:55Z

5

0

transformers

[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-160m", "base_model:finetune:EleutherAI/pythia-160m", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2024-04-19T14:32:35Z

--- license: apache-2.0 tags: - generated_from_trainer base_model: EleutherAI/pythia-160m model-index: - name: robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10 results: [] ---  # robust_llm_pythia-160m_ian-022_PasswordMatch_n-its-10 This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) 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: 1e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

Resi/layfi-v1-docvqa

Resi

2024-04-19T14:29:51Z

5

0

transformers

[ "transformers", "safetensors", "layoutlmv2", "document-question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

document-question-answering

2024-04-19T14:29:17Z

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

Raul569/lora_outfit_recommender_model

Raul569

2024-04-19T14:26:21Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-04-19T14:26:09Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** Raul569 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

MR-Eder/llama3-1000-steps-wiki-de-conversation-lora

MR-Eder

2024-04-19T14:23:18Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-04-19T14:22:50Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** MR-Eder - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Sif10/summarization

Sif10

2024-04-19T14:22:38Z

22

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

2024-04-19T10:30:44Z

--- license: apache-2.0 base_model: google-t5/t5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: summarization results: [] ---  # summarization This model is a fine-tuned version of [google-t5/t5-base](https://huggingface.co/google-t5/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2491 - Rouge1: 0.3279 - Rouge2: 0.2271 - Rougel: 0.3003 - Rougelsum: 0.3005 - Gen Len: 18.9811 ## 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: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.372 | 1.0 | 4189 | 0.2643 | 0.3326 | 0.2341 | 0.3055 | 0.3053 | 18.9784 | | 0.3303 | 2.0 | 8378 | 0.2558 | 0.3379 | 0.2401 | 0.3112 | 0.3112 | 18.9808 | | 0.3069 | 3.0 | 12567 | 0.2482 | 0.34 | 0.241 | 0.3129 | 0.313 | 18.9815 | | 0.3057 | 4.0 | 16756 | 0.2491 | 0.3279 | 0.2271 | 0.3003 | 0.3005 | 18.9811 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

coralexbadea/llama3-sql-16bit

coralexbadea

2024-04-19T14:21:23Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-04-19T13:30:07Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** coralexbadea - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

dylanebert/mvdream

dylanebert

2024-04-19T14:17:31Z

27

5

diffusers

[ "diffusers", "safetensors", "text-to-3d", "arxiv:2308.16512", "license:openrail", "diffusers:MVDreamPipeline", "region:us" ]

text-to-3d

2024-04-19T14:17:10Z

--- license: openrail pipeline_tag: text-to-3d --- This is a copy of [ashawkey/mvdream-sd2.1-diffusers](https://huggingface.co/ashawkey/mvdream-sd2.1-diffusers). It is hosted here for persistence throughout the ML for 3D course. # MVDream-diffusers Model Card This is a port of https://huggingface.co/MVDream/MVDream into diffusers. For usage, please check: https://github.com/ashawkey/mvdream_diffusers ## Citation ``` @article{shi2023MVDream, author = {Shi, Yichun and Wang, Peng and Ye, Jianglong and Mai, Long and Li, Kejie and Yang, Xiao}, title = {MVDream: Multi-view Diffusion for 3D Generation}, journal = {arXiv:2308.16512}, year = {2023}, } ``` ## Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

brugmark/all-MiniLM-L6-v2-personal-project-default-2024-04-19

brugmark

2024-04-19T14:14:31Z

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "bert", "fill-mask", "generated_from_trainer", "base_model:sentence-transformers/all-MiniLM-L6-v2", "base_model:finetune:sentence-transformers/all-MiniLM-L6-v2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2024-04-19T13:12:36Z

--- license: apache-2.0 base_model: sentence-transformers/all-MiniLM-L6-v2 tags: - generated_from_trainer model-index: - name: all-MiniLM-L6-v2-personal-project-default-2024-04-19 results: [] ---  # all-MiniLM-L6-v2-personal-project-default-2024-04-19 This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset. It achieves the following results on the evaluation set: - eval_loss: 10.7201 - eval_runtime: 3708.4666 - eval_samples_per_second: 8.168 - eval_steps_per_second: 0.255 - step: 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

MR-Eder/llama3-1000-steps-wiki-de-conversation-merged-16bit-GGUF

MR-Eder

2024-04-19T14:10:52Z

7

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-04-19T14:04:35Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** MR-Eder - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

idoru/jetmoe-8b-MyRus-kto

idoru

2024-04-19T14:01:19Z

8

0

transformers

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

text-generation

2024-04-19T13:57:01Z

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

OneGate/Llama3-OGSQL-FT-8B

OneGate

2024-04-19T14:00:49Z

43

2

transformers

[ "transformers", "safetensors", "llama", "text-generation", "Text-to-sql", "conversational", "license:cc-by-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-04-19T11:45:16Z

--- license: cc-by-4.0 tags: - Text-to-sql library_name: transformers --- ### Llama3-OGSQL-8B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657ad4e2583493c1d1efb05b/YzOlyYJEeD4HWAIhcdHGB.png) ### Model Description Llama3-OGSQL-8B was fine-tuned on the most recent and state of the art models (LLAMA 3) for the task of converting natural language text into SQL queries. The model has been trained on more than 270 million tokens, ensuring robust performance and high accuracy in SQL generation tasks. - **Model type**: Auto-regressive language model - **Language(s) (NLP)**: SQL (target language for generation) - **Finetuned from model**: Llama3-8B ## Use Case OGSQL-7B is designed to facilitate the conversion of natural language queries into structured SQL commands, aiding in database querying without the need for manual SQL knowledge. ## How to Get Started with the Model ```python # Example code to load and use the model from transformers import AutoModelForSeq2SeqLM, AutoTokenizer model_name = "Llama3-OGSQL-8B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) def generate_sql(query): inputs = tokenizer.encode(query, return_tensors="pt") outputs = model.generate(inputs) return tokenizer.decode(outputs[0], skip_special_tokens=True) # Example use query = """ using this context: -- Create Customers Table CREATE TABLE Customers ( customer_id INTEGER PRIMARY KEY, name TEXT NOT NULL, email TEXT, join_date DATE ); -- Create Products Table CREATE TABLE Products ( product_id INTEGER PRIMARY KEY, name TEXT NOT NULL, price DECIMAL(10, 2) ); -- Create Orders Table CREATE TABLE Orders ( order_id INTEGER PRIMARY KEY, customer_id INTEGER, product_id INTEGER, order_date DATE, quantity INTEGER, total_price DECIMAL(10, 2), FOREIGN KEY (customer_id) REFERENCES Customers(customer_id), FOREIGN KEY (product_id) REFERENCES Products(product_id) ); show me all the orders from last month , sort by date """ print(generate_sql(query)) ``` ## alternatively you can use this notebook: [![Colab notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pQuIuCdoFMG76AH3BNZzep8PgRaZkkYS?usp=sharing)

erlend123/emotion-analysis

erlend123

2024-04-19T13:59:55Z

6

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "en", "dataset:dair-ai/emotion", "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

2024-04-09T10:11:19Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: emotion-analysis-3000 results: [] datasets: - dair-ai/emotion language: - en ---  # emotion-analysis-3000 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: 0.3205 - Accuracy: 0.9015 - F1: 0.9014 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 233 | 0.2691 | 0.9070 | 0.9068 | | No log | 2.0 | 466 | 0.2963 | 0.8928 | 0.8922 | | 0.2332 | 3.0 | 699 | 0.3205 | 0.9015 | 0.9014 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

dylanebert/imagedream

dylanebert

2024-04-19T13:57:32Z

8

0

diffusers

[ "diffusers", "safetensors", "image-to-3d", "arxiv:2312.02201", "license:openrail", "diffusers:MVDreamPipeline", "region:us" ]

image-to-3d

2024-04-19T13:57:16Z

--- license: openrail pipeline_tag: image-to-3d --- This is a copy of [ashawkey/imagedream-ipmv-diffusers](https://huggingface.co/ashawkey/imagedream-ipmv-diffusers). It is hosted here for persistence throughout the ML for 3D course. # MVDream-diffusers Model Card This is a port of https://huggingface.co/Peng-Wang/ImageDream into diffusers. For usage, please check: https://github.com/ashawkey/mvdream_diffusers ## Citation ``` @article{wang2023imagedream, title={ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation}, author={Wang, Peng and Shi, Yichun}, journal={arXiv preprint arXiv:2312.02201}, year={2023} } ``` ## Misuse, Malicious Use, and Out-of-Scope Use The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.

sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx

sosoai

2024-04-19T13:56:07Z

7

0

mlx

[ "mlx", "safetensors", "llama", "region:us" ]

null

2024-04-19T13:40:45Z

--- tags: - mlx --- # sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx This model was converted to MLX format from [`sosoai/Hansoldeco-llama3-8b-unsloth-v0.1`]() using mlx-lm version **0.9.0**. Refer to the [original model card](https://huggingface.co/sosoai/Hansoldeco-llama3-8b-unsloth-v0.1) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("sosoai/Hansoldeco-llama3-8b-unsloth-v0.1-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

mgoin/Meta-Llama-3-8B-Instruct-Marlin

mgoin

2024-04-19T13:52:09Z

112

0

transformers

[ "transformers", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "marlin", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "4-bit", "gptq", "region:us" ]

text-generation

2024-04-18T19:03:30Z

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - marlin license: other license_name: llama3 license_link: LICENSE --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td>Training Data </td> <td>Params </td> <td>Context length </td> <td>GQA </td> <td>Token count </td> <td>Knowledge cutoff </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-8B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3) To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td>Time (GPU hours) </td> <td>Power Consumption (W) </td> <td>Carbon Emitted(tCO2eq) </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td>Category </td> <td>Benchmark </td> <td>Llama 3 8B </td> <td>Llama2 7B </td> <td>Llama2 13B </td> <td>Llama 3 70B </td> <td>Llama2 70B </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td>Benchmark </td> <td>Llama 3 8B </td> <td>Llama 2 7B </td> <td>Llama 2 13B </td> <td>Llama 3 70B </td> <td>Llama 2 70B </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. Safety For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. Refusals In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks CBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### Cyber Security We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### Child Safety Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos

Augusto777/swinv2-tiny-patch4-window8-256-dmae-va-U5-42B

Augusto777

2024-04-19T13:43:20Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "swinv2", "image-classification", "generated_from_trainer", "base_model:microsoft/swinv2-tiny-patch4-window8-256", "base_model:finetune:microsoft/swinv2-tiny-patch4-window8-256", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-04-19T04:23:12Z

--- license: apache-2.0 base_model: microsoft/swinv2-tiny-patch4-window8-256 tags: - generated_from_trainer metrics: - accuracy model-index: - name: swinv2-tiny-patch4-window8-256-dmae-va-U5-42B results: [] ---  # swinv2-tiny-patch4-window8-256-dmae-va-U5-42B This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9637 - Accuracy: 0.6667 ## 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: 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: 42 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 0.9 | 7 | 7.8663 | 0.1167 | | 6.936 | 1.94 | 15 | 7.7572 | 0.1167 | | 6.936 | 2.97 | 23 | 7.1790 | 0.1167 | | 6.7016 | 4.0 | 31 | 5.9033 | 0.1167 | | 5.5439 | 4.9 | 38 | 4.6116 | 0.1167 | | 5.5439 | 5.94 | 46 | 3.2830 | 0.1167 | | 3.6477 | 6.97 | 54 | 2.2014 | 0.1167 | | 2.2506 | 8.0 | 62 | 1.5647 | 0.45 | | 2.2506 | 8.9 | 69 | 1.3160 | 0.45 | | 1.5088 | 9.94 | 77 | 1.3676 | 0.3333 | | 1.3868 | 10.97 | 85 | 1.3390 | 0.45 | | 1.3868 | 12.0 | 93 | 1.3223 | 0.3833 | | 1.351 | 12.9 | 100 | 1.3156 | 0.45 | | 1.3271 | 13.94 | 108 | 1.3485 | 0.4833 | | 1.3271 | 14.97 | 116 | 1.2646 | 0.4833 | | 1.2322 | 16.0 | 124 | 1.2308 | 0.4833 | | 1.2322 | 16.9 | 131 | 1.2160 | 0.5 | | 1.22 | 17.94 | 139 | 1.2015 | 0.5 | | 1.1899 | 18.97 | 147 | 1.2008 | 0.5 | | 1.1899 | 20.0 | 155 | 1.1606 | 0.5 | | 1.109 | 20.9 | 162 | 1.1182 | 0.5667 | | 1.0603 | 21.94 | 170 | 1.0855 | 0.5333 | | 1.0603 | 22.97 | 178 | 1.0763 | 0.5667 | | 1.0264 | 24.0 | 186 | 1.1153 | 0.5833 | | 1.0086 | 24.9 | 193 | 1.0770 | 0.65 | | 1.0086 | 25.94 | 201 | 1.0041 | 0.6167 | | 0.9301 | 26.97 | 209 | 0.9637 | 0.6667 | | 0.9077 | 28.0 | 217 | 0.9824 | 0.5833 | | 0.9077 | 28.9 | 224 | 0.9485 | 0.6 | | 0.8725 | 29.94 | 232 | 0.9294 | 0.6167 | | 0.8203 | 30.97 | 240 | 0.9348 | 0.6167 | | 0.8203 | 32.0 | 248 | 0.9295 | 0.6 | | 0.8211 | 32.9 | 255 | 0.9167 | 0.6 | | 0.8211 | 33.94 | 263 | 0.9281 | 0.5833 | | 0.7916 | 34.97 | 271 | 0.8803 | 0.6333 | | 0.7822 | 36.0 | 279 | 0.8785 | 0.6333 | | 0.7822 | 36.9 | 286 | 0.8906 | 0.6 | | 0.7937 | 37.94 | 294 | 0.8899 | 0.6 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

xshubhamx/tiny-llama-lora

xshubhamx

2024-04-19T13:42:28Z

0

null

[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]

null

2024-04-16T15:23:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tiny-llama results: [] ---  # tiny-llama This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6966 - Accuracy: 0.8195 - Precision: 0.8222 - Recall: 0.8195 - Precision Macro: 0.7955 - Recall Macro: 0.7536 - Macro Fpr: 0.0148 - Weighted Fpr: 0.0141 - Weighted Specificity: 0.9765 - Macro Specificity: 0.9873 - Weighted Sensitivity: 0.8327 - Macro Sensitivity: 0.7536 - F1 Micro: 0.8327 - F1 Macro: 0.7609 - F1 Weighted: 0.8291 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Precision Macro | Recall Macro | Macro Fpr | Weighted Fpr | Weighted Specificity | Macro Specificity | Weighted Sensitivity | Macro Sensitivity | F1 Micro | F1 Macro | F1 Weighted | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:---------------:|:------------:|:---------:|:------------:|:--------------------:|:-----------------:|:--------------------:|:-----------------:|:--------:|:--------:|:-----------:| | 1.0444 | 1.0 | 642 | 0.5968 | 0.8056 | 0.8050 | 0.8056 | 0.7122 | 0.6995 | 0.0175 | 0.0169 | 0.9730 | 0.9852 | 0.8056 | 0.6995 | 0.8056 | 0.6986 | 0.8014 | | 0.4788 | 2.0 | 1284 | 0.6966 | 0.8195 | 0.8222 | 0.8195 | 0.8092 | 0.7825 | 0.0161 | 0.0155 | 0.9755 | 0.9863 | 0.8195 | 0.7825 | 0.8195 | 0.7849 | 0.8172 | | 0.3354 | 3.0 | 1926 | 0.8046 | 0.8327 | 0.8276 | 0.8327 | 0.8058 | 0.7582 | 0.0148 | 0.0141 | 0.9758 | 0.9872 | 0.8327 | 0.7582 | 0.8327 | 0.7742 | 0.8282 | | 0.0571 | 4.0 | 2569 | 1.1143 | 0.8265 | 0.8312 | 0.8265 | 0.7904 | 0.7763 | 0.0152 | 0.0148 | 0.9772 | 0.9869 | 0.8265 | 0.7763 | 0.8265 | 0.7690 | 0.8262 | | 0.0187 | 5.0 | 3211 | 1.1104 | 0.8319 | 0.8316 | 0.8319 | 0.7745 | 0.7724 | 0.0149 | 0.0142 | 0.9770 | 0.9873 | 0.8319 | 0.7724 | 0.8319 | 0.7638 | 0.8303 | | 0.0071 | 6.0 | 3853 | 1.1445 | 0.8242 | 0.8210 | 0.8242 | 0.7684 | 0.7384 | 0.0157 | 0.0150 | 0.9755 | 0.9866 | 0.8242 | 0.7384 | 0.8242 | 0.7451 | 0.8209 | | 0.0002 | 7.0 | 4495 | 1.2032 | 0.8327 | 0.8302 | 0.8327 | 0.7985 | 0.7529 | 0.0148 | 0.0141 | 0.9765 | 0.9873 | 0.8327 | 0.7529 | 0.8327 | 0.7617 | 0.8293 | | 0.0028 | 8.0 | 5138 | 1.1918 | 0.8257 | 0.8226 | 0.8257 | 0.7738 | 0.7493 | 0.0155 | 0.0149 | 0.9756 | 0.9868 | 0.8257 | 0.7493 | 0.8257 | 0.7552 | 0.8229 | | 0.0 | 9.0 | 5780 | 1.2181 | 0.8311 | 0.8286 | 0.8311 | 0.7935 | 0.7522 | 0.0150 | 0.0143 | 0.9764 | 0.9872 | 0.8311 | 0.7522 | 0.8311 | 0.7592 | 0.8276 | | 0.0018 | 10.0 | 6420 | 1.2265 | 0.8327 | 0.8301 | 0.8327 | 0.7955 | 0.7536 | 0.0148 | 0.0141 | 0.9765 | 0.9873 | 0.8327 | 0.7536 | 0.8327 | 0.7609 | 0.8291 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.1

Raelina/RaemuMix

Raelina

2024-04-19T13:39:47Z

0

4

null

[ "anime", "stable-diffusion-1.5", "license:creativeml-openrail-m", "region:us" ]

null

2023-07-21T09:46:53Z

--- license: creativeml-openrail-m tags: - anime - stable-diffusion-1.5 --- Instruction and example images go here https://civitai.com/models/113362/raemumix

michaelw37/sc40

michaelw37

2024-04-19T13:39:33Z

5

0

transformers

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

text-generation

2024-04-19T13:38:03Z

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

GladiusTn/llama3_ocr_to_xml_A1

GladiusTn

2024-04-19T13:36:42Z

6

0

transformers

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

text-generation

2024-04-19T13:30:52Z

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

paloalma/ECE-TW3-JRGL-V1

paloalma

2024-04-19T13:32:48Z

4,222

1

transformers

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "ShinojiResearch/Senku-70B-Full", "152334H/miqu-1-70b-sf", "conversational", "arxiv:2312.06281", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2024-04-03T20:54:20Z

--- license: apache-2.0 tags: - merge - mergekit - ShinojiResearch/Senku-70B-Full - 152334H/miqu-1-70b-sf --- # ECE-TW3-JRGL-V1 ## This model has been produced by : - [Louis Garcia](https://www.linkedin.com/in/louis-garcia-profil/), engineering student at [French Engineering School ECE](https://www.ece.fr/en/) - [Matthieu Jollard](https://www.linkedin.com/in/matthieu-jollard/), engineering student at [French Engineering School ECE](https://www.ece.fr/en/) ## Under the supervision of : - [Andre-Louis Rochet](https://www.linkedin.com/in/andrelouisrochet/), Lecturer at ECE & Co-Founder of [TW3 Partners](https://tw3partners.fr/) - [Paul Lemaistre](https://www.linkedin.com/in/paul-lemaistre/), CTO of [TW3 Partners](https://tw3partners.fr/) ## With the contribution of : - ECE engineering school as sponsor and financial contributor - RunPod as financial contributor ## About ECE >_**ECE**, a multi-program, multi-campus, and multi-sector engineering school specializing in digital engineering, > trains engineers and technology experts for the 21st century, capable of meeting the challenges of the dual digital and sustainable development revolutions. >[French Engineering School ECE](https://www.ece.fr/en/)_ ## Description ECE-TW3-JRGL-V1 is a merge of the following models using **[mergekit](https://github.com/cg123/mergekit)**: * [ShinojiResearch/Senku-70B-Full](https://huggingface.co/ShinojiResearch/Senku-70B-Full) * [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) ```yaml slices: - sources: - model: ShinojiResearch/Senku-70B-Full layer_range: [0, 80] - model: 152334H/miqu-1-70b-sf layer_range: [0, 80] merge_method: slerp base_model: 152334H/miqu-1-70b-sf parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: float16 ``` ## Results - ECE-TW3-JRGL-v1 scores 83.07 on [EQ-Bench V2](https://eqbench.com/index.html) --- @misc{paech2023eqbench, title={EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models}, author={Samuel J. Paech}, year={2023}, eprint={2312.06281}, archivePrefix={arXiv}, primaryClass={cs.CL} } ---

sosoai/hansoldeco-llama3-8b-unsloth-lora-v0.1

sosoai

2024-04-19T13:32:29Z

3

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:adapter:unsloth/llama-3-8b-bnb-4bit", "region:us" ]

null

2024-04-19T13:31:55Z

--- library_name: peft base_model: unsloth/llama-3-8b-bnb-4bit --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0

LoneStriker/opus-v1.2-llama-3-8b-6.0bpw-h6-exl2

LoneStriker

2024-04-19T13:28:44Z

7

1

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "unsloth", "axolotl", "conversational", "en", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]

text-generation

2024-04-19T13:25:56Z

--- language: - en pipeline_tag: text-generation tags: - unsloth - axolotl license: cc-by-nc-nd-4.0 --- # Llama 3 DreamGen Opus V1 <div style="display: flex; flex-direction: row; align-items: center;"> <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style=" border-radius: 12px; margin-right: 12px; margin-top: 0px; margin-bottom: 0px; max-width: 100px; height: auto; "/> Models for **(steerable) story-writing and role-playing**. [All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31). </div> ## Resources - [**Opus V1 prompting guide**](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy. - [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`. - [Python code](example/prompt/format.py) to format the prompt correctly. - Join the community on [**Discord**](https://dreamgen.com/discord) to get early access to new models. <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style=" padding: 12px; border-radius: 12px; border: 2px solid #f9a8d4; background: rgb(9, 9, 11); "/> ## Prompting <details> <summary>The models use an extended version of ChatML.</summary> ``` <|im_start|>system (Story description in the right format here) (Typically consists of plot description, style description and characters)<|im_end|> <|im_start|>user (Your instruction on how the story should continue)<|im_end|> <|im_start|>text names= Alice (Continuation of the story from the Alice character)<|im_end|> <|im_start|>text (Continuation of the story from no character in particular (pure narration))<|im_end|> <|im_start|>user (Your instruction on how the story should continue)<|im_end|> <|im_start|>text names= Bob (Continuation of the story from the Bob character)<|im_end|> ``` The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names. Pay attention to the following: - The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play. - There can be multiple subsequent message with a `text` role, especially if names are involved. - There can be multiple names attached to a message. - The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names. </details> While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance. Here's how you can prompt the model for the following tasks ### Steerable Story-writing and Role-playing: - [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing) - [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing) - Input: - System prompt: You provide story / role-play description, which consists of: - Plot description - Style description - Characters and their descriptions - Conversation turns: - Text / message turn: This represents part of the story or role play - Instruction: This tells the model what should happen next - Output: Continuation of the story / role-play. ### Story plot summarization - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description) - Input: A story, or a few chapters of a story. - Output: A description of the story or chapters. #### Examples: <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary> <blockquote> Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic. </blockquote> </details> <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary> <blockquote> The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth. One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic. As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary> <blockquote> The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary> <blockquote> A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses. </blockquote> </details> ### Story character description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description) - Input: A story, or a few chapters of a story, set of characters. - Output: A description of the characters. #### Examples: <details> <summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary> <blockquote> Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui. </blockquote> </details> <details> <summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary> <blockquote> The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision. </blockquote> </details> ### Story style description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description) - Input: A story, or a few chapters of a story. - Output: A description the style of the story. <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary> <blockquote> The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose. </blockquote> </details> <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary> <blockquote> The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory. </blockquote> </details> ### Story description to chapters - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions) - Input: A brief plot description and the desired number of chapters. - Output: A description for each chapter. ### And more... ## Sampling params For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`. You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures. ## Dataset The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long. All story-writing and role-playing examples were based on human-written text. ![token count distribution](images/token_count_cum__token_bucket.png) ## Running the model The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization. I recommend using these model versions: - 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b) - 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq) - 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq) ### Running on DreamGen.com (free) You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api). ### Running Locally - **Make sure your prompt is as close as possible to the Opus V1** - Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly. - [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1) - [Read the prompt formatting code](example/prompt/format.py) - Make sure `<|im_start|>` and `<|im_end|>` are tokenized correctly - **vLLM** - [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU. - [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario. - **SillyTavern** - [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models - SillyTavern (staging) comes with built-in DreamGen preset for RP - Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti - Make sure to unselect `Skip special tokens`, otherwise it won't work - This is just an attempt at approximating the Opus V1 prompt, it won't be perfect - Character cards specifically rewritten for the built-in DreamGen preset: - [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card) - [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony) - **LM Studio** - [Config](configs/lmstudio/preset.json) - Just like ChatML, just changed "assistant" to "text" role. - **There's a bug** in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280). - **HuggingFace** - [Chat template](tokenizer_config.json#L51) - Just like ChatML, just changed "assistant" to "text" role. ## Known Issues - **34B repetition**: - The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes. - **GGUF**: - The tokenization might be messed up. Some users reported that `<|im_start|>` and `<|im_end|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer). ## License - This model is intended for personal use only, other use is not permitted.

0x0mom/sl25

0x0mom

2024-04-19T13:28:15Z

7

0

transformers

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

text-generation

2024-04-19T09:39:38Z

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

Alefiah/UrduSum6

Alefiah

2024-04-19T13:26:44Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "mt5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-04-19T13:22:17Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: UrduSum6 results: [] ---  # UrduSum6 This model is a fine-tuned version of [ahmed0189/mT5-Arabic-text-summarization](https://huggingface.co/ahmed0189/mT5-Arabic-text-summarization) on the None 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: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 1.0 | 147 | 3.6410 | 4.6547 | 1.7375 | 4.8048 | 4.8048 | 31.8514 | ### Framework versions - Transformers 4.28.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.13.3

Raul569/oufit_recommender_19_Apr_2024_v2

Raul569

2024-04-19T13:26:08Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2024-04-19T13:25:14Z

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

LoneStriker/opus-v1.2-llama-3-8b-5.0bpw-h6-exl2

LoneStriker

2024-04-19T13:25:53Z

8

2

transformers

[ "transformers", "pytorch", "safetensors", "llama", "text-generation", "unsloth", "axolotl", "conversational", "en", "license:cc-by-nc-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "5-bit", "exl2", "region:us" ]

text-generation

2024-04-19T13:23:27Z

--- language: - en pipeline_tag: text-generation tags: - unsloth - axolotl license: cc-by-nc-nd-4.0 --- # Llama 3 DreamGen Opus V1 <div style="display: flex; flex-direction: row; align-items: center;"> <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style=" border-radius: 12px; margin-right: 12px; margin-top: 0px; margin-bottom: 0px; max-width: 100px; height: auto; "/> Models for **(steerable) story-writing and role-playing**. [All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31). </div> ## Resources - [**Opus V1 prompting guide**](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy. - [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`. - [Python code](example/prompt/format.py) to format the prompt correctly. - Join the community on [**Discord**](https://dreamgen.com/discord) to get early access to new models. <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style=" padding: 12px; border-radius: 12px; border: 2px solid #f9a8d4; background: rgb(9, 9, 11); "/> ## Prompting <details> <summary>The models use an extended version of ChatML.</summary> ``` <|im_start|>system (Story description in the right format here) (Typically consists of plot description, style description and characters)<|im_end|> <|im_start|>user (Your instruction on how the story should continue)<|im_end|> <|im_start|>text names= Alice (Continuation of the story from the Alice character)<|im_end|> <|im_start|>text (Continuation of the story from no character in particular (pure narration))<|im_end|> <|im_start|>user (Your instruction on how the story should continue)<|im_end|> <|im_start|>text names= Bob (Continuation of the story from the Bob character)<|im_end|> ``` The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names. Pay attention to the following: - The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play. - There can be multiple subsequent message with a `text` role, especially if names are involved. - There can be multiple names attached to a message. - The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names. </details> While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance. Here's how you can prompt the model for the following tasks ### Steerable Story-writing and Role-playing: - [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing) - [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing) - Input: - System prompt: You provide story / role-play description, which consists of: - Plot description - Style description - Characters and their descriptions - Conversation turns: - Text / message turn: This represents part of the story or role play - Instruction: This tells the model what should happen next - Output: Continuation of the story / role-play. ### Story plot summarization - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description) - Input: A story, or a few chapters of a story. - Output: A description of the story or chapters. #### Examples: <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary> <blockquote> Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic. </blockquote> </details> <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary> <blockquote> The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth. One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic. As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary> <blockquote> The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary> <blockquote> A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses. </blockquote> </details> ### Story character description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description) - Input: A story, or a few chapters of a story, set of characters. - Output: A description of the characters. #### Examples: <details> <summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary> <blockquote> Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui. </blockquote> </details> <details> <summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary> <blockquote> The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision. </blockquote> </details> ### Story style description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description) - Input: A story, or a few chapters of a story. - Output: A description the style of the story. <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary> <blockquote> The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose. </blockquote> </details> <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary> <blockquote> The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory. </blockquote> </details> ### Story description to chapters - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions) - Input: A brief plot description and the desired number of chapters. - Output: A description for each chapter. ### And more... ## Sampling params For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`. You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures. ## Dataset The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long. All story-writing and role-playing examples were based on human-written text. ![token count distribution](images/token_count_cum__token_bucket.png) ## Running the model The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization. I recommend using these model versions: - 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b) - 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq) - 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq) ### Running on DreamGen.com (free) You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api). ### Running Locally - **Make sure your prompt is as close as possible to the Opus V1** - Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly. - [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1) - [Read the prompt formatting code](example/prompt/format.py) - Make sure `<|im_start|>` and `<|im_end|>` are tokenized correctly - **vLLM** - [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU. - [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario. - **SillyTavern** - [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models - SillyTavern (staging) comes with built-in DreamGen preset for RP - Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti - Make sure to unselect `Skip special tokens`, otherwise it won't work - This is just an attempt at approximating the Opus V1 prompt, it won't be perfect - Character cards specifically rewritten for the built-in DreamGen preset: - [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card) - [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony) - **LM Studio** - [Config](configs/lmstudio/preset.json) - Just like ChatML, just changed "assistant" to "text" role. - **There's a bug** in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280). - **HuggingFace** - [Chat template](tokenizer_config.json#L51) - Just like ChatML, just changed "assistant" to "text" role. ## Known Issues - **34B repetition**: - The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes. - **GGUF**: - The tokenization might be messed up. Some users reported that `<|im_start|>` and `<|im_end|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer). ## License - This model is intended for personal use only, other use is not permitted.

yujiepan/chatglm3-tiny-random

yujiepan

2024-04-19T13:22:22Z

5

0

transformers

[ "transformers", "safetensors", "chatglm", "feature-extraction", "text-generation", "conversational", "custom_code", "region:us" ]

text-generation

2024-03-30T15:46:40Z

--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This model is randomly initialized, using the config from [THUDM/chatglm3-6b-128k](https://huggingface.co/THUDM/chatglm3-6b-128k/blob/main/config.json) but with smaller size. Note the model is in float16. Codes: ```python import transformers import torch import os from huggingface_hub import create_repo, upload_folder source_model_id = 'THUDM/chatglm3-6b-128k' tiny_random_name = 'chatglm3-tiny-random' save_path = f'/tmp/yujiepan/{tiny_random_name}' repo_id = f'yujiepan/{tiny_random_name}' config = transformers.AutoConfig.from_pretrained( source_model_id, trust_remote_code=True) config.hidden_size = 4 config.ffn_hidden_size = 6 config.num_attention_heads = 4 config.kv_channels = 2 config.num_layers = 2 config.torch_dtype = torch.float16 model = transformers.AutoModelForCausalLM.from_config( config, trust_remote_code=True, torch_dtype=torch.float16) model = model.half() tokenizer = transformers.AutoTokenizer.from_pretrained( source_model_id, trust_remote_code=True) # result = transformers.pipelines.pipeline( # 'text-generation', # model=model, tokenizer=tokenizer, # device=0, # max_new_tokens=16, # )('Hello') # print(result) model = model.cuda() response, history = model.chat(tokenizer, "Hi", history=[], max_length=32) print(response) model.save_pretrained(save_path) tokenizer.save_pretrained(save_path) os.system(f'ls -alh {save_path}') create_repo(repo_id, exist_ok=True) upload_folder(repo_id=repo_id, folder_path=save_path) ```

yujiepan/llama-2-tiny-3layers-random

yujiepan

2024-04-19T13:22:12Z

139

1

transformers

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

text-generation

2023-08-31T09:30:50Z

--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- # yujiepan/llama-2-tiny-3layers-random This model is **randomly initialized**, using the config from [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/yujiepan/llama-2-tiny-3layers-random/blob/main/config.json) but with the following modifications: ```json { "hidden_size": 8, "intermediate_size": 32, "num_attention_heads": 2, "num_hidden_layers": 3, "num_key_value_heads": 2, } ```

yujiepan/llama-2-tiny-random

yujiepan

2024-04-19T13:22:10Z

2,541

1

transformers

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

text-generation

2023-08-21T05:31:01Z

--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- # yujiepan/llama-2-tiny-random This model is **randomly initialized**, using the config from [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/yujiepan/llama-2-tiny-random/blob/main/config.json) but with the following modifications: ```json { "hidden_size": 8, "intermediate_size": 32, "num_attention_heads": 2, "num_hidden_layers": 1, "num_key_value_heads": 2, } ```

yujiepan/mistral-tiny-random

yujiepan

2024-04-19T13:22:05Z

33

0

transformers

[ "transformers", "safetensors", "openvino", "mistral", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2023-12-12T14:20:47Z

--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This model is randomly initialized, using the config from [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) but with smaller size. Codes: ```python from optimum.intel.openvino import OVModelForCausalLM from transformers import pipeline from huggingface_hub import create_repo, upload_folder import torch import transformers import os model_id = 'mistralai/Mistral-7B-v0.1' save_path = '/tmp/yujiepan/mistral-tiny-random' repo_id = 'yujiepan/mistral-tiny-random' config = transformers.AutoConfig.from_pretrained(model_id) config.hidden_size = 8 config.intermediate_size = 32 config.num_attention_heads = 4 config.num_hidden_layers = 2 config.num_key_value_heads = 2 print(config) tokenizer = transformers.AutoTokenizer.from_pretrained(model_id) tokenizer.save_pretrained(save_path) model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16) model = model.half() pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False, device='cuda') print(pipe('Hello World!')) model.save_pretrained(save_path) ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True) ovmodel = ovmodel.half() ovmodel.save_pretrained(save_path) os.system(f'ls -alh {save_path}') create_repo(repo_id, exist_ok=True) upload_folder(repo_id=repo_id, folder_path=save_path) ```

yujiepan/gemma-tiny-random

yujiepan

2024-04-19T13:22:04Z

481

0

transformers

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

text-generation

2024-02-21T18:43:38Z

--- library_name: transformers pipeline_tag: text-generation inference: true widget: - text: Hello! example_title: Hello world group: Python --- This model is randomly initialized, using the config from [https://huggingface.co/google/gemma-7b-it] but with smaller size. Note the model is in float16. Codes: ```python from transformers import pipeline from huggingface_hub import create_repo, upload_folder import torch import transformers import os model_id = 'google/gemma-7b-it' save_path = '/tmp/yujiepan/gemma-tiny-random' repo_id = 'yujiepan/gemma-tiny-random' config = transformers.AutoConfig.from_pretrained(model_id) config.hidden_size = 8 config.head_dim = 2 config.intermediate_size = 16 config.num_attention_heads = 4 config.num_hidden_layers = 2 config.num_key_value_heads = 2 print(config) tokenizer = transformers.AutoTokenizer.from_pretrained(model_id) tokenizer.save_pretrained(save_path) model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16) model = model.half() pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False, device='cuda') print(pipe('Hello World!')) model.save_pretrained(save_path) # ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True) # ovmodel = ovmodel.half() # ovmodel.save_pretrained(save_path) os.system(f'ls -alh {save_path}') create_repo(repo_id, exist_ok=True) upload_folder(repo_id=repo_id, folder_path=save_path) ```

NatLibFi/HogwartsSortingHat-fastText

NatLibFi

2024-04-19T13:21:31Z

0

null

[ "glam", "lam", "subject indexing", "annif", "hogwarts", "text-classification", "en", "license:cc-by-4.0", "region:us" ]

text-classification

2024-04-19T13:06:41Z

--- license: cc-by-4.0 language: - en pipeline_tag: text-classification tags: - glam - lam - subject indexing - annif - hogwarts --- # Hogwarts Sorting Hat using Annif and its fastText backend The model is the output of [this Annif tutorial exercise](https://github.com/NatLibFi/Annif-tutorial/blob/master/exercises/OPT_hogwarts.md). > The original Sorting Hat reads the thoughts of the student, but Annif generally does not have access to that kind of information, so we will simply use the name of the student as input. We will train a fastText model on the names of characters from the Harry Potter novels whose house is known. To make it possible to generalize the model to new, unseen names, we will use character n-grams to split all names into chunks of 1 to 4 characters - for example harry becomes [h, ha, har, harr, a, ar, arr, arry ...]. fastText can do this when given the minn and maxn parameters, which set the minimum and maximum length of character n-grams to generate from input text.

spietari/Reinforce-Pixelcopter-PLE-v0

spietari

2024-04-19T13:20:05Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2024-04-18T15:31:11Z

--- 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: 103.20 +/- 55.03 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

t1msan/swin-base-patch4-window7-224-in22k-Kontur-competition-1.3K

t1msan

2024-04-19T13:17:28Z

8

0

transformers

[ "transformers", "tensorboard", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-base-patch4-window7-224-in22k", "base_model:finetune:microsoft/swin-base-patch4-window7-224-in22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2024-04-18T19:48:58Z

--- license: apache-2.0 base_model: microsoft/swin-base-patch4-window7-224-in22k tags: - generated_from_trainer datasets: - imagefolder model-index: - name: swin-base-patch4-window7-224-in22k-Kontur-competition-1.3K results: [] ---  # swin-base-patch4-window7-224-in22k-Kontur-competition-1.3K This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0008 ## 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: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0593 | 0.99 | 55 | 0.0294 | | 0.0098 | 1.99 | 111 | 0.0315 | | 0.0066 | 3.0 | 167 | 0.0322 | | 0.0179 | 4.0 | 223 | 0.0068 | | 0.0078 | 4.99 | 278 | 0.0033 | | 0.0015 | 5.99 | 334 | 0.0008 | | 0.0017 | 7.0 | 390 | 0.0078 | | 0.0008 | 8.0 | 446 | 0.0027 | | 0.0019 | 8.99 | 501 | 0.0011 | | 0.0014 | 9.87 | 550 | 0.0036 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

birdy654/CHECK_P_MISTRAL

birdy654

2024-04-19T13:16:37Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]

null

2024-04-19T13:13:15Z

--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.2 --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0

OwOOwO/dumbo-krillin103

OwOOwO

2024-04-19T13:11:45Z

6

0

transformers

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

text-generation

2024-04-19T13:08:42Z

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

DandinPower/deberta-v3-base-cotat

DandinPower

2024-04-19T13:11:34Z

12

0

transformers

[ "transformers", "safetensors", "deberta-v2", "text-classification", "nycu-112-2-datamining-hw2", "generated_from_trainer", "en", "dataset:DandinPower/review_cleanonlytitleandtext", "base_model:microsoft/deberta-v3-base", "base_model:finetune:microsoft/deberta-v3-base", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-04-19T12:42:34Z

--- language: - en license: mit base_model: microsoft/deberta-v3-base tags: - nycu-112-2-datamining-hw2 - generated_from_trainer datasets: - DandinPower/review_cleanonlytitleandtext metrics: - accuracy model-index: - name: deberta-v3-base-cotat results: - task: name: Text Classification type: text-classification dataset: name: DandinPower/review_cleanonlytitleandtext type: DandinPower/review_cleanonlytitleandtext metrics: - name: Accuracy type: accuracy value: 0.623 ---  # deberta-v3-base-cotat This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the DandinPower/review_cleanonlytitleandtext dataset. It achieves the following results on the evaluation set: - Loss: 1.4985 - Accuracy: 0.623 - Macro F1: 0.6247 ## 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: 4.5e-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: 1500 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:| | 1.0223 | 0.14 | 500 | 0.9610 | 0.592 | 0.5971 | | 1.0108 | 0.29 | 1000 | 0.9378 | 0.6044 | 0.6083 | | 0.9323 | 0.43 | 1500 | 0.9605 | 0.589 | 0.5652 | | 0.9651 | 0.57 | 2000 | 0.9845 | 0.5797 | 0.5687 | | 0.928 | 0.71 | 2500 | 0.9521 | 0.5907 | 0.5656 | | 0.9205 | 0.86 | 3000 | 0.9073 | 0.603 | 0.5740 | | 0.9243 | 1.0 | 3500 | 0.8876 | 0.616 | 0.6113 | | 0.8545 | 1.14 | 4000 | 0.8631 | 0.6267 | 0.6290 | | 0.8267 | 1.29 | 4500 | 0.8908 | 0.624 | 0.6185 | | 0.8175 | 1.43 | 5000 | 0.8771 | 0.6173 | 0.6222 | | 0.8613 | 1.57 | 5500 | 0.9564 | 0.6209 | 0.6081 | | 0.8138 | 1.71 | 6000 | 0.9246 | 0.6089 | 0.6063 | | 0.7314 | 1.86 | 6500 | 0.9030 | 0.6329 | 0.6313 | | 0.8287 | 2.0 | 7000 | 0.8753 | 0.6211 | 0.6235 | | 0.6963 | 2.14 | 7500 | 0.9700 | 0.6247 | 0.6257 | | 0.7034 | 2.29 | 8000 | 0.9592 | 0.6234 | 0.6220 | | 0.679 | 2.43 | 8500 | 0.8994 | 0.6233 | 0.6272 | | 0.7207 | 2.57 | 9000 | 1.0013 | 0.6236 | 0.6183 | | 0.6992 | 2.71 | 9500 | 0.9385 | 0.6169 | 0.6219 | | 0.7032 | 2.86 | 10000 | 0.9247 | 0.6366 | 0.6364 | | 0.6949 | 3.0 | 10500 | 0.9615 | 0.6239 | 0.6281 | | 0.5581 | 3.14 | 11000 | 1.0439 | 0.6217 | 0.6267 | | 0.55 | 3.29 | 11500 | 1.1205 | 0.6259 | 0.6232 | | 0.5496 | 3.43 | 12000 | 1.1122 | 0.6226 | 0.6267 | | 0.5462 | 3.57 | 12500 | 1.0692 | 0.6251 | 0.6263 | | 0.5121 | 3.71 | 13000 | 1.1563 | 0.6197 | 0.6214 | | 0.531 | 3.86 | 13500 | 1.1123 | 0.6261 | 0.6256 | | 0.5256 | 4.0 | 14000 | 1.1194 | 0.6247 | 0.6264 | | 0.3908 | 4.14 | 14500 | 1.3631 | 0.6204 | 0.6210 | | 0.4439 | 4.29 | 15000 | 1.4810 | 0.6204 | 0.6211 | | 0.4252 | 4.43 | 15500 | 1.4454 | 0.6211 | 0.6217 | | 0.3721 | 4.57 | 16000 | 1.5315 | 0.6204 | 0.6231 | | 0.369 | 4.71 | 16500 | 1.4797 | 0.6184 | 0.6190 | | 0.3907 | 4.86 | 17000 | 1.4857 | 0.6219 | 0.6234 | | 0.4022 | 5.0 | 17500 | 1.4985 | 0.623 | 0.6247 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

LoneStriker/opus-v1.2-llama-3-8b-GGUF

LoneStriker

2024-04-19T13:11:11Z

86

14

null

[ "gguf", "unsloth", "axolotl", "text-generation", "en", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2024-04-19T12:59:08Z

--- language: - en pipeline_tag: text-generation tags: - unsloth - axolotl license: cc-by-nc-nd-4.0 --- # Llama 3 DreamGen Opus V1 <div style="display: flex; flex-direction: row; align-items: center;"> <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/logo-1024.png" alt="model logo" style=" border-radius: 12px; margin-right: 12px; margin-top: 0px; margin-bottom: 0px; max-width: 100px; height: auto; "/> Models for **(steerable) story-writing and role-playing**. [All Opus V1 models, including quants](https://huggingface.co/collections/dreamgen/opus-v1-65d092a6f8ab7fc669111b31). </div> ## Resources - [**Opus V1 prompting guide**](https://dreamgen.com/docs/models/opus/v1) with many (interactive) examples and prompts that you can copy. - [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing) for interactive role-play using `opus-v1.2-7b`. - [Python code](example/prompt/format.py) to format the prompt correctly. - Join the community on [**Discord**](https://dreamgen.com/discord) to get early access to new models. <img src="/dreamgen/opus-v1.2-llama-3-8b/resolve/main/images/story_writing.webp" alt="story writing on dreamgen.com" style=" padding: 12px; border-radius: 12px; border: 2px solid #f9a8d4; background: rgb(9, 9, 11); "/> ## Prompting <details> <summary>The models use an extended version of ChatML.</summary> ``` <|im_start|>system (Story description in the right format here) (Typically consists of plot description, style description and characters)<|im_end|> <|im_start|>user (Your instruction on how the story should continue)<|im_end|> <|im_start|>text names= Alice (Continuation of the story from the Alice character)<|im_end|> <|im_start|>text (Continuation of the story from no character in particular (pure narration))<|im_end|> <|im_start|>user (Your instruction on how the story should continue)<|im_end|> <|im_start|>text names= Bob (Continuation of the story from the Bob character)<|im_end|> ``` The Opus V1 extension is the addition of the `text` role, and the addition / modification of role names. Pay attention to the following: - The `text` messages can (but don't have to have) `names`, names are used to indicate the "active" character during role-play. - There can be multiple subsequent message with a `text` role, especially if names are involved. - There can be multiple names attached to a message. - The format for names is `names= {{name[0]}}; {{name[1]}}`, beware of the spaces after `names=` and after the `;`. This spacing leads to most natural tokenization for the names. </details> While the main goal for the models is great story-writing and role-playing performance, the models are also capable of several writing related tasks as well as general assistance. Here's how you can prompt the model for the following tasks ### Steerable Story-writing and Role-playing: - [Story-writing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-writing) - [Role-playing prompt guide](https://dreamgen.com/docs/models/opus/v1#task-role-playing) - Input: - System prompt: You provide story / role-play description, which consists of: - Plot description - Style description - Characters and their descriptions - Conversation turns: - Text / message turn: This represents part of the story or role play - Instruction: This tells the model what should happen next - Output: Continuation of the story / role-play. ### Story plot summarization - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-plot-description) - Input: A story, or a few chapters of a story. - Output: A description of the story or chapters. #### Examples: <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 50 words</summary> <blockquote> Malygris, a powerful magician, summons his long-dead lover Nylissa from the grave using necromancy. However, upon seeing her, he realizes that she is not the same person he once knew, leading him to question the nature of love, memory, and the power of magic. </blockquote> </details> <details> <summary>Summary of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">short fiction story</a> in 200 words</summary> <blockquote> The story is a dark fantasy set in the city of Susran, where an ancient and powerful magician named Malygris dwells in a tower atop a conical hill. Malygris, who has mastered all forms of sorcery and necromancy, is plagued by a deep sense of ennui and melancholy. He seeks solace in the memory of Nylissa, a girl he once loved in his youth. One day, Malygris decides to summon Nylissa's spirit from the grave using his necromantic powers. He performs the necessary rituals and incantations, and Nylissa appears before him as a ghostly apparition. However, as Malygris gazes upon her, he becomes increasingly disillusioned. He notices subtle changes in her appearance and behavior that do not match his memories of her. He begins to question whether this is truly the same Nylissa he once knew, or if it is just another illusion conjured by his magic. As Malygris grapples with this uncertainty, he becomes increasingly despondent and disillusioned with life itself. He realizes that even his magic cannot bring back the past or make him truly happy. In the end, he dismisses Nylissa's spirit and returns to his lonely tower, resigned to his fate as an old and weary sorcerer. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 150 words</summary> <blockquote> The article discusses the recent human deaths from diseases transmitted by animals, specifically Alaskapox and bubonic plague. Dr. Leana Wen, an emergency physician and former Baltimore health commissioner, explains the various ways household pets can transmit diseases to humans. These include fecal-oral transmission, scratches and bites, and vector-borne diseases like ticks, fleas, and mosquitoes. Wen emphasizes the importance of vaccinating pets, maintaining cleanliness, and taking commonsense precautions to reduce disease transmission. She also advises that individuals who are immunocompromised, babies, and pregnant women should take additional care when interacting with animals. </blockquote> </details> <details> <summary>Summary of <a href="https://edition.cnn.com/2024/02/29/health/pets-diseases-viruses-bacteria-wellness/index.html">CNN News article</a> in 50 words</summary> <blockquote> A doctor discusses the risks of diseases transmitted from animals to humans, focusing on household pets like cats and dogs. They emphasize the importance of vaccination, hygiene, and regular veterinary care to prevent these illnesses. </blockquote> </details> ### Story character description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-char-description) - Input: A story, or a few chapters of a story, set of characters. - Output: A description of the characters. #### Examples: <details> <summary>Description of the Malygris character from <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a> in 50 words</summary> <blockquote> Malygris is an ancient and powerful magician, characterized by his long white beard, cold green eyes, and a violet robe. He is a master of necromancy and possesses a vast knowledge of forbidden magic. Despite his immense power, he is plagued by a deep sense of melancholy and ennui. </blockquote> </details> <details> <summary>Description of the prince character from <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a> in 50 words</summary> <blockquote> The prince is a devoted and determined individual, driven by his love for his late queen. He is described as a graceful youth in the beginning, but as he grows older, his dedication to the Pearl of Love transforms him into a grave and intent man. His sense of color and design evolves over time, reflecting his growing maturity and artistic vision. </blockquote> </details> ### Story style description - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-style-description) - Input: A story, or a few chapters of a story. - Output: A description the style of the story. <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-last-incantation">this story</a></summary> <blockquote> The story is written in a descriptive and evocative style, with a focus on the atmosphere and setting. The author uses vivid and detailed language to create a sense of place and time, and employs a variety of literary devices such as similes, metaphors, and allusions to enhance the mood and tone of the narrative. The sentence structure is varied, with a mix of short and long sentences that create a rhythmic flow to the prose. </blockquote> </details> <details> <summary>Style description of <a href="https://www.libraryofshortstories.com/onlinereader/the-pearl-of-love">this story</a></summary> <blockquote> The writing style is descriptive and evocative, with a focus on the beauty and grandeur of the Pearl of Love. The author uses vivid imagery and sensory details to create a rich and immersive atmosphere. The tone is reverential and contemplative, reflecting the prince's deep love for his queen and his dedication to creating a lasting monument to her memory. </blockquote> </details> ### Story description to chapters - [Prompt guide](https://dreamgen.com/docs/models/opus/v1#task-story-description-to-chapter-descriptions) - Input: A brief plot description and the desired number of chapters. - Output: A description for each chapter. ### And more... ## Sampling params For story-writing and role-play, I recommend "Min P" based sampling with `min_p` in the range `[0.01, 0.1]` and with `temperature` in the range `[0.5, 1.5]`, depending on your preferences. A good starting point would be `min_p=0.1; temperature=0.8`. You may also benefit from setting presence, frequency and repetition penalties, especially at lower temperatures. ## Dataset The fine-tuning dataset consisted of ~100M tokens of steerable story-writing, role-playing, writing-assistant and general-assistant examples. Each example was up to 31000 tokens long. All story-writing and role-playing examples were based on human-written text. ![token count distribution](images/token_count_cum__token_bucket.png) ## Running the model The model is should be compatible with any software that supports the base model, but beware of prompting and tokenization. I recommend using these model versions: - 7B: [no quant (opus-v1.2-7b)](https://huggingface.co/dreamgen/opus-v1.2-7b) - 34B: [no quant (opus-v1-34b)](https://huggingface.co/dreamgen/opus-v1-34b) or [awq (opus-v1-34b-awq)](https://huggingface.co/dreamgen/opus-v1-34b-awq) - 34B: [no quant (opus-v1.2-70b)](https://huggingface.co/dreamgen/opus-v1.2-70b) or [awq (opus-v1.2-70b-awq)](https://huggingface.co/dreamgen/opus-v1.2-70b-awq) ### Running on DreamGen.com (free) You can run the models on [dreamgen.com](https://dreamgen.com) for free — you can use the built-in UI for story-writing & role-playing, or use [the API](https://dreamgen.com/docs/api). ### Running Locally - **Make sure your prompt is as close as possible to the Opus V1** - Regardless of which backend you use, it's important that you format your prompt well and that the tokenization works correctly. - [Read the prompt guide](https://dreamgen.com/docs/models/opus/v1) - [Read the prompt formatting code](example/prompt/format.py) - Make sure `<|im_start|>` and `<|im_end|>` are tokenized correctly - **vLLM** - [**Google Colab**](https://colab.research.google.com/drive/1J178fH6IdQOXNi-Njgdacf5QgAxsdT20?usp=sharing): This is a simple interactive Google Colab to do role-play with the 7B model, it should fit on the T4 GPU. - [Code](example/prompt/interactive.py): This is simple script for interactive chat for one hard-coded scenario. - **SillyTavern** - [Official SillyTavern documentation for DreamGen](https://docs.sillytavern.app/usage/api-connections/dreamgen/) -- applies to both the API an local models - SillyTavern (staging) comes with built-in DreamGen preset for RP - Other presets can be found [here](https://huggingface.co/dreamgen/opus-v1.2-llama-3-8b/tree/main/configs/silly_tavern), v2 kindly provided by @MarinaraSpaghetti - Make sure to unselect `Skip special tokens`, otherwise it won't work - This is just an attempt at approximating the Opus V1 prompt, it won't be perfect - Character cards specifically rewritten for the built-in DreamGen preset: - [Seraphina](configs/silly_tavern/cards/Seraphina.png) (based on the default Seraphina card) - [Lara Lightland](configs/silly_tavern/cards/LaraLightland.png) (based on the card by Deffcolony) - **LM Studio** - [Config](configs/lmstudio/preset.json) - Just like ChatML, just changed "assistant" to "text" role. - **There's a bug** in LM Studio if you delete a message or click "Continue", [see here for details](https://discord.com/channels/1110598183144399058/1212665261128417280/1212665261128417280). - **HuggingFace** - [Chat template](tokenizer_config.json#L51) - Just like ChatML, just changed "assistant" to "text" role. ## Known Issues - **34B repetition**: - The 34B sometimes gets stuck repeating the same word, or synonyms. This seems to be a common problem across various Yi 34B fine-tunes. - **GGUF**: - The tokenization might be messed up. Some users reported that `<|im_start|>` and `<|im_end|>` are tokenized as multiple tokens. Also llama.cpp may not tokenize correctly (the Yi tokenizer is subtly different from the Llama 2 tokenizer). ## License - This model is intended for personal use only, other use is not permitted.

numen-tech/Meta-Llama-3-8B-Instruct-w4a16g128asym

numen-tech

2024-04-19T13:08:47Z

0

1

null

[ "arxiv:2308.13137", "license:other", "region:us" ]

null

2024-04-19T13:06:04Z

--- license: other license_name: llama3 license_link: LICENSE --- 4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct).

NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-GGUF

NikolayKozloff

2024-04-19T13:05:04Z

4

1

null

[ "gguf", "alignment-handbook", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "dataset:maxidl/distilabel-capybara-dpo-7k-binarized_en_de", "base_model:mistralai/Mistral-7B-v0.1", "base_model:quantized:mistralai/Mistral-7B-v0.1", "endpoints_compatible", "region:us", "conversational" ]

null

2024-04-19T13:04:37Z

--- tags: - alignment-handbook - generated_from_trainer - llama-cpp - gguf-my-repo base_model: mistralai/Mistral-7B-v0.1 datasets: - maxidl/distilabel-capybara-dpo-7k-binarized_en_de model-index: - name: Mistral-7B-v0.1-capybara-orpo-en-de results: [] --- # NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF This model was converted to GGUF format from [`maxidl/Mistral-7B-v0.1-capybara-orpo-en-de`](https://huggingface.co/maxidl/Mistral-7B-v0.1-capybara-orpo-en-de) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/maxidl/Mistral-7B-v0.1-capybara-orpo-en-de) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF --model mistral-7b-v0.1-capybara-orpo-en-de.Q8_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/Mistral-7B-v0.1-capybara-orpo-en-de-Q8_0-GGUF --model mistral-7b-v0.1-capybara-orpo-en-de.Q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mistral-7b-v0.1-capybara-orpo-en-de.Q8_0.gguf -n 128 ```

himanshue2e/gemma-2b-g

himanshue2e

2024-04-19T13:03:26Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b", "base_model:adapter:google/gemma-2b", "license:gemma", "region:us" ]

null

2024-04-18T13:00:38Z

--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b model-index: - name: gemma-2b-g results: [] ---  # gemma-2b-g This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9563 ## 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: 2.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 500 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.016 | 2 | 0.9410 | | No log | 0.032 | 4 | 0.9443 | | No log | 0.048 | 6 | 0.9413 | | No log | 0.064 | 8 | 0.9398 | | No log | 0.08 | 10 | 0.9401 | | No log | 0.096 | 12 | 0.9406 | | No log | 0.112 | 14 | 0.9404 | | No log | 0.128 | 16 | 0.9409 | | No log | 0.144 | 18 | 0.9412 | | No log | 0.16 | 20 | 0.9412 | | No log | 0.176 | 22 | 0.9411 | | No log | 0.192 | 24 | 0.9408 | | No log | 0.208 | 26 | 0.9412 | | No log | 0.224 | 28 | 0.9411 | | No log | 0.24 | 30 | 0.9408 | | No log | 0.256 | 32 | 0.9406 | | No log | 0.272 | 34 | 0.9404 | | No log | 0.288 | 36 | 0.9406 | | No log | 0.304 | 38 | 0.9409 | | No log | 0.32 | 40 | 0.9414 | | No log | 0.336 | 42 | 0.9419 | | No log | 0.352 | 44 | 0.9425 | | No log | 0.368 | 46 | 0.9425 | | No log | 0.384 | 48 | 0.9416 | | No log | 0.4 | 50 | 0.9408 | | No log | 0.416 | 52 | 0.9403 | | No log | 0.432 | 54 | 0.9398 | | No log | 0.448 | 56 | 0.9393 | | No log | 0.464 | 58 | 0.9385 | | No log | 0.48 | 60 | 0.9390 | | No log | 0.496 | 62 | 0.9394 | | No log | 0.512 | 64 | 0.9392 | | No log | 0.528 | 66 | 0.9386 | | No log | 0.544 | 68 | 0.9385 | | No log | 0.56 | 70 | 0.9380 | | No log | 0.576 | 72 | 0.9373 | | No log | 0.592 | 74 | 0.9369 | | No log | 0.608 | 76 | 0.9367 | | No log | 0.624 | 78 | 0.9369 | | No log | 0.64 | 80 | 0.9370 | | No log | 0.656 | 82 | 0.9371 | | No log | 0.672 | 84 | 0.9366 | | No log | 0.688 | 86 | 0.9361 | | No log | 0.704 | 88 | 0.9361 | | No log | 0.72 | 90 | 0.9354 | | No log | 0.736 | 92 | 0.9352 | | No log | 0.752 | 94 | 0.9354 | | No log | 0.768 | 96 | 0.9352 | | No log | 0.784 | 98 | 0.9350 | | No log | 0.8 | 100 | 0.9349 | | No log | 0.816 | 102 | 0.9353 | | No log | 0.832 | 104 | 0.9349 | | No log | 0.848 | 106 | 0.9346 | | No log | 0.864 | 108 | 0.9341 | | No log | 0.88 | 110 | 0.9335 | | No log | 0.896 | 112 | 0.9327 | | No log | 0.912 | 114 | 0.9321 | | No log | 0.928 | 116 | 0.9323 | | No log | 0.944 | 118 | 0.9327 | | No log | 0.96 | 120 | 0.9325 | | No log | 0.976 | 122 | 0.9318 | | No log | 0.992 | 124 | 0.9316 | | No log | 1.008 | 126 | 0.9321 | | No log | 1.024 | 128 | 0.9332 | | No log | 1.04 | 130 | 0.9351 | | No log | 1.056 | 132 | 0.9370 | | No log | 1.072 | 134 | 0.9383 | | No log | 1.088 | 136 | 0.9390 | | No log | 1.104 | 138 | 0.9386 | | No log | 1.12 | 140 | 0.9378 | | No log | 1.1360 | 142 | 0.9375 | | No log | 1.152 | 144 | 0.9380 | | No log | 1.168 | 146 | 0.9380 | | No log | 1.184 | 148 | 0.9376 | | No log | 1.2 | 150 | 0.9381 | | No log | 1.216 | 152 | 0.9390 | | No log | 1.232 | 154 | 0.9400 | | No log | 1.248 | 156 | 0.9410 | | No log | 1.264 | 158 | 0.9411 | | No log | 1.28 | 160 | 0.9405 | | No log | 1.296 | 162 | 0.9402 | | No log | 1.312 | 164 | 0.9400 | | No log | 1.328 | 166 | 0.9399 | | No log | 1.3440 | 168 | 0.9397 | | No log | 1.3600 | 170 | 0.9398 | | No log | 1.376 | 172 | 0.9403 | | No log | 1.392 | 174 | 0.9412 | | No log | 1.408 | 176 | 0.9424 | | No log | 1.424 | 178 | 0.9432 | | No log | 1.44 | 180 | 0.9417 | | No log | 1.456 | 182 | 0.9403 | | No log | 1.472 | 184 | 0.9397 | | No log | 1.488 | 186 | 0.9393 | | No log | 1.504 | 188 | 0.9391 | | No log | 1.52 | 190 | 0.9385 | | No log | 1.536 | 192 | 0.9385 | | No log | 1.552 | 194 | 0.9387 | | No log | 1.568 | 196 | 0.9393 | | No log | 1.584 | 198 | 0.9402 | | No log | 1.6 | 200 | 0.9410 | | No log | 1.616 | 202 | 0.9410 | | No log | 1.6320 | 204 | 0.9417 | | No log | 1.6480 | 206 | 0.9414 | | No log | 1.6640 | 208 | 0.9410 | | No log | 1.6800 | 210 | 0.9402 | | No log | 1.696 | 212 | 0.9400 | | No log | 1.712 | 214 | 0.9398 | | No log | 1.728 | 216 | 0.9397 | | No log | 1.744 | 218 | 0.9395 | | No log | 1.76 | 220 | 0.9398 | | No log | 1.776 | 222 | 0.9400 | | No log | 1.792 | 224 | 0.9403 | | No log | 1.808 | 226 | 0.9403 | | No log | 1.8240 | 228 | 0.9399 | | No log | 1.8400 | 230 | 0.9392 | | No log | 1.8560 | 232 | 0.9385 | | No log | 1.8720 | 234 | 0.9385 | | No log | 1.888 | 236 | 0.9390 | | No log | 1.904 | 238 | 0.9394 | | No log | 1.92 | 240 | 0.9395 | | No log | 1.936 | 242 | 0.9392 | | No log | 1.952 | 244 | 0.9391 | | No log | 1.968 | 246 | 0.9390 | | No log | 1.984 | 248 | 0.9386 | | No log | 2.0 | 250 | 0.9380 | | No log | 2.016 | 252 | 0.9381 | | No log | 2.032 | 254 | 0.9401 | | No log | 2.048 | 256 | 0.9431 | | No log | 2.064 | 258 | 0.9469 | | No log | 2.08 | 260 | 0.9507 | | No log | 2.096 | 262 | 0.9529 | | No log | 2.112 | 264 | 0.9524 | | No log | 2.128 | 266 | 0.9501 | | No log | 2.144 | 268 | 0.9478 | | No log | 2.16 | 270 | 0.9466 | | No log | 2.176 | 272 | 0.9463 | | No log | 2.192 | 274 | 0.9458 | | No log | 2.208 | 276 | 0.9454 | | No log | 2.224 | 278 | 0.9451 | | No log | 2.24 | 280 | 0.9456 | | No log | 2.2560 | 282 | 0.9468 | | No log | 2.2720 | 284 | 0.9477 | | No log | 2.288 | 286 | 0.9484 | | No log | 2.304 | 288 | 0.9486 | | No log | 2.32 | 290 | 0.9479 | | No log | 2.336 | 292 | 0.9473 | | No log | 2.352 | 294 | 0.9473 | | No log | 2.368 | 296 | 0.9473 | | No log | 2.384 | 298 | 0.9475 | | No log | 2.4 | 300 | 0.9479 | | No log | 2.416 | 302 | 0.9490 | | No log | 2.432 | 304 | 0.9499 | | No log | 2.448 | 306 | 0.9501 | | No log | 2.464 | 308 | 0.9498 | | No log | 2.48 | 310 | 0.9491 | | No log | 2.496 | 312 | 0.9489 | | No log | 2.512 | 314 | 0.9490 | | No log | 2.528 | 316 | 0.9487 | | No log | 2.544 | 318 | 0.9483 | | No log | 2.56 | 320 | 0.9483 | | No log | 2.576 | 322 | 0.9483 | | No log | 2.592 | 324 | 0.9485 | | No log | 2.608 | 326 | 0.9487 | | No log | 2.624 | 328 | 0.9492 | | No log | 2.64 | 330 | 0.9493 | | No log | 2.656 | 332 | 0.9488 | | No log | 2.672 | 334 | 0.9487 | | No log | 2.6880 | 336 | 0.9486 | | No log | 2.7040 | 338 | 0.9485 | | No log | 2.7200 | 340 | 0.9481 | | No log | 2.7360 | 342 | 0.9477 | | No log | 2.752 | 344 | 0.9478 | | No log | 2.768 | 346 | 0.9482 | | No log | 2.784 | 348 | 0.9487 | | No log | 2.8 | 350 | 0.9483 | | No log | 2.816 | 352 | 0.9481 | | No log | 2.832 | 354 | 0.9480 | | No log | 2.848 | 356 | 0.9480 | | No log | 2.864 | 358 | 0.9479 | | No log | 2.88 | 360 | 0.9481 | | No log | 2.896 | 362 | 0.9484 | | No log | 2.912 | 364 | 0.9488 | | No log | 2.928 | 366 | 0.9490 | | No log | 2.944 | 368 | 0.9489 | | No log | 2.96 | 370 | 0.9487 | | No log | 2.976 | 372 | 0.9484 | | No log | 2.992 | 374 | 0.9476 | | No log | 3.008 | 376 | 0.9468 | | No log | 3.024 | 378 | 0.9471 | | No log | 3.04 | 380 | 0.9481 | | No log | 3.056 | 382 | 0.9499 | | No log | 3.072 | 384 | 0.9521 | | No log | 3.088 | 386 | 0.9543 | | No log | 3.104 | 388 | 0.9562 | | No log | 3.12 | 390 | 0.9572 | | No log | 3.136 | 392 | 0.9577 | | No log | 3.152 | 394 | 0.9577 | | No log | 3.168 | 396 | 0.9577 | | No log | 3.184 | 398 | 0.9574 | | No log | 3.2 | 400 | 0.9570 | | No log | 3.216 | 402 | 0.9569 | | No log | 3.232 | 404 | 0.9567 | | No log | 3.248 | 406 | 0.9565 | | No log | 3.2640 | 408 | 0.9564 | | No log | 3.2800 | 410 | 0.9562 | | No log | 3.296 | 412 | 0.9561 | | No log | 3.312 | 414 | 0.9561 | | No log | 3.328 | 416 | 0.9562 | | No log | 3.344 | 418 | 0.9565 | | No log | 3.36 | 420 | 0.9568 | | No log | 3.376 | 422 | 0.9570 | | No log | 3.392 | 424 | 0.9572 | | No log | 3.408 | 426 | 0.9573 | | No log | 3.424 | 428 | 0.9572 | | No log | 3.44 | 430 | 0.9569 | | No log | 3.456 | 432 | 0.9570 | | No log | 3.472 | 434 | 0.9572 | | No log | 3.488 | 436 | 0.9574 | | No log | 3.504 | 438 | 0.9575 | | No log | 3.52 | 440 | 0.9577 | | No log | 3.536 | 442 | 0.9577 | | No log | 3.552 | 444 | 0.9578 | | No log | 3.568 | 446 | 0.9579 | | No log | 3.584 | 448 | 0.9577 | | No log | 3.6 | 450 | 0.9575 | | No log | 3.616 | 452 | 0.9575 | | No log | 3.632 | 454 | 0.9575 | | No log | 3.648 | 456 | 0.9576 | | No log | 3.664 | 458 | 0.9576 | | No log | 3.68 | 460 | 0.9574 | | No log | 3.6960 | 462 | 0.9573 | | No log | 3.7120 | 464 | 0.9571 | | No log | 3.7280 | 466 | 0.9569 | | No log | 3.7440 | 468 | 0.9567 | | No log | 3.76 | 470 | 0.9565 | | No log | 3.776 | 472 | 0.9563 | | No log | 3.792 | 474 | 0.9563 | | No log | 3.808 | 476 | 0.9563 | | No log | 3.824 | 478 | 0.9564 | | No log | 3.84 | 480 | 0.9565 | | No log | 3.856 | 482 | 0.9565 | | No log | 3.872 | 484 | 0.9566 | | No log | 3.888 | 486 | 0.9566 | | No log | 3.904 | 488 | 0.9565 | | No log | 3.92 | 490 | 0.9565 | | No log | 3.936 | 492 | 0.9565 | | No log | 3.952 | 494 | 0.9564 | | No log | 3.968 | 496 | 0.9564 | | No log | 3.984 | 498 | 0.9564 | | 0.814 | 4.0 | 500 | 0.9563 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

ws11yrin/Huggy-ppo-mlagents

ws11yrin

2024-04-19T13:01:30Z

4

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]

reinforcement-learning

2024-04-19T13:00:46Z

--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # **ppo** Agent playing **Huggy** This is a trained model of a **ppo** agent playing **Huggy** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: u8208306949/mlagents-ppo-Huggy 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Jacque008/unsloth-llama-3-8b-instruct_4396_ori_refer_fwd_epoch2_merge

Jacque008

2024-04-19T13:01:30Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct", "base_model:finetune:unsloth/llama-3-8b-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-04-19T13:01:02Z

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct --- # Uploaded model - **Developed by:** Jacque008 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

indiehackers/gemma-telugu-instruct-test1

indiehackers

2024-04-19T12:58:47Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:unsloth/gemma-7b-it-bnb-4bit", "base_model:finetune:unsloth/gemma-7b-it-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-04-19T12:58:39Z

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

larrydai/peft-starcoder-lora-a100

larrydai

2024-04-19T12:58:09Z

1

0

peft

[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:bigcode/starcoderbase-1b", "base_model:adapter:bigcode/starcoderbase-1b", "license:bigcode-openrail-m", "region:us" ]

null

2024-04-19T09:29:44Z

--- license: bigcode-openrail-m library_name: peft tags: - generated_from_trainer base_model: bigcode/starcoderbase-1b model-index: - name: peft-starcoder-lora-a100 results: [] ---  # peft-starcoder-lora-a100 This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9744 ## 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: 0.0005 - 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: cosine - lr_scheduler_warmup_steps: 30 - training_steps: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.7228 | 0.5 | 100 | 1.0017 | | 0.8952 | 1.0 | 200 | 0.9744 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

Raul569/oufit_recommender_19_Apr_2024_v1

Raul569

2024-04-19T12:51:21Z

0

transformers

[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-04-19T12:51:20Z

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

Raul569/SFTOpenLM-Dolly15k

Raul569

2024-04-19T12:51:07Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:openlm-research/open_llama_3b", "base_model:adapter:openlm-research/open_llama_3b", "license:apache-2.0", "region:us" ]

null

2024-04-19T12:50:45Z

--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: openlm-research/open_llama_3b model-index: - name: SFTOpenLM-Dolly15k results: [] ---  # SFTOpenLM-Dolly15k This model is a fine-tuned version of [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_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: 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: linear - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.1.0+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1

choprahetarth/starcoder2

choprahetarth

2024-04-19T12:49:08Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:bigcode/starcoder2-3b", "base_model:adapter:bigcode/starcoder2-3b", "license:bigcode-openrail-m", "region:us" ]

null

2024-04-19T12:41:24Z

--- license: bigcode-openrail-m library_name: peft tags: - trl - sft - generated_from_trainer base_model: bigcode/starcoder2-3b model-index: - name: starcoder2 results: [] ---  # starcoder2 This model is a fine-tuned version of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) 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: 0.0002 - train_batch_size: 10 - eval_batch_size: 8 - seed: 1234 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 160 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 343 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

thusinh1969/LLaMA-2-finetune-100k-CP45600-19APRIL2024

thusinh1969

2024-04-19T12:46:43Z

6

0

transformers

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

text-generation

2024-04-19T12:44:08Z

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

jgrc3/unipelt_adapter_classification_trained_lr0_0001

jgrc3

2024-04-19T12:43:41Z

0

adapter-transformers

[ "adapter-transformers", "roberta", "dataset:BigTMiami/amazon_helpfulness", "region:us" ]

null

2024-04-19T12:43:38Z

--- tags: - adapter-transformers - roberta datasets: - BigTMiami/amazon_helpfulness --- # Adapter `jgrc3/unipelt_adapter_classification_trained_lr0_0001` for roberta-base An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification. This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") adapter_name = model.load_adapter("jgrc3/unipelt_adapter_classification_trained_lr0_0001", source="hf", set_active=True) ``` ## Architecture & Training  ## Evaluation results  ## Citation

erlend123/emotion-analysis-binary-trans

erlend123

2024-04-19T12:43:04Z

7

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-04-19T11:42:45Z

--- tags: - generated_from_trainer model-index: - name: emotion-analysis-ntnu results: [] ---  # emotion-analysis-ntnu This model was trained from scratch 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

DeepMount00/OCR_corrector

DeepMount00

2024-04-19T12:41:51Z

151

15

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "it", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2024-04-10T05:50:46Z

--- language: - it license: apache-2.0 library_name: transformers --- # Italian OCR Error Correction Sequence-to-Sequence Model ## Model Details This model represents the first version of an experimental sequence-to-sequence architecture designed specifically for the Italian language. It aims to correct approximately 93% of the errors generated by low-quality Optical Character Recognition (OCR) systems, which tend to perform poorly on Italian text. By taking raw, OCR-scanned text as input, the model outputs the corrected version of the text, significantly reducing errors and improving readability and accuracy. ## Intended Use - **Primary Use**: This model is intended for use in processing and correcting Italian text that has been digitized using OCR technology. It is particularly useful for texts scanned at low quality, where the OCR's error rate is noticeably high. - **Users**: It is designed for developers, researchers, and archivists working with Italian historical documents, books, and any digitized material where OCR errors are prevalent. ## Limitations - While the model corrects approximately 93% of OCR errors, there may be certain types of errors or specific contexts where its performance could be lower. - The model is specifically trained on Italian text and may not perform well on texts in other languages or texts that include significant amounts of non-Italian languages. ## How to Use ```python import torch from transformers import AutoTokenizer, AutoModelForSeq2SeqLM device = "cuda" if torch.cuda.is_available() else "cpu" MODEL_NAME = "DeepMount00/OCR_corrector" model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME).eval() tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) model.to(device) my_text = "In un'epca lontnaa, un re goernava le sue tere con saggez2a e giustiia. Sotot il suo regno, il rgeno prosperava e la getne era flice. Ma un gionro, un drgoa feroce attcò il regno, semniando ditruzione e paurra tra i suoi abtanti." inputs = tokenizer(my_text, return_tensors="pt").to(device) outputs = model.generate(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], num_beams=2, max_length=1050, top_k=10) clean_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(clean_text) ```

namkyeong/facebook_wav2vec2-xls-r-300m_meet_tr_p_10_30h

namkyeong

2024-04-19T12:35:04Z

9

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-04-17T05:49:21Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: facebook_wav2vec2-xls-r-300m_meet_tr_p_10_30h results: [] ---  # facebook_wav2vec2-xls-r-300m_meet_tr_p_10_30h This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5611 - Cer: 0.1334 ## 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: 7 - eval_batch_size: 56 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.2651 | 0.21 | 500 | 5.9159 | 1.0 | | 2.8965 | 0.43 | 1000 | 2.0493 | 0.4184 | | 1.3122 | 0.64 | 1500 | 1.0627 | 0.2433 | | 1.0682 | 0.85 | 2000 | 0.8674 | 0.2083 | | 0.9281 | 1.07 | 2500 | 0.7413 | 0.1749 | | 0.8103 | 1.28 | 3000 | 0.7032 | 0.1652 | | 0.7823 | 1.5 | 3500 | 0.6806 | 0.1616 | | 0.7429 | 1.71 | 4000 | 0.6430 | 0.1547 | | 0.7375 | 1.92 | 4500 | 0.6253 | 0.1533 | | 0.6299 | 2.14 | 5000 | 0.6375 | 0.1423 | | 0.5801 | 2.35 | 5500 | 0.6086 | 0.1398 | | 0.5735 | 2.56 | 6000 | 0.5808 | 0.1394 | | 0.5448 | 2.78 | 6500 | 0.5736 | 0.1351 | | 0.5555 | 2.99 | 7000 | 0.5611 | 0.1334 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.15.2

fangzhaoz/hellaswag_dora_llama_merged

fangzhaoz

2024-04-19T12:34:25Z

5

0

transformers

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

text-generation

2024-04-19T12:31:30Z

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

aamonten/gemma-2b-danish-chatml

aamonten

2024-04-19T12:31:01Z

5

0

transformers

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

text-generation

2024-04-19T12:27:44Z

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

tb2pi-persistent/Llama-2-7b-chat-hf-tb2pi-merged-v7

tb2pi-persistent

2024-04-19T12:27:33Z

4

0

transformers

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

text-generation

2024-04-18T12:55:19Z

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

madelineoliver/ToolsBaer-EML-to-Office-365-Importer

madelineoliver

2024-04-19T12:14:55Z

0

null

[ "region:us" ]

null

2024-04-19T12:14:25Z

Users can export EML files into Office 365 formats using the ToolsBaer EML to Office 365 Migrator Software. Programs that convert EML to Office 365 can easily handle EML files of any size or quality. Throughout the conversion process, there is no loss of data or damage to the original files. This program can be utilized without the need for previous technological expertise because of its user-friendly interface. The program has two distinct filtration feature types, both of which are safe to use. The File Mode lets users export individual EML files from local folders, while the Folder Mode lets users upload the data of certain folders that store EML files. It has undergone many tests, all of which have shown it to be a secure utility. The email attributes that are exported by the program include name, CC, BCC, to, from, hyperlinks, photos, and attachments. EML files that are imported to an Office 365 account can be utilized with Windows 11, Windows 8.1, Windows 8, Windows 7, XP, Vista, and other operating systems. Download the free trial version of the program and install it on your Windows system. Read More:- http://www.toolsbaer.com/eml-to-office-365-conversion/

amine-01/Pyramids

amine-01

2024-04-19T12:13:54Z

0

ml-agents

[ "ml-agents", "tensorboard", "onnx", "Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]

reinforcement-learning

2024-04-19T11:01:14Z

--- library_name: ml-agents tags: - Pyramids - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids --- # **ppo** Agent playing **Pyramids** This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: amine-01/Pyramids 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

Woutervdbos/ppo-LunarLander-v2

Woutervdbos

2024-04-19T12:11:16Z

1

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2024-04-19T12:10:55Z

--- 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: 269.86 +/- 10.81 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 ... ```

manishiitg/open-aditi-v6-gemma

manishiitg

2024-04-19T12:08:14Z

0

peft

[ "peft", "safetensors", "gemma", "axolotl", "generated_from_trainer", "license:gemma", "region:us" ]

null

2024-04-07T15:24:55Z

--- license: gemma library_name: peft tags: - axolotl - generated_from_trainer base_model: google/gemma-7B model-index: - name: open-aditi-chat-hi-1.25-gemma results: [] --- Preview of dataset trained on: https://huggingface.co/datasets/manishiitg/aditi-syn-v2 The synthetic dataset (https://huggingface.co/datasets/manishiitg/aditi-syn-v2) and the full data creation pipeline (https://github.com/manishiitg/aditi_dataset) have been open-sourced, enabling transparency and fostering further research in this domain. The dataset is a rich tapestry of Hinglish (a blend of Hindi and English) data, as well as a diverse array of tasks spanning tools, retrieval-augmented generation (RAG), mathematics, and reasoning – all in the Hindi language. LMJudge Eval ============ https://github.com/manishiitg/IndicLMJudge #### LLM Judge Language: hi | Model | Language | Score | No# Questions | | --- | --- | --- | --- | | mistralai/Mixtral-8x7B-Instruct-v0.1 | hi | 8.7148 | 554 | | Qwen/Qwen1.5-72B-Chat-AWQ | hi | 8.3695 | 554 | | manishiitg/open-aditi-v6-llama3 | hi | 8.2659 | 551 | | Qwen/Qwen1.5-14B-Chat | hi | 8.2404 | 554 | | google/gemma-7b-it | hi | 7.9152 | 554 | | manishiitg/open-aditi-v6-gemma | hi | 7.8634 | 549 | | Qwen/Qwen1.5-7B-Chat | hi | 7.8587 | 554 | | manishiitg/open-aditi-hi-v3 | hi | 7.7644 | 554 | | manishiitg/open-aditi-hi-v4 | hi | 7.6150 | 554 | | manishiitg/open-aditi-hi-v2 | hi | 7.2518 | 554 | | teknium/OpenHermes-2.5-Mistral-7B | hi | 7.2489 | 554 | | ai4bharat/Airavata | hi | 6.9468 | 554 | | 01-ai/Yi-34B-Chat | hi | 6.5801 | 554 | | manishiitg/open-aditi-hi-v1 | hi | 4.7022 | 554 | | sarvamai/OpenHathi-7B-Hi-v0.1-Base | hi | 4.2834 | 598 | | Qwen/Qwen1.5-4B-Chat | hi | 4.1101 | 554 | #### LLM Judge Language: en | Model | Language | Score | No# Questions | | --- | --- | --- | --- | | Qwen/Qwen1.5-14B-Chat | en | 9.1947 | 356 | | Qwen/Qwen1.5-72B-Chat-AWQ | en | 9.1618 | 356 | | Qwen/Qwen1.5-7B-Chat | en | 9.1570 | 356 | | 01-ai/Yi-34B-Chat | en | 9.1368 | 356 | | mistralai/Mixtral-8x7B-Instruct-v0.1 | en | 9.1306 | 356 | | manishiitg/open-aditi-v6-gemma | en | 9.1003 | 356 | | teknium/OpenHermes-2.5-Mistral-7B | en | 9.0230 | 356 | | manishiitg/open-aditi-v6-llama3 | en | 9.0197 | 356 | | manishiitg/open-aditi-hi-v3 | en | 8.9615 | 356 | | manishiitg/open-aditi-hi-v4 | en | 8.9188 | 356 | | google/gemma-7b-it | en | 8.8191 | 356 | | Qwen/Qwen1.5-4B-Chat | en | 8.7500 | 356 | | google/gemma-2b-it | en | 8.4671 | 356 | | manishiitg/open-aditi-hi-v2 | en | 8.4584 | 356 | | ai4bharat/Airavata | en | 7.3834 | 356 | | manishiitg/open-aditi-hi-v1 | en | 6.6559 | 356 | | sarvamai/OpenHathi-7B-Hi-v0.1-Base | en | 5.9567 | 312 | DHARMA TINY EVAL ============ #### Language Hi | Model | ARC-Easy | bigbench | truthful_qa | BoolQ | winogrande | agieval | ARC-Challenge | MMLU | openbookqa | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | open-aditi-hi-v2 | 0.6245 | 0.4959 | 0.3866 | 0.7192 | 0.5353 | 0.2945 | 0.4828 | 0.3457 | 0.5279 | | open-aditi-hi-v3 | 0.6803 | 0.4553 | 0.2788 | 0.7385 | 0.5390 | 0.2178 | 0.4914 | 0.3346 | 0.5688 | | open-aditi-hi-v4 | 0.6989 | 0.4526 | 0.2714 | 0.7231 | 0.5167 | 0.2331 | 0.5302 | 0.3123 | 0.5316 | | open-aditi-v6-gemma | 0.7212 | 0.4146 | 0.3234 | 0.6923 | 0.4870 | 0.2638 | 0.4957 | 0.3680 | 0.4349 | | open-aditi-v6-llama3 | 0.5688 | 0.4119 | 0.2268 | 0.6500 | 0.4498 | 0.2331 | 0.4310 | 0.3420 | 0.3792 | | open-aditi-hi-v1 | 0.4572 | 0.3767 | 0.2230 | 0.6346 | 0.4647 | 0.1840 | 0.3405 | 0.3271 | 0.3532 | | OpenHermes-2.5-Mistral-7B | 0.3309 | 0.4201 | 0.3197 | 0.6077 | 0.4981 | 0.2331 | 0.3276 | 0.3086 | 0.3086 | | OpenHathi-7B-Hi-v0.1-Base | 0.2862 | 0.3333 | 0.5130 | 0.6077 | 0.4907 | 0.2301 | 0.3017 | 0.2677 | 0.1933 | | Airavata | 0.2751 | 0.1274 | 0.2268 | 0.0615 | 0.3866 | 0.1104 | 0.2845 | 0.1450 | 0.3383 | | gemma-7b-it | 0.1227 | 0.0786 | 0.0743 | 0.1808 | 0.1561 | 0.0491 | 0.1078 | 0.0818 | 0.0855 | #### Language En | Model | ARC-Easy | bigbench | truthful_qa | BoolQ | winogrande | agieval | ARC-Challenge | MMLU | openbookqa | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | OpenHermes-2.5-Mistral-7B | 0.8922 | 0.5745 | 0.3197 | 0.8346 | 0.6989 | 0.4908 | 0.7802 | 0.5911 | 0.7621 | | open-aditi-hi-v2 | 0.8625 | 0.5149 | 0.3532 | 0.8192 | 0.6877 | 0.4571 | 0.7500 | 0.5613 | 0.7732 | | open-aditi-hi-v4 | 0.8959 | 0.5041 | 0.2862 | 0.8423 | 0.6914 | 0.4571 | 0.7716 | 0.5651 | 0.7138 | | open-aditi-hi-v3 | 0.8773 | 0.4986 | 0.3048 | 0.8385 | 0.6766 | 0.4663 | 0.7371 | 0.5613 | 0.7249 | | Qwen1.5-7B-Chat | 0.8922 | 0.5122 | 0.2007 | 0.8000 | 0.6654 | 0.4294 | 0.7759 | 0.5799 | 0.7621 | | open-aditi-v6-gemma | 0.8699 | 0.4959 | 0.2602 | 0.7385 | 0.5465 | 0.4540 | 0.7371 | 0.5167 | 0.6654 | | open-aditi-v6-llama3 | 0.8810 | 0.4634 | 0.1822 | 0.7577 | 0.5353 | 0.4110 | 0.7457 | 0.5688 | 0.6506 | | open-aditi-hi-v1 | 0.8104 | 0.3902 | 0.2491 | 0.6962 | 0.5539 | 0.3681 | 0.6379 | 0.5056 | 0.5911 | | Airavata | 0.7026 | 0.4282 | 0.3123 | 0.7192 | 0.5651 | 0.3313 | 0.5172 | 0.3792 | 0.5093 | | OpenHathi-7B-Hi-v0.1-Base | 0.4684 | 0.3062 | 0.4758 | 0.6346 | 0.5167 | 0.2577 | 0.3017 | 0.2788 | 0.2714 | Task: BoolQ Metric: score Task: ARC-Easy Metric: score Task: openbookqa Metric: score Task: winogrande Metric: score Task: ARC-Challenge Metric: score Task: truthful_qa Metric: score Task: bigbench Metric: score Task: MMLU Metric: score Task: agieval Metric: score  [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: google/gemma-7B model_type: AutoModelForCausalLM tokenizer_type: AutoTokenizer tokenizer_config: philschmid/gemma-tokenizer-chatml tokenizer_use_fast: true load_in_8bit: false load_in_4bit: true strict: false datasets: - path: manishiitg/aditi-syn-train-small-v3 type: completion # 25 has only sythentic data, and has judge removed data hub_model_id: manishiitg/open-aditi-chat-hi-1.25-gemma hf_use_auth_token: true wandb_project: open-aditi-chat-hi-1.25-gemma dataset_prepared_path: manishiitg push_dataset_to_hub: manishiitg val_set_size: .1 output_dir: /sky-notebook/manishiitg/open-aditi-chat-hi-1.25-gemma adapter: qlora lora_model_dir: save_safetensors: true sequence_len: 2048 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true wandb_entity: wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 4 num_epochs: 1 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 adam_beta2: 0.95 adam_epsilon: 0.00001 max_grad_norm: 1.0 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: auto_resume_from_checkpoints: true ## manage check point resume from here local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 2 eval_table_size: eval_table_max_new_tokens: 128 save_steps: 20 ## increase based on your dataset save_strategy: steps debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: ``` </details> # open-aditi-chat-hi-1.25-gemma This model is a fine-tuned version of [google/gemma-7B](https://huggingface.co/google/gemma-7B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0992 ## 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: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.8213 | 0.0 | 1 | 8.4429 | | 0.9759 | 0.5 | 121 | 2.0992 | ### Framework versions - PEFT 0.9.0 - Transformers 4.40.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0

jorgefg03/deberta-v3-base-autext

jorgefg03

2024-04-19T12:05:43Z

5

0

transformers

[ "transformers", "safetensors", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-04-19T11:18:04Z

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

leliuga/gemma-7b-it-bnb-4bit

leliuga

2024-04-19T12:04:18Z

4

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "conversational", "base_model:google/gemma-7b-it", "base_model:quantized:google/gemma-7b-it", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]

text-generation

2024-03-18T20:56:54Z

--- base_model: google/gemma-7b-it license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false model_creator: Google model_name: gemma-7b-it quantized_by: Leliuga pipeline_tag: text-generation tags: - gemma - arxiv:1910.09700 --- # gemma-7b-it - bnb 4bit - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-7b-it](https://huggingface.co/google/gemma-7b-it) ## Description This model is 4bit quantized version of [gemma-7b-it](https://huggingface.co/google/gemma-7b-it) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<pad>".

leliuga/gemma-7b-bnb-4bit

leliuga

2024-04-19T12:04:01Z

6

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "base_model:google/gemma-7b", "base_model:quantized:google/gemma-7b", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]

text-generation

2024-03-18T20:39:10Z

--- base_model: google/gemma-7b license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false model_creator: Google model_name: gemma-7b quantized_by: Leliuga pipeline_tag: text-generation tags: - gemma - arxiv:1910.09700 --- # gemma-7b - bnb 4bit - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-7b](https://huggingface.co/google/gemma-7b) ## Description This model is 4bit quantized version of [gemma-7b](https://huggingface.co/google/gemma-7b) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as <<pad>>.

leliuga/gemma-2b-it-bnb-4bit

leliuga

2024-04-19T12:03:37Z

9

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "conversational", "base_model:google/gemma-2b-it", "base_model:quantized:google/gemma-2b-it", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]

text-generation

2024-03-18T20:34:10Z

--- base_model: google/gemma-2b-it license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false model_creator: Google model_name: gemma-2b-it quantized_by: Leliuga pipeline_tag: text-generation tags: - gemma - arxiv:1910.09700 --- # gemma-2b-it - bnb 4bit - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) ## Description This model is 4bit quantized version of [gemma-2b-it](https://huggingface.co/google/gemma-2b-it) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<pad>".

kiroloskhela/Sentiment-Bert

kiroloskhela

2024-04-19T12:03:25Z

18

1

transformers

[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-04-05T07:19:13Z

# Emotion Analysis Model (Arabic) This repository contains a model for emotion analysis in Arabic text. The model predicts the emotion associated with a given text input. It was developed by Kirolos Amgad Khela and trained on the Emotone dataset and the SetFit/Emotion dataset after transcribing text to Arabic. ## Model Details - **Model type:** Text classification - **Language(s):** Arabic - **Finetuned from model:** MarBert ## Usage ### Direct Use The model can be directly used for emotion analysis. It takes text inputs in Arabic and predicts the corresponding emotion associated with the text. ## Training Details ### Training Data The model was trained using the Emotone dataset and the SetFit/Emotion dataset after translate text to Arabic. ### Training Procedure #### Preprocessing The training data was preprocessed by cleaning the text from English and removing punctuations. #### Training Hyperparameters - **Output Directory:** "train" - **Logging Directory:** "logs" - **Evaluation Strategy:** "epoch" - **Per Device Train Batch Size:** 32 - **Per Device Eval Batch Size:** 32 - **Gradient Accumulation Steps:** 1 - **Number of Train Epochs:** 3 - **Learning Rate:** 3e-5 - **Warmup Ratio:** 0.1 - **Weight Decay:** 0.01 - **Adam Beta1:** 0.9 - **Adam Beta2:** 0.999 - **Adam Epsilon:** 1e-8 - **FP16:** True - **Save Strategy:** "epoch" - **Save Total Limit:** 3 - **Load Best Model at End:** True - **Metric for Best Model:** "macro_f1" - **Greater is Better:** True ## Dataset The Emotone dataset and the SetFit/Emotion dataset are available for training. These datasets contain Arabic text annotated with emotion labels. ## Fine-tuning in Colab To fine-tune the model on a custom dataset, you can use the provided Colab notebook. Follow the steps outlined in the notebook to upload your dataset, configure the training parameters, and start the fine-tuning process. ## Fine-tuned Model and Dataset The fine-tuned model files and dataset are available in this [Google Drive folder](https://drive.google.com/drive/folders/1UWcBal3Myn4SipHhIX9bNpmbkQsO0Yow?usp=sharing). You can download the necessary files from this folder. ## Accuracy The model achieved an accuracy of 82% on the evaluation dataset. ## Authors - Kirolos Amgad Khela ## Contact For any inquiries about the model, please contact Kirolos Amgad Khela at [email protected].

leliuga/gemma-2b-bnb-4bit

leliuga

2024-04-19T12:03:22Z

18

0

transformers

[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "base_model:google/gemma-2b", "base_model:quantized:google/gemma-2b", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]

text-generation

2024-03-18T20:19:24Z

--- base_model: google/gemma-2b license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms inference: false model_creator: Google model_name: gemma-2b quantized_by: Leliuga pipeline_tag: text-generation tags: - gemma - arxiv:1910.09700 --- # gemma-2b - bnb 4bit - Model creator: [Google](https://huggingface.co/google) - Original model: [gemma-2b](https://huggingface.co/google/gemma-2b) ## Description This model is 4bit quantized version of [gemma-2b](https://huggingface.co/google/gemma-2b) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as "<pad>".

leliuga/mistral-7b-instruct-v0.1-bnb-4bit

leliuga

2024-04-19T12:01:31Z

75

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:2310.06825", "arxiv:1910.09700", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]

text-generation

2024-03-17T21:35:43Z

--- base_model: mistralai/Mistral-7B-Instruct-v0.1 license: apache-2.0 inference: false model_creator: Mistral AI_ model_name: Mistral-7B-Instruct-v0.1 quantized_by: Leliuga pipeline_tag: text-generation tags: - mistral - arxiv:2310.06825 - arxiv:1910.09700 --- # Mistral-7B-Instruct-v0.1 - bnb 4bit - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) ## Description This model is 4bit quantized version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.

leliuga/Mistral-7B-v0.1-bnb-4bit

leliuga

2024-04-19T12:01:12Z

4

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:2310.06825", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "base_model:quantized:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]

text-generation

2024-03-18T21:51:32Z

--- base_model: mistralai/Mistral-7B-v0.1 license: apache-2.0 inference: false model_creator: Mistral AI_ model_name: Mistral-7B-v0.1 quantized_by: Leliuga pipeline_tag: text-generation tags: - mistral - arxiv:2310.06825 - arxiv:1910.09700 --- # Mistral-7B-v0.1 - bnb 4bit - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) ## Description This model is 4bit quantized version of [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.

leliuga/Llama-2-13b-chat-hf-bnb-4bit

leliuga

2024-04-19T11:59:53Z

75

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "llama-2", "arxiv:2307.09288", "arxiv:1910.09700", "conversational", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:quantized:meta-llama/Llama-2-13b-chat-hf", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]

text-generation

2024-03-17T22:28:16Z

--- base_model: meta-llama/Llama-2-13b-chat-hf license: apache-2.0 inference: false model_creator: Meta model_name: Llama-2-13b-chat-hf quantized_by: Leliuga pipeline_tag: text-generation tags: - llama-2 - arxiv:2307.09288 - arxiv:1910.09700 --- # Llama-2-13b-chat-hf - bnb 4bit - Model creator: [Meta](https://huggingface.co/meta-llama) - Original model: [Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) ## Description This model is 4bit quantized version of [Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.

leliuga/Llama-2-13b-hf-bnb-4bit

leliuga

2024-04-19T11:59:30Z

5

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "llama-2", "arxiv:2307.09288", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-13b-hf", "base_model:quantized:meta-llama/Llama-2-13b-hf", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "region:us" ]

text-generation

2024-03-17T22:59:01Z

--- base_model: meta-llama/Llama-2-13b-hf license: apache-2.0 inference: false model_creator: Meta model_name: Llama-2-13b-hf quantized_by: Leliuga pipeline_tag: text-generation tags: - llama-2 - arxiv:2307.09288 - arxiv:1910.09700 --- # Llama-2-13b-hf - bnb 4bit - Model creator: [Meta](https://huggingface.co/meta-llama) - Original model: [Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) ## Description This model is 4bit quantized version of [Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) using bitsandbytes. It's designed for fine-tuning! The PAD token is set as UNK.

AdinaM/hackathon_datacrowd

AdinaM

2024-04-19T11:42:23Z

4

0

transformers

[ "transformers", "safetensors", "llama", "moe", "fr", "it", "de", "es", "en", "license:apache-2.0", "model-index", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2024-04-18T14:33:05Z

--- language: - fr - it - de - es - en license: apache-2.0 tags: - moe model-index: - name: Mixtral-8x22B-v0.1 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: 70.48 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 88.73 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 77.81 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 51.08 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 84.53 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 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: 74.15 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mistral-community/Mixtral-8x22B-v0.1 name: Open LLM Leaderboard --- # Mixtral-8x22B > [!TIP] > MistralAI has uploaded weights to their organization at [mistralai/Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) and [mistralai/Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1) too. > [!TIP] > Kudos to [@v2ray](https://huggingface.co/v2ray) for converting the checkpoints and uploading them in `transformers` compatible format. Go give them a follow! Converted to HuggingFace Transformers format using the script [here](https://huggingface.co/v2ray/Mixtral-8x22B-v0.1/blob/main/convert.py). The Mixtral-8x22B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. ## Run the model ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistral-community/Mixtral-8x22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) text = "Hello my name is" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem: ### In half-precision Note `float16` precision only works on GPU devices <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistral-community/Mixtral-8x22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16).to(0) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Lower precision using (8-bit & 4-bit) using `bitsandbytes` <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistral-community/Mixtral-8x22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ### Load the model with Flash Attention 2 <details> <summary> Click to expand </summary> ```diff + import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "mistral-community/Mixtral-8x22B-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_id) + model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True) text = "Hello my name is" + inputs = tokenizer(text, return_tensors="pt").to(0) outputs = model.generate(**inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` </details> ## Notice Mixtral-8x22B-v0.1 is a pretrained base model and therefore does not have any moderation mechanisms. # The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall. # [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_mistral-community__Mixtral-8x22B-v0.1) | Metric |Value| |---------------------------------|----:| |Avg. |74.46| |AI2 Reasoning Challenge (25-Shot)|70.48| |HellaSwag (10-Shot) |88.73| |MMLU (5-Shot) |77.81| |TruthfulQA (0-shot) |51.08| |Winogrande (5-shot) |84.53| |GSM8k (5-shot) |74.15|

Yakonrus/SDXL_Controlnet_Tile_Realistic_v2

Yakonrus

2024-04-19T11:33:29Z

303

1

diffusers

[ "diffusers", "Controlnet", "Tile", "stable diffustion", "image-feature-extraction", "license:openrail", "region:us" ]

image-feature-extraction

2024-04-19T11:26:55Z

--- library_name: diffusers pipeline_tag: image-feature-extraction tags: - Controlnet - Tile - stable diffustion license: openrail --- This is just a renamed copy of the original controlnet https://huggingface.co/TTPlanet/TTPLanet_SDXL_Controlnet_Tile_Realistic (V2) that works out of the box with diffusers in fp16 mode. ```py controlnet = ControlNetModel.from_pretrained( "Yakonrus/SDXL_Controlnet_Tile_Realistic_v2", torch_dtype=torch.float16, variant="fp16" ) ```

adammoss/gpt-pretrain-lm-w1

adammoss

2024-04-19T11:30:20Z

5

0

transformers

[ "transformers", "safetensors", "gptmodel", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-04-18T14:42:22Z

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

pavi156/my_awesome_qa_model

pavi156

2024-04-19T11:28:38Z

5

0

transformers

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

question-answering

2024-04-18T13:28:56Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: my_awesome_qa_model results: [] ---  # my_awesome_qa_model 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: 1.9998 ## 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: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 250 | 2.7540 | | 2.9386 | 2.0 | 500 | 2.1089 | | 2.9386 | 3.0 | 750 | 1.9998 | ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.15.2

jorgefg03/bert-base-uncased-autext

jorgefg03

2024-04-19T11:22:39Z

5

0

transformers

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

text-classification

2024-04-19T09:45:48Z

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

automerger/Multiverseex26Meliodaspercival_01_experiment26t3q-7B

automerger

2024-04-19T11:20:12Z

0

null

[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:MaziyarPanahi/MeliodasPercival_01_Experiment26T3q", "base_model:merge:MaziyarPanahi/MeliodasPercival_01_Experiment26T3q", "base_model:allknowingroger/MultiverseEx26-7B-slerp", "base_model:merge:allknowingroger/MultiverseEx26-7B-slerp", "license:apache-2.0", "region:us" ]

null

2024-04-19T11:20:11Z

--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - allknowingroger/MultiverseEx26-7B-slerp - MaziyarPanahi/MeliodasPercival_01_Experiment26T3q --- # Multiverseex26Meliodaspercival_01_experiment26t3q-7B Multiverseex26Meliodaspercival_01_experiment26t3q-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration. * [allknowingroger/MultiverseEx26-7B-slerp](https://huggingface.co/allknowingroger/MultiverseEx26-7B-slerp) * [MaziyarPanahi/MeliodasPercival_01_Experiment26T3q](https://huggingface.co/MaziyarPanahi/MeliodasPercival_01_Experiment26T3q) ## 🧩 Configuration ```yaml slices: - sources: - model: allknowingroger/MultiverseEx26-7B-slerp layer_range: [0, 32] - model: MaziyarPanahi/MeliodasPercival_01_Experiment26T3q layer_range: [0, 32] merge_method: slerp base_model: allknowingroger/MultiverseEx26-7B-slerp parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "automerger/Multiverseex26Meliodaspercival_01_experiment26t3q-7B" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

MelanieKoe/w2v2-base-pretrained_lr5e-5_at1_da1-p4

MelanieKoe

2024-04-19T11:16:41Z

6

0

transformers

[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-04-19T05:35:32Z

--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - wer model-index: - name: w2v2-base-pretrained_lr5e-5_at1_da1-p4 results: [] ---  # w2v2-base-pretrained_lr5e-5_at1_da1-p4 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6341 - Wer: 0.1039 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 18.6256 | 5.21 | 250 | 4.2622 | 1.0 | | 3.3901 | 10.42 | 500 | 3.2209 | 1.0 | | 3.0963 | 15.62 | 750 | 3.1175 | 1.0 | | 2.0992 | 20.83 | 1000 | 0.5962 | 0.4402 | | 0.2069 | 26.04 | 1250 | 0.4456 | 0.1310 | | 0.0849 | 31.25 | 1500 | 0.4902 | 0.1200 | | 0.0596 | 36.46 | 1750 | 0.5079 | 0.1176 | | 0.0437 | 41.67 | 2000 | 0.5362 | 0.1136 | | 0.0355 | 46.88 | 2250 | 0.5433 | 0.1156 | | 0.0281 | 52.08 | 2500 | 0.5994 | 0.1136 | | 0.0238 | 57.29 | 2750 | 0.6018 | 0.1112 | | 0.02 | 62.5 | 3000 | 0.5970 | 0.1120 | | 0.0181 | 67.71 | 3250 | 0.6282 | 0.1083 | | 0.0167 | 72.92 | 3500 | 0.6120 | 0.1075 | | 0.0145 | 78.12 | 3750 | 0.6404 | 0.1047 | | 0.014 | 83.33 | 4000 | 0.6341 | 0.1039 | ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.14.6 - Tokenizers 0.14.1

reecursion/stress-RoBERTa

reecursion

2024-04-19T11:16:38Z

5

0

transformers

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

text-classification

2024-04-19T11:15:55Z

--- license: mit base_model: FacebookAI/roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-chatbot-stress results: [] ---  # roberta-chatbot-stress This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9676 - Accuracy: 0.8275 ## 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 - lr_scheduler_warmup_steps: 500 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4295 | 1.0 | 249 | 0.4018 | 0.8157 | | 0.4911 | 2.0 | 498 | 0.4656 | 0.8039 | | 0.472 | 3.0 | 747 | 0.6054 | 0.8431 | | 0.1464 | 4.0 | 996 | 0.9441 | 0.8157 | | 0.08 | 5.0 | 1245 | 0.9676 | 0.8275 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

RJ3vans/CMV1spanTagger

RJ3vans

2024-04-19T11:13:24Z

105

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-02T23:29:04Z

This model identifies compound verb phrases (including conjoins and coordinators) in an input sentence. Try the test sentence: John kicked the ball [and] chased after it. Please note that it is necessary for you to highlight the verb phrase coordinator "and" using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger. The model should tag the tokens in the sentence with information about whether or not they are contained within a compound verb phrase. If you find the model useful, please cite my thesis which presents the dataset used for finetuning: Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf) There you will find more information about the tagging scheme. The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton.

RJ3vans/CMN1spanTagger

RJ3vans

2024-04-19T11:11:21Z

8

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-03-02T23:29:04Z

This model identifies compound noun phrases in an input sentence. Try the test sentence: The inquiry, which continues, will recall John Smith [and] Peter Montgomery next month for further questioning. Please note that it is necessary for you to highlight the noun phrase coordinator "and" using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger. The model should tag the tokens in the sentence with information about whether or not they are contained within a compound noun phrase. If you find the model useful, please cite my thesis which presents the dataset used for finetuning: Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf) There you will find more information about the tagging scheme. The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton.

RJ3vans/SSCCVspanTagger

RJ3vans

2024-04-19T11:03:19Z

53

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-05-17T14:59:38Z

Try the test sentences: My name is Sarah and I live in London[, which] is the largest city in the UK. John thought [that] that was a strange idea. It was on Tuesdays [when] Peter took Tess for a walk. John was so large [that] he had to crouch to fit through the front door. Please note that it is necessary for you to highlight the left clause boundary using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger. The model should tag the tokens in the sentence with information about whether or not they are contained within particular types of syntactic constituents. If you find the model useful, please cite my thesis which presents the dataset used for finetuning: Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf) There you will find more information about the tagging scheme.

DavidAU/UNA-TheBeagle-7b-v1-Q6_K-GGUF

DavidAU

2024-04-19T11:00:32Z

3

0

transformers

[ "transformers", "gguf", "generated_from_trainer", "llama-cpp", "gguf-my-repo", "dataset:jondurbin/bagel-v0.3", "license:cc-by-nc-nd-4.0", "endpoints_compatible", "region:us" ]

null

2024-04-19T11:00:16Z

--- license: cc-by-nc-nd-4.0 library_name: transformers tags: - generated_from_trainer - llama-cpp - gguf-my-repo datasets: - jondurbin/bagel-v0.3 model-index: - name: UNA-TheBeagle-7b-v1 results: [] --- # DavidAU/UNA-TheBeagle-7b-v1-Q6_K-GGUF This model was converted to GGUF format from [`fblgit/UNA-TheBeagle-7b-v1`](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo DavidAU/UNA-TheBeagle-7b-v1-Q6_K-GGUF --model una-thebeagle-7b-v1.Q6_K.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo DavidAU/UNA-TheBeagle-7b-v1-Q6_K-GGUF --model una-thebeagle-7b-v1.Q6_K.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m una-thebeagle-7b-v1.Q6_K.gguf -n 128 ```

RJ3vans/DeBERTaSSCCVspanTagger

RJ3vans

2024-04-19T10:58:13Z

106

0

transformers

[ "transformers", "pytorch", "deberta-v2", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-07-14T15:08:04Z

Try the test sentences: My name is Sarah and I live in London[, which] is the largest city in the UK. John thought [that] that was a strange idea. It was on Tuesdays [when] Peter took Tess for a walk. John was so large [that] he had to crouch to fit through the front door. Please note that it is necessary for you to highlight the left clause boundary using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger. The model should tag the tokens in the sentence with information about whether or not they are contained within particular types of syntactic constituents. If you find the model useful, please cite my thesis which presents the dataset used for finetuning: Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf) There you will find more information about the tagging scheme.