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<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # badokorach/mobilebert-uncased-finetuned-agic-140224 This model is a fine-tuned version of [badokorach/mobilebert-uncased-finetuned-agic-181223](https://huggingface.co/badokorach/mobilebert-uncased-finetuned-agic-181223) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0044 - Validation Loss: 0.0 - Epoch: 9 ## 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: - optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': None, 'class_name': 'CustomLearningRateScheduler', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2736, 'warmup_steps': 304, 'end_learning_rate': 1e-05}, 'registered_name': 'CustomLearningRateScheduler'}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 2.0770 | 0.0 | 0 | | 0.1217 | 0.0 | 1 | | 0.0462 | 0.0 | 2 | | 0.0279 | 0.0 | 3 | | 0.0163 | 0.0 | 4 | | 0.0149 | 0.0 | 5 | | 0.0084 | 0.0 | 6 | | 0.0086 | 0.0 | 7 | | 0.0055 | 0.0 | 8 | | 0.0044 | 0.0 | 9 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "badokorach/mobilebert-uncased-finetuned-agic-181223", "model-index": [{"name": "badokorach/mobilebert-uncased-finetuned-agic-140224", "results": []}]}
question-answering
badokorach/mobilebert-uncased-finetuned-agic-140224
[ "transformers", "tf", "mobilebert", "question-answering", "generated_from_keras_callback", "base_model:badokorach/mobilebert-uncased-finetuned-agic-181223", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-14T11:37:03+00:00
[]
[]
TAGS #transformers #tf #mobilebert #question-answering #generated_from_keras_callback #base_model-badokorach/mobilebert-uncased-finetuned-agic-181223 #license-mit #endpoints_compatible #region-us
badokorach/mobilebert-uncased-finetuned-agic-140224 =================================================== This model is a fine-tuned version of badokorach/mobilebert-uncased-finetuned-agic-181223 on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.0044 * Validation Loss: 0.0 * Epoch: 9 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: * optimizer: {'name': 'Adam', 'weight\_decay': None, 'clipnorm': None, 'global\_clipnorm': None, 'clipvalue': None, 'use\_ema': False, 'ema\_momentum': 0.99, 'ema\_overwrite\_frequency': None, 'jit\_compile': True, 'is\_legacy\_optimizer': False, 'learning\_rate': {'module': None, 'class\_name': 'CustomLearningRateScheduler', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 2736, 'warmup\_steps': 304, 'end\_learning\_rate': 1e-05}, 'registered\_name': 'CustomLearningRateScheduler'}, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} * training\_precision: mixed\_float16 ### Training results ### Framework versions * Transformers 4.35.2 * TensorFlow 2.15.0 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': None, 'class\\_name': 'CustomLearningRateScheduler', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 2736, 'warmup\\_steps': 304, 'end\\_learning\\_rate': 1e-05}, 'registered\\_name': 'CustomLearningRateScheduler'}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tf #mobilebert #question-answering #generated_from_keras_callback #base_model-badokorach/mobilebert-uncased-finetuned-agic-181223 #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': None, 'class\\_name': 'CustomLearningRateScheduler', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 2736, 'warmup\\_steps': 304, 'end\\_learning\\_rate': 1e-05}, 'registered\\_name': 'CustomLearningRateScheduler'}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 70, 304, 4, 31 ]
[ "passage: TAGS\n#transformers #tf #mobilebert #question-answering #generated_from_keras_callback #base_model-badokorach/mobilebert-uncased-finetuned-agic-181223 #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': None, 'class\\_name': 'CustomLearningRateScheduler', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 2736, 'warmup\\_steps': 304, 'end\\_learning\\_rate': 1e-05}, 'registered\\_name': 'CustomLearningRateScheduler'}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
null
kenchenxingyu/flan-large-single-label-emotion-human7
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T11:40:54+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
peft
# 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 0.7.1
{"library_name": "peft", "base_model": "deepseek-ai/deepseek-coder-33b-instruct"}
null
NikitaZagainov/notebook-generation-deepseek-33b-5ep
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:deepseek-ai/deepseek-coder-33b-instruct", "region:us" ]
2024-02-14T11:42:36+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.1
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ 45, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-deepseek-ai/deepseek-coder-33b-instruct #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1" ]
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null
null
transformers
# Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed. ```bash pip install transformers==4.29.2 pip install einops==0.6.1 pip install accelerate==0.19.0 pip install torch==2.0.0 ``` ```python import torch from transformers import pipeline generate_text = pipeline( model="Shishir1807/Moas_Explicit_OLM_v2", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <|prompt|>Why is drinking water so healthy?</s><|answer|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python import torch from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "Shishir1807/Moas_Explicit_OLM_v2", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "Shishir1807/Moas_Explicit_OLM_v2", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Shishir1807/Moas_Explicit_OLM_v2" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<|prompt|>How are you?</s><|answer|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 3200, padding_idx=0) (layers): ModuleList( (0-25): 26 x LlamaDecoderLayer( (self_attn): LlamaAttention( (q_proj): Linear(in_features=3200, out_features=3200, bias=False) (k_proj): Linear(in_features=3200, out_features=3200, bias=False) (v_proj): Linear(in_features=3200, out_features=3200, bias=False) (o_proj): Linear(in_features=3200, out_features=3200, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=3200, out_features=8640, bias=False) (down_proj): Linear(in_features=8640, out_features=3200, bias=False) (up_proj): Linear(in_features=3200, out_features=8640, bias=False) (act_fn): SiLUActivation() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=3200, out_features=32000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
{"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"}
text-generation
Shishir1807/Moas_Explicit_OLM_v2
[ "transformers", "pytorch", "llama", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]
2024-02-14T11:45:48+00:00
[]
[ "en" ]
TAGS #transformers #pytorch #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us
# Model Card ## Summary This model was trained using H2O LLM Studio. - Base model: openlm-research/open_llama_3b ## Usage To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed. You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'. You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ## Quantization and sharding You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting . ## Model Architecture ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
[ "# Model Card", "## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: openlm-research/open_llama_3b", "## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.\n\n\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:", "## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .", "## Model Architecture", "## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.", "## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it." ]
[ "TAGS\n#transformers #pytorch #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us \n", "# Model Card", "## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: openlm-research/open_llama_3b", "## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.\n\n\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:", "## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .", "## Model Architecture", "## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.", "## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it." ]
[ 59, 3, 33, 174, 34, 4, 42, 518 ]
[ "passage: TAGS\n#transformers #pytorch #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #en #autotrain_compatible #text-generation-inference #region-us \n# Model Card## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: openlm-research/open_llama_3b## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers', 'accelerate' and 'torch' libraries installed.\n\n\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .## Model Architecture## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models." ]
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null
null
peft
<!-- 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. --> # vilsonrodrigues/falcon-7b-instruct-sharded This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - 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: 0.03 - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "vilsonrodrigues/falcon-7b-instruct-sharded", "model-index": [{"name": "vilsonrodrigues/falcon-7b-instruct-sharded", "results": []}]}
null
kertob/falcon_drone_commands
[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:vilsonrodrigues/falcon-7b-instruct-sharded", "license:apache-2.0", "region:us" ]
2024-02-14T11:53:33+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #license-apache-2.0 #region-us
# vilsonrodrigues/falcon-7b-instruct-sharded This model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - 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: 0.03 - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
[ "# vilsonrodrigues/falcon-7b-instruct-sharded\n\nThis model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 250\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #license-apache-2.0 #region-us \n", "# vilsonrodrigues/falcon-7b-instruct-sharded\n\nThis model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 250\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
[ 61, 53, 6, 12, 8, 3, 141, 4, 39 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-vilsonrodrigues/falcon-7b-instruct-sharded #license-apache-2.0 #region-us \n# vilsonrodrigues/falcon-7b-instruct-sharded\n\nThis model is a fine-tuned version of vilsonrodrigues/falcon-7b-instruct-sharded on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 250\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
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transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
null
Lollitor/FineTunedMarked2
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T11:53:44+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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peft
# Model Card for Model ID This is an adapter prepared to return True or False depending on whether the student's answer ("student_answer") is correct based on the question ("question") and comparing it with a given answer ("best_answer"). The prompt has the following structure: ``` <s>[INST]Analyze the question, the expected answer, and the student's response. Determine if the student's answer is correct or not. It only returns True if the student's answer is correct with respect to the expected answer or False otherwise. Add a brief comment explaining why the answer is correct or incorrect.\n\n Question: {question}\n Expected Answer: {best_answer}\n Student Answer: {student_answer}[/INST]" ``` ## 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 In Google Colab: ``` !pip install -q -U transformers peft accelerate optimum !pip install datasets==2.15.0 !pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu117/ from peft import AutoPeftModelForCausalLM from rich import print from transformers import GenerationConfig, AutoTokenizer import torch model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ" adapter = "nmarafo/Mistral-7B-Instruct-v0.2-TrueFalse-Feedback-GPTQ" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, return_token_type_ids=False) tokenizer.pad_token = tokenizer.eos_token model = AutoPeftModelForCausalLM.from_pretrained(adapter, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map="cuda") def predict(question, best_answer, student_answer): system_message = "Analyze the question, the expected answer, and the student's response. Determine if the student's answer is conceptually correct in relation to the expected answer, regardless of the exact wording. Return True if the student's answer is correct or False otherwise. Add a brief comment explaining the rationale behind the answer being correct or incorrect." prompt = f"{system_message}\n\nQuestion: {question}\nBest Answer: {best_answer}\nStudent Answer: {student_answer}" prompt_template=f"<s>[INST]{prompt}[/INST]" encoding = tokenizer(prompt_template, return_tensors='pt', padding=True, truncation=True, max_length=512) input_ids = encoding['input_ids'].cuda() attention_mask = encoding['attention_mask'].cuda() output = model.generate(input_ids, attention_mask=attention_mask, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512, pad_token_id=tokenizer.eos_token_id) response = tokenizer.decode(output[0], skip_special_tokens=True) return response question="Mention all the Canary Island" best_answer="Tenerife, Fuerteventura, Gran Canaria, Lanzarote, La Palma, La Gomera, El Hierro, La Graciosa" student_answer="Tenerife" print(predict(question, best_answer, student_answer)) ``` # To perform inference on the test dataset example load the model from the checkpoint persisted_model = AutoPeftModelForCausalLM.from_pretrained( adapter, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map="cuda") # Some gen config knobs generation_config = GenerationConfig( penalty_alpha=0.6, do_sample = True, top_k=5, temperature=0.5, repetition_penalty=1.2, max_new_tokens=512 ) [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 0.8.2
{"language": ["en", "es"], "license": "apache-2.0", "library_name": "peft", "datasets": ["nmarafo/truthful_qa_TrueFalse-Feedback"], "base_model": "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"}
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nmarafo/Mistral-7B-Instruct-v0.2-TrueFalse-Feedback-GPTQ
[ "peft", "safetensors", "en", "es", "dataset:nmarafo/truthful_qa_TrueFalse-Feedback", "arxiv:1910.09700", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "has_space", "region:us" ]
2024-02-14T11:57:59+00:00
[ "1910.09700" ]
[ "en", "es" ]
TAGS #peft #safetensors #en #es #dataset-nmarafo/truthful_qa_TrueFalse-Feedback #arxiv-1910.09700 #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #license-apache-2.0 #has_space #region-us
# Model Card for Model ID This is an adapter prepared to return True or False depending on whether the student's answer ("student_answer") is correct based on the question ("question") and comparing it with a given answer ("best_answer"). The prompt has the following structure: ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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 In Google Colab: # To perform inference on the test dataset example load the model from the checkpoint persisted_model = AutoPeftModelForCausalLM.from_pretrained( adapter, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map="cuda") # Some gen config knobs generation_config = GenerationConfig( penalty_alpha=0.6, do_sample = True, top_k=5, temperature=0.5, repetition_penalty=1.2, max_new_tokens=512 ) ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID\n\nThis is an adapter prepared to return True or False depending on whether the student's answer (\"student_answer\") is correct based on the question (\"question\") and comparing it with a given answer (\"best_answer\").\nThe prompt has the following structure:", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\nIn Google Colab:", "# To perform inference on the test dataset example load the model from the checkpoint\npersisted_model = AutoPeftModelForCausalLM.from_pretrained(\n adapter,\n low_cpu_mem_usage=True,\n return_dict=True,\n torch_dtype=torch.float16,\n device_map=\"cuda\")", "# Some gen config knobs\ngeneration_config = GenerationConfig(\n penalty_alpha=0.6, \n do_sample = True, \n top_k=5, \n temperature=0.5, \n repetition_penalty=1.2,\n max_new_tokens=512\n)", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #safetensors #en #es #dataset-nmarafo/truthful_qa_TrueFalse-Feedback #arxiv-1910.09700 #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #license-apache-2.0 #has_space #region-us \n", "# Model Card for Model ID\n\nThis is an adapter prepared to return True or False depending on whether the student's answer (\"student_answer\") is correct based on the question (\"question\") and comparing it with a given answer (\"best_answer\").\nThe prompt has the following structure:", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\nIn Google Colab:", "# To perform inference on the test dataset example load the model from the checkpoint\npersisted_model = AutoPeftModelForCausalLM.from_pretrained(\n adapter,\n low_cpu_mem_usage=True,\n return_dict=True,\n torch_dtype=torch.float16,\n device_map=\"cuda\")", "# Some gen config knobs\ngeneration_config = GenerationConfig(\n penalty_alpha=0.6, \n do_sample = True, \n top_k=5, \n temperature=0.5, \n repetition_penalty=1.2,\n max_new_tokens=512\n)", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 85, 63, 3, 54, 28, 3, 4, 9, 9, 10, 42, 14, 82, 56, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #en #es #dataset-nmarafo/truthful_qa_TrueFalse-Feedback #arxiv-1910.09700 #base_model-TheBloke/Mistral-7B-Instruct-v0.2-GPTQ #license-apache-2.0 #has_space #region-us \n# Model Card for Model ID\n\nThis is an adapter prepared to return True or False depending on whether the student's answer (\"student_answer\") is correct based on the question (\"question\") and comparing it with a given answer (\"best_answer\").\nThe prompt has the following structure:## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\nIn Google Colab:# To perform inference on the test dataset example load the model from the checkpoint\npersisted_model = AutoPeftModelForCausalLM.from_pretrained(\n adapter,\n low_cpu_mem_usage=True,\n return_dict=True,\n torch_dtype=torch.float16,\n device_map=\"cuda\")# Some gen config knobs\ngeneration_config = GenerationConfig(\n penalty_alpha=0.6, \n do_sample = True, \n top_k=5, \n temperature=0.5, \n repetition_penalty=1.2,\n max_new_tokens=512\n)## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]" ]
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transformers
# **SINHALA QUESTION AND ANSWER MODEL by Indramal** > **Contact details:** [Indramal Wansekara Profile Website](https://www.indramal.com/) **Sinhala Example 1:** ``` - Question: Notre dame හි Scholastic සඟරාව ප්‍රකාශනය ආරම්භ කළේ කවදාද? - Answer: 1876 ​​සැප්තැම්බර් මාසයේදී - Context: අනෙකුත් බොහෝ විශ්ව විද්‍යාල වල මෙන්ම, Notre Dame හි සිසුන් ප්‍රවෘත්ති මාධ්‍ය ආයතන ගණනාවක් පවත්වාගෙන යයි. සිසුන් විසින් පවත්වාගෙන යනු ලබන අලෙවිසැල් නවයට පුවත්පත් තුනක්, ගුවන්විදුලිය සහ රූපවාහිනී මධ්‍යස්ථානයක් සහ සඟරා සහ සඟරා කිහිපයක් ඇතුළත් වේ. 1876 ​​සැප්තැම්බර් මාසයේදී එක් පිටුවක සඟරාවක් ලෙස ආරම්භ කරන ලද Scholastic සඟරාව මසකට දෙවරක් නිකුත් කරනු ලබන අතර එක්සත් ජනපදයේ පැරණිතම අඛණ්ඩ සාමූහික ප්‍රකාශනය ලෙස ප්‍රකාශ කරයි. අනෙක් සඟරාව වන ජග්ලර් වසරකට දෙවරක් නිකුත් වන අතර ශිෂ්‍ය සාහිත්‍යය සහ කලා කෘති කෙරෙහි අවධානය යොමු කරයි. Dome වාර්ෂික පොත වාර්ෂිකව ප්‍රකාශයට පත් කෙරේ. පුවත්පත්වලට විවිධ ප්‍රකාශන අවශ්‍යතා ඇත, ඔබ්සර්වර් දිනපතා ප්‍රකාශයට පත් කරන අතර ප්‍රධාන වශයෙන් විශ්ව විද්‍යාල සහ වෙනත් ප්‍රවෘත්ති වාර්තා කරයි, සහ නොට්‍රේ ඩේම් සහ ශාන්ත මරියා විද්‍යාලයේ සිසුන්ගෙන් කාර්ය මණ්ඩලයක් ඇත. Scholastic සහ The Dome මෙන් නොව, The Observer ස්වාධීන ප්‍රකාශනයක් වන අතර විශ්වවිද්‍යාලයෙන් පීඨ උපදේශකයෙකු හෝ කතුවැකි අධීක්‍ෂණයක් නොමැත. 1987 දී, ඔබ්සර්වර් විසින් ගතානුගතික නැඹුරුවක් පෙන්වීමට පටන් ගත් බව සමහර සිසුන් විශ්වාස කරන විට, ලිබරල් පුවත්පතක්, Common Sense ප්‍රකාශයට පත් කරන ලදී. එලෙසම, 2003 දී, එම පත්‍රය ලිබරල් නැඹුරුවක් පෙන්නුම් කරන බව අනෙකුත් සිසුන් විශ්වාස කළ විට, කොන්සර්වේටිව් කඩදාසි Irish Rover නිෂ්පාදනයට ගියේය. The Observer තරම් නිතර ප්‍රකාශනය වන පත්‍රිකාවක්වත් නැත; කෙසේ වෙතත්, තුනම සියලුම සිසුන්ට බෙදා හරිනු ලැබේ. අවසාන වශයෙන්, 2008 වසන්තයේ දී දේශපාලන විද්‍යා පර්යේෂණ සඳහා උපාධි අපේක්ෂක සඟරාවක්, Beyond Politics, එහි මංගල දර්ශනය විය ``` **Sinhala Example 2:** ``` - Question: Notre Dame හි නවක සිසුන් සඳහා කාලය කළමනාකරණය කිරීම සඳහා උපකාර සපයන ආයතනය කුමක්ද? - Answer: ඉගෙනුම් සම්පත් මධ්‍යස්ථානයක් - Context: Notre Dame හි සියලුම උපාධි අපේක්ෂකයින් පාසලේ උපාධි අපේක්ෂක විද්‍යාල පහෙන් එකක කොටසක් හෝ පළමු වසර අධ්‍යයන වැඩසටහනේ සිටී. පළමු වසර අධ්‍යයන වැඩසටහන 1962 දී ආරම්භ කරන ලද්දේ ඔවුන් ප්‍රධාන පෙළක් ප්‍රකාශයට පත් කිරීමට පෙර පාසලට ඔවුන්ගේ පළමු වසරේ පැමිණෙන නවකයන්ට මග පෙන්වීම සඳහා ය. සෑම සිසුවෙකුටම වැඩසටහනෙන් අධ්‍යයන උපදේශකයෙකු ලබා දෙන අතර ඔහු ඔවුන් උනන්දුවක් දක්වන ඕනෑම ප්‍රධාන පෙළකට නිරාවරණය වන පන්ති තෝරා ගැනීමට උපකාර කරයි. මෙම වැඩසටහනට කාල කළමනාකරණය, සහයෝගී ඉගෙනීම සහ විෂය ඉගැන්වීම් සපයන ඉගෙනුම් සම්පත් මධ්‍යස්ථානයක් ද ඇතුළත් වේ. මෙම වැඩසටහන මීට පෙර එක්සත් ජනපද ප්‍රවෘත්ති සහ ලෝක වාර්තාව විසින් කැපී පෙනෙන ලෙස පිළිගෙන ඇත. ``` ## Citation If you want to cite this model you can use this: ``` @misc {indramal_wansekara_2024, author = { {Indramal Wansekara} }, title = { SINHALA_QUESTION_AND_ANSWER (Revision 19236c6) }, year = 2024, url = { https://huggingface.co/Indramal/SINHALA_QUESTION_AND_ANSWER }, doi = { 10.57967/hf/1767 }, publisher = { Hugging Face } } ```
{"language": ["si"], "license": "apache-2.0", "tags": ["sinhala", "question&answer", "sri lanka"], "datasets": ["Indramal/SINHALA_QUESTION_AND_ANSWER_DATASET"], "widget": [{"text": "Notre dame \u0dc4\u0dd2 Scholastic \u0dc3\u0d9f\u0dbb\u0dcf\u0dc0 \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0db1\u0dba \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dc5\u0dda \u0d9a\u0dc0\u0daf\u0dcf\u0daf?", "example_title": "Sinhala Example 1", "context": "\u0d85\u0db1\u0dd9\u0d9a\u0dd4\u0dad\u0dca \u0db6\u0ddc\u0dc4\u0ddd \u0dc0\u0dd2\u0dc1\u0dca\u0dc0 \u0dc0\u0dd2\u0daf\u0dca\u200d\u0dba\u0dcf\u0dbd \u0dc0\u0dbd \u0db8\u0dd9\u0db1\u0dca\u0db8, Notre Dame \u0dc4\u0dd2 \u0dc3\u0dd2\u0dc3\u0dd4\u0db1\u0dca \u0db4\u0dca\u200d\u0dbb\u0dc0\u0dd8\u0dad\u0dca\u0dad\u0dd2 \u0db8\u0dcf\u0db0\u0dca\u200d\u0dba \u0d86\u0dba\u0dad\u0db1 \u0d9c\u0dab\u0db1\u0dcf\u0dc0\u0d9a\u0dca \u0db4\u0dc0\u0dad\u0dca\u0dc0\u0dcf\u0d9c\u0dd9\u0db1 \u0dba\u0dba\u0dd2. \u0dc3\u0dd2\u0dc3\u0dd4\u0db1\u0dca \u0dc0\u0dd2\u0dc3\u0dd2\u0db1\u0dca \u0db4\u0dc0\u0dad\u0dca\u0dc0\u0dcf\u0d9c\u0dd9\u0db1 \u0dba\u0db1\u0dd4 \u0dbd\u0db6\u0db1 \u0d85\u0dbd\u0dd9\u0dc0\u0dd2\u0dc3\u0dd0\u0dbd\u0dca \u0db1\u0dc0\u0dba\u0da7 \u0db4\u0dd4\u0dc0\u0dad\u0dca\u0db4\u0dad\u0dca \u0dad\u0dd4\u0db1\u0d9a\u0dca, \u0d9c\u0dd4\u0dc0\u0db1\u0dca\u0dc0\u0dd2\u0daf\u0dd4\u0dbd\u0dd2\u0dba \u0dc3\u0dc4 \u0dbb\u0dd6\u0db4\u0dc0\u0dcf\u0dc4\u0dd2\u0db1\u0dd3 \u0db8\u0db0\u0dca\u200d\u0dba\u0dc3\u0dca\u0dae\u0dcf\u0db1\u0dba\u0d9a\u0dca \u0dc3\u0dc4 \u0dc3\u0d9f\u0dbb\u0dcf \u0dc3\u0dc4 \u0dc3\u0d9f\u0dbb\u0dcf \u0d9a\u0dd2\u0dc4\u0dd2\u0db4\u0dba\u0d9a\u0dca \u0d87\u0dad\u0dd4\u0dc5\u0dad\u0dca \u0dc0\u0dda. 1876 \u200b\u200b\u0dc3\u0dd0\u0db4\u0dca\u0dad\u0dd0\u0db8\u0dca\u0db6\u0dbb\u0dca \u0db8\u0dcf\u0dc3\u0dba\u0dda\u0daf\u0dd3 \u0d91\u0d9a\u0dca \u0db4\u0dd2\u0da7\u0dd4\u0dc0\u0d9a \u0dc3\u0d9f\u0dbb\u0dcf\u0dc0\u0d9a\u0dca \u0dbd\u0dd9\u0dc3 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1 \u0dbd\u0daf Scholastic \u0dc3\u0d9f\u0dbb\u0dcf\u0dc0 \u0db8\u0dc3\u0d9a\u0da7 \u0daf\u0dd9\u0dc0\u0dbb\u0d9a\u0dca \u0db1\u0dd2\u0d9a\u0dd4\u0dad\u0dca \u0d9a\u0dbb\u0db1\u0dd4 \u0dbd\u0db6\u0db1 \u0d85\u0dad\u0dbb \u0d91\u0d9a\u0dca\u0dc3\u0dad\u0dca \u0da2\u0db1\u0db4\u0daf\u0dba\u0dda \u0db4\u0dd0\u0dbb\u0dab\u0dd2\u0dad\u0db8 \u0d85\u0d9b\u0dab\u0dca\u0da9 \u0dc3\u0dcf\u0db8\u0dd6\u0dc4\u0dd2\u0d9a \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0db1\u0dba \u0dbd\u0dd9\u0dc3 \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1 \u0d9a\u0dbb\u0dba\u0dd2. \u0d85\u0db1\u0dd9\u0d9a\u0dca \u0dc3\u0d9f\u0dbb\u0dcf\u0dc0 \u0dc0\u0db1 \u0da2\u0d9c\u0dca\u0dbd\u0dbb\u0dca \u0dc0\u0dc3\u0dbb\u0d9a\u0da7 \u0daf\u0dd9\u0dc0\u0dbb\u0d9a\u0dca \u0db1\u0dd2\u0d9a\u0dd4\u0dad\u0dca \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0dc1\u0dd2\u0dc2\u0dca\u200d\u0dba \u0dc3\u0dcf\u0dc4\u0dd2\u0dad\u0dca\u200d\u0dba\u0dba \u0dc3\u0dc4 \u0d9a\u0dbd\u0dcf \u0d9a\u0dd8\u0dad\u0dd2 \u0d9a\u0dd9\u0dbb\u0dd9\u0dc4\u0dd2 \u0d85\u0dc0\u0db0\u0dcf\u0db1\u0dba \u0dba\u0ddc\u0db8\u0dd4 \u0d9a\u0dbb\u0dba\u0dd2. Dome \u0dc0\u0dcf\u0dbb\u0dca\u0dc2\u0dd2\u0d9a \u0db4\u0ddc\u0dad \u0dc0\u0dcf\u0dbb\u0dca\u0dc2\u0dd2\u0d9a\u0dc0 \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0dba\u0da7 \u0db4\u0dad\u0dca \u0d9a\u0dd9\u0dbb\u0dda. \u0db4\u0dd4\u0dc0\u0dad\u0dca\u0db4\u0dad\u0dca\u0dc0\u0dbd\u0da7 \u0dc0\u0dd2\u0dc0\u0dd2\u0db0 \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0db1 \u0d85\u0dc0\u0dc1\u0dca\u200d\u0dba\u0dad\u0dcf \u0d87\u0dad, \u0d94\u0db6\u0dca\u0dc3\u0dbb\u0dca\u0dc0\u0dbb\u0dca \u0daf\u0dd2\u0db1\u0db4\u0dad\u0dcf \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0dba\u0da7 \u0db4\u0dad\u0dca \u0d9a\u0dbb\u0db1 \u0d85\u0dad\u0dbb \u0db4\u0dca\u200d\u0dbb\u0db0\u0dcf\u0db1 \u0dc0\u0dc1\u0dba\u0dd9\u0db1\u0dca \u0dc0\u0dd2\u0dc1\u0dca\u0dc0 \u0dc0\u0dd2\u0daf\u0dca\u200d\u0dba\u0dcf\u0dbd \u0dc3\u0dc4 \u0dc0\u0dd9\u0db1\u0dad\u0dca \u0db4\u0dca\u200d\u0dbb\u0dc0\u0dd8\u0dad\u0dca\u0dad\u0dd2 \u0dc0\u0dcf\u0dbb\u0dca\u0dad\u0dcf \u0d9a\u0dbb\u0dba\u0dd2, \u0dc3\u0dc4 \u0db1\u0ddc\u0da7\u0dca\u200d\u0dbb\u0dda \u0da9\u0dda\u0db8\u0dca \u0dc3\u0dc4 \u0dc1\u0dcf\u0db1\u0dca\u0dad \u0db8\u0dbb\u0dd2\u0dba\u0dcf \u0dc0\u0dd2\u0daf\u0dca\u200d\u0dba\u0dcf\u0dbd\u0dba\u0dda \u0dc3\u0dd2\u0dc3\u0dd4\u0db1\u0dca\u0d9c\u0dd9\u0db1\u0dca \u0d9a\u0dcf\u0dbb\u0dca\u0dba \u0db8\u0dab\u0dca\u0da9\u0dbd\u0dba\u0d9a\u0dca \u0d87\u0dad. Scholastic \u0dc3\u0dc4 The Dome \u0db8\u0dd9\u0db1\u0dca \u0db1\u0ddc\u0dc0, The Observer \u0dc3\u0dca\u0dc0\u0dcf\u0db0\u0dd3\u0db1 \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0db1\u0dba\u0d9a\u0dca \u0dc0\u0db1 \u0d85\u0dad\u0dbb \u0dc0\u0dd2\u0dc1\u0dca\u0dc0\u0dc0\u0dd2\u0daf\u0dca\u200d\u0dba\u0dcf\u0dbd\u0dba\u0dd9\u0db1\u0dca \u0db4\u0dd3\u0da8 \u0d8b\u0db4\u0daf\u0dda\u0dc1\u0d9a\u0dba\u0dd9\u0d9a\u0dd4 \u0dc4\u0ddd \u0d9a\u0dad\u0dd4\u0dc0\u0dd0\u0d9a\u0dd2 \u0d85\u0db0\u0dd3\u0d9a\u0dca\u200d\u0dc2\u0dab\u0dba\u0d9a\u0dca \u0db1\u0ddc\u0db8\u0dd0\u0dad. 1987 \u0daf\u0dd3, \u0d94\u0db6\u0dca\u0dc3\u0dbb\u0dca\u0dc0\u0dbb\u0dca \u0dc0\u0dd2\u0dc3\u0dd2\u0db1\u0dca \u0d9c\u0dad\u0dcf\u0db1\u0dd4\u0d9c\u0dad\u0dd2\u0d9a \u0db1\u0dd0\u0db9\u0dd4\u0dbb\u0dd4\u0dc0\u0d9a\u0dca \u0db4\u0dd9\u0db1\u0dca\u0dc0\u0dd3\u0db8\u0da7 \u0db4\u0da7\u0db1\u0dca \u0d9c\u0dad\u0dca \u0db6\u0dc0 \u0dc3\u0db8\u0dc4\u0dbb \u0dc3\u0dd2\u0dc3\u0dd4\u0db1\u0dca \u0dc0\u0dd2\u0dc1\u0dca\u0dc0\u0dcf\u0dc3 \u0d9a\u0dbb\u0db1 \u0dc0\u0dd2\u0da7, \u0dbd\u0dd2\u0db6\u0dbb\u0dbd\u0dca \u0db4\u0dd4\u0dc0\u0dad\u0dca\u0db4\u0dad\u0d9a\u0dca, Common Sense \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0dba\u0da7 \u0db4\u0dad\u0dca \u0d9a\u0dbb\u0db1 \u0dbd\u0daf\u0dd3. \u0d91\u0dbd\u0dd9\u0dc3\u0db8, 2003 \u0daf\u0dd3, \u0d91\u0db8 \u0db4\u0dad\u0dca\u200d\u0dbb\u0dba \u0dbd\u0dd2\u0db6\u0dbb\u0dbd\u0dca \u0db1\u0dd0\u0db9\u0dd4\u0dbb\u0dd4\u0dc0\u0d9a\u0dca \u0db4\u0dd9\u0db1\u0dca\u0db1\u0dd4\u0db8\u0dca \u0d9a\u0dbb\u0db1 \u0db6\u0dc0 \u0d85\u0db1\u0dd9\u0d9a\u0dd4\u0dad\u0dca \u0dc3\u0dd2\u0dc3\u0dd4\u0db1\u0dca \u0dc0\u0dd2\u0dc1\u0dca\u0dc0\u0dcf\u0dc3 \u0d9a\u0dc5 \u0dc0\u0dd2\u0da7, \u0d9a\u0ddc\u0db1\u0dca\u0dc3\u0dbb\u0dca\u0dc0\u0dda\u0da7\u0dd2\u0dc0\u0dca \u0d9a\u0da9\u0daf\u0dcf\u0dc3\u0dd2 Irish Rover \u0db1\u0dd2\u0dc2\u0dca\u0db4\u0dcf\u0daf\u0db1\u0dba\u0da7 \u0d9c\u0dd2\u0dba\u0dda\u0dba. The Observer \u0dad\u0dbb\u0db8\u0dca \u0db1\u0dd2\u0dad\u0dbb \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0db1\u0dba \u0dc0\u0db1 \u0db4\u0dad\u0dca\u200d\u0dbb\u0dd2\u0d9a\u0dcf\u0dc0\u0d9a\u0dca\u0dc0\u0dad\u0dca \u0db1\u0dd0\u0dad; \u0d9a\u0dd9\u0dc3\u0dda \u0dc0\u0dd9\u0dad\u0dad\u0dca, \u0dad\u0dd4\u0db1\u0db8 \u0dc3\u0dd2\u0dba\u0dbd\u0dd4\u0db8 \u0dc3\u0dd2\u0dc3\u0dd4\u0db1\u0dca\u0da7 \u0db6\u0dd9\u0daf\u0dcf \u0dc4\u0dbb\u0dd2\u0db1\u0dd4 \u0dbd\u0dd0\u0db6\u0dda. \u0d85\u0dc0\u0dc3\u0dcf\u0db1 \u0dc0\u0dc1\u0dba\u0dd9\u0db1\u0dca, 2008 \u0dc0\u0dc3\u0db1\u0dca\u0dad\u0dba\u0dda \u0daf\u0dd3 \u0daf\u0dda\u0dc1\u0db4\u0dcf\u0dbd\u0db1 \u0dc0\u0dd2\u0daf\u0dca\u200d\u0dba\u0dcf \u0db4\u0dbb\u0dca\u0dba\u0dda\u0dc2\u0dab \u0dc3\u0db3\u0dc4\u0dcf \u0d8b\u0db4\u0dcf\u0db0\u0dd2 \u0d85\u0db4\u0dda\u0d9a\u0dca\u0dc2\u0d9a \u0dc3\u0d9f\u0dbb\u0dcf\u0dc0\u0d9a\u0dca, Beyond Politics, \u0d91\u0dc4\u0dd2 \u0db8\u0d82\u0d9c\u0dbd \u0daf\u0dbb\u0dca\u0dc1\u0db1\u0dba \u0dc0\u0dd2\u0dba."}, {"text": "Notre Dame \u0dc4\u0dd2 \u0db1\u0dc0\u0d9a \u0dc3\u0dd2\u0dc3\u0dd4\u0db1\u0dca \u0dc3\u0db3\u0dc4\u0dcf \u0d9a\u0dcf\u0dbd\u0dba \u0d9a\u0dc5\u0db8\u0db1\u0dcf\u0d9a\u0dbb\u0dab\u0dba \u0d9a\u0dd2\u0dbb\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0d8b\u0db4\u0d9a\u0dcf\u0dbb \u0dc3\u0db4\u0dba\u0db1 \u0d86\u0dba\u0dad\u0db1\u0dba \u0d9a\u0dd4\u0db8\u0d9a\u0dca\u0daf?", "example_title": "Sinhala Example 2", "context": "Notre Dame \u0dc4\u0dd2 \u0dc3\u0dd2\u0dba\u0dbd\u0dd4\u0db8 \u0d8b\u0db4\u0dcf\u0db0\u0dd2 \u0d85\u0db4\u0dda\u0d9a\u0dca\u0dc2\u0d9a\u0dba\u0dd2\u0db1\u0dca \u0db4\u0dcf\u0dc3\u0dbd\u0dda \u0d8b\u0db4\u0dcf\u0db0\u0dd2 \u0d85\u0db4\u0dda\u0d9a\u0dca\u0dc2\u0d9a \u0dc0\u0dd2\u0daf\u0dca\u200d\u0dba\u0dcf\u0dbd \u0db4\u0dc4\u0dd9\u0db1\u0dca \u0d91\u0d9a\u0d9a \u0d9a\u0ddc\u0da7\u0dc3\u0d9a\u0dca \u0dc4\u0ddd \u0db4\u0dc5\u0db8\u0dd4 \u0dc0\u0dc3\u0dbb \u0d85\u0db0\u0dca\u200d\u0dba\u0dba\u0db1 \u0dc0\u0dd0\u0da9\u0dc3\u0da7\u0dc4\u0db1\u0dda \u0dc3\u0dd2\u0da7\u0dd3. \u0db4\u0dc5\u0db8\u0dd4 \u0dc0\u0dc3\u0dbb \u0d85\u0db0\u0dca\u200d\u0dba\u0dba\u0db1 \u0dc0\u0dd0\u0da9\u0dc3\u0da7\u0dc4\u0db1 1962 \u0daf\u0dd3 \u0d86\u0dbb\u0db8\u0dca\u0db7 \u0d9a\u0dbb\u0db1 \u0dbd\u0daf\u0dca\u0daf\u0dda \u0d94\u0dc0\u0dd4\u0db1\u0dca \u0db4\u0dca\u200d\u0dbb\u0db0\u0dcf\u0db1 \u0db4\u0dd9\u0dc5\u0d9a\u0dca \u0db4\u0dca\u200d\u0dbb\u0d9a\u0dcf\u0dc1\u0dba\u0da7 \u0db4\u0dad\u0dca \u0d9a\u0dd2\u0dbb\u0dd3\u0db8\u0da7 \u0db4\u0dd9\u0dbb \u0db4\u0dcf\u0dc3\u0dbd\u0da7 \u0d94\u0dc0\u0dd4\u0db1\u0dca\u0d9c\u0dda \u0db4\u0dc5\u0db8\u0dd4 \u0dc0\u0dc3\u0dbb\u0dda \u0db4\u0dd0\u0db8\u0dd2\u0dab\u0dd9\u0db1 \u0db1\u0dc0\u0d9a\u0dba\u0db1\u0dca\u0da7 \u0db8\u0d9c \u0db4\u0dd9\u0db1\u0dca\u0dc0\u0dd3\u0db8 \u0dc3\u0db3\u0dc4\u0dcf \u0dba. \u0dc3\u0dd1\u0db8 \u0dc3\u0dd2\u0dc3\u0dd4\u0dc0\u0dd9\u0d9a\u0dd4\u0da7\u0db8 \u0dc0\u0dd0\u0da9\u0dc3\u0da7\u0dc4\u0db1\u0dd9\u0db1\u0dca \u0d85\u0db0\u0dca\u200d\u0dba\u0dba\u0db1 \u0d8b\u0db4\u0daf\u0dda\u0dc1\u0d9a\u0dba\u0dd9\u0d9a\u0dd4 \u0dbd\u0db6\u0dcf \u0daf\u0dd9\u0db1 \u0d85\u0dad\u0dbb \u0d94\u0dc4\u0dd4 \u0d94\u0dc0\u0dd4\u0db1\u0dca \u0d8b\u0db1\u0db1\u0dca\u0daf\u0dd4\u0dc0\u0d9a\u0dca \u0daf\u0d9a\u0dca\u0dc0\u0db1 \u0d95\u0db1\u0dd1\u0db8 \u0db4\u0dca\u200d\u0dbb\u0db0\u0dcf\u0db1 \u0db4\u0dd9\u0dc5\u0d9a\u0da7 \u0db1\u0dd2\u0dbb\u0dcf\u0dc0\u0dbb\u0dab\u0dba \u0dc0\u0db1 \u0db4\u0db1\u0dca\u0dad\u0dd2 \u0dad\u0ddd\u0dbb\u0dcf \u0d9c\u0dd0\u0db1\u0dd3\u0db8\u0da7 \u0d8b\u0db4\u0d9a\u0dcf\u0dbb \u0d9a\u0dbb\u0dba\u0dd2. \u0db8\u0dd9\u0db8 \u0dc0\u0dd0\u0da9\u0dc3\u0da7\u0dc4\u0db1\u0da7 \u0d9a\u0dcf\u0dbd \u0d9a\u0dc5\u0db8\u0db1\u0dcf\u0d9a\u0dbb\u0dab\u0dba, \u0dc3\u0dc4\u0dba\u0ddd\u0d9c\u0dd3 \u0d89\u0d9c\u0dd9\u0db1\u0dd3\u0db8 \u0dc3\u0dc4 \u0dc0\u0dd2\u0dc2\u0dba \u0d89\u0d9c\u0dd0\u0db1\u0dca\u0dc0\u0dd3\u0db8\u0dca \u0dc3\u0db4\u0dba\u0db1 \u0d89\u0d9c\u0dd9\u0db1\u0dd4\u0db8\u0dca \u0dc3\u0db8\u0dca\u0db4\u0dad\u0dca \u0db8\u0db0\u0dca\u200d\u0dba\u0dc3\u0dca\u0dae\u0dcf\u0db1\u0dba\u0d9a\u0dca \u0daf \u0d87\u0dad\u0dd4\u0dc5\u0dad\u0dca \u0dc0\u0dda. \u0db8\u0dd9\u0db8 \u0dc0\u0dd0\u0da9\u0dc3\u0da7\u0dc4\u0db1 \u0db8\u0dd3\u0da7 \u0db4\u0dd9\u0dbb \u0d91\u0d9a\u0dca\u0dc3\u0dad\u0dca \u0da2\u0db1\u0db4\u0daf \u0db4\u0dca\u200d\u0dbb\u0dc0\u0dd8\u0dad\u0dca\u0dad\u0dd2 \u0dc3\u0dc4 \u0dbd\u0ddd\u0d9a \u0dc0\u0dcf\u0dbb\u0dca\u0dad\u0dcf\u0dc0 \u0dc0\u0dd2\u0dc3\u0dd2\u0db1\u0dca \u0d9a\u0dd0\u0db4\u0dd3 \u0db4\u0dd9\u0db1\u0dd9\u0db1 \u0dbd\u0dd9\u0dc3 \u0db4\u0dd2\u0dc5\u0dd2\u0d9c\u0dd9\u0db1 \u0d87\u0dad."}]}
question-answering
Indramal/SINHALA_QUESTION_AND_ANSWER
[ "transformers", "pytorch", "safetensors", "bert", "question-answering", "sinhala", "question&answer", "sri lanka", "si", "dataset:Indramal/SINHALA_QUESTION_AND_ANSWER_DATASET", "doi:10.57967/hf/1767", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-14T11:59:41+00:00
[]
[ "si" ]
TAGS #transformers #pytorch #safetensors #bert #question-answering #sinhala #question&answer #sri lanka #si #dataset-Indramal/SINHALA_QUESTION_AND_ANSWER_DATASET #doi-10.57967/hf/1767 #license-apache-2.0 #endpoints_compatible #region-us
# SINHALA QUESTION AND ANSWER MODEL by Indramal > Contact details: Indramal Wansekara Profile Website Sinhala Example 1: Sinhala Example 2: If you want to cite this model you can use this:
[ "# SINHALA QUESTION AND ANSWER MODEL by Indramal\n\n> Contact details: Indramal Wansekara Profile Website\n\n\nSinhala Example 1:\n\n\nSinhala Example 2:\n\n\nIf you want to cite this model you can use this:" ]
[ "TAGS\n#transformers #pytorch #safetensors #bert #question-answering #sinhala #question&answer #sri lanka #si #dataset-Indramal/SINHALA_QUESTION_AND_ANSWER_DATASET #doi-10.57967/hf/1767 #license-apache-2.0 #endpoints_compatible #region-us \n", "# SINHALA QUESTION AND ANSWER MODEL by Indramal\n\n> Contact details: Indramal Wansekara Profile Website\n\n\nSinhala Example 1:\n\n\nSinhala Example 2:\n\n\nIf you want to cite this model you can use this:" ]
[ 94, 46 ]
[ "passage: TAGS\n#transformers #pytorch #safetensors #bert #question-answering #sinhala #question&answer #sri lanka #si #dataset-Indramal/SINHALA_QUESTION_AND_ANSWER_DATASET #doi-10.57967/hf/1767 #license-apache-2.0 #endpoints_compatible #region-us \n# SINHALA QUESTION AND ANSWER MODEL by Indramal\n\n> Contact details: Indramal Wansekara Profile Website\n\n\nSinhala Example 1:\n\n\nSinhala Example 2:\n\n\nIf you want to cite this model you can use this:" ]
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null
null
peft
# 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 0.7.1
{"library_name": "peft", "base_model": "bert-base-uncased"}
null
alitolga/627_bert-base-uncased_PrefixTuning
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:bert-base-uncased", "region:us" ]
2024-02-14T12:01:19+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-bert-base-uncased #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.1
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-bert-base-uncased #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
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[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-bert-base-uncased #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1" ]
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null
null
peft
# 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 0.7.1
{"library_name": "peft", "base_model": "gpt2"}
null
alitolga/627_gpt2_PrefixTuning
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:gpt2", "region:us" ]
2024-02-14T12:01:27+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-gpt2 #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.1
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-gpt2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ 31, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-gpt2 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1" ]
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null
null
peft
# 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 0.7.1
{"library_name": "peft", "base_model": "t5-base"}
null
alitolga/627_t5-base_PrefixTuning
[ "peft", "safetensors", "arxiv:1910.09700", "base_model:t5-base", "region:us" ]
2024-02-14T12:02:09+00:00
[ "1910.09700" ]
[]
TAGS #peft #safetensors #arxiv-1910.09700 #base_model-t5-base #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.1
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-t5-base #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]
[ 31, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-t5-base #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1" ]
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null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Finetuned_Final_LM_200k This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.5453 - Accuracy: 0.8429 - F1: 0.8410 - Precision: 0.8604 - Recall: 0.8429 ## 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: 16 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:| | 0.1882 | 0.08 | 500 | 0.7728 | 0.8338 | 0.8300 | 0.8666 | 0.8338 | | 0.1178 | 0.16 | 1000 | 1.0142 | 0.8365 | 0.8349 | 0.8494 | 0.8365 | | 0.2868 | 0.24 | 1500 | 2.3359 | 0.8444 | 0.8423 | 0.8636 | 0.8444 | | 0.3269 | 0.32 | 2000 | 2.4489 | 0.8399 | 0.8375 | 0.8607 | 0.8399 | | 0.1704 | 0.4 | 2500 | 2.3116 | 0.8440 | 0.8424 | 0.8593 | 0.8440 | | 0.2567 | 0.48 | 3000 | 2.3376 | 0.8403 | 0.8384 | 0.8565 | 0.8403 | | 0.1004 | 0.56 | 3500 | 2.1410 | 0.8440 | 0.8420 | 0.8625 | 0.8440 | | 0.1368 | 0.64 | 4000 | 2.3633 | 0.8463 | 0.8446 | 0.8617 | 0.8463 | | 0.1003 | 0.72 | 4500 | 2.3986 | 0.8437 | 0.8418 | 0.8605 | 0.8437 | | 0.1889 | 0.8 | 5000 | 2.5537 | 0.8437 | 0.8419 | 0.8595 | 0.8437 | | 0.0424 | 0.88 | 5500 | 2.4177 | 0.8440 | 0.8420 | 0.8625 | 0.8440 | | 0.3186 | 0.96 | 6000 | 2.5633 | 0.8429 | 0.8411 | 0.8594 | 0.8429 | | 0.2532 | 1.04 | 6500 | 2.4783 | 0.8433 | 0.8413 | 0.8615 | 0.8433 | | 0.1323 | 1.12 | 7000 | 2.5693 | 0.8440 | 0.8421 | 0.8620 | 0.8440 | | 0.1018 | 1.2 | 7500 | 2.5286 | 0.8440 | 0.8420 | 0.8623 | 0.8440 | | 0.1762 | 1.28 | 8000 | 2.4495 | 0.8429 | 0.8408 | 0.8620 | 0.8429 | | 0.2621 | 1.36 | 8500 | 2.3865 | 0.8448 | 0.8428 | 0.8633 | 0.8448 | | 0.0256 | 1.44 | 9000 | 2.4784 | 0.8459 | 0.8439 | 0.8646 | 0.8459 | | 0.1207 | 1.52 | 9500 | 2.5304 | 0.8440 | 0.8422 | 0.8607 | 0.8440 | | 0.1659 | 1.6 | 10000 | 2.5637 | 0.8433 | 0.8413 | 0.8610 | 0.8433 | | 0.196 | 1.68 | 10500 | 2.5453 | 0.8429 | 0.8410 | 0.8604 | 0.8429 | ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "model-index": [{"name": "Finetuned_Final_LM_200k", "results": []}]}
text-classification
ManojAlexender/Finetuned_Final_LM_200k
[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T12:03:06+00:00
[]
[]
TAGS #transformers #safetensors #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
Finetuned\_Final\_LM\_200k ========================== This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.5453 * Accuracy: 0.8429 * F1: 0.8410 * Precision: 0.8604 * Recall: 0.8429 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: 16 * eval\_batch\_size: 64 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 50 * num\_epochs: 2 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 45, 159, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 64\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 50\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
<!-- 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-dataset This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.2599 - Wer: 48.5207 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 40 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | No log | 1.6 | 10 | 0.3733 | 50.2959 | | No log | 3.2 | 20 | 0.2663 | 52.0710 | | 0.2997 | 4.8 | 30 | 0.2667 | 48.5207 | | 0.2997 | 6.4 | 40 | 0.2599 | 48.5207 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
{"language": ["hi"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "model-index": [{"name": "whisper-small-dataset", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "None", "args": "config: hi, split: test"}, "metrics": [{"type": "wer", "value": 48.5207100591716, "name": "Wer"}]}]}]}
automatic-speech-recognition
himanshugrad/whisper-small-dataset
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-large-v3", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2024-02-14T12:10:02+00:00
[]
[ "hi" ]
TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #endpoints_compatible #region-us
whisper-small-dataset ===================== This model is a fine-tuned version of openai/whisper-large-v3 on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: * Loss: 0.2599 * Wer: 48.5207 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: 4 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 5 * training\_steps: 40 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* training\\_steps: 40\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* training\\_steps: 40\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ 107, 158, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #hi #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* training\\_steps: 40\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
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# PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 2000000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'mathreader/ppo-LunarLander-v2-v2' 'batch_size': 512 'minibatch_size': 128} ```
{"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "12.73 +/- 113.21", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
mathreader/ppo-LunarLander-v2-v2
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
2024-02-14T12:10:12+00:00
[]
[]
TAGS #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
# PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
[ "# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters" ]
[ "TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n", "# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters" ]
[ 51, 37 ]
[ "passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "bigscience/bloom-560m"}
null
KapitalK/my_new_model
[ "peft", "arxiv:1910.09700", "base_model:bigscience/bloom-560m", "region:us" ]
2024-02-14T12:16:24+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-bigscience/bloom-560m #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloom-560m #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 31, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloom-560m #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
ctranslate2
Convert from: Watarungurunnn/whisper-large-v3-ja # Whisper large-v3 model for CTranslate2 This repository contains the conversion of [Watarungurunnn/whisper-large-v3-ja](https://huggingface.co/Watarungurunnn/whisper-large-v3-ja) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("large-v3") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model Watarungurunnn/whisper-large-v3-ja --output_dir faster-whisper-large-v3-ja \ --copy_files tokenizer.json preprocessor_config.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-large-v3).**
{"language": ["en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "yue"], "license": "mit", "library_name": "ctranslate2", "tags": ["audio", "automatic-speech-recognition"]}
automatic-speech-recognition
JhonVanced/faster-whisper-large-v3-ja
[ "ctranslate2", "audio", "automatic-speech-recognition", "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "yue", "license:mit", "region:us" ]
2024-02-14T12:20:05+00:00
[]
[ "en", "zh", "de", "es", "ru", "ko", "fr", "ja", "pt", "tr", "pl", "ca", "nl", "ar", "sv", "it", "id", "hi", "fi", "vi", "he", "uk", "el", "ms", "cs", "ro", "da", "hu", "ta", "no", "th", "ur", "hr", "bg", "lt", "la", "mi", "ml", "cy", "sk", "te", "fa", "lv", "bn", "sr", "az", "sl", "kn", "et", "mk", "br", "eu", "is", "hy", "ne", "mn", "bs", "kk", "sq", "sw", "gl", "mr", "pa", "si", "km", "sn", "yo", "so", "af", "oc", "ka", "be", "tg", "sd", "gu", "am", "yi", "lo", "uz", "fo", "ht", "ps", "tk", "nn", "mt", "sa", "lb", "my", "bo", "tl", "mg", "as", "tt", "haw", "ln", "ha", "ba", "jw", "su", "yue" ]
TAGS #ctranslate2 #audio #automatic-speech-recognition #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #yue #license-mit #region-us
Convert from: Watarungurunnn/whisper-large-v3-ja # Whisper large-v3 model for CTranslate2 This repository contains the conversion of Watarungurunnn/whisper-large-v3-ja to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. ## Example ## Conversion details The original model was converted with the following command: Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the 'compute_type' option in CTranslate2. ## More information For more information about the original model, see its model card.
[ "# Whisper large-v3 model for CTranslate2\n\nThis repository contains the conversion of Watarungurunnn/whisper-large-v3-ja to the CTranslate2 model format.\n\nThis model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper.", "## Example", "## Conversion details\n\nThe original model was converted with the following command:\n\n\n\nNote that the model weights are saved in FP16. This type can be changed when the model is loaded using the 'compute_type' option in CTranslate2.", "## More information\n\nFor more information about the original model, see its model card." ]
[ "TAGS\n#ctranslate2 #audio #automatic-speech-recognition #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #yue #license-mit #region-us \n", "# Whisper large-v3 model for CTranslate2\n\nThis repository contains the conversion of Watarungurunnn/whisper-large-v3-ja to the CTranslate2 model format.\n\nThis model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper.", "## Example", "## Conversion details\n\nThe original model was converted with the following command:\n\n\n\nNote that the model weights are saved in FP16. This type can be changed when the model is loaded using the 'compute_type' option in CTranslate2.", "## More information\n\nFor more information about the original model, see its model card." ]
[ 241, 75, 3, 54, 16 ]
[ "passage: TAGS\n#ctranslate2 #audio #automatic-speech-recognition #en #zh #de #es #ru #ko #fr #ja #pt #tr #pl #ca #nl #ar #sv #it #id #hi #fi #vi #he #uk #el #ms #cs #ro #da #hu #ta #no #th #ur #hr #bg #lt #la #mi #ml #cy #sk #te #fa #lv #bn #sr #az #sl #kn #et #mk #br #eu #is #hy #ne #mn #bs #kk #sq #sw #gl #mr #pa #si #km #sn #yo #so #af #oc #ka #be #tg #sd #gu #am #yi #lo #uz #fo #ht #ps #tk #nn #mt #sa #lb #my #bo #tl #mg #as #tt #haw #ln #ha #ba #jw #su #yue #license-mit #region-us \n# Whisper large-v3 model for CTranslate2\n\nThis repository contains the conversion of Watarungurunnn/whisper-large-v3-ja to the CTranslate2 model format.\n\nThis model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper.## Example## Conversion details\n\nThe original model was converted with the following command:\n\n\n\nNote that the model weights are saved in FP16. This type can be changed when the model is loaded using the 'compute_type' option in CTranslate2.## More information\n\nFor more information about the original model, see its model card." ]
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null
null
transformers
# Jaskier-7b-dpo-v4.3 **This is work-in-progress model, may not be ready for production use** **WARNING** If you are not running your predictions in full precision (32 bit), you might get an INST loop (output that look like INSTINSTINSTINSTINST). We are aware of the issue and are working on a fix. As a temporary hack, you can use following biases: ``` sequence_bias = {(16289,): -10.0, (733, 16289): -10.0, (28793, 16289): -10.0, (3133,): -10.0, (2,): 4.0} model("<s><unk>[INST] Can Poland into space? [/INST]<unk>", max_new_tokens=128, do_sample=False, sequence_bias=sequence_bias) ``` ``` [{'generated_text': "<s><unk>[INST] Can Poland into space? [/INST]<unk>Poland has been advancing its space capabilities, collaborating with other European countries through the Polish Space Agency established in 2016. While the country doesn't have its own operational launch vehicles, Polish scientists contribute to various international space programs and research."}] ``` Please notice the `<unk>` tokens. Adding them reduces the possibility of random Inst tokens. This indicates, that there might be some issue with padding or unk tokens during training. Here is a more stable version, although it may still have some issues with the INST loop: https://huggingface.co/bardsai/jaskier-7b-dpo-v5.6 Model based on `paulml/OGNO-7B` (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1. ## Changelog - 2024-02-14: Initial release ## About bards.ai At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: bards.ai Let us know if you use our model :). Also, if you need any help, feel free to contact us at [email protected]
{"language": ["en"], "license": "cc-by-4.0", "library_name": "transformers", "tags": ["llm", "7b"], "datasets": ["jondurbin/truthy-dpo-v0.1"]}
text-generation
bardsai/jaskier-7b-dpo-v4.3
[ "transformers", "safetensors", "mistral", "text-generation", "llm", "7b", "conversational", "en", "dataset:jondurbin/truthy-dpo-v0.1", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-14T12:21:12+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #mistral #text-generation #llm #7b #conversational #en #dataset-jondurbin/truthy-dpo-v0.1 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Jaskier-7b-dpo-v4.3 This is work-in-progress model, may not be ready for production use WARNING If you are not running your predictions in full precision (32 bit), you might get an INST loop (output that look like INSTINSTINSTINSTINST). We are aware of the issue and are working on a fix. As a temporary hack, you can use following biases: Please notice the '<unk>' tokens. Adding them reduces the possibility of random Inst tokens. This indicates, that there might be some issue with padding or unk tokens during training. Here is a more stable version, although it may still have some issues with the INST loop: URL Model based on 'paulml/OGNO-7B' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1. ## Changelog - 2024-02-14: Initial release ## About URL At URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL
[ "# Jaskier-7b-dpo-v4.3\n\nThis is work-in-progress model, may not be ready for production use\n\nWARNING\nIf you are not running your predictions in full precision (32 bit), you might get an INST loop (output that look like INSTINSTINSTINSTINST). We are aware of the issue and are working on a fix.\n\nAs a temporary hack, you can use following biases:\n\n\n\n\nPlease notice the '<unk>' tokens. Adding them reduces the possibility of random Inst tokens. This indicates, that there might be some issue with padding or unk tokens during training.\nHere is a more stable version, although it may still have some issues with the INST loop: URL\n\nModel based on 'paulml/OGNO-7B' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1.", "## Changelog\n\n- 2024-02-14: Initial release", "## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #llm #7b #conversational #en #dataset-jondurbin/truthy-dpo-v0.1 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Jaskier-7b-dpo-v4.3\n\nThis is work-in-progress model, may not be ready for production use\n\nWARNING\nIf you are not running your predictions in full precision (32 bit), you might get an INST loop (output that look like INSTINSTINSTINSTINST). We are aware of the issue and are working on a fix.\n\nAs a temporary hack, you can use following biases:\n\n\n\n\nPlease notice the '<unk>' tokens. Adding them reduces the possibility of random Inst tokens. This indicates, that there might be some issue with padding or unk tokens during training.\nHere is a more stable version, although it may still have some issues with the INST loop: URL\n\nModel based on 'paulml/OGNO-7B' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1.", "## Changelog\n\n- 2024-02-14: Initial release", "## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL" ]
[ 84, 208, 13, 81 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #llm #7b #conversational #en #dataset-jondurbin/truthy-dpo-v0.1 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Jaskier-7b-dpo-v4.3\n\nThis is work-in-progress model, may not be ready for production use\n\nWARNING\nIf you are not running your predictions in full precision (32 bit), you might get an INST loop (output that look like INSTINSTINSTINSTINST). We are aware of the issue and are working on a fix.\n\nAs a temporary hack, you can use following biases:\n\n\n\n\nPlease notice the '<unk>' tokens. Adding them reduces the possibility of random Inst tokens. This indicates, that there might be some issue with padding or unk tokens during training.\nHere is a more stable version, although it may still have some issues with the INST loop: URL\n\nModel based on 'paulml/OGNO-7B' (downstream version of Mistral7B) finetuned using Direct Preference Optimization on jondurbin/truthy-dpo-v0.1.## Changelog\n\n- 2024-02-14: Initial release## About URL\n\nAt URL, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: URL\n\nLet us know if you use our model :). Also, if you need any help, feel free to contact us at info@URL" ]
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# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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]
{"language": ["en"], "license": "other", "datasets": ["Karlend/RVC_Models"], "license_name": "discord", "license_link": "LICENSE"}
null
UnchangedSpawn/RVC-Models
[ "en", "dataset:Karlend/RVC_Models", "arxiv:1910.09700", "license:other", "region:us" ]
2024-02-14T12:21:53+00:00
[ "1910.09700" ]
[ "en" ]
TAGS #en #dataset-Karlend/RVC_Models #arxiv-1910.09700 #license-other #region-us
# Model Card for Model ID This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#en #dataset-Karlend/RVC_Models #arxiv-1910.09700 #license-other #region-us \n", "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 34, 29, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#en #dataset-Karlend/RVC_Models #arxiv-1910.09700 #license-other #region-us \n# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
diffusers
# DreamBooth trained by AutoTrain Text encoder was not trained.
{"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A house plan of a 1 kanal house having car porch parking of 2 cars, gate on the south west side of the house, 2 or 3 bedrooms, front lawn, kitchen, launge, drawing and dining", "inference": true}
text-to-image
HamzaWajid1/Finetune_try
[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]
2024-02-14T12:22:00+00:00
[]
[]
TAGS #diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us
# DreamBooth trained by AutoTrain Text encoder was not trained.
[ "# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]
[ "TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n", "# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]
[ 41, 19 ]
[ "passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]
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null
null
transformers
<!-- 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. --> # finetune-BERT-squad This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1278 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 125 | 2.7399 | | No log | 2.0 | 250 | 2.1507 | | No log | 3.0 | 375 | 2.1278 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "finetune-BERT-squad", "results": []}]}
question-answering
muratsimsek003/finetune-BERT-squad
[ "transformers", "tensorboard", "safetensors", "bert", "question-answering", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-14T12:26:24+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us
finetune-BERT-squad =================== This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.1278 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 61, 98, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #question-answering #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "openai-community/gpt2"}
null
KapitalK/GPT_Something
[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "region:us" ]
2024-02-14T12:27:04+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-openai-community/gpt2 #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-openai-community/gpt2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 33, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-openai-community/gpt2 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
sample-factory
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r mathreader/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "11.73 +/- 4.72", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
mathreader/rl_course_vizdoom_health_gathering_supreme
[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-14T12:28:36+00:00
[]
[]
TAGS #sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: URL Documentation for how to use Sample-Factory can be found at URL ## Downloading the model After installing Sample-Factory, download the model with: ## Using the model To run the model after download, use the 'enjoy' script corresponding to this environment: You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag. See URL for more details ## Training with this model To continue training with this model, use the 'train' script corresponding to this environment: Note, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at.
[ "## Downloading the model\n\nAfter installing Sample-Factory, download the model with:", "## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details", "## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at." ]
[ "TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "## Downloading the model\n\nAfter installing Sample-Factory, download the model with:", "## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details", "## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at." ]
[ 34, 19, 59, 67 ]
[ "passage: TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n## Downloading the model\n\nAfter installing Sample-Factory, download the model with:## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at." ]
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null
null
setfit
# SetFit with lighteternal/stsb-xlm-r-greek-transfer This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [lighteternal/stsb-xlm-r-greek-transfer](https://huggingface.co/lighteternal/stsb-xlm-r-greek-transfer) - **Classification head:** a OneVsRestClassifier instance - **Maximum Sequence Length:** 400 tokens <!-- - **Number of Classes:** Unknown --> <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Evaluation ### Metrics | Label | Accuracy | |:--------|:---------| | **all** | 0.1589 | ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("st-karlos-efood/setfit-multilabel-one-vs-rest-feb-2024") # Run inference preds = model("παστα ατομικη") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Training Set Metrics | Training set | Min | Median | Max | |:-------------|:----|:-------|:----| | Word count | 1 | 8.6048 | 116 | ### Training Hyperparameters - batch_size: (48, 48) - num_epochs: (5, 5) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 10 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results | Epoch | Step | Training Loss | Validation Loss | |:------:|:----:|:-------------:|:---------------:| | 0.0008 | 1 | 0.2009 | - | | 0.0377 | 50 | 0.1674 | - | | 0.0754 | 100 | 0.1593 | - | | 0.1131 | 150 | 0.1793 | - | | 0.1508 | 200 | 0.176 | - | | 0.1885 | 250 | 0.1818 | - | | 0.2262 | 300 | 0.1209 | - | | 0.2640 | 350 | 0.1546 | - | | 0.3017 | 400 | 0.0996 | - | | 0.3394 | 450 | 0.1108 | - | | 0.3771 | 500 | 0.1163 | - | | 0.4148 | 550 | 0.1102 | - | | 0.4525 | 600 | 0.1477 | - | | 0.4902 | 650 | 0.0973 | - | | 0.5279 | 700 | 0.1324 | - | | 0.5656 | 750 | 0.1792 | - | | 0.6033 | 800 | 0.1026 | - | | 0.6410 | 850 | 0.1461 | - | | 0.6787 | 900 | 0.117 | - | | 0.7164 | 950 | 0.0907 | - | | 0.7541 | 1000 | 0.0904 | - | | 0.7919 | 1050 | 0.1168 | - | | 0.8296 | 1100 | 0.0831 | - | | 0.8673 | 1150 | 0.0623 | - | | 0.9050 | 1200 | 0.0802 | - | | 0.9427 | 1250 | 0.0802 | - | | 0.9804 | 1300 | 0.1212 | - | | 1.0181 | 1350 | 0.0872 | - | | 1.0558 | 1400 | 0.1068 | - | | 1.0935 | 1450 | 0.0975 | - | | 1.1312 | 1500 | 0.096 | - | | 1.1689 | 1550 | 0.0649 | - | | 1.2066 | 1600 | 0.1004 | - | | 1.2443 | 1650 | 0.0818 | - | | 1.2821 | 1700 | 0.0714 | - | | 1.3198 | 1750 | 0.0875 | - | | 1.3575 | 1800 | 0.0893 | - | | 1.3952 | 1850 | 0.1132 | - | | 1.4329 | 1900 | 0.1127 | - | | 1.4706 | 1950 | 0.0707 | - | | 1.5083 | 2000 | 0.0819 | - | | 1.5460 | 2050 | 0.0954 | - | | 1.5837 | 2100 | 0.0948 | - | | 1.6214 | 2150 | 0.0953 | - | | 1.6591 | 2200 | 0.0813 | - | | 1.6968 | 2250 | 0.0974 | - | | 1.7345 | 2300 | 0.0785 | - | | 1.7722 | 2350 | 0.086 | - | | 1.8100 | 2400 | 0.0808 | - | | 1.8477 | 2450 | 0.1014 | - | | 1.8854 | 2500 | 0.112 | - | | 1.9231 | 2550 | 0.0765 | - | | 1.9608 | 2600 | 0.0694 | - | | 1.9985 | 2650 | 0.0915 | - | | 2.0362 | 2700 | 0.087 | - | | 2.0739 | 2750 | 0.0831 | - | | 2.1116 | 2800 | 0.1223 | - | | 2.1493 | 2850 | 0.0897 | - | | 2.1870 | 2900 | 0.0937 | - | | 2.2247 | 2950 | 0.0862 | - | | 2.2624 | 3000 | 0.0977 | - | | 2.3002 | 3050 | 0.0563 | - | | 2.3379 | 3100 | 0.1197 | - | | 2.3756 | 3150 | 0.095 | - | | 2.4133 | 3200 | 0.0702 | - | | 2.4510 | 3250 | 0.0823 | - | | 2.4887 | 3300 | 0.1309 | - | | 2.5264 | 3350 | 0.0612 | - | | 2.5641 | 3400 | 0.0994 | - | | 2.6018 | 3450 | 0.0904 | - | | 2.6395 | 3500 | 0.0678 | - | | 2.6772 | 3550 | 0.0896 | - | | 2.7149 | 3600 | 0.0753 | - | | 2.7526 | 3650 | 0.0997 | - | | 2.7903 | 3700 | 0.0956 | - | | 2.8281 | 3750 | 0.1016 | - | | 2.8658 | 3800 | 0.0784 | - | | 2.9035 | 3850 | 0.0911 | - | | 2.9412 | 3900 | 0.0485 | - | | 2.9789 | 3950 | 0.1078 | - | | 3.0166 | 4000 | 0.0659 | - | | 3.0543 | 4050 | 0.0802 | - | | 3.0920 | 4100 | 0.12 | - | | 3.1297 | 4150 | 0.0519 | - | | 3.1674 | 4200 | 0.047 | - | | 3.2051 | 4250 | 0.0906 | - | | 3.2428 | 4300 | 0.0999 | - | | 3.2805 | 4350 | 0.059 | - | | 3.3183 | 4400 | 0.0533 | - | | 3.3560 | 4450 | 0.1033 | - | | 3.3937 | 4500 | 0.0871 | - | | 3.4314 | 4550 | 0.065 | - | | 3.4691 | 4600 | 0.1487 | - | | 3.5068 | 4650 | 0.0542 | - | | 3.5445 | 4700 | 0.0846 | - | | 3.5822 | 4750 | 0.0756 | - | | 3.6199 | 4800 | 0.0518 | - | | 3.6576 | 4850 | 0.1035 | - | | 3.6953 | 4900 | 0.1129 | - | | 3.7330 | 4950 | 0.1319 | - | | 3.7707 | 5000 | 0.0804 | - | | 3.8084 | 5050 | 0.108 | - | | 3.8462 | 5100 | 0.1246 | - | | 3.8839 | 5150 | 0.0923 | - | | 3.9216 | 5200 | 0.1048 | - | | 3.9593 | 5250 | 0.0951 | - | | 3.9970 | 5300 | 0.1015 | - | | 4.0347 | 5350 | 0.0888 | - | | 4.0724 | 5400 | 0.0917 | - | | 4.1101 | 5450 | 0.0823 | - | | 4.1478 | 5500 | 0.0882 | - | | 4.1855 | 5550 | 0.0807 | - | | 4.2232 | 5600 | 0.0997 | - | | 4.2609 | 5650 | 0.0782 | - | | 4.2986 | 5700 | 0.1165 | - | | 4.3363 | 5750 | 0.0837 | - | | 4.3741 | 5800 | 0.1098 | - | | 4.4118 | 5850 | 0.0564 | - | | 4.4495 | 5900 | 0.0715 | - | | 4.4872 | 5950 | 0.0858 | - | | 4.5249 | 6000 | 0.0889 | - | | 4.5626 | 6050 | 0.0719 | - | | 4.6003 | 6100 | 0.1076 | - | | 4.6380 | 6150 | 0.1044 | - | | 4.6757 | 6200 | 0.0914 | - | | 4.7134 | 6250 | 0.1078 | - | | 4.7511 | 6300 | 0.1137 | - | | 4.7888 | 6350 | 0.0666 | - | | 4.8265 | 6400 | 0.1009 | - | | 4.8643 | 6450 | 0.0537 | - | | 4.9020 | 6500 | 0.0576 | - | | 4.9397 | 6550 | 0.1366 | - | | 4.9774 | 6600 | 0.1009 | - | ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.35.2 - PyTorch: 2.1.0+cu121 - Datasets: 2.17.0 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->
{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "widget": [{"text": "\u03c0\u03b1\u03c3\u03c4\u03b1 \u03b1\u03c4\u03bf\u03bc\u03b9\u03ba\u03b7"}, {"text": "mikel mini croissant \u03c3\u03bf\u03ba\u03bf\u03bb\u03b1\u03c4\u03b1"}, {"text": "tasty nat nut \u03c0\u03b1\u03c3\u03c4\u03b5\u03bb\u03b9 \u03c3\u03bf\u03c5\u03c3\u03b1\u03bc\u03b9"}, {"text": "\u03c3\u03ba\u03b9\u03bf\u03c5\u03c6\u03b9\u03c7\u03c4\u03b1 \u03c3\u03b1\u03bb\u03c4\u03c3\u03b1 \u03bd\u03c4\u03bf\u03bc\u03b1\u03c4\u03b1\u03c2 \u03bc\u03c5\u03b6\u03b7\u03b8\u03c1\u03b1 \u03c3\u03b1\u03bb\u03c4\u03c3\u03b1 \u03bd\u03c4\u03bf\u03bc\u03b1\u03c4\u03b1\u03c2 \u03b5\u03bb\u03b9\u03b5\u03c2 \u03ba\u03b1\u03c0\u03c0\u03b1\u03c1\u03b7 \u03bc\u03c5\u03b6\u03b7\u03b8\u03c1\u03b1"}, {"text": "\u03ba\u03c1\u03b1\u03c3\u03b9 \u03c1\u03bf\u03b6\u03b5 \u03bb\u03b9\u03b1\u03bd\u03bf\u03c2"}], "pipeline_tag": "text-classification", "inference": false, "base_model": "lighteternal/stsb-xlm-r-greek-transfer", "model-index": [{"name": "SetFit with lighteternal/stsb-xlm-r-greek-transfer", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.1588785046728972, "name": "Accuracy"}]}]}]}
text-classification
st-karlos-efood/setfit-multilabel-one-vs-rest-feb-2024
[ "setfit", "safetensors", "xlm-roberta", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:lighteternal/stsb-xlm-r-greek-transfer", "model-index", "region:us" ]
2024-02-14T12:31:25+00:00
[ "2209.11055" ]
[]
TAGS #setfit #safetensors #xlm-roberta #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-lighteternal/stsb-xlm-r-greek-transfer #model-index #region-us
SetFit with lighteternal/stsb-xlm-r-greek-transfer ================================================== This is a SetFit model that can be used for Text Classification. This SetFit model uses lighteternal/stsb-xlm-r-greek-transfer as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a Sentence Transformer with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. Model Details ------------- ### Model Description * Model Type: SetFit * Sentence Transformer body: lighteternal/stsb-xlm-r-greek-transfer * Classification head: a OneVsRestClassifier instance * Maximum Sequence Length: 400 tokens ### Model Sources * Repository: SetFit on GitHub * Paper: Efficient Few-Shot Learning Without Prompts * Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts Evaluation ---------- ### Metrics Uses ---- ### Direct Use for Inference First install the SetFit library: Then you can load this model and run inference. Training Details ---------------- ### Training Set Metrics ### Training Hyperparameters * batch\_size: (48, 48) * num\_epochs: (5, 5) * max\_steps: -1 * sampling\_strategy: oversampling * num\_iterations: 10 * body\_learning\_rate: (2e-05, 2e-05) * head\_learning\_rate: 2e-05 * loss: CosineSimilarityLoss * distance\_metric: cosine\_distance * margin: 0.25 * end\_to\_end: False * use\_amp: False * warmup\_proportion: 0.1 * seed: 42 * eval\_max\_steps: -1 * load\_best\_model\_at\_end: False ### Training Results ### Framework Versions * Python: 3.10.12 * SetFit: 1.0.3 * Sentence Transformers: 2.3.1 * Transformers: 4.35.2 * PyTorch: 2.1.0+cu121 * Datasets: 2.17.0 * Tokenizers: 0.15.1 ### BibTeX
[ "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: lighteternal/stsb-xlm-r-greek-transfer\n* Classification head: a OneVsRestClassifier instance\n* Maximum Sequence Length: 400 tokens", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (48, 48)\n* num\\_epochs: (5, 5)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 10\n* body\\_learning\\_rate: (2e-05, 2e-05)\n* head\\_learning\\_rate: 2e-05\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.17.0\n* Tokenizers: 0.15.1", "### BibTeX" ]
[ "TAGS\n#setfit #safetensors #xlm-roberta #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-lighteternal/stsb-xlm-r-greek-transfer #model-index #region-us \n", "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: lighteternal/stsb-xlm-r-greek-transfer\n* Classification head: a OneVsRestClassifier instance\n* Maximum Sequence Length: 400 tokens", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (48, 48)\n* num\\_epochs: (5, 5)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 10\n* body\\_learning\\_rate: (2e-05, 2e-05)\n* head\\_learning\\_rate: 2e-05\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.17.0\n* Tokenizers: 0.15.1", "### BibTeX" ]
[ 75, 60, 56, 8, 31, 7, 178, 4, 58, 6 ]
[ "passage: TAGS\n#setfit #safetensors #xlm-roberta #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-lighteternal/stsb-xlm-r-greek-transfer #model-index #region-us \n### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: lighteternal/stsb-xlm-r-greek-transfer\n* Classification head: a OneVsRestClassifier instance\n* Maximum Sequence Length: 400 tokens### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts\n\n\nEvaluation\n----------### Metrics\n\n\n\nUses\n----### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------### Training Set Metrics### Training Hyperparameters\n\n\n* batch\\_size: (48, 48)\n* num\\_epochs: (5, 5)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 10\n* body\\_learning\\_rate: (2e-05, 2e-05)\n* head\\_learning\\_rate: 2e-05\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False### Training Results### Framework Versions\n\n\n* Python: 3.10.12\n* SetFit: 1.0.3\n* Sentence Transformers: 2.3.1\n* Transformers: 4.35.2\n* PyTorch: 2.1.0+cu121\n* Datasets: 2.17.0\n* Tokenizers: 0.15.1### BibTeX" ]
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null
null
transformers
# FrankenCRIA v1.3-m.1 ## What is FrankenCRIA? <p align="center"> <img src="https://github.com/davzoku/cria/blob/main/assets/frankencria-icon-512x512.png?raw=true" width="300" height="300" alt="FrankenCRIA Logo"> <br> <i>This is a frankenmerge of <a href="https://huggingface.co/davzoku/cria-llama2-7b-v1.3">davzoku/cria-llama2-7b-v1.3</a>.</i> </p> The configuration is the same as [Undi95/Mistral-11B-v0.1](https://huggingface.co/Undi95/Mistral-11B-v0.1), [mlabonne/FrankenBeagle14-11B](https://huggingface.co/mlabonne/FrankenBeagle14-11B) and the DUS technique used in [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0). Please be aware that this model is highly experimental, and no further training has been conducted following the merge. Therefore, the model performance may not meet expectations, as described in the [SOLAR paper](https://arxiv.org/abs/2312.15166) ## 📦 FrankenCRIA Model Release FrankenCRIA v1.3 comes with several variants. - [davzoku/frankencria-llama2-11b-v1.3-m.1](https://huggingface.co/davzoku/frankencria-llama2-11b-v1.3-m.1): 11B FrankenMerge inspired by [Undi95/Mistral-11B-v0.1](https://huggingface.co/Undi95/Mistral-11B-v0.1) - [davzoku/frankencria-llama2-11b-v1.3-m.2](https://huggingface.co/davzoku/frankencria-llama2-12.5b-v1.3-m.2): 12.5B interleaving FrankenMerge inspired by [vilm/vinallama-12.5b-chat-DUS](https://huggingface.co/vilm/vinallama-12.5b-chat-DUS) ## 🧩 Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3) ### Configuration The following YAML configuration was used to produce this model. ```yaml # https://huggingface.co/Undi95/Mistral-11B-v0.1 slices: - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [0, 24] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ```
{"language": "en", "license": "llama2", "library_name": "transformers", "tags": ["mergekit", "merge", "llama-2"], "datasets": ["mlabonne/CodeLlama-2-20k"], "inference": false, "model_type": "llama", "pipeline_tag": "text-generation", "base_model": ["davzoku/cria-llama2-7b-v1.3"]}
text-generation
davzoku/frankencria-llama2-11b-v1.3-m.1
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "llama-2", "en", "dataset:mlabonne/CodeLlama-2-20k", "arxiv:2312.15166", "base_model:davzoku/cria-llama2-7b-v1.3", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]
2024-02-14T12:32:00+00:00
[ "2312.15166" ]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #arxiv-2312.15166 #base_model-davzoku/cria-llama2-7b-v1.3 #license-llama2 #autotrain_compatible #text-generation-inference #region-us
# FrankenCRIA v1.3-m.1 ## What is FrankenCRIA? <p align="center"> <img src="URL width="300" height="300" alt="FrankenCRIA Logo"> <br> <i>This is a frankenmerge of <a href="URL </p> The configuration is the same as Undi95/Mistral-11B-v0.1, mlabonne/FrankenBeagle14-11B and the DUS technique used in upstage/SOLAR-10.7B-v1.0. Please be aware that this model is highly experimental, and no further training has been conducted following the merge. Therefore, the model performance may not meet expectations, as described in the SOLAR paper ## FrankenCRIA Model Release FrankenCRIA v1.3 comes with several variants. - davzoku/frankencria-llama2-11b-v1.3-m.1: 11B FrankenMerge inspired by Undi95/Mistral-11B-v0.1 - davzoku/frankencria-llama2-11b-v1.3-m.2: 12.5B interleaving FrankenMerge inspired by vilm/vinallama-12.5b-chat-DUS ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * davzoku/cria-llama2-7b-v1.3 ### Configuration The following YAML configuration was used to produce this model.
[ "# FrankenCRIA v1.3-m.1", "## What is FrankenCRIA?\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"FrankenCRIA Logo\"> <br>\n <i>This is a frankenmerge of <a href=\"URL\n</p>\n\nThe configuration is the same as Undi95/Mistral-11B-v0.1, mlabonne/FrankenBeagle14-11B and the DUS technique used in upstage/SOLAR-10.7B-v1.0.\n\n\nPlease be aware that this model is highly experimental, and no further training has been conducted following the merge. \nTherefore, the model performance may not meet expectations, as described in the SOLAR paper", "## FrankenCRIA Model Release\n\nFrankenCRIA v1.3 comes with several variants.\n\n- davzoku/frankencria-llama2-11b-v1.3-m.1: 11B FrankenMerge inspired by Undi95/Mistral-11B-v0.1\n- davzoku/frankencria-llama2-11b-v1.3-m.2: 12.5B interleaving FrankenMerge inspired by vilm/vinallama-12.5b-chat-DUS", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* davzoku/cria-llama2-7b-v1.3", "### Configuration\n\nThe following YAML configuration was used to produce this model." ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #arxiv-2312.15166 #base_model-davzoku/cria-llama2-7b-v1.3 #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n", "# FrankenCRIA v1.3-m.1", "## What is FrankenCRIA?\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"FrankenCRIA Logo\"> <br>\n <i>This is a frankenmerge of <a href=\"URL\n</p>\n\nThe configuration is the same as Undi95/Mistral-11B-v0.1, mlabonne/FrankenBeagle14-11B and the DUS technique used in upstage/SOLAR-10.7B-v1.0.\n\n\nPlease be aware that this model is highly experimental, and no further training has been conducted following the merge. \nTherefore, the model performance may not meet expectations, as described in the SOLAR paper", "## FrankenCRIA Model Release\n\nFrankenCRIA v1.3 comes with several variants.\n\n- davzoku/frankencria-llama2-11b-v1.3-m.1: 11B FrankenMerge inspired by Undi95/Mistral-11B-v0.1\n- davzoku/frankencria-llama2-11b-v1.3-m.2: 12.5B interleaving FrankenMerge inspired by vilm/vinallama-12.5b-chat-DUS", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* davzoku/cria-llama2-7b-v1.3", "### Configuration\n\nThe following YAML configuration was used to produce this model." ]
[ 101, 11, 153, 108, 4, 17, 31, 17 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #arxiv-2312.15166 #base_model-davzoku/cria-llama2-7b-v1.3 #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n# FrankenCRIA v1.3-m.1## What is FrankenCRIA?\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"FrankenCRIA Logo\"> <br>\n <i>This is a frankenmerge of <a href=\"URL\n</p>\n\nThe configuration is the same as Undi95/Mistral-11B-v0.1, mlabonne/FrankenBeagle14-11B and the DUS technique used in upstage/SOLAR-10.7B-v1.0.\n\n\nPlease be aware that this model is highly experimental, and no further training has been conducted following the merge. \nTherefore, the model performance may not meet expectations, as described in the SOLAR paper## FrankenCRIA Model Release\n\nFrankenCRIA v1.3 comes with several variants.\n\n- davzoku/frankencria-llama2-11b-v1.3-m.1: 11B FrankenMerge inspired by Undi95/Mistral-11B-v0.1\n- davzoku/frankencria-llama2-11b-v1.3-m.2: 12.5B interleaving FrankenMerge inspired by vilm/vinallama-12.5b-chat-DUS## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* davzoku/cria-llama2-7b-v1.3### Configuration\n\nThe following YAML configuration was used to produce this model." ]
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null
null
transformers
<!-- 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. --> # speecht5_tts_finetuned_voxpopuli_fr This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.5233 ## 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: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.6236 | 0.5 | 167 | 0.5358 | | 0.5737 | 1.0 | 334 | 0.5233 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["facebook/voxpopuli"], "base_model": "microsoft/speecht5_tts", "pipeline_tag": "text-to-speech", "model-index": [{"name": "speecht5-tts-finetuned-voxpopuli-fr", "results": []}]}
text-to-speech
ChuGyouk/speecht5-finetuned-voxpopuli-fr
[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "text-to-speech", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]
2024-02-14T12:32:06+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #text-to-speech #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
speecht5\_tts\_finetuned\_voxpopuli\_fr ======================================= This model is a fine-tuned version of microsoft/speecht5\_tts on the voxpopuli dataset. It achieves the following results on the evaluation set: * Loss: 0.5233 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: 4 * eval\_batch\_size: 2 * seed: 42 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 1 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #text-to-speech #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 81, 160, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #text-to-speech #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
null
flair
### Demo: How to use in Flair Requires: - **[Flair](https://github.com/flairNLP/flair/)>=0.14.0** (`pip install flair` or `pip install git+https://github.com/flairNLP/flair.git`) ```python from flair.data import Sentence from flair.models.prefixed_tagger import PrefixedSequenceTagger from flair.tokenization import SciSpacyTokenizer # load tagger tagger = PrefixedSequenceTagger.load("hunflair/hunflair2-ner") # make example sentence sentence = Sentence("The mutation in the ABCD1 gene causes X-linked adrenoleukodystrophy, " "a neurodegenerative disease, which is exacerbated by exposure to high " "levels of mercury in dolphin populations.", use_tokenizer=SciSpacyTokenizer()) # predict NER tags tagger.predict(sentence) # print sentence print(sentence) # print predicted NER spans print('The following NER tags are found:') # iterate over entities and print for entity in sentence.get_spans('ner'): print(entity) ```
{"tags": ["flair", "token-classification", "sequence-tagger-model"]}
token-classification
hunflair/hunflair2-ner
[ "flair", "pytorch", "token-classification", "sequence-tagger-model", "region:us" ]
2024-02-14T12:34:03+00:00
[]
[]
TAGS #flair #pytorch #token-classification #sequence-tagger-model #region-us
### Demo: How to use in Flair Requires: - Flair>=0.14.0 ('pip install flair' or 'pip install git+URL
[ "### Demo: How to use in Flair\n\nRequires:\n- Flair>=0.14.0 ('pip install flair' or 'pip install git+URL" ]
[ "TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #region-us \n", "### Demo: How to use in Flair\n\nRequires:\n- Flair>=0.14.0 ('pip install flair' or 'pip install git+URL" ]
[ 28, 37 ]
[ "passage: TAGS\n#flair #pytorch #token-classification #sequence-tagger-model #region-us \n### Demo: How to use in Flair\n\nRequires:\n- Flair>=0.14.0 ('pip install flair' or 'pip install git+URL" ]
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# Lora of owari/尾張/尾张 (Azur Lane) ## What Is This? This is the LoRA model of waifu owari/尾張/尾张 (Azur Lane). ## How Is It Trained? * This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). * The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs). * The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest). * Dataset used for training is the `stage3-p480-800` in [CyberHarem/owari_azurlane](https://huggingface.co/datasets/CyberHarem/owari_azurlane), which contains 795 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 2, resolution is 720x720, clustering into 20 buckets. * Trained for 7960 steps, 40 checkpoints were saved and evaluated. * **Trigger word is `owari_azurlane`.** * Pruned core tags for this waifu are `breasts, long_hair, braid, hair_over_one_eye, horns, large_breasts, yellow_eyes, blonde_hair, mole, twin_braids, dark_skin, bangs, earrings, hair_ornament, very_long_hair, dark-skinned_female, mole_under_mouth, hairclip, huge_breasts`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. ## How to Use It? ### If You Are Using A1111 WebUI v1.7+ **Just use it like the classic LoRA**. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 1393, you need to download [`1393/owari_azurlane.pt`](https://huggingface.co/CyberHarem/owari_azurlane/resolve/main/1393/owari_azurlane.pt) as the embedding and [`1393/owari_azurlane.safetensors`](https://huggingface.co/CyberHarem/owari_azurlane/resolve/main/1393/owari_azurlane.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters. ## Which Step Should I Use? We selected 5 good steps for you to choose. The best one is step 1393. 1720 images (1.86 GiB) were generated for auto-testing. ![Metrics Plot](metrics_plot.png) The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). Here are the preview of the recommended steps: | Step | Epoch | CCIP | AI Corrupt | Bikini Plus | Score | Download | pattern_0 | pattern_1_0 | pattern_1_1 | pattern_2_0 | pattern_2_1 | pattern_2_2 | pattern_3 | pattern_4 | portrait_0 | portrait_1 | portrait_2 | full_body_0 | full_body_1 | profile_0 | profile_1 | free_0 | free_1 | shorts | maid_0 | maid_1 | miko | yukata | suit | china | bikini_0 | bikini_1 | bikini_2 | sit | squat | kneel | jump | crossed_arms | angry | smile | cry | grin | n_lie_0 | n_lie_1 | n_stand_0 | n_stand_1 | n_stand_2 | n_sex_0 | n_sex_1 | |-------:|--------:|:----------|:-------------|:--------------|:----------|:--------------------------------------------------------------------------------------------------|:------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------------|:--------------------------------------------|:--------------------------------------------|:----------------------------------------------|:----------------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:------------------------------------|:--------------------------------|:------------------------------------|:--------------------------------|:----------------------------------|:----------------------------------------|:----------------------------------------|:----------------------------------------|:------------------------------|:----------------------------------|:----------------------------------|:--------------------------------|:------------------------------------------------|:----------------------------------|:----------------------------------|:------------------------------|:--------------------------------|:--------------------------------------|:--------------------------------------|:------------------------------------------|:------------------------------------------|:------------------------------------------|:--------------------------------------|:--------------------------------------| | 1393 | 8 | **0.975** | 0.935 | **0.851** | **0.959** | [Download](https://huggingface.co/CyberHarem/owari_azurlane/resolve/main/1393/owari_azurlane.zip) | ![pattern_0](1393/previews/pattern_0.png) | ![pattern_1_0](1393/previews/pattern_1_0.png) | ![pattern_1_1](1393/previews/pattern_1_1.png) | ![pattern_2_0](1393/previews/pattern_2_0.png) | ![pattern_2_1](1393/previews/pattern_2_1.png) | ![pattern_2_2](1393/previews/pattern_2_2.png) | ![pattern_3](1393/previews/pattern_3.png) | ![pattern_4](1393/previews/pattern_4.png) | ![portrait_0](1393/previews/portrait_0.png) | ![portrait_1](1393/previews/portrait_1.png) | ![portrait_2](1393/previews/portrait_2.png) | ![full_body_0](1393/previews/full_body_0.png) | ![full_body_1](1393/previews/full_body_1.png) | ![profile_0](1393/previews/profile_0.png) | ![profile_1](1393/previews/profile_1.png) | ![free_0](1393/previews/free_0.png) | ![free_1](1393/previews/free_1.png) | ![shorts](1393/previews/shorts.png) | ![maid_0](1393/previews/maid_0.png) | ![maid_1](1393/previews/maid_1.png) | ![miko](1393/previews/miko.png) | ![yukata](1393/previews/yukata.png) | ![suit](1393/previews/suit.png) | ![china](1393/previews/china.png) | ![bikini_0](1393/previews/bikini_0.png) | ![bikini_1](1393/previews/bikini_1.png) | ![bikini_2](1393/previews/bikini_2.png) | ![sit](1393/previews/sit.png) | ![squat](1393/previews/squat.png) | ![kneel](1393/previews/kneel.png) | ![jump](1393/previews/jump.png) | ![crossed_arms](1393/previews/crossed_arms.png) | ![angry](1393/previews/angry.png) | ![smile](1393/previews/smile.png) | ![cry](1393/previews/cry.png) | ![grin](1393/previews/grin.png) | ![n_lie_0](1393/previews/n_lie_0.png) | ![n_lie_1](1393/previews/n_lie_1.png) | ![n_stand_0](1393/previews/n_stand_0.png) | ![n_stand_1](1393/previews/n_stand_1.png) | ![n_stand_2](1393/previews/n_stand_2.png) | ![n_sex_0](1393/previews/n_sex_0.png) | ![n_sex_1](1393/previews/n_sex_1.png) | | 1592 | 9 | 0.954 | 0.915 | 0.842 | 0.856 | [Download](https://huggingface.co/CyberHarem/owari_azurlane/resolve/main/1592/owari_azurlane.zip) | ![pattern_0](1592/previews/pattern_0.png) | ![pattern_1_0](1592/previews/pattern_1_0.png) | ![pattern_1_1](1592/previews/pattern_1_1.png) | ![pattern_2_0](1592/previews/pattern_2_0.png) | ![pattern_2_1](1592/previews/pattern_2_1.png) | ![pattern_2_2](1592/previews/pattern_2_2.png) | ![pattern_3](1592/previews/pattern_3.png) | ![pattern_4](1592/previews/pattern_4.png) | ![portrait_0](1592/previews/portrait_0.png) | ![portrait_1](1592/previews/portrait_1.png) | ![portrait_2](1592/previews/portrait_2.png) | ![full_body_0](1592/previews/full_body_0.png) | ![full_body_1](1592/previews/full_body_1.png) | ![profile_0](1592/previews/profile_0.png) | ![profile_1](1592/previews/profile_1.png) | ![free_0](1592/previews/free_0.png) | ![free_1](1592/previews/free_1.png) | ![shorts](1592/previews/shorts.png) | ![maid_0](1592/previews/maid_0.png) | ![maid_1](1592/previews/maid_1.png) | ![miko](1592/previews/miko.png) | ![yukata](1592/previews/yukata.png) | ![suit](1592/previews/suit.png) | ![china](1592/previews/china.png) | ![bikini_0](1592/previews/bikini_0.png) | ![bikini_1](1592/previews/bikini_1.png) | ![bikini_2](1592/previews/bikini_2.png) | ![sit](1592/previews/sit.png) | ![squat](1592/previews/squat.png) | ![kneel](1592/previews/kneel.png) | ![jump](1592/previews/jump.png) | ![crossed_arms](1592/previews/crossed_arms.png) | ![angry](1592/previews/angry.png) | ![smile](1592/previews/smile.png) | ![cry](1592/previews/cry.png) | ![grin](1592/previews/grin.png) | ![n_lie_0](1592/previews/n_lie_0.png) | ![n_lie_1](1592/previews/n_lie_1.png) | ![n_stand_0](1592/previews/n_stand_0.png) | ![n_stand_1](1592/previews/n_stand_1.png) | ![n_stand_2](1592/previews/n_stand_2.png) | ![n_sex_0](1592/previews/n_sex_0.png) | ![n_sex_1](1592/previews/n_sex_1.png) | | 398 | 3 | 0.949 | **0.951** | 0.842 | 0.835 | [Download](https://huggingface.co/CyberHarem/owari_azurlane/resolve/main/398/owari_azurlane.zip) | ![pattern_0](398/previews/pattern_0.png) | ![pattern_1_0](398/previews/pattern_1_0.png) | ![pattern_1_1](398/previews/pattern_1_1.png) | ![pattern_2_0](398/previews/pattern_2_0.png) | ![pattern_2_1](398/previews/pattern_2_1.png) | ![pattern_2_2](398/previews/pattern_2_2.png) | ![pattern_3](398/previews/pattern_3.png) | ![pattern_4](398/previews/pattern_4.png) | ![portrait_0](398/previews/portrait_0.png) | ![portrait_1](398/previews/portrait_1.png) | ![portrait_2](398/previews/portrait_2.png) | ![full_body_0](398/previews/full_body_0.png) | ![full_body_1](398/previews/full_body_1.png) | ![profile_0](398/previews/profile_0.png) | ![profile_1](398/previews/profile_1.png) | ![free_0](398/previews/free_0.png) | ![free_1](398/previews/free_1.png) | ![shorts](398/previews/shorts.png) | ![maid_0](398/previews/maid_0.png) | ![maid_1](398/previews/maid_1.png) | ![miko](398/previews/miko.png) | ![yukata](398/previews/yukata.png) | ![suit](398/previews/suit.png) | ![china](398/previews/china.png) | ![bikini_0](398/previews/bikini_0.png) | ![bikini_1](398/previews/bikini_1.png) | ![bikini_2](398/previews/bikini_2.png) | ![sit](398/previews/sit.png) | ![squat](398/previews/squat.png) | ![kneel](398/previews/kneel.png) | ![jump](398/previews/jump.png) | ![crossed_arms](398/previews/crossed_arms.png) | ![angry](398/previews/angry.png) | ![smile](398/previews/smile.png) | ![cry](398/previews/cry.png) | ![grin](398/previews/grin.png) | ![n_lie_0](398/previews/n_lie_0.png) | ![n_lie_1](398/previews/n_lie_1.png) | ![n_stand_0](398/previews/n_stand_0.png) | ![n_stand_1](398/previews/n_stand_1.png) | ![n_stand_2](398/previews/n_stand_2.png) | ![n_sex_0](398/previews/n_sex_0.png) | ![n_sex_1](398/previews/n_sex_1.png) | | 7960 | 41 | 0.946 | 0.901 | 0.834 | 0.815 | [Download](https://huggingface.co/CyberHarem/owari_azurlane/resolve/main/7960/owari_azurlane.zip) | ![pattern_0](7960/previews/pattern_0.png) | ![pattern_1_0](7960/previews/pattern_1_0.png) | ![pattern_1_1](7960/previews/pattern_1_1.png) | ![pattern_2_0](7960/previews/pattern_2_0.png) | ![pattern_2_1](7960/previews/pattern_2_1.png) | ![pattern_2_2](7960/previews/pattern_2_2.png) | ![pattern_3](7960/previews/pattern_3.png) | ![pattern_4](7960/previews/pattern_4.png) | ![portrait_0](7960/previews/portrait_0.png) | ![portrait_1](7960/previews/portrait_1.png) | ![portrait_2](7960/previews/portrait_2.png) | ![full_body_0](7960/previews/full_body_0.png) | ![full_body_1](7960/previews/full_body_1.png) | ![profile_0](7960/previews/profile_0.png) | ![profile_1](7960/previews/profile_1.png) | ![free_0](7960/previews/free_0.png) | ![free_1](7960/previews/free_1.png) | ![shorts](7960/previews/shorts.png) | ![maid_0](7960/previews/maid_0.png) | ![maid_1](7960/previews/maid_1.png) | ![miko](7960/previews/miko.png) | ![yukata](7960/previews/yukata.png) | ![suit](7960/previews/suit.png) | ![china](7960/previews/china.png) | ![bikini_0](7960/previews/bikini_0.png) | ![bikini_1](7960/previews/bikini_1.png) | ![bikini_2](7960/previews/bikini_2.png) | ![sit](7960/previews/sit.png) | ![squat](7960/previews/squat.png) | ![kneel](7960/previews/kneel.png) | ![jump](7960/previews/jump.png) | ![crossed_arms](7960/previews/crossed_arms.png) | ![angry](7960/previews/angry.png) | ![smile](7960/previews/smile.png) | ![cry](7960/previews/cry.png) | ![grin](7960/previews/grin.png) | ![n_lie_0](7960/previews/n_lie_0.png) | ![n_lie_1](7960/previews/n_lie_1.png) | ![n_stand_0](7960/previews/n_stand_0.png) | ![n_stand_1](7960/previews/n_stand_1.png) | ![n_stand_2](7960/previews/n_stand_2.png) | ![n_sex_0](7960/previews/n_sex_0.png) | ![n_sex_1](7960/previews/n_sex_1.png) | | 4179 | 22 | 0.946 | 0.901 | 0.825 | 0.799 | [Download](https://huggingface.co/CyberHarem/owari_azurlane/resolve/main/4179/owari_azurlane.zip) | ![pattern_0](4179/previews/pattern_0.png) | ![pattern_1_0](4179/previews/pattern_1_0.png) | ![pattern_1_1](4179/previews/pattern_1_1.png) | ![pattern_2_0](4179/previews/pattern_2_0.png) | ![pattern_2_1](4179/previews/pattern_2_1.png) | ![pattern_2_2](4179/previews/pattern_2_2.png) | ![pattern_3](4179/previews/pattern_3.png) | ![pattern_4](4179/previews/pattern_4.png) | ![portrait_0](4179/previews/portrait_0.png) | ![portrait_1](4179/previews/portrait_1.png) | ![portrait_2](4179/previews/portrait_2.png) | ![full_body_0](4179/previews/full_body_0.png) | ![full_body_1](4179/previews/full_body_1.png) | ![profile_0](4179/previews/profile_0.png) | ![profile_1](4179/previews/profile_1.png) | ![free_0](4179/previews/free_0.png) | ![free_1](4179/previews/free_1.png) | ![shorts](4179/previews/shorts.png) | ![maid_0](4179/previews/maid_0.png) | ![maid_1](4179/previews/maid_1.png) | ![miko](4179/previews/miko.png) | ![yukata](4179/previews/yukata.png) | ![suit](4179/previews/suit.png) | ![china](4179/previews/china.png) | ![bikini_0](4179/previews/bikini_0.png) | ![bikini_1](4179/previews/bikini_1.png) | ![bikini_2](4179/previews/bikini_2.png) | ![sit](4179/previews/sit.png) | ![squat](4179/previews/squat.png) | ![kneel](4179/previews/kneel.png) | ![jump](4179/previews/jump.png) | ![crossed_arms](4179/previews/crossed_arms.png) | ![angry](4179/previews/angry.png) | ![smile](4179/previews/smile.png) | ![cry](4179/previews/cry.png) | ![grin](4179/previews/grin.png) | ![n_lie_0](4179/previews/n_lie_0.png) | ![n_lie_1](4179/previews/n_lie_1.png) | ![n_stand_0](4179/previews/n_stand_0.png) | ![n_stand_1](4179/previews/n_stand_1.png) | ![n_stand_2](4179/previews/n_stand_2.png) | ![n_sex_0](4179/previews/n_sex_0.png) | ![n_sex_1](4179/previews/n_sex_1.png) | ## Anything Else? Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. ## All Steps We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * [Steps From 6169 to 7960](all/0.md) * [Steps From 4179 to 5970](all/1.md) * [Steps From 2189 to 3980](all/2.md) * [Steps From 199 to 1990](all/3.md)
{"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/owari_azurlane"], "pipeline_tag": "text-to-image"}
text-to-image
CyberHarem/owari_azurlane
[ "art", "not-for-all-audiences", "text-to-image", "dataset:CyberHarem/owari_azurlane", "license:mit", "region:us" ]
2024-02-14T12:35:30+00:00
[]
[]
TAGS #art #not-for-all-audiences #text-to-image #dataset-CyberHarem/owari_azurlane #license-mit #region-us
Lora of owari/尾張/尾张 (Azur Lane) =============================== What Is This? ------------- This is the LoRA model of waifu owari/尾張/尾张 (Azur Lane). How Is It Trained? ------------------ * This model is trained with HCP-Diffusion. * The auto-training framework is maintained by DeepGHS Team. * The base model used for training is deepghs/animefull-latest. * Dataset used for training is the 'stage3-p480-800' in CyberHarem/owari\_azurlane, which contains 795 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 2, resolution is 720x720, clustering into 20 buckets. * Trained for 7960 steps, 40 checkpoints were saved and evaluated. * Trigger word is 'owari\_azurlane'. * Pruned core tags for this waifu are 'breasts, long\_hair, braid, hair\_over\_one\_eye, horns, large\_breasts, yellow\_eyes, blonde\_hair, mole, twin\_braids, dark\_skin, bangs, earrings, hair\_ornament, very\_long\_hair, dark-skinned\_female, mole\_under\_mouth, hairclip, huge\_breasts'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. How to Use It? -------------- ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 1393, you need to download '1393/owari\_azurlane.pt' as the embedding and '1393/owari\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters. Which Step Should I Use? ------------------------ We selected 5 good steps for you to choose. The best one is step 1393. 1720 images (1.86 GiB) were generated for auto-testing. !Metrics Plot The base model used for generating preview images is Meina/MeinaMix\_V11. Here are the preview of the recommended steps: Anything Else? -------------- Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. All Steps --------- We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * Steps From 6169 to 7960 * Steps From 4179 to 5970 * Steps From 2189 to 3980 * Steps From 199 to 1990
[ "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 1393, you need to download '1393/owari\\_azurlane.pt' as the embedding and '1393/owari\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 1393.\n\n\n1720 images (1.86 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 6169 to 7960\n* Steps From 4179 to 5970\n* Steps From 2189 to 3980\n* Steps From 199 to 1990" ]
[ "TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/owari_azurlane #license-mit #region-us \n", "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 1393, you need to download '1393/owari\\_azurlane.pt' as the embedding and '1393/owari\\_azurlane.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 1393.\n\n\n1720 images (1.86 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 6169 to 7960\n* Steps From 4179 to 5970\n* Steps From 2189 to 3980\n* Steps From 199 to 1990" ]
[ 44, 38, 472 ]
[ "passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/owari_azurlane #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file." ]
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null
null
stable-baselines3
# **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```
{"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.21 +/- 0.11", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
arekpaterak/a2c-PandaReachDense-v3
[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-14T12:36:33+00:00
[]
[]
TAGS #stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 41, 45, 17 ]
[ "passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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transformers
# [MaziyarPanahi/LWM-Text-256K-GGUF](https://huggingface.co/MaziyarPanahi/LWM-Text-256K-GGUF) - Model creator: [LargeWorldModel](https://huggingface.co/LargeWorldModel) - Original model: [LargeWorldModel/LWM-Text-256K](https://huggingface.co/LargeWorldModel/LWM-Text-256K) ## Description [MaziyarPanahi/LWM-Text-256K-GGUF](https://huggingface.co/MaziyarPanahi/LWM-Text-256K-GGUF) contains GGUF format model files for [LargeWorldModel/LWM-Text-256K](https://huggingface.co/LargeWorldModel/LWM-Text-256K). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/LWM-Text-256K-GGUF](https://huggingface.co/MaziyarPanahi/LWM-Text-256K-GGUF) and below it, a specific filename to download, such as: LWM-Text-256K-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/LWM-Text-256K-GGUF LWM-Text-256K-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/LWM-Text-256K-GGUF](https://huggingface.co/MaziyarPanahi/LWM-Text-256K-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/LWM-Text-256K-GGUF LWM-Text-256K-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m LWM-Text-256K-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./LWM-Text-256K-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./LWM-Text-256K-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
{"tags": ["quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "region:us"], "model_name": "LWM-Text-256K-GGUF", "base_model": "LargeWorldModel/LWM-Text-256K", "inference": false, "model_creator": "LargeWorldModel", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"}
text-generation
MaziyarPanahi/LWM-Text-256K-GGUF
[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "pytorch", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "region:us", "base_model:LargeWorldModel/LWM-Text-256K" ]
2024-02-14T12:37:39+00:00
[]
[]
TAGS #transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #pytorch #llama #text-generation #autotrain_compatible #text-generation-inference #region-us #base_model-LargeWorldModel/LWM-Text-256K
# MaziyarPanahi/LWM-Text-256K-GGUF - Model creator: LargeWorldModel - Original model: LargeWorldModel/LWM-Text-256K ## Description MaziyarPanahi/LWM-Text-256K-GGUF contains GGUF format model files for LargeWorldModel/LWM-Text-256K. ## How to use Thanks to TheBloke for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL. Here is an incomplete list of clients and libraries that are known to support GGUF: * URL. The source project for GGUF. Offers a CLI and a server option. * text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection. * URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use. * ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * URL ### In 'text-generation-webui' Under Download Model, you can enter the model repo: MaziyarPanahi/LWM-Text-256K-GGUF and below it, a specific filename to download, such as: LWM-Text-256K-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the 'huggingface-hub' Python library: Then you can download any individual model file to the current directory, at high speed, with a command like this: </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI. To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer': And set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1': Windows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command. </details> ## Example 'URL' command Make sure you are using 'URL' from commit d0cee0d or later. Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins' For other parameters and how to use them, please refer to the URL documentation ## How to run in 'text-generation-webui' Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL. ## How to run from Python code You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: llama-cpp-python docs. #### First install the package Run one of the following commands, according to your system: #### Simple llama-cpp-python example code ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * LangChain + llama-cpp-python * LangChain + ctransformers
[ "# MaziyarPanahi/LWM-Text-256K-GGUF\n- Model creator: LargeWorldModel\n- Original model: LargeWorldModel/LWM-Text-256K", "## Description\nMaziyarPanahi/LWM-Text-256K-GGUF contains GGUF format model files for LargeWorldModel/LWM-Text-256K.", "## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.", "### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw", "## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL", "### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/LWM-Text-256K-GGUF and below it, a specific filename to download, such as: LWM-Text-256K-GGUF.Q4_K_M.gguf.\n\nThen click Download.", "### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n</details>\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>", "## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation", "## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.", "## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code", "## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers" ]
[ "TAGS\n#transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #pytorch #llama #text-generation #autotrain_compatible #text-generation-inference #region-us #base_model-LargeWorldModel/LWM-Text-256K \n", "# MaziyarPanahi/LWM-Text-256K-GGUF\n- Model creator: LargeWorldModel\n- Original model: LargeWorldModel/LWM-Text-256K", "## Description\nMaziyarPanahi/LWM-Text-256K-GGUF contains GGUF format model files for LargeWorldModel/LWM-Text-256K.", "## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.", "### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw", "## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL", "### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/LWM-Text-256K-GGUF and below it, a specific filename to download, such as: LWM-Text-256K-GGUF.Q4_K_M.gguf.\n\nThen click Download.", "### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n</details>\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>", "## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation", "## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.", "## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code", "## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers" ]
[ 87, 38, 38, 26, 401, 323, 84, 77, 218, 182, 49, 77, 36, 19, 12, 50 ]
[ "passage: TAGS\n#transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #pytorch #llama #text-generation #autotrain_compatible #text-generation-inference #region-us #base_model-LargeWorldModel/LWM-Text-256K \n# MaziyarPanahi/LWM-Text-256K-GGUF\n- Model creator: LargeWorldModel\n- Original model: LargeWorldModel/LWM-Text-256K## Description\nMaziyarPanahi/LWM-Text-256K-GGUF contains GGUF format model files for LargeWorldModel/LWM-Text-256K.## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "passage: ### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/LWM-Text-256K-GGUF and below it, a specific filename to download, such as: LWM-Text-256K-GGUF.Q4_K_M.gguf.\n\nThen click Download." ]
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null
null
diffusers
# The Female 10Th doctor <Gallery /> ## Trigger words You should use `10ma_s` to trigger the image generation. You should use `1girl` to trigger the image generation. You should use `solo` to trigger the image generation. You should use `long hair` to trigger the image generation. You should use `female 10th doctor` to trigger the image generation. You should use `black hair` to trigger the image generation. You should use `green eyes` to trigger the image generation. You should use `absurdly long hair` to trigger the image generation. You should use `natural white back hair bow` to trigger the image generation. You should use `green hair` to trigger the image generation. You should use `closed mouth` to trigger the image generation. You should use `{{{{{girl&#39;s face}}}}}` to trigger the image generation. You should use `{{{{{breast}}}}}` to trigger the image generation. You should use `{{{{{girl body}}}}}` to trigger the image generation. You should use `time cotton fabric` to trigger the image generation. You should use `brown with blue stripe` to trigger the image generation. You should use `navy with rust stripes` to trigger the image generation. You should use `analysis` to trigger the image generation. You should use `brown suit` to trigger the image generation. You should use `light blue shirt` to trigger the image generation. You should use `brown pants` to trigger the image generation. You should use `brown pinstripe suit` to trigger the image generation. You should use `brown with blue stripes` to trigger the image generation. You should use `brown cotton with blue pinstripe` to trigger the image generation. You should use `four button single vent with three` to trigger the image generation. You should use `pocket detail` to trigger the image generation. You should use `brown snit` to trigger the image generation. You should use `lining` to trigger the image generation. You should use `blue acetate lining` to trigger the image generation. You should use `pahe blue` to trigger the image generation. You should use `stwirly tie lien shirt + necktie to vary` to trigger the image generation. You should use `very long brown wool trench coat` to trigger the image generation. You should use `brown al cantara` to trigger the image generation. You should use `sofa fabric` to trigger the image generation. You should use `roan ii` to trigger the image generation. You should use `single breosbey` to trigger the image generation. You should use `with vent + button detail` to trigger the image generation. You should use `inside pocket detail` to trigger the image generation. You should use `brown Long wool trench coat lining` to trigger the image generation. You should use `navy blue with seif satirl` to trigger the image generation. You should use `stripe` to trigger the image generation. You should use `brown coat pocket` to trigger the image generation. You should use `lininy trim` to trigger the image generation. You should use `rust dvpion` to trigger the image generation. You should use `doctor who style` to trigger the image generation. You should use `with the 10th doctor&#39;s sonic screwdriver` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/EuellBraceros01/TheFemale10ThDoctor/tree/main) them in the Files & versions tab.
{"license": "apache-2.0", "tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "10ma_s, 1girl, solo, long hair, female 10th doctor, black hair,green eyes,absurdly long hair, natural white back hair bow,green hair, closed mouth, {{{{{girl's face}}}}}, {{{{{breast}}}}}, {{{{{girl body}}}}}, time cotton fabric, brown with blue stripe, navy with rust stripes, analysis, brown suit, light blue shirt, brown pants, brown pinstripe suit, brown with blue stripes, brown cotton with blue pinstripe, four button single vent with three, pocket detail, brown snit, lining, blue acetate lining, pahe blue, stwirly tie lien shirt + necktie to vary, very long brown wool trench coat, brown al cantara, sofa fabric, roan ii, single breosbey, with vent + button detail, inside pocket detail, brown Long wool trench coat lining, navy blue with seif satirl, stripe, brown coat pocket, lininy trim, rust dvpion, doctor who style, with the 10th doctor's sonic screwdriver,", "parameters": {"negative_prompt": "(EasyNegative:1.2), ugly, (extra arm:1.5), bad hands, extra hands, bad legs, bad hair, (extra hair:1.5), breasts, (bad tails:1.5), (missing hands:1.5), (missing arms:1.5), bad arms, bare legs, bad ears, bad skirt, bad skin, lowres, text, bad long hair, fix hand, extra hand, extra very long hair, bad sleeveless, bad detached sleeves, extra sleeveless, extra detached sleeves, bad sleeveless shirt, extra face, fix brown suit, extra very long brown trench coat"}, "output": {"url": "images/6289759d-d76c-4fcb-99a0-de3f2f32a436.webp"}}], "base_model": "runwayml/stable-diffusion-v1-5", "instance_prompt": "10ma_s, 1girl, solo, long hair, female 10th doctor, black hair, green eyes, absurdly long hair, natural white back hair bow, green hair, closed mouth, {{{{{girl's face}}}}}, {{{{{breast}}}}}, {{{{{girl body}}}}}, time cotton fabric, brown with blue stripe, navy with rust stripes, analysis, brown suit, light blue shirt, brown pants, brown pinstripe suit, brown with blue stripes, brown cotton with blue pinstripe, four button single vent with three, pocket detail, brown snit, lining, blue acetate lining, pahe blue, stwirly tie lien shirt + necktie to vary, very long brown wool trench coat, brown al cantara, sofa fabric, roan ii, single breosbey, with vent + button detail, inside pocket detail, brown Long wool trench coat lining, navy blue with seif satirl, stripe, brown coat pocket, lininy trim, rust dvpion, doctor who style, with the 10th doctor's sonic screwdriver"}
text-to-image
EuellBraceros01/TheFemale10ThDoctor
[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:runwayml/stable-diffusion-v1-5", "license:apache-2.0", "region:us" ]
2024-02-14T12:37:55+00:00
[]
[]
TAGS #diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #region-us
# The Female 10Th doctor <Gallery /> ## Trigger words You should use '10ma_s' to trigger the image generation. You should use '1girl' to trigger the image generation. You should use 'solo' to trigger the image generation. You should use 'long hair' to trigger the image generation. You should use 'female 10th doctor' to trigger the image generation. You should use 'black hair' to trigger the image generation. You should use 'green eyes' to trigger the image generation. You should use 'absurdly long hair' to trigger the image generation. You should use 'natural white back hair bow' to trigger the image generation. You should use 'green hair' to trigger the image generation. You should use 'closed mouth' to trigger the image generation. You should use '{{{{{girl&#39;s face}}}}}' to trigger the image generation. You should use '{{{{{breast}}}}}' to trigger the image generation. You should use '{{{{{girl body}}}}}' to trigger the image generation. You should use 'time cotton fabric' to trigger the image generation. You should use 'brown with blue stripe' to trigger the image generation. You should use 'navy with rust stripes' to trigger the image generation. You should use 'analysis' to trigger the image generation. You should use 'brown suit' to trigger the image generation. You should use 'light blue shirt' to trigger the image generation. You should use 'brown pants' to trigger the image generation. You should use 'brown pinstripe suit' to trigger the image generation. You should use 'brown with blue stripes' to trigger the image generation. You should use 'brown cotton with blue pinstripe' to trigger the image generation. You should use 'four button single vent with three' to trigger the image generation. You should use 'pocket detail' to trigger the image generation. You should use 'brown snit' to trigger the image generation. You should use 'lining' to trigger the image generation. You should use 'blue acetate lining' to trigger the image generation. You should use 'pahe blue' to trigger the image generation. You should use 'stwirly tie lien shirt + necktie to vary' to trigger the image generation. You should use 'very long brown wool trench coat' to trigger the image generation. You should use 'brown al cantara' to trigger the image generation. You should use 'sofa fabric' to trigger the image generation. You should use 'roan ii' to trigger the image generation. You should use 'single breosbey' to trigger the image generation. You should use 'with vent + button detail' to trigger the image generation. You should use 'inside pocket detail' to trigger the image generation. You should use 'brown Long wool trench coat lining' to trigger the image generation. You should use 'navy blue with seif satirl' to trigger the image generation. You should use 'stripe' to trigger the image generation. You should use 'brown coat pocket' to trigger the image generation. You should use 'lininy trim' to trigger the image generation. You should use 'rust dvpion' to trigger the image generation. You should use 'doctor who style' to trigger the image generation. You should use 'with the 10th doctor&#39;s sonic screwdriver' to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab.
[ "# The Female 10Th doctor\n\n<Gallery />", "## Trigger words\n\nYou should use '10ma_s' to trigger the image generation.\n\nYou should use '1girl' to trigger the image generation.\n\nYou should use 'solo' to trigger the image generation.\n\nYou should use 'long hair' to trigger the image generation.\n\nYou should use 'female 10th doctor' to trigger the image generation.\n\nYou should use 'black hair' to trigger the image generation.\n\nYou should use 'green eyes' to trigger the image generation.\n\nYou should use 'absurdly long hair' to trigger the image generation.\n\nYou should use 'natural white back hair bow' to trigger the image generation.\n\nYou should use 'green hair' to trigger the image generation.\n\nYou should use 'closed mouth' to trigger the image generation.\n\nYou should use '{{{{{girl&#39;s face}}}}}' to trigger the image generation.\n\nYou should use '{{{{{breast}}}}}' to trigger the image generation.\n\nYou should use '{{{{{girl body}}}}}' to trigger the image generation.\n\nYou should use 'time cotton fabric' to trigger the image generation.\n\nYou should use 'brown with blue stripe' to trigger the image generation.\n\nYou should use 'navy with rust stripes' to trigger the image generation.\n\nYou should use 'analysis' to trigger the image generation.\n\nYou should use 'brown suit' to trigger the image generation.\n\nYou should use 'light blue shirt' to trigger the image generation.\n\nYou should use 'brown pants' to trigger the image generation.\n\nYou should use 'brown pinstripe suit' to trigger the image generation.\n\nYou should use 'brown with blue stripes' to trigger the image generation.\n\nYou should use 'brown cotton with blue pinstripe' to trigger the image generation.\n\nYou should use 'four button single vent with three' to trigger the image generation.\n\nYou should use 'pocket detail' to trigger the image generation.\n\nYou should use 'brown snit' to trigger the image generation.\n\nYou should use 'lining' to trigger the image generation.\n\nYou should use 'blue acetate lining' to trigger the image generation.\n\nYou should use 'pahe blue' to trigger the image generation.\n\nYou should use 'stwirly tie lien shirt + necktie to vary' to trigger the image generation.\n\nYou should use 'very long brown wool trench coat' to trigger the image generation.\n\nYou should use 'brown al cantara' to trigger the image generation.\n\nYou should use 'sofa fabric' to trigger the image generation.\n\nYou should use 'roan ii' to trigger the image generation.\n\nYou should use 'single breosbey' to trigger the image generation.\n\nYou should use 'with vent + button detail' to trigger the image generation.\n\nYou should use 'inside pocket detail' to trigger the image generation.\n\nYou should use 'brown Long wool trench coat lining' to trigger the image generation.\n\nYou should use 'navy blue with seif satirl' to trigger the image generation.\n\nYou should use 'stripe' to trigger the image generation.\n\nYou should use 'brown coat pocket' to trigger the image generation.\n\nYou should use 'lininy trim' to trigger the image generation.\n\nYou should use 'rust dvpion' to trigger the image generation.\n\nYou should use 'doctor who style' to trigger the image generation.\n\nYou should use 'with the 10th doctor&#39;s sonic screwdriver' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #region-us \n", "# The Female 10Th doctor\n\n<Gallery />", "## Trigger words\n\nYou should use '10ma_s' to trigger the image generation.\n\nYou should use '1girl' to trigger the image generation.\n\nYou should use 'solo' to trigger the image generation.\n\nYou should use 'long hair' to trigger the image generation.\n\nYou should use 'female 10th doctor' to trigger the image generation.\n\nYou should use 'black hair' to trigger the image generation.\n\nYou should use 'green eyes' to trigger the image generation.\n\nYou should use 'absurdly long hair' to trigger the image generation.\n\nYou should use 'natural white back hair bow' to trigger the image generation.\n\nYou should use 'green hair' to trigger the image generation.\n\nYou should use 'closed mouth' to trigger the image generation.\n\nYou should use '{{{{{girl&#39;s face}}}}}' to trigger the image generation.\n\nYou should use '{{{{{breast}}}}}' to trigger the image generation.\n\nYou should use '{{{{{girl body}}}}}' to trigger the image generation.\n\nYou should use 'time cotton fabric' to trigger the image generation.\n\nYou should use 'brown with blue stripe' to trigger the image generation.\n\nYou should use 'navy with rust stripes' to trigger the image generation.\n\nYou should use 'analysis' to trigger the image generation.\n\nYou should use 'brown suit' to trigger the image generation.\n\nYou should use 'light blue shirt' to trigger the image generation.\n\nYou should use 'brown pants' to trigger the image generation.\n\nYou should use 'brown pinstripe suit' to trigger the image generation.\n\nYou should use 'brown with blue stripes' to trigger the image generation.\n\nYou should use 'brown cotton with blue pinstripe' to trigger the image generation.\n\nYou should use 'four button single vent with three' to trigger the image generation.\n\nYou should use 'pocket detail' to trigger the image generation.\n\nYou should use 'brown snit' to trigger the image generation.\n\nYou should use 'lining' to trigger the image generation.\n\nYou should use 'blue acetate lining' to trigger the image generation.\n\nYou should use 'pahe blue' to trigger the image generation.\n\nYou should use 'stwirly tie lien shirt + necktie to vary' to trigger the image generation.\n\nYou should use 'very long brown wool trench coat' to trigger the image generation.\n\nYou should use 'brown al cantara' to trigger the image generation.\n\nYou should use 'sofa fabric' to trigger the image generation.\n\nYou should use 'roan ii' to trigger the image generation.\n\nYou should use 'single breosbey' to trigger the image generation.\n\nYou should use 'with vent + button detail' to trigger the image generation.\n\nYou should use 'inside pocket detail' to trigger the image generation.\n\nYou should use 'brown Long wool trench coat lining' to trigger the image generation.\n\nYou should use 'navy blue with seif satirl' to trigger the image generation.\n\nYou should use 'stripe' to trigger the image generation.\n\nYou should use 'brown coat pocket' to trigger the image generation.\n\nYou should use 'lininy trim' to trigger the image generation.\n\nYou should use 'rust dvpion' to trigger the image generation.\n\nYou should use 'doctor who style' to trigger the image generation.\n\nYou should use 'with the 10th doctor&#39;s sonic screwdriver' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]
[ 62, 11, 740, 28 ]
[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-runwayml/stable-diffusion-v1-5 #license-apache-2.0 #region-us \n# The Female 10Th doctor\n\n<Gallery />" ]
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null
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<h1 align="center"> OCR Tamil - Easy, Accurate and Simple to use Tamil OCR - (ஒளி எழுத்துணரி)</h1> <p align="center">❤️️❤️️Please star✨ it if you like❤️️❤️️</p> <p align="center"> <a href="LICENSE"> <img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/MIT.svg" alt="LICENSE"> </a> <a href="https://huggingface.co/spaces/GnanaPrasath/ocr_tamil"> <img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/huggingface.svg" alt="HuggingSpace"> </a> <a href="https://colab.research.google.com/drive/11QPPj3EmpoIqnpuIznKeP1icxvVOjfux?usp=sharing"> <img src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/colab.svg" alt="colab"> </a> </p> <div align="center"> <p> <a href="https://github.com/gnana70/tamil_ocr"> <img width="50%" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/logo_1.gif"> </a> </p> </div> OCR Tamil can help you extract text from signboard, nameplates, storefronts etc., from Natural Scenes with high accuracy. This version of OCR is much more robust to tilted text compared to the Tesseract, Paddle OCR and Easy OCR as they are primarily built to work on the documents texts and not on natural scenes. This model is work in progress, feel free to contribute!!! ## Languages Supported 🔛 **➡️ English** **➡️ Tamil (தமிழ்)** ## Accuracy 🎯 ✔️ English > 98% ✔️ Tamil > 95% ## Comparison between Tesseract OCR and OCR Tamil ⚖️ Input Image | OCR TAMIL 🏆 | Tesseract | EasyOCR | |:--------------------------------------------------------------------------:|:--------------------:|:-----------------:|:-----------------:| | <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/4.jpg"> | வாழ்கவளமுடன்✅ | க்‌ க்கஸாரகளள௮ஊகஎளமுடன்‌ ❌ | வாழக வளமுடன்❌| | <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/11.jpg"> | தமிழ்வாழ்க✅ | **NO OUTPUT** ❌ | தமிழ்வாழ்க✅ | | <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/2.jpg"> | கோபி ✅ | **NO OUTPUT** ❌ | ப99❌ | | <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/6.jpg"> | தாம்பரம் ✅ | **NO OUTPUT** ❌ | தாம்பரம❌ | | <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/1.jpg"> | நெடுஞ்சாலைத் ✅ | **NO OUTPUT** ❌ |நெடுஞ்சாலைத் ✅ | | <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/5.jpg"> | அண்ணாசாலை ✅ | **NO OUTPUT** ❌ | ல@I9❌ | | <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/10.jpg"> | ரெடிமேடஸ் ❌ |**NO OUTPUT** ❌ | ரெடிமேடஸ் ❌ | **Obtained Tesseract and EasyOCR results using the [Colab notebook](https://colab.research.google.com/drive/1ylZm6afur85Pe6I10N2_tzuBFl2VIxkW?usp=sharing) with Tamil and english as language** ## How to Install and Use OCR Tamil 👨🏼‍💻 ### Quick links🌐 📔 Detailed explanation on [Medium article](https://gnana70.medium.com/ocr-tamil-easy-accurate-and-simple-to-use-tamil-ocr-b03b98697f7b). ✍️ Experiment in [Colab notebook](https://colab.research.google.com/drive/11QPPj3EmpoIqnpuIznKeP1icxvVOjfux?usp=sharing) 🤗 Test it in [Huggingface spaces](https://huggingface.co/spaces/GnanaPrasath/ocr_tamil) ### Pip install instructions🐍 In your command line, run the following command ```pip install ocr_tamil``` If you are using jupyter notebook , install like ```!pip install ocr_tamil``` ### Python Usage - Single image inference **Text Recognition only** ```python from ocr_tamil.ocr import OCR image_path = r"test_images\1.jpg" # insert your own path here ocr = OCR() text_list = ocr.predict(image_path) print(text_list[0]) ## OUTPUT : நெடுஞ்சாலைத் ``` <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/1_180.jpg"> **Text Detect + Recognition** ```python from ocr_tamil.ocr import OCR image_path = r"test_images\0.jpg" # insert your own image path here ocr = OCR(detect=True) texts = ocr.predict(image_path) print(text_list[0]) ## OUTPUT : கொடைக்கானல் Kodaikanal ``` <img width="400" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/0.jpg"> ### Batch inference mode 💻 **Text Recognition only** ```python from ocr_tamil.ocr import OCR image_path = [r"test_images\1.jpg",r"test_images\2.jpg"] # insert your own image paths here ocr = OCR() text_list = ocr.predict(image_path) for text in text_list: print(text) ## OUTPUT : நெடுஞ்சாலைத் ## OUTPUT : கோபி ``` **Text Detect + Recognition** ```python from ocr_tamil.ocr import OCR image_path = [r"test_images\0.jpg",r"test_images\tamil_sentence.jpg"] # insert your own image paths here ocr = OCR(detect=True) text_list = ocr.predict(image_path) for text in text_list: print(text) ## OUTPUT : கொடைக்கானல் Kodaikanal ## OUTPUT : செரியர் யற்கை மூலிகைகளில் இருந்து ஈர்த்தெடுக்க்கப்பட்ட வீரிய உட்பொருட்களை உள்ளடக்கி எந்த இரசாயன சேர்க்கைகளும் இல்லாமல் உருவாக்கப்பட்ட இந்தியாவின் முதல் சித்த தயாரிப்பு ``` **Tested using Python 3.10 on Windows & Linux (Ubuntu 22.04) Machines** ## Applications⚡ 1. ADAS system navigation based on the signboards + maps (hybrid approach) 🚁 2. License plate recognition 🚘 ## Limitations⛔ 1. Unable to read the text if they are present in rotated forms <p align="left"> <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/9.jpg"> <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/8.jpg"> </p> 2. Currently supports Only English and Tamil Language 3. Document Text reading capability is limited. Auto identification of Paragraph, line are not supported along with Text detection model inability to detect and crop the Tamil text leads to accuracy decrease (**WORKAROUND** Can use your own text detection model along with OCR tamil text recognition model) <p align="center"> <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/tamil_sentence.jpg"> </p> <p align="center"> <span>Cropped Text from Text detection Model</span> </p> <p align="center"> <img width="200" alt="teaser" src="https://github.com/gnana70/tamil_ocr/raw/main/test_images/tamil_sentence_crop.jpg"> </p> <p align="center"> Character **இ** missing due to text detection model error </p> **?**யற்கை மூலிகைகளில் இருந்து ஈர்த்தெடுக்கக்கப்பட்ட வீரிய உட்பொருட்களை உள்ளடக்கி எந்த இரசாயன சேர்க்கைகளும் இல்லாமல் உருவாக்கப்பட்ட இந்தியாவின் முதல் சித்த தயாரிப்பு ## Acknowledgements 👏 **Text detection** - [CRAFT TEXT DECTECTION](https://github.com/clovaai/CRAFT-pytorch) **Text recognition** - [PARSEQ](https://github.com/baudm/parseq) ```bibtex @InProceedings{bautista2022parseq, title={Scene Text Recognition with Permuted Autoregressive Sequence Models}, author={Bautista, Darwin and Atienza, Rowel}, booktitle={European Conference on Computer Vision}, pages={178--196}, month={10}, year={2022}, publisher={Springer Nature Switzerland}, address={Cham}, doi={10.1007/978-3-031-19815-1_11}, url={https://doi.org/10.1007/978-3-031-19815-1_11} } ``` ```bibtex @inproceedings{baek2019character, title={Character Region Awareness for Text Detection}, author={Baek, Youngmin and Lee, Bado and Han, Dongyoon and Yun, Sangdoo and Lee, Hwalsuk}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={9365--9374}, year={2019} } ``` ## Citation ```bibtex @InProceedings{GnanaPrasath, title={Tamil OCR}, author={Gnana Prasath D}, month={01}, year={2024}, url={https://github.com/gnana70/tamil_ocr} } ``` ![logo](https://github.com/gnana70/tamil_ocr/raw/main/test_images/logo_1.gif)
{"language": ["ta", "en"], "license": "mit", "tags": ["ocr", "optical character recognition", "text recognition", "tamil"], "metrics": ["accuracy"], "pipeline_tag": "image-to-text"}
image-to-text
GnanaPrasath/ocr_tamil
[ "ocr", "optical character recognition", "text recognition", "tamil", "image-to-text", "ta", "en", "license:mit", "region:us" ]
2024-02-14T12:38:41+00:00
[]
[ "ta", "en" ]
TAGS #ocr #optical character recognition #text recognition #tamil #image-to-text #ta #en #license-mit #region-us
OCR Tamil - Easy, Accurate and Simple to use Tamil OCR - (ஒளி எழுத்துணரி) ========================================================================== ️️️️Please star it if you like️️️️ [![](URL alt=)](LICENSE) [![teaser](URL </p> <ol start=) - Currently supports Only English and Tamil Language - Document Text reading capability is limited. Auto identification of Paragraph, line are not supported along with Text detection model inability to detect and crop the Tamil text leads to accuracy decrease (WORKAROUND Can use your own text detection model along with OCR tamil text recognition model) ![teaser](URL </p> <p align=) Cropped Text from Text detection Model ![teaser](URL </p> <p align=) Character இ missing due to text detection model error ?யற்கை மூலிகைகளில் இருந்து ஈர்த்தெடுக்கக்கப்பட்ட வீரிய உட்பொருட்களை உள்ளடக்கி எந்த இரசாயன சேர்க்கைகளும் இல்லாமல் உருவாக்கப்பட்ட இந்தியாவின் முதல் சித்த தயாரிப்பு Acknowledgements ---------------- Text detection - CRAFT TEXT DECTECTION Text recognition - PARSEQ !logo](URL <img width=)
[]
[ "TAGS\n#ocr #optical character recognition #text recognition #tamil #image-to-text #ta #en #license-mit #region-us \n" ]
[ 34 ]
[ "passage: TAGS\n#ocr #optical character recognition #text recognition #tamil #image-to-text #ta #en #license-mit #region-us \n" ]
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null
null
transformers
<!-- 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. --> # chordektomie This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.3610 - Accuracy: 0.9091 ## 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: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.694 | 1.0 | 6 | 0.6934 | 0.5909 | | 0.656 | 2.0 | 12 | 0.6910 | 0.4545 | | 0.5986 | 3.0 | 18 | 0.5668 | 0.8182 | | 0.4783 | 4.0 | 24 | 0.4786 | 0.9091 | | 0.3821 | 5.0 | 30 | 0.4230 | 0.8636 | | 0.2763 | 6.0 | 36 | 0.4426 | 0.8182 | | 0.2312 | 7.0 | 42 | 0.3846 | 0.8636 | | 0.1928 | 8.0 | 48 | 0.3662 | 0.8636 | | 0.1642 | 9.0 | 54 | 0.3672 | 0.9091 | | 0.1565 | 10.0 | 60 | 0.3610 | 0.9091 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "chordektomie", "results": []}]}
audio-classification
Anguuuuus/chordektomie
[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-14T12:39:32+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us
chordektomie ============ This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.3610 * Accuracy: 0.9091 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: 3e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 66, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
null
diffusers
# DreamBooth - SidXXD/aiti_db-real_person_1 This is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "dreambooth"], "base_model": "stabilityai/stable-diffusion-2-1-base", "instance_prompt": "a photo of sks person", "inference": true}
text-to-image
SidXXD/aiti_db-real_person_1
[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:stabilityai/stable-diffusion-2-1-base", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]
2024-02-14T12:42:44+00:00
[]
[]
TAGS #diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-stabilityai/stable-diffusion-2-1-base #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
# DreamBooth - SidXXD/aiti_db-real_person_1 This is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks person using DreamBooth. You can find some example images in the following. DreamBooth for the text encoder was enabled: False.
[ "# DreamBooth - SidXXD/aiti_db-real_person_1\n\nThis is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks person using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
[ "TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-stabilityai/stable-diffusion-2-1-base #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# DreamBooth - SidXXD/aiti_db-real_person_1\n\nThis is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks person using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
[ 99, 86 ]
[ "passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-stabilityai/stable-diffusion-2-1-base #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# DreamBooth - SidXXD/aiti_db-real_person_1\n\nThis is a dreambooth model derived from stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks person using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "ai-forever/mGPT"}
null
KapitalK/mGPT
[ "peft", "arxiv:1910.09700", "base_model:ai-forever/mGPT", "region:us" ]
2024-02-14T12:43:10+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-ai-forever/mGPT #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-ai-forever/mGPT #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 31, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-ai-forever/mGPT #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
automatic-speech-recognition
BlahBlah314/Whisper_LargeV3FR_V3-8
[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T12:46:00+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# NeuralTrix-7B-v1 NeuralTrix-7B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B) * [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) * [AiMavenAi/AiMaven-Prometheus](https://huggingface.co/AiMavenAi/AiMaven-Prometheus) It was then trained with DPO using: * https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1 ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: mlabonne/OmniBeagle-7B parameters: density: 0.65 weight: 0.4 - model: flemmingmiguel/MBX-7B-v3 parameters: density: 0.6 weight: 0.35 - model: AiMavenAi/AiMaven-Prometheus parameters: density: 0.6 weight: 0.35 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "CultriX/NeuralTrix-7B-v1" 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"]) ```
{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "AiMavenAi/AiMaven-Prometheus"], "base_model": ["mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "AiMavenAi/AiMaven-Prometheus"]}
text-generation
Kquant03/NeuralTrix-7B-dpo-laser
[ "transformers", "pytorch", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "mlabonne/OmniBeagle-7B", "flemmingmiguel/MBX-7B-v3", "AiMavenAi/AiMaven-Prometheus", "base_model:mlabonne/OmniBeagle-7B", "base_model:flemmingmiguel/MBX-7B-v3", "base_model:AiMavenAi/AiMaven-Prometheus", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-14T12:48:52+00:00
[]
[]
TAGS #transformers #pytorch #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-AiMavenAi/AiMaven-Prometheus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# NeuralTrix-7B-v1 NeuralTrix-7B-v1 is a merge of the following models using LazyMergekit: * mlabonne/OmniBeagle-7B * flemmingmiguel/MBX-7B-v3 * AiMavenAi/AiMaven-Prometheus It was then trained with DPO using: * URL ## Configuration ## Usage
[ "# NeuralTrix-7B-v1\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus\n\nIt was then trained with DPO using: \n* URL", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #pytorch #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-AiMavenAi/AiMaven-Prometheus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# NeuralTrix-7B-v1\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus\n\nIt was then trained with DPO using: \n* URL", "## Configuration", "## Usage" ]
[ 155, 84, 4, 3 ]
[ "passage: TAGS\n#transformers #pytorch #mistral #text-generation #merge #mergekit #lazymergekit #mlabonne/OmniBeagle-7B #flemmingmiguel/MBX-7B-v3 #AiMavenAi/AiMaven-Prometheus #base_model-mlabonne/OmniBeagle-7B #base_model-flemmingmiguel/MBX-7B-v3 #base_model-AiMavenAi/AiMaven-Prometheus #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralTrix-7B-v1\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus\n\nIt was then trained with DPO using: \n* URL## Configuration## Usage" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "facebook/bart-base"}
null
KapitalK/bart-test
[ "peft", "arxiv:1910.09700", "base_model:facebook/bart-base", "region:us" ]
2024-02-14T12:49:21+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-facebook/bart-base #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-facebook/bart-base #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 28, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-facebook/bart-base #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
transformers
<!-- 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-base_ag_news This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the ag_news dataset. It achieves the following results on the evaluation set: - Loss: 0.1897 ## 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 - 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 | |:-------------:|:-----:|:-----:|:---------------:| | 0.1879 | 1.0 | 3750 | 0.2106 | | 0.1556 | 2.0 | 7500 | 0.1951 | | 0.126 | 3.0 | 11250 | 0.1897 | | 0.0576 | 4.0 | 15000 | 0.1921 | | 0.0386 | 5.0 | 18750 | 0.2283 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.1+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["ag_news"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base_ag_news", "results": []}]}
text-classification
feelwoo/roberta-base_ag_news
[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "dataset:ag_news", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T12:49:47+00:00
[]
[]
TAGS #transformers #safetensors #roberta #text-classification #generated_from_trainer #dataset-ag_news #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us
roberta-base\_ag\_news ====================== This model is a fine-tuned version of roberta-base on the ag\_news dataset. It achieves the following results on the evaluation set: * Loss: 0.1897 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 * 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 ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.1+cu121 * Datasets 2.14.6 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #dataset-ag_news #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]
[ 66, 116, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #dataset-ag_news #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]
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null
null
peft
<!-- 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. --> # llama2-13b-llama2-finetune2 This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2316 ## 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: 2 - eval_batch_size: 8 - 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 - lr_scheduler_warmup_steps: 5 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5665 | 1.16 | 50 | 0.7637 | | 0.4424 | 2.31 | 100 | 0.3439 | | 0.2782 | 3.47 | 150 | 0.2923 | | 0.2387 | 4.62 | 200 | 0.2656 | | 0.2085 | 5.78 | 250 | 0.2509 | | 0.1832 | 6.94 | 300 | 0.2402 | | 0.167 | 8.09 | 350 | 0.2349 | | 0.1527 | 9.25 | 400 | 0.2342 | | 0.143 | 10.4 | 450 | 0.2323 | | 0.1449 | 11.56 | 500 | 0.2316 | ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
{"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "meta-llama/Llama-2-13b-hf", "model-index": [{"name": "llama2-13b-llama2-finetune2", "results": []}]}
null
kaushalpowar/llama2-13b-llama2-finetune2
[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-hf", "region:us" ]
2024-02-14T12:52:15+00:00
[]
[]
TAGS #peft #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-13b-hf #region-us
llama2-13b-llama2-finetune2 =========================== This model is a fine-tuned version of meta-llama/Llama-2-13b-hf on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.2316 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: 2 * eval\_batch\_size: 8 * 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 * lr\_scheduler\_warmup\_steps: 5 * training\_steps: 500 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.2.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* training\\_steps: 500\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#peft #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-13b-hf #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* training\\_steps: 500\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 39, 158, 4, 44 ]
[ "passage: TAGS\n#peft #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-13b-hf #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2.5e-05\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 8\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 5\n* training\\_steps: 500\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
automatic-speech-recognition
BlahBlah314/Whisper_LargeV3FR_V3-9
[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T12:53:29+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
stable-baselines3
# **A2C** Agent playing **CartPole-v1** This is a trained model of a **A2C** agent playing **CartPole-v1** 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 ... ```
{"library_name": "stable-baselines3", "tags": ["CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Stoub/ecl-Ecosserat-A2C-CartPole-v1
[ "stable-baselines3", "CartPole-v1", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-14T12:54:59+00:00
[]
[]
TAGS #stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# A2C Agent playing CartPole-v1 This is a trained model of a A2C agent playing CartPole-v1 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# A2C Agent playing CartPole-v1\nThis is a trained model of a A2C agent playing CartPole-v1\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# A2C Agent playing CartPole-v1\nThis is a trained model of a A2C agent playing CartPole-v1\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 39, 41, 17 ]
[ "passage: TAGS\n#stable-baselines3 #CartPole-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing CartPole-v1\nThis is a trained model of a A2C agent playing CartPole-v1\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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# Marcoro14-7B-slerp Marcoro14-7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): * [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) * [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1) ## 🧩 Configuration ```yaml slices: - sources: - model: AIDC-ai-business/Marcoroni-7B-v3 layer_range: [0, 32] - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1 layer_range: [0, 32] merge_method: slerp base_model: AIDC-ai-business/Marcoroni-7B-v3 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 ```
{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "AIDC-ai-business/Marcoroni-7B-v3", "EmbeddedLLM/Mistral-7B-Merge-14-v0.1"]}
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aratancoder/Marcoro14-7B-slerp
[ "merge", "mergekit", "lazymergekit", "AIDC-ai-business/Marcoroni-7B-v3", "EmbeddedLLM/Mistral-7B-Merge-14-v0.1", "license:apache-2.0", "region:us" ]
2024-02-14T12:58:58+00:00
[]
[]
TAGS #merge #mergekit #lazymergekit #AIDC-ai-business/Marcoroni-7B-v3 #EmbeddedLLM/Mistral-7B-Merge-14-v0.1 #license-apache-2.0 #region-us
# Marcoro14-7B-slerp Marcoro14-7B-slerp is a merge of the following models using mergekit: * AIDC-ai-business/Marcoroni-7B-v3 * EmbeddedLLM/Mistral-7B-Merge-14-v0.1 ## Configuration
[ "# Marcoro14-7B-slerp\n\nMarcoro14-7B-slerp is a merge of the following models using mergekit:\n* AIDC-ai-business/Marcoroni-7B-v3\n* EmbeddedLLM/Mistral-7B-Merge-14-v0.1", "## Configuration" ]
[ "TAGS\n#merge #mergekit #lazymergekit #AIDC-ai-business/Marcoroni-7B-v3 #EmbeddedLLM/Mistral-7B-Merge-14-v0.1 #license-apache-2.0 #region-us \n", "# Marcoro14-7B-slerp\n\nMarcoro14-7B-slerp is a merge of the following models using mergekit:\n* AIDC-ai-business/Marcoroni-7B-v3\n* EmbeddedLLM/Mistral-7B-Merge-14-v0.1", "## Configuration" ]
[ 61, 62, 4 ]
[ "passage: TAGS\n#merge #mergekit #lazymergekit #AIDC-ai-business/Marcoroni-7B-v3 #EmbeddedLLM/Mistral-7B-Merge-14-v0.1 #license-apache-2.0 #region-us \n# Marcoro14-7B-slerp\n\nMarcoro14-7B-slerp is a merge of the following models using mergekit:\n* AIDC-ai-business/Marcoroni-7B-v3\n* EmbeddedLLM/Mistral-7B-Merge-14-v0.1## Configuration" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "openai-community/gpt2"}
null
KapitalK/gpt2
[ "peft", "arxiv:1910.09700", "base_model:openai-community/gpt2", "region:us" ]
2024-02-14T13:02:33+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-openai-community/gpt2 #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-openai-community/gpt2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 33, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-openai-community/gpt2 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
stable-baselines3
# **A2C** Agent playing **PandaReachDense-v3** This is a trained model of a **A2C** agent playing **PandaReachDense-v3** 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 ... ```
{"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.86 +/- 1.05", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
hugo-massonnat/a2c-PandaReachDense-v3
[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]
2024-02-14T13:02:34+00:00
[]
[]
TAGS #stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
# A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code
[ "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ "TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]
[ 41, 45, 17 ]
[ "passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]
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# PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'Facepalm0/ppo_from_scratch-LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
{"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "47.83 +/- 43.84", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
Facepalm0/ppo_from_scratch-LunarLander-v2
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
2024-02-14T13:03:12+00:00
[]
[]
TAGS #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
# PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
[ "# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters" ]
[ "TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n", "# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters" ]
[ 51, 37 ]
[ "passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n\n # Hyperparameters" ]
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transformers
# [MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF](https://huggingface.co/MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF) - Model creator: [FelixChao](https://huggingface.co/FelixChao) - Original model: [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2) ## Description [MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF](https://huggingface.co/MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF) contains GGUF format model files for [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF](https://huggingface.co/MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF) and below it, a specific filename to download, such as: WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF](https://huggingface.co/MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
{"tags": ["quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "transformers", "safetensors", "mistral", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us"], "model_name": "WestSeverus-7B-DPO-v2-GGUF", "base_model": "FelixChao/WestSeverus-7B-DPO-v2", "inference": false, "model_creator": "FelixChao", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"}
text-generation
MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF
[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:FelixChao/WestSeverus-7B-DPO-v2" ]
2024-02-14T13:04:43+00:00
[]
[]
TAGS #transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #safetensors #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #base_model-FelixChao/WestSeverus-7B-DPO-v2
# MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF - Model creator: FelixChao - Original model: FelixChao/WestSeverus-7B-DPO-v2 ## Description MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF contains GGUF format model files for FelixChao/WestSeverus-7B-DPO-v2. ## How to use Thanks to TheBloke for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL. Here is an incomplete list of clients and libraries that are known to support GGUF: * URL. The source project for GGUF. Offers a CLI and a server option. * text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection. * URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use. * ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * URL ### In 'text-generation-webui' Under Download Model, you can enter the model repo: MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF and below it, a specific filename to download, such as: WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the 'huggingface-hub' Python library: Then you can download any individual model file to the current directory, at high speed, with a command like this: </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI. To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer': And set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1': Windows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command. </details> ## Example 'URL' command Make sure you are using 'URL' from commit d0cee0d or later. Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins' For other parameters and how to use them, please refer to the URL documentation ## How to run in 'text-generation-webui' Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL. ## How to run from Python code You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: llama-cpp-python docs. #### First install the package Run one of the following commands, according to your system: #### Simple llama-cpp-python example code ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * LangChain + llama-cpp-python * LangChain + ctransformers
[ "# MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF\n- Model creator: FelixChao\n- Original model: FelixChao/WestSeverus-7B-DPO-v2", "## Description\nMaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF contains GGUF format model files for FelixChao/WestSeverus-7B-DPO-v2.", "## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.", "### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw", "## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL", "### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF and below it, a specific filename to download, such as: WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf.\n\nThen click Download.", "### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n</details>\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>", "## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation", "## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.", "## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code", "## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers" ]
[ "TAGS\n#transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #safetensors #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #base_model-FelixChao/WestSeverus-7B-DPO-v2 \n", "# MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF\n- Model creator: FelixChao\n- Original model: FelixChao/WestSeverus-7B-DPO-v2", "## Description\nMaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF contains GGUF format model files for FelixChao/WestSeverus-7B-DPO-v2.", "## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.", "### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw", "## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL", "### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF and below it, a specific filename to download, such as: WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf.\n\nThen click Download.", "### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n</details>\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>", "## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation", "## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.", "## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code", "## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers" ]
[ 108, 47, 46, 26, 401, 323, 84, 85, 218, 182, 49, 77, 36, 19, 12, 50 ]
[ "passage: TAGS\n#transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #safetensors #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #base_model-FelixChao/WestSeverus-7B-DPO-v2 \n# MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF\n- Model creator: FelixChao\n- Original model: FelixChao/WestSeverus-7B-DPO-v2## Description\nMaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF contains GGUF format model files for FelixChao/WestSeverus-7B-DPO-v2.## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "passage: ### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/WestSeverus-7B-DPO-v2-GGUF and below it, a specific filename to download, such as: WestSeverus-7B-DPO-v2-GGUF.Q4_K_M.gguf.\n\nThen click Download." ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
heldJan/llama-2-7b-froozen_CLIP_test
[ "transformers", "safetensors", "VideoChatGPT", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T13:04:48+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #VideoChatGPT #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #VideoChatGPT #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #VideoChatGPT #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
null
mtc/meta-llama-Llama-2-7b-hf-pubmed-summarization-1000-last-lora-full-adapter
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T13:06:46+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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{"library_name": "transformers", "tags": []}
text-generation
mtc/meta-llama-Llama-2-7b-hf-pubmed-summarization-1000-last_merged
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-14T13:06:48+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 56, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# TinyPoliticaLlama-1.1B-slerp TinyPoliticaLlama-1.1B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [h4rz3rk4s3/TinyNewsLlama-1.1B](https://huggingface.co/h4rz3rk4s3/TinyNewsLlama-1.1B) * [h4rz3rk4s3/TinyParlaMintLlama-1.1B](https://huggingface.co/h4rz3rk4s3/TinyParlaMintLlama-1.1B) ## 🧩 Configuration ```yaml slices: - sources: - model: h4rz3rk4s3/TinyNewsLlama-1.1B layer_range: [0, 21] - model: h4rz3rk4s3/TinyParlaMintLlama-1.1B layer_range: [0, 21] merge_method: slerp base_model: h4rz3rk4s3/TinyNewsLlama-1.1B 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 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "h4rz3rk4s3/TinyPoliticaLlama-1.1B-slerp" 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"]) ```
{"tags": ["merge", "mergekit", "lazymergekit", "h4rz3rk4s3/TinyNewsLlama-1.1B", "h4rz3rk4s3/TinyParlaMintLlama-1.1B"], "base_model": ["h4rz3rk4s3/TinyNewsLlama-1.1B", "h4rz3rk4s3/TinyParlaMintLlama-1.1B"]}
text-generation
h4rz3rk4s3/TinyPoliticaLlama-1.1B-slerp
[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "h4rz3rk4s3/TinyNewsLlama-1.1B", "h4rz3rk4s3/TinyParlaMintLlama-1.1B", "conversational", "base_model:h4rz3rk4s3/TinyNewsLlama-1.1B", "base_model:h4rz3rk4s3/TinyParlaMintLlama-1.1B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-14T13:07:28+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #h4rz3rk4s3/TinyNewsLlama-1.1B #h4rz3rk4s3/TinyParlaMintLlama-1.1B #conversational #base_model-h4rz3rk4s3/TinyNewsLlama-1.1B #base_model-h4rz3rk4s3/TinyParlaMintLlama-1.1B #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
# TinyPoliticaLlama-1.1B-slerp TinyPoliticaLlama-1.1B-slerp is a merge of the following models using LazyMergekit: * h4rz3rk4s3/TinyNewsLlama-1.1B * h4rz3rk4s3/TinyParlaMintLlama-1.1B ## Configuration ## Usage
[ "# TinyPoliticaLlama-1.1B-slerp\n\nTinyPoliticaLlama-1.1B-slerp is a merge of the following models using LazyMergekit:\n* h4rz3rk4s3/TinyNewsLlama-1.1B\n* h4rz3rk4s3/TinyParlaMintLlama-1.1B", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #h4rz3rk4s3/TinyNewsLlama-1.1B #h4rz3rk4s3/TinyParlaMintLlama-1.1B #conversational #base_model-h4rz3rk4s3/TinyNewsLlama-1.1B #base_model-h4rz3rk4s3/TinyParlaMintLlama-1.1B #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# TinyPoliticaLlama-1.1B-slerp\n\nTinyPoliticaLlama-1.1B-slerp is a merge of the following models using LazyMergekit:\n* h4rz3rk4s3/TinyNewsLlama-1.1B\n* h4rz3rk4s3/TinyParlaMintLlama-1.1B", "## Configuration", "## Usage" ]
[ 149, 76, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #h4rz3rk4s3/TinyNewsLlama-1.1B #h4rz3rk4s3/TinyParlaMintLlama-1.1B #conversational #base_model-h4rz3rk4s3/TinyNewsLlama-1.1B #base_model-h4rz3rk4s3/TinyParlaMintLlama-1.1B #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# TinyPoliticaLlama-1.1B-slerp\n\nTinyPoliticaLlama-1.1B-slerp is a merge of the following models using LazyMergekit:\n* h4rz3rk4s3/TinyNewsLlama-1.1B\n* h4rz3rk4s3/TinyParlaMintLlama-1.1B## Configuration## Usage" ]
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llama.cpp conversion of https://huggingface.co/nakodanei/Blue-Orchid-2x7b/ except for f16 and q8_0, every quant is using the `merge.imatrix` `merge.imatrix` is a merge of `kalomaze-group_10_merged.172chunks.imatrix` and `wiki.train.400chunks.imatrix`, which took ~10min + ~20min to calulate on my machine. full wiki.train would have taken 10h for more info on imatrix handling see https://github.com/ggerganov/llama.cpp/pull/5302 ### ppl (512 wiki.test, 300chunks) | quant | ppl (lower is better) | |--------------------|-----| | f16(baseline) | 5.8839 +/- 0.05173 | | q8_0 | 5.8880 +/- 0.05178 | | q5_k_m | 5.8912 +/- 0.05177 | | q5_k_m(without-imat) | 5.8893 +/- 0.05174 | | q4_k_m | 5.9248 +/- 0.05216 | | q4_k_m(without-imat) | 5.9492 +/- 0.05249 | | iq3_xxs | 6.1984 +/- 0.05475 | | iq3_xxs(only-wiki) | 6.1796 +/- 0.05446 | | iq3_xxs(only-kal) | 6.1984 +/- 0.05475 | | iq3_xxs(withou-imat) | 6.4228 +/- 0.05756 | ### Interesting observations despite `merge.imatrix` being different from `kalomaze-group_10_merged.172chunks.imatrix`, they produce the exact same quantized iq3_xxs model file. (same hash, checked multiple times) q5_k_m has a lower perplexity with the imatrix. but that probably is caused by kalomaze-group_10_merged diverging enough from wiki.
{"language": ["en"], "license": "apache-2.0", "tags": ["not-for-all-audiences", "writing", "roleplay", "gguf", "gguf-imatrix"], "base_model": ["nakodanei/Blue-Orchid-2x7b"], "model_type": "mixtral", "quantized_by": "Green-Sky"}
null
Green-Sky/nakodanei-Blue-Orchid-2x7b-GGUF-iMatrix
[ "gguf", "not-for-all-audiences", "writing", "roleplay", "gguf-imatrix", "en", "base_model:nakodanei/Blue-Orchid-2x7b", "license:apache-2.0", "region:us" ]
2024-02-14T13:09:06+00:00
[]
[ "en" ]
TAGS #gguf #not-for-all-audiences #writing #roleplay #gguf-imatrix #en #base_model-nakodanei/Blue-Orchid-2x7b #license-apache-2.0 #region-us
URL conversion of URL except for f16 and q8\_0, every quant is using the 'merge.imatrix' 'merge.imatrix' is a merge of 'kalomaze-group\_10\_merged.172chunks.imatrix' and 'URL.400chunks.imatrix', which took ~10min + ~20min to calulate on my machine. full URL would have taken 10h for more info on imatrix handling see URL ### ppl (512 URL, 300chunks) ### Interesting observations despite 'merge.imatrix' being different from 'kalomaze-group\_10\_merged.172chunks.imatrix', they produce the exact same quantized iq3\_xxs model file. (same hash, checked multiple times) q5\_k\_m has a lower perplexity with the imatrix. but that probably is caused by kalomaze-group\_10\_merged diverging enough from wiki.
[ "### ppl (512 URL, 300chunks)", "### Interesting observations\n\n\ndespite 'merge.imatrix' being different from 'kalomaze-group\\_10\\_merged.172chunks.imatrix', they produce the exact same quantized iq3\\_xxs model file. (same hash, checked multiple times)\n\n\nq5\\_k\\_m has a lower perplexity with the imatrix. but that probably is caused by kalomaze-group\\_10\\_merged diverging enough from wiki." ]
[ "TAGS\n#gguf #not-for-all-audiences #writing #roleplay #gguf-imatrix #en #base_model-nakodanei/Blue-Orchid-2x7b #license-apache-2.0 #region-us \n", "### ppl (512 URL, 300chunks)", "### Interesting observations\n\n\ndespite 'merge.imatrix' being different from 'kalomaze-group\\_10\\_merged.172chunks.imatrix', they produce the exact same quantized iq3\\_xxs model file. (same hash, checked multiple times)\n\n\nq5\\_k\\_m has a lower perplexity with the imatrix. but that probably is caused by kalomaze-group\\_10\\_merged diverging enough from wiki." ]
[ 58, 13, 112 ]
[ "passage: TAGS\n#gguf #not-for-all-audiences #writing #roleplay #gguf-imatrix #en #base_model-nakodanei/Blue-Orchid-2x7b #license-apache-2.0 #region-us \n### ppl (512 URL, 300chunks)### Interesting observations\n\n\ndespite 'merge.imatrix' being different from 'kalomaze-group\\_10\\_merged.172chunks.imatrix', they produce the exact same quantized iq3\\_xxs model file. (same hash, checked multiple times)\n\n\nq5\\_k\\_m has a lower perplexity with the imatrix. but that probably is caused by kalomaze-group\\_10\\_merged diverging enough from wiki." ]
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null
null
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# PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'exp_name': 'ppo' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 500000 'learning_rate': 0.0003 'num_envs': 16 'num_steps': 1024 'anneal_lr': True 'gae': True 'gamma': 0.999 'gae_lambda': 0.98 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'itsdhanoob/cleanRlPPO-lunar_lander' 'batch_size': 16384 'minibatch_size': 4096} ```
{"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-164.42 +/- 70.38", "name": "mean_reward", "verified": false}]}]}]}
reinforcement-learning
itsdhanoob/cleanRlPPO-lunar_lander
[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]
2024-02-14T13:10:35+00:00
[]
[]
TAGS #tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us
# PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters
[ "# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters" ]
[ "TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n", "# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters" ]
[ 51, 37 ]
[ "passage: TAGS\n#tensorboard #LunarLander-v2 #ppo #deep-reinforcement-learning #reinforcement-learning #custom-implementation #deep-rl-course #model-index #region-us \n# PPO Agent Playing LunarLander-v2\n\n This is a trained model of a PPO agent playing LunarLander-v2.\n \n # Hyperparameters" ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
null
AntoineGourru/Mistral_qlora_telecom_2
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T13:10:54+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
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# CroissantLLM - Base (190k steps, Final version) This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens. To play with the final model, we recommend using the Chat version: https://huggingface.co/croissantllm/CroissantLLMChat-v0.1. https://arxiv.org/abs/2402.00786 ## Abstract We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models. ## Citation Our work can be cited as: ```bash @misc{faysse2024croissantllm, title={CroissantLLM: A Truly Bilingual French-English Language Model}, author={Manuel Faysse and Patrick Fernandes and Nuno Guerreiro and António Loison and Duarte Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro Martins and Antoni Bigata Casademunt and François Yvon and André Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo}, year={2024}, eprint={2402.00786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Usage This model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "croissantllm/CroissantLLMBase" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") inputs = tokenizer("I am so tired I could sleep right now. -> Je suis si fatigué que je pourrais m'endormir maintenant.\nHe is heading to the market. -> Il va au marché.\nWe are running on the beach. ->", return_tensors="pt").to(model.device) tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60, temperature=0.3) print(tokenizer.decode(tokens[0])) # remove bos token inputs = tokenizer("Capitales: France -> Paris, Italie -> Rome, Allemagne -> Berlin, Espagne ->", return_tensors="pt", add_special_tokens=True).to(model.device) tokens = model.generate(**inputs, max_length=100, do_sample=True, top_p=0.95, top_k=60) print(tokenizer.decode(tokens[0])) ``` *** Quantization of Model [croissantllm/CroissantLLMBase](https://huggingface.co/croissantllm/CroissantLLMBase). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline [8668cbd2081063e33a128251312e6de9744d0a64]
{"language": ["fr", "en"], "license": "mit", "tags": ["legal", "code", "text-generation-inference", "art"], "datasets": ["cerebras/SlimPajama-627B", "uonlp/CulturaX", "pg19", "bigcode/starcoderdata", "croissantllm/croissant_dataset"], "pipeline_tag": "text2text-generation"}
text2text-generation
nold/CroissantLLMBase-GGUF
[ "gguf", "legal", "code", "text-generation-inference", "art", "text2text-generation", "fr", "en", "dataset:cerebras/SlimPajama-627B", "dataset:uonlp/CulturaX", "dataset:pg19", "dataset:bigcode/starcoderdata", "dataset:croissantllm/croissant_dataset", "arxiv:2402.00786", "license:mit", "region:us" ]
2024-02-14T13:15:08+00:00
[ "2402.00786" ]
[ "fr", "en" ]
TAGS #gguf #legal #code #text-generation-inference #art #text2text-generation #fr #en #dataset-cerebras/SlimPajama-627B #dataset-uonlp/CulturaX #dataset-pg19 #dataset-bigcode/starcoderdata #dataset-croissantllm/croissant_dataset #arxiv-2402.00786 #license-mit #region-us
# CroissantLLM - Base (190k steps, Final version) This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens. To play with the final model, we recommend using the Chat version: URL URL ## Abstract We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models. Our work can be cited as: ## Usage This model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies. * Quantization of Model croissantllm/CroissantLLMBase. Created using llm-quantizer Pipeline [8668cbd2081063e33a128251312e6de9744d0a64]
[ "# CroissantLLM - Base (190k steps, Final version)\n\nThis model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens.\n\nTo play with the final model, we recommend using the Chat version: URL\n\n\nURL", "## Abstract\nWe introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.\nTo that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.\nTo assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.\nThis work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.\n\nOur work can be cited as:", "## Usage\n\nThis model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies.\n\n\n\n\n*\n\nQuantization of Model croissantllm/CroissantLLMBase. Created using llm-quantizer Pipeline [8668cbd2081063e33a128251312e6de9744d0a64]" ]
[ "TAGS\n#gguf #legal #code #text-generation-inference #art #text2text-generation #fr #en #dataset-cerebras/SlimPajama-627B #dataset-uonlp/CulturaX #dataset-pg19 #dataset-bigcode/starcoderdata #dataset-croissantllm/croissant_dataset #arxiv-2402.00786 #license-mit #region-us \n", "# CroissantLLM - Base (190k steps, Final version)\n\nThis model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens.\n\nTo play with the final model, we recommend using the Chat version: URL\n\n\nURL", "## Abstract\nWe introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.\nTo that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.\nTo assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.\nThis work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.\n\nOur work can be cited as:", "## Usage\n\nThis model is a base model, that is, it is not finetuned for Chat function and works best with few-shot prompting strategies.\n\n\n\n\n*\n\nQuantization of Model croissantllm/CroissantLLMBase. Created using llm-quantizer Pipeline [8668cbd2081063e33a128251312e6de9744d0a64]" ]
[ 106, 62, 336, 85 ]
[ "passage: TAGS\n#gguf #legal #code #text-generation-inference #art #text2text-generation #fr #en #dataset-cerebras/SlimPajama-627B #dataset-uonlp/CulturaX #dataset-pg19 #dataset-bigcode/starcoderdata #dataset-croissantllm/croissant_dataset #arxiv-2402.00786 #license-mit #region-us \n# CroissantLLM - Base (190k steps, Final version)\n\nThis model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens.\n\nTo play with the final model, we recommend using the Chat version: URL\n\n\nURL## Abstract\nWe introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.\nTo that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.\nTo assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.\nThis work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.\n\nOur work can be cited as:" ]
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null
null
transformers
#### Description Optimize your engagement with [This project](https://huggingface.co/OEvortex/OEvortex/HelpingAI-unvelite) by seamlessly integrating GGUF Format model files. Please Subscribe to my youtube channel [OEvortex](https://youtube.com/@OEvortex) ### GGUF Technical Specifications Delve into the intricacies of GGUF, a meticulously crafted format that builds upon the robust foundation of the GGJT model. Tailored for heightened extensibility and user-centric functionality, GGUF introduces a suite of indispensable features: **Single-file Deployment:** Streamline distribution and loading effortlessly. GGUF models have been meticulously architected for seamless deployment, necessitating no external files for supplementary information. **Extensibility:** Safeguard the future of your models. GGUF seamlessly accommodates the integration of new features into GGML-based executors, ensuring compatibility with existing models. **mmap Compatibility:** Prioritize efficiency. GGUF models are purposefully engineered to support mmap, facilitating rapid loading and saving, thus optimizing your workflow. **User-Friendly:** Simplify your coding endeavors. Load and save models effortlessly, irrespective of the programming language used, obviating the dependency on external libraries. **Full Information:** A comprehensive repository in a single file. GGUF models encapsulate all requisite information for loading, eliminating the need for users to furnish additional data. The differentiator between GGJT and GGUF lies in the deliberate adoption of a key-value structure for hyperparameters (now termed metadata). Bid farewell to untyped lists, and embrace a structured approach that seamlessly accommodates new metadata without compromising compatibility with existing models. Augment your model with supplementary information for enhanced inference and model identification. **QUANTIZATION_METHODS:** | Method | Quantization | Advantages | Trade-offs | |---|---|---|---| | q2_k | 2-bit integers | Significant model size reduction | Minimal impact on accuracy | | q3_k_l | 3-bit integers | Balance between model size reduction and accuracy preservation | Moderate impact on accuracy | | q3_k_m | 3-bit integers | Enhanced accuracy with mixed precision | Increased computational complexity | | q3_k_s | 3-bit integers | Improved model efficiency with structured pruning | Reduced accuracy | | q4_0 | 4-bit integers | Significant model size reduction | Moderate impact on accuracy | | q4_1 | 4-bit integers | Enhanced accuracy with mixed precision | Increased computational complexity | | q4_k_m | 4-bit integers | Optimized model size and accuracy with mixed precision and structured pruning | Reduced accuracy | | q4_k_s | 4-bit integers | Improved model efficiency with structured pruning | Reduced accuracy | | q5_0 | 5-bit integers | Balance between model size reduction and accuracy preservation | Moderate impact on accuracy | | q5_1 | 5-bit integers | Enhanced accuracy with mixed precision | Increased computational complexity | | q5_k_m | 5-bit integers | Optimized model size and accuracy with mixed precision and structured pruning | Reduced accuracy | | q5_k_s | 5-bit integers | Improved model efficiency with structured pruning | Reduced accuracy | | q6_k | 6-bit integers | Balance between model size reduction and accuracy preservation | Moderate impact on accuracy | | q8_0 | 8-bit integers | Significant model size reduction | Minimal impact on accuracy |
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["HelpingAI", "lite", "code"], "base_model": "OEvortex/lite-hermes", "inference": false}
null
OEvortex/HelpingAI-unvelite-GGUF
[ "transformers", "gguf", "HelpingAI", "lite", "code", "en", "base_model:OEvortex/lite-hermes", "license:mit", "region:us" ]
2024-02-14T13:23:10+00:00
[]
[ "en" ]
TAGS #transformers #gguf #HelpingAI #lite #code #en #base_model-OEvortex/lite-hermes #license-mit #region-us
#### Description Optimize your engagement with This project by seamlessly integrating GGUF Format model files. Please Subscribe to my youtube channel OEvortex ### GGUF Technical Specifications Delve into the intricacies of GGUF, a meticulously crafted format that builds upon the robust foundation of the GGJT model. Tailored for heightened extensibility and user-centric functionality, GGUF introduces a suite of indispensable features: Single-file Deployment: Streamline distribution and loading effortlessly. GGUF models have been meticulously architected for seamless deployment, necessitating no external files for supplementary information. Extensibility: Safeguard the future of your models. GGUF seamlessly accommodates the integration of new features into GGML-based executors, ensuring compatibility with existing models. mmap Compatibility: Prioritize efficiency. GGUF models are purposefully engineered to support mmap, facilitating rapid loading and saving, thus optimizing your workflow. User-Friendly: Simplify your coding endeavors. Load and save models effortlessly, irrespective of the programming language used, obviating the dependency on external libraries. Full Information: A comprehensive repository in a single file. GGUF models encapsulate all requisite information for loading, eliminating the need for users to furnish additional data. The differentiator between GGJT and GGUF lies in the deliberate adoption of a key-value structure for hyperparameters (now termed metadata). Bid farewell to untyped lists, and embrace a structured approach that seamlessly accommodates new metadata without compromising compatibility with existing models. Augment your model with supplementary information for enhanced inference and model identification. QUANTIZATION\_METHODS:
[ "#### Description\n\n\nOptimize your engagement with This project by seamlessly integrating GGUF Format model files.\nPlease Subscribe to my youtube channel OEvortex", "### GGUF Technical Specifications\n\n\nDelve into the intricacies of GGUF, a meticulously crafted format that builds upon the robust foundation of the GGJT model. Tailored for heightened extensibility and user-centric functionality, GGUF introduces a suite of indispensable features:\n\n\nSingle-file Deployment: Streamline distribution and loading effortlessly. GGUF models have been meticulously architected for seamless deployment, necessitating no external files for supplementary information.\n\n\nExtensibility: Safeguard the future of your models. GGUF seamlessly accommodates the integration of new features into GGML-based executors, ensuring compatibility with existing models.\n\n\nmmap Compatibility: Prioritize efficiency. GGUF models are purposefully engineered to support mmap, facilitating rapid loading and saving, thus optimizing your workflow.\n\n\nUser-Friendly: Simplify your coding endeavors. Load and save models effortlessly, irrespective of the programming language used, obviating the dependency on external libraries.\n\n\nFull Information: A comprehensive repository in a single file. GGUF models encapsulate all requisite information for loading, eliminating the need for users to furnish additional data.\n\n\nThe differentiator between GGJT and GGUF lies in the deliberate adoption of a key-value structure for hyperparameters (now termed metadata). Bid farewell to untyped lists, and embrace a structured approach that seamlessly accommodates new metadata without compromising compatibility with existing models. Augment your model with supplementary information for enhanced inference and model identification.\n\n\nQUANTIZATION\\_METHODS:" ]
[ "TAGS\n#transformers #gguf #HelpingAI #lite #code #en #base_model-OEvortex/lite-hermes #license-mit #region-us \n", "#### Description\n\n\nOptimize your engagement with This project by seamlessly integrating GGUF Format model files.\nPlease Subscribe to my youtube channel OEvortex", "### GGUF Technical Specifications\n\n\nDelve into the intricacies of GGUF, a meticulously crafted format that builds upon the robust foundation of the GGJT model. Tailored for heightened extensibility and user-centric functionality, GGUF introduces a suite of indispensable features:\n\n\nSingle-file Deployment: Streamline distribution and loading effortlessly. GGUF models have been meticulously architected for seamless deployment, necessitating no external files for supplementary information.\n\n\nExtensibility: Safeguard the future of your models. GGUF seamlessly accommodates the integration of new features into GGML-based executors, ensuring compatibility with existing models.\n\n\nmmap Compatibility: Prioritize efficiency. GGUF models are purposefully engineered to support mmap, facilitating rapid loading and saving, thus optimizing your workflow.\n\n\nUser-Friendly: Simplify your coding endeavors. Load and save models effortlessly, irrespective of the programming language used, obviating the dependency on external libraries.\n\n\nFull Information: A comprehensive repository in a single file. GGUF models encapsulate all requisite information for loading, eliminating the need for users to furnish additional data.\n\n\nThe differentiator between GGJT and GGUF lies in the deliberate adoption of a key-value structure for hyperparameters (now termed metadata). Bid farewell to untyped lists, and embrace a structured approach that seamlessly accommodates new metadata without compromising compatibility with existing models. Augment your model with supplementary information for enhanced inference and model identification.\n\n\nQUANTIZATION\\_METHODS:" ]
[ 41, 34, 397 ]
[ "passage: TAGS\n#transformers #gguf #HelpingAI #lite #code #en #base_model-OEvortex/lite-hermes #license-mit #region-us \n#### Description\n\n\nOptimize your engagement with This project by seamlessly integrating GGUF Format model files.\nPlease Subscribe to my youtube channel OEvortex### GGUF Technical Specifications\n\n\nDelve into the intricacies of GGUF, a meticulously crafted format that builds upon the robust foundation of the GGJT model. Tailored for heightened extensibility and user-centric functionality, GGUF introduces a suite of indispensable features:\n\n\nSingle-file Deployment: Streamline distribution and loading effortlessly. GGUF models have been meticulously architected for seamless deployment, necessitating no external files for supplementary information.\n\n\nExtensibility: Safeguard the future of your models. GGUF seamlessly accommodates the integration of new features into GGML-based executors, ensuring compatibility with existing models.\n\n\nmmap Compatibility: Prioritize efficiency. GGUF models are purposefully engineered to support mmap, facilitating rapid loading and saving, thus optimizing your workflow.\n\n\nUser-Friendly: Simplify your coding endeavors. Load and save models effortlessly, irrespective of the programming language used, obviating the dependency on external libraries.\n\n\nFull Information: A comprehensive repository in a single file. GGUF models encapsulate all requisite information for loading, eliminating the need for users to furnish additional data.\n\n\nThe differentiator between GGJT and GGUF lies in the deliberate adoption of a key-value structure for hyperparameters (now termed metadata). Bid farewell to untyped lists, and embrace a structured approach that seamlessly accommodates new metadata without compromising compatibility with existing models. Augment your model with supplementary information for enhanced inference and model identification.\n\n\nQUANTIZATION\\_METHODS:" ]
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null
null
transformers
<!-- 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. --> # lyfi-expert-classification This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## 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: 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 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0583 | 0.25 | 500 | 0.0002 | 1.0 | | 0.0002 | 0.5 | 1000 | 0.0001 | 1.0 | | 0.0001 | 0.75 | 1500 | 0.0000 | 1.0 | | 0.0 | 1.0 | 2000 | 0.0000 | 1.0 | | 0.0 | 1.25 | 2500 | 0.0000 | 1.0 | | 0.0 | 1.5 | 3000 | 0.0000 | 1.0 | | 0.0 | 1.75 | 3500 | 0.0000 | 1.0 | | 0.0 | 2.0 | 4000 | 0.0000 | 1.0 | | 0.0 | 2.25 | 4500 | 0.0000 | 1.0 | | 0.0 | 2.5 | 5000 | 0.0000 | 1.0 | | 0.0 | 2.75 | 5500 | 0.0000 | 1.0 | | 0.0 | 3.0 | 6000 | 0.0000 | 1.0 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.3.0.dev20240212 - Datasets 2.16.1 - Tokenizers 0.15.0
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-multilingual-cased", "model-index": [{"name": "lyfi-expert-classification", "results": []}]}
text-classification
MrHungry/lyfi-expert-classification
[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T13:23:52+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
lyfi-expert-classification ========================== This model is a fine-tuned version of bert-base-multilingual-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0000 * Accuracy: 1.0 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: 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 * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.36.2 * Pytorch 2.3.0.dev20240212 * Datasets 2.16.1 * Tokenizers 0.15.0
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.3.0.dev20240212\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.3.0.dev20240212\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
[ 71, 98, 4, 36 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-multilingual-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.3.0.dev20240212\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-arxiv This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 0.0008 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 500 - training_steps: 10 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.2 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.15.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-arxiv", "results": []}]}
text2text-generation
mtc/bart-base-arxiv
[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T13:27:05+00:00
[]
[]
TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# bart-base-arxiv This model is a fine-tuned version of facebook/bart-base 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: 0.0008 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 500 - training_steps: 10 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.2 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.15.2
[ "# bart-base-arxiv\n\nThis model is a fine-tuned version of facebook/bart-base on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0008\n- train_batch_size: 8\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: polynomial\n- lr_scheduler_warmup_steps: 500\n- training_steps: 10\n- mixed_precision_training: Native AMP\n- label_smoothing_factor: 0.2", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.14.4\n- Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bart-base-arxiv\n\nThis model is a fine-tuned version of facebook/bart-base on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0008\n- train_batch_size: 8\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: polynomial\n- lr_scheduler_warmup_steps: 500\n- training_steps: 10\n- mixed_precision_training: Native AMP\n- label_smoothing_factor: 0.2", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.14.4\n- Tokenizers 0.15.2" ]
[ 64, 31, 6, 12, 8, 3, 151, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bart-base-arxiv\n\nThis model is a fine-tuned version of facebook/bart-base on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0008\n- train_batch_size: 8\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: polynomial\n- lr_scheduler_warmup_steps: 500\n- training_steps: 10\n- mixed_precision_training: Native AMP\n- label_smoothing_factor: 0.2### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.14.4\n- Tokenizers 0.15.2" ]
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null
null
transformers
# NeuralTrix-bf16 NeuralTrix-bf16 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [bardsai/jaskier-7b-dpo-v3.3](https://huggingface.co/bardsai/jaskier-7b-dpo-v3.3) * [CultriX/NeuralTrix-v4-bf16](https://huggingface.co/CultriX/NeuralTrix-v4-bf16) * [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) ## 🧩 Configuration ```yaml models: - model: eren23/dpo-binarized-NeuralTrix-7B # no parameters necessary for base model - model: bardsai/jaskier-7b-dpo-v3.3 parameters: density: 0.65 weight: 0.4 - model: CultriX/NeuralTrix-v4-bf16 parameters: density: 0.6 weight: 0.35 - model: CultriX/NeuralTrix-7B-dpo parameters: density: 0.6 weight: 0.35 merge_method: dare_ties base_model: eren23/dpo-binarized-NeuralTrix-7B parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "CultriX/" 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"]) ```
{"tags": ["merge", "mergekit", "lazymergekit", "bardsai/jaskier-7b-dpo-v3.3", "CultriX/NeuralTrix-v4-bf16", "CultriX/NeuralTrix-7B-dpo"], "base_model": ["bardsai/jaskier-7b-dpo-v3.3", "CultriX/NeuralTrix-v4-bf16", "CultriX/NeuralTrix-7B-dpo"]}
text-generation
CultriX/NeuralTrix-bf16
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "bardsai/jaskier-7b-dpo-v3.3", "CultriX/NeuralTrix-v4-bf16", "CultriX/NeuralTrix-7B-dpo", "base_model:bardsai/jaskier-7b-dpo-v3.3", "base_model:CultriX/NeuralTrix-v4-bf16", "base_model:CultriX/NeuralTrix-7B-dpo", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-14T13:30:21+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-7B-dpo #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# NeuralTrix-bf16 NeuralTrix-bf16 is a merge of the following models using LazyMergekit: * bardsai/jaskier-7b-dpo-v3.3 * CultriX/NeuralTrix-v4-bf16 * CultriX/NeuralTrix-7B-dpo ## Configuration ## Usage
[ "# NeuralTrix-bf16\n\nNeuralTrix-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-7B-dpo", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-7B-dpo #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# NeuralTrix-bf16\n\nNeuralTrix-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-7B-dpo", "## Configuration", "## Usage" ]
[ 164, 73, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-7B-dpo #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-7B-dpo #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralTrix-bf16\n\nNeuralTrix-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-7B-dpo## Configuration## Usage" ]
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null
null
transformers
--- # 🦜 EmertonMonarch-7B EmertonOmniBeagle-7B-dpo is a DPO fine-tune of [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/OmniBeagle-7B) using the [yleo/emerton_dpo_pairs_judge](https://huggingface.co/datasets/yleo/emerton_dpo_pairs_judge) preference dataset created from [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, LLM-Blender is used to judge between GPT4 and GPT4 Turbo. ## 🔍 Applications This model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template. ## 🏆 Evaluation ### Open LLM Leaderboard To come... ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "yleo/EmertonMonarch-7B" messages = [{"role": "user", "content": "How to improve LLM fine-tuning?"}] 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"]) ```
{"license": "cc-by-nc-4.0", "tags": ["dpo"], "datasets": ["yleo/emerton_dpo_pairs_judge"], "base_model": "mlabonne/Monarch-7B"}
text-generation
yleo/EmertonMonarch-7B
[ "transformers", "safetensors", "mistral", "text-generation", "dpo", "conversational", "dataset:yleo/emerton_dpo_pairs_judge", "base_model:mlabonne/Monarch-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-14T13:31:06+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #dpo #conversational #dataset-yleo/emerton_dpo_pairs_judge #base_model-mlabonne/Monarch-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
--- # EmertonMonarch-7B EmertonOmniBeagle-7B-dpo is a DPO fine-tune of mlabonne/Monarch-7B using the yleo/emerton_dpo_pairs_judge preference dataset created from Intel/orca_dpo_pairs by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, LLM-Blender is used to judge between GPT4 and GPT4 Turbo. ## Applications This model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template. ## Evaluation ### Open LLM Leaderboard To come... ## Usage
[ "# EmertonMonarch-7B\n\nEmertonOmniBeagle-7B-dpo is a DPO fine-tune of mlabonne/Monarch-7B using the yleo/emerton_dpo_pairs_judge preference dataset created from Intel/orca_dpo_pairs by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, LLM-Blender is used to judge between GPT4 and GPT4 Turbo.", "## Applications\n\nThis model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.", "## Evaluation", "### Open LLM Leaderboard\n\nTo come...", "## Usage" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #dpo #conversational #dataset-yleo/emerton_dpo_pairs_judge #base_model-mlabonne/Monarch-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# EmertonMonarch-7B\n\nEmertonOmniBeagle-7B-dpo is a DPO fine-tune of mlabonne/Monarch-7B using the yleo/emerton_dpo_pairs_judge preference dataset created from Intel/orca_dpo_pairs by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, LLM-Blender is used to judge between GPT4 and GPT4 Turbo.", "## Applications\n\nThis model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.", "## Evaluation", "### Open LLM Leaderboard\n\nTo come...", "## Usage" ]
[ 96, 104, 33, 3, 10, 3 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #dpo #conversational #dataset-yleo/emerton_dpo_pairs_judge #base_model-mlabonne/Monarch-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# EmertonMonarch-7B\n\nEmertonOmniBeagle-7B-dpo is a DPO fine-tune of mlabonne/Monarch-7B using the yleo/emerton_dpo_pairs_judge preference dataset created from Intel/orca_dpo_pairs by replacing gpt 3.5 answer by a gpt4 Turbo answer. Then, LLM-Blender is used to judge between GPT4 and GPT4 Turbo.## Applications\n\nThis model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.## Evaluation### Open LLM Leaderboard\n\nTo come...## Usage" ]
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# pflowtts_uk_elevenlabs Model Checkpoints ## Overview This repository hosts the trained checkpoints of the [GitHub - skypro1111/pflowtts_pytorch_uk](https://github.com/skypro1111/pflowtts_pytorch_uk) model, trained by [@skypro1111](https://github.com/skypro1111). The model is trained using a synthetic dataset [Huggingface - skypro1111/elevenlabs_dataset](https://huggingface.co/datasets/skypro1111/elevenlabs_dataset) generated with ChatGPT-4 and ElevenLabs.io. ## Usage The trained model checkpoints are intended for use in TTS applications, research, and development, providing a resource for those looking to incorporate high-quality Ukrainian speech synthesis into their projects. To utilize these checkpoints in your projects, refer to the GitHub repository for implementation details and examples: [GitHub - skypro1111/pflowtts_pytorch_uk](https://github.com/skypro1111/pflowtts_pytorch_uk) ## Model Details - **Training Dataset**: Synthetic dataset comprising 1,388 audio files and their corresponding texts, totaling 2 hours and 20 minutes of speech. - **Objective**: The model aims to demonstrate the efficacy of synthetic datasets in training robust and versatile TTS systems. ## License The checkpoints are distributed under the MIT License, allowing for both academic and commercial use, provided that proper credit is given. ## Citation If you find these checkpoints useful in your research or application, please consider citing the repository as follows: ``` @misc{pflowtts_uk_elevenlabs_checkpoints, author = {@skypro1111}, title = {pflowtts_uk_elevenlabs Checkpoints}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face Repository}, howpublished = {\url{https://huggingface.co/skypro1111/pyflowtts_uk_elevenlabs}} } ``` ## Acknowledgments Special thanks to the services provided by ChatGPT-4 and ElevenLabs.io for making the generation of the synthetic dataset possible, thereby facilitating the development of this TTS model. --- © 2024 @skypro1111. All Rights Reserved.
{"language": ["uk"], "license": "mit", "datasets": ["skypro1111/elevenlabs_dataset"]}
null
skypro1111/pyflowtts_uk_elevenlabs
[ "uk", "dataset:skypro1111/elevenlabs_dataset", "license:mit", "region:us" ]
2024-02-14T13:32:26+00:00
[]
[ "uk" ]
TAGS #uk #dataset-skypro1111/elevenlabs_dataset #license-mit #region-us
# pflowtts_uk_elevenlabs Model Checkpoints ## Overview This repository hosts the trained checkpoints of the GitHub - skypro1111/pflowtts_pytorch_uk model, trained by @skypro1111. The model is trained using a synthetic dataset Huggingface - skypro1111/elevenlabs_dataset generated with ChatGPT-4 and URL. ## Usage The trained model checkpoints are intended for use in TTS applications, research, and development, providing a resource for those looking to incorporate high-quality Ukrainian speech synthesis into their projects. To utilize these checkpoints in your projects, refer to the GitHub repository for implementation details and examples: GitHub - skypro1111/pflowtts_pytorch_uk ## Model Details - Training Dataset: Synthetic dataset comprising 1,388 audio files and their corresponding texts, totaling 2 hours and 20 minutes of speech. - Objective: The model aims to demonstrate the efficacy of synthetic datasets in training robust and versatile TTS systems. ## License The checkpoints are distributed under the MIT License, allowing for both academic and commercial use, provided that proper credit is given. If you find these checkpoints useful in your research or application, please consider citing the repository as follows: ## Acknowledgments Special thanks to the services provided by ChatGPT-4 and URL for making the generation of the synthetic dataset possible, thereby facilitating the development of this TTS model. --- © 2024 @skypro1111. All Rights Reserved.
[ "# pflowtts_uk_elevenlabs Model Checkpoints", "## Overview\n\nThis repository hosts the trained checkpoints of the GitHub - skypro1111/pflowtts_pytorch_uk model, trained by @skypro1111. The model is trained using a synthetic dataset Huggingface - skypro1111/elevenlabs_dataset generated with ChatGPT-4 and URL.", "## Usage\n\nThe trained model checkpoints are intended for use in TTS applications, research, and development, providing a resource for those looking to incorporate high-quality Ukrainian speech synthesis into their projects. \n\nTo utilize these checkpoints in your projects, refer to the GitHub repository for implementation details and examples:\n\nGitHub - skypro1111/pflowtts_pytorch_uk", "## Model Details\n\n- Training Dataset: Synthetic dataset comprising 1,388 audio files and their corresponding texts, totaling 2 hours and 20 minutes of speech.\n- Objective: The model aims to demonstrate the efficacy of synthetic datasets in training robust and versatile TTS systems.", "## License\n\nThe checkpoints are distributed under the MIT License, allowing for both academic and commercial use, provided that proper credit is given.\n\nIf you find these checkpoints useful in your research or application, please consider citing the repository as follows:", "## Acknowledgments\n\nSpecial thanks to the services provided by ChatGPT-4 and URL for making the generation of the synthetic dataset possible, thereby facilitating the development of this TTS model.\n\n---\n\n© 2024 @skypro1111. All Rights Reserved." ]
[ "TAGS\n#uk #dataset-skypro1111/elevenlabs_dataset #license-mit #region-us \n", "# pflowtts_uk_elevenlabs Model Checkpoints", "## Overview\n\nThis repository hosts the trained checkpoints of the GitHub - skypro1111/pflowtts_pytorch_uk model, trained by @skypro1111. The model is trained using a synthetic dataset Huggingface - skypro1111/elevenlabs_dataset generated with ChatGPT-4 and URL.", "## Usage\n\nThe trained model checkpoints are intended for use in TTS applications, research, and development, providing a resource for those looking to incorporate high-quality Ukrainian speech synthesis into their projects. \n\nTo utilize these checkpoints in your projects, refer to the GitHub repository for implementation details and examples:\n\nGitHub - skypro1111/pflowtts_pytorch_uk", "## Model Details\n\n- Training Dataset: Synthetic dataset comprising 1,388 audio files and their corresponding texts, totaling 2 hours and 20 minutes of speech.\n- Objective: The model aims to demonstrate the efficacy of synthetic datasets in training robust and versatile TTS systems.", "## License\n\nThe checkpoints are distributed under the MIT License, allowing for both academic and commercial use, provided that proper credit is given.\n\nIf you find these checkpoints useful in your research or application, please consider citing the repository as follows:", "## Acknowledgments\n\nSpecial thanks to the services provided by ChatGPT-4 and URL for making the generation of the synthetic dataset possible, thereby facilitating the development of this TTS model.\n\n---\n\n© 2024 @skypro1111. All Rights Reserved." ]
[ 28, 16, 82, 90, 67, 56, 57 ]
[ "passage: TAGS\n#uk #dataset-skypro1111/elevenlabs_dataset #license-mit #region-us \n# pflowtts_uk_elevenlabs Model Checkpoints## Overview\n\nThis repository hosts the trained checkpoints of the GitHub - skypro1111/pflowtts_pytorch_uk model, trained by @skypro1111. The model is trained using a synthetic dataset Huggingface - skypro1111/elevenlabs_dataset generated with ChatGPT-4 and URL.## Usage\n\nThe trained model checkpoints are intended for use in TTS applications, research, and development, providing a resource for those looking to incorporate high-quality Ukrainian speech synthesis into their projects. \n\nTo utilize these checkpoints in your projects, refer to the GitHub repository for implementation details and examples:\n\nGitHub - skypro1111/pflowtts_pytorch_uk## Model Details\n\n- Training Dataset: Synthetic dataset comprising 1,388 audio files and their corresponding texts, totaling 2 hours and 20 minutes of speech.\n- Objective: The model aims to demonstrate the efficacy of synthetic datasets in training robust and versatile TTS systems.## License\n\nThe checkpoints are distributed under the MIT License, allowing for both academic and commercial use, provided that proper credit is given.\n\nIf you find these checkpoints useful in your research or application, please consider citing the repository as follows:## Acknowledgments\n\nSpecial thanks to the services provided by ChatGPT-4 and URL for making the generation of the synthetic dataset possible, thereby facilitating the development of this TTS model.\n\n---\n\n© 2024 @skypro1111. All Rights Reserved." ]
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# No Copyright Music for AI Model Examples Welcome to our collection of no copyright music, specially curated for use with AI model examples. This resource is designed for developers, researchers, and creators who need high-quality music without the constraints of copyright for their AI-driven projects. ## Overview This project aims to provide a diverse library of music tracks that are safe to use for various applications, including but not limited to, machine learning demos, AI model testing, and more. Our collection ensures that you can focus on innovation and creativity without worrying about copyright infringement. ## Music List : You can listen and download it directly from [this Space](https://lenylvt-nocopyrightmusic-player.hf.space/) | Music Name | By | Full Version | Instrumental Version | Vocals Version | | --- | --- | --- | --- | --- | | Always Be There | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Always%20Be%20There.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Always%20Be%20There%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Always%20Be%20There%20_%20Vocals.wav?download=true) | | Baila Conmigo | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Baila%20Conmigo.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Baila%20Conmigo%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Baila%20Conmigo%20_%20Vocals.wav?download=true) | | GetToYou | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/GetToYou.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/GetToYou%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/GetToYou%20_%20Vocals.wav?download=true) | | Higher | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Higher.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Higher%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Higher%20_%20Vocals.wav?download=true) | | I Try | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/I%20Try.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/I%20Try%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/I%20Try%20_%20Vocals.wav?download=true) | | Latin Pop | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Latin%20Pop.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Latin%20Pop%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Latin%20Pop%20_%20Vocals.wav?download=true) | | Memories | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Memories.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Memories%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Memories%20_%20Vocals.wav?download=true) | | One Way To Know | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/One%20Way%20To%20Know.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/One%20Way%20To%20Know%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/One%20Way%20To%20Know%20_%20Vocals.wav?download=true) | | Run Away | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Run%20Away.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Run%20Away%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Run%20Away%20_%20Vocals.wav?download=true) | | Save Me | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Save%20Me.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Save%20Me%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Save%20Me%20_%20Vocals.wav?download=true) | | There You Were | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/There%20You%20Were.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/There%20You%20Were%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/There%20You%20Were%20_%20Vocals.wav?download=true) | | Whatever It Takes | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Whatever%20It%20Takes.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Whatever%20It%20Takes%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/Whatever%20It%20Takes%20_%20Vocals.wav?download=true) | | You Found Me | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/You%20Found%20Me.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/You%20Found%20Me%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/You%20Found%20Me%20_%20Vocals.wav?download=true) | | You Know | Liborio Conti | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/You%20Know.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/You%20Know%20_%20Instrumental.wav?download=true) | [Download](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/resolve/main/You%20Know%20_%20Vocals.wav?download=true) | ## Contributing We welcome contributions from the community! If you have music you'd like to add to this collection, please go on [Community Tab](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/discussions) to submit your tracks. ## License All music in this collection is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to share, remix, adapt, and build upon the work, even commercially, as long as they credit the original creation. By using any track from this collection, you agree to give appropriate credit ("By" column in "Music List"), provide a link to the license (https://creativecommons.org/licenses/by/4.0/), and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. For more details about the CC BY 4.0 license, please visit [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/). ## Contact For questions, suggestions, or to report an issue, please contact us at [Community Tab](https://huggingface.co/Lenylvt/NoCopyrightMusic_for_AIModelExamples/discussions). Thank you for exploring our no copyright music collection for AI model examples. We hope this resource enhances your projects and fosters innovation in the AI community.
{"license": "cc-by-4.0"}
null
Lenylvt/NoCopyrightMusic_for_AIModelExamples
[ "license:cc-by-4.0", "has_space", "region:us" ]
2024-02-14T13:32:30+00:00
[]
[]
TAGS #license-cc-by-4.0 #has_space #region-us
No Copyright Music for AI Model Examples ======================================== Welcome to our collection of no copyright music, specially curated for use with AI model examples. This resource is designed for developers, researchers, and creators who need high-quality music without the constraints of copyright for their AI-driven projects. Overview -------- This project aims to provide a diverse library of music tracks that are safe to use for various applications, including but not limited to, machine learning demos, AI model testing, and more. Our collection ensures that you can focus on innovation and creativity without worrying about copyright infringement. Music List : ------------ You can listen and download it directly from this Space Contributing ------------ We welcome contributions from the community! If you have music you'd like to add to this collection, please go on Community Tab to submit your tracks. License ------- All music in this collection is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license allows others to share, remix, adapt, and build upon the work, even commercially, as long as they credit the original creation. By using any track from this collection, you agree to give appropriate credit ("By" column in "Music List"), provide a link to the license (URL and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. For more details about the CC BY 4.0 license, please visit URL Contact ------- For questions, suggestions, or to report an issue, please contact us at Community Tab. Thank you for exploring our no copyright music collection for AI model examples. We hope this resource enhances your projects and fosters innovation in the AI community.
[]
[ "TAGS\n#license-cc-by-4.0 #has_space #region-us \n" ]
[ 19 ]
[ "passage: TAGS\n#license-cc-by-4.0 #has_space #region-us \n" ]
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null
null
transformers
# FrankenCRIA v1.3-m.2 ## What is FrankenCRIA? <p align="center"> <img src="https://github.com/davzoku/cria/blob/main/assets/frankencria-icon-512x512.png?raw=true" width="300" height="300" alt="FrankenCRIA Logo"> <br> <i>This is a frankenmerge of <a href="https://huggingface.co/davzoku/cria-llama2-7b-v1.3">davzoku/cria-llama2-7b-v1.3</a>.</i> </p> The configuration is the same as [vilm/vinallama-12.5b-chat-DUS](https://huggingface.co/vilm/vinallama-12.5b-chat-DUS). Please be aware that this model is highly experimental, and no further training has been conducted following the merge. Therefore, the model performance may not meet expectations, as described in the [SOLAR paper](https://arxiv.org/abs/2312.15166) ## 📦 FrankenCRIA Model Release FrankenCRIA v1.3 comes with several variants. - [davzoku/frankencria-llama2-11b-v1.3-m.1](https://huggingface.co/davzoku/frankencria-llama2-11b-v1.3-m.1): 11B FrankenMerge inspired by [Undi95/Mistral-11B-v0.1](https://huggingface.co/Undi95/Mistral-11B-v0.1) - [davzoku/frankencria-llama2-12.5b-v1.3-m.2](https://huggingface.co/davzoku/frankencria-llama2-12.5b-v1.3-m.2): 12.5B interleaving FrankenMerge inspired by [vilm/vinallama-12.5b-chat-DUS](https://huggingface.co/vilm/vinallama-12.5b-chat-DUS) ## 🧩 Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3) ### Configuration The following YAML configuration was used to produce this model: ```yaml # https://huggingface.co/vilm/vinallama-12.5b-chat-DUS slices: - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [0, 16] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [8, 16] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [8, 16] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [16, 24] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [16, 24] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [24, 28] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [24, 28] - sources: - model: davzoku/cria-llama2-7b-v1.3 layer_range: [28, 32] merge_method: passthrough dtype: bfloat16 ```
{"language": "en", "license": "llama2", "library_name": "transformers", "tags": ["mergekit", "merge", "llama-2"], "datasets": ["mlabonne/CodeLlama-2-20k"], "inference": false, "model_type": "llama", "pipeline_tag": "text-generation", "base_model": ["davzoku/cria-llama2-7b-v1.3"]}
text-generation
davzoku/frankencria-llama2-12.5b-v1.3-m.2
[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "llama-2", "en", "dataset:mlabonne/CodeLlama-2-20k", "arxiv:2312.15166", "base_model:davzoku/cria-llama2-7b-v1.3", "license:llama2", "autotrain_compatible", "text-generation-inference", "region:us" ]
2024-02-14T13:32:59+00:00
[ "2312.15166" ]
[ "en" ]
TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #arxiv-2312.15166 #base_model-davzoku/cria-llama2-7b-v1.3 #license-llama2 #autotrain_compatible #text-generation-inference #region-us
# FrankenCRIA v1.3-m.2 ## What is FrankenCRIA? <p align="center"> <img src="URL width="300" height="300" alt="FrankenCRIA Logo"> <br> <i>This is a frankenmerge of <a href="URL </p> The configuration is the same as vilm/vinallama-12.5b-chat-DUS. Please be aware that this model is highly experimental, and no further training has been conducted following the merge. Therefore, the model performance may not meet expectations, as described in the SOLAR paper ## FrankenCRIA Model Release FrankenCRIA v1.3 comes with several variants. - davzoku/frankencria-llama2-11b-v1.3-m.1: 11B FrankenMerge inspired by Undi95/Mistral-11B-v0.1 - davzoku/frankencria-llama2-12.5b-v1.3-m.2: 12.5B interleaving FrankenMerge inspired by vilm/vinallama-12.5b-chat-DUS ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * davzoku/cria-llama2-7b-v1.3 ### Configuration The following YAML configuration was used to produce this model:
[ "# FrankenCRIA v1.3-m.2", "## What is FrankenCRIA?\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"FrankenCRIA Logo\"> <br>\n <i>This is a frankenmerge of <a href=\"URL\n</p>\n\nThe configuration is the same as vilm/vinallama-12.5b-chat-DUS.\n\n\nPlease be aware that this model is highly experimental, and no further training has been conducted following the merge. \nTherefore, the model performance may not meet expectations, as described in the SOLAR paper", "## FrankenCRIA Model Release\n\nFrankenCRIA v1.3 comes with several variants.\n\n- davzoku/frankencria-llama2-11b-v1.3-m.1: 11B FrankenMerge inspired by Undi95/Mistral-11B-v0.1\n- davzoku/frankencria-llama2-12.5b-v1.3-m.2: 12.5B interleaving FrankenMerge inspired by vilm/vinallama-12.5b-chat-DUS", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* davzoku/cria-llama2-7b-v1.3", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #arxiv-2312.15166 #base_model-davzoku/cria-llama2-7b-v1.3 #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n", "# FrankenCRIA v1.3-m.2", "## What is FrankenCRIA?\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"FrankenCRIA Logo\"> <br>\n <i>This is a frankenmerge of <a href=\"URL\n</p>\n\nThe configuration is the same as vilm/vinallama-12.5b-chat-DUS.\n\n\nPlease be aware that this model is highly experimental, and no further training has been conducted following the merge. \nTherefore, the model performance may not meet expectations, as described in the SOLAR paper", "## FrankenCRIA Model Release\n\nFrankenCRIA v1.3 comes with several variants.\n\n- davzoku/frankencria-llama2-11b-v1.3-m.1: 11B FrankenMerge inspired by Undi95/Mistral-11B-v0.1\n- davzoku/frankencria-llama2-12.5b-v1.3-m.2: 12.5B interleaving FrankenMerge inspired by vilm/vinallama-12.5b-chat-DUS", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* davzoku/cria-llama2-7b-v1.3", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
[ 101, 11, 122, 108, 4, 17, 31, 17 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #llama-2 #en #dataset-mlabonne/CodeLlama-2-20k #arxiv-2312.15166 #base_model-davzoku/cria-llama2-7b-v1.3 #license-llama2 #autotrain_compatible #text-generation-inference #region-us \n# FrankenCRIA v1.3-m.2## What is FrankenCRIA?\n\n<p align=\"center\">\n <img src=\"URL width=\"300\" height=\"300\" alt=\"FrankenCRIA Logo\"> <br>\n <i>This is a frankenmerge of <a href=\"URL\n</p>\n\nThe configuration is the same as vilm/vinallama-12.5b-chat-DUS.\n\n\nPlease be aware that this model is highly experimental, and no further training has been conducted following the merge. \nTherefore, the model performance may not meet expectations, as described in the SOLAR paper## FrankenCRIA Model Release\n\nFrankenCRIA v1.3 comes with several variants.\n\n- davzoku/frankencria-llama2-11b-v1.3-m.1: 11B FrankenMerge inspired by Undi95/Mistral-11B-v0.1\n- davzoku/frankencria-llama2-12.5b-v1.3-m.2: 12.5B interleaving FrankenMerge inspired by vilm/vinallama-12.5b-chat-DUS## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* davzoku/cria-llama2-7b-v1.3### Configuration\n\nThe following YAML configuration was used to produce this model:" ]
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null
null
peft
# Qwen-1.5-1.8B-SQL Model ## Description This model, `deltawi/Qwen-1.5-1.8B-SQL`, is fine-tuned on SQL generation based on questions and context. It's designed to generate SQL queries from natural language descriptions, leveraging the [Qwen 1.5 - 1.8B model](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat). ## Installation To use this model, you need to install the `transformers` library from Hugging Face. You can do this using pip: ```bash pip install transformers ``` ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Set the device device = "mps" # replace with your device: "cpu", "cuda", "mps" # Load the model model = AutoModelForCausalLM.from_pretrained( "deltawi/Qwen-1.5-1.8B-SQL", device_map="auto" ) # Load the tokenizer tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B-Chat") # Define your question and context Question = "Your question here" Context = """ Your SQL context here """ # Create the prompt prompt = f"Question: {Question}\nContext: {Context}" messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] # Prepare the input text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) # Generate the response generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] # Decode the response response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] print(response) ``` ## More details - Base Model: Qwen 1.5-1.8B - Fine-tuned for: SQL Query Generation - Fine-tuning using LoRA: r=64 - Training Data: [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) ### Framework versions - PEFT 0.8.2
{"library_name": "peft", "base_model": "Qwen/Qwen1.5-1.8B-Chat"}
null
deltawi/Qwen-1.5-1.8B-SQL
[ "peft", "safetensors", "base_model:Qwen/Qwen1.5-1.8B-Chat", "region:us" ]
2024-02-14T13:33:09+00:00
[]
[]
TAGS #peft #safetensors #base_model-Qwen/Qwen1.5-1.8B-Chat #region-us
# Qwen-1.5-1.8B-SQL Model ## Description This model, 'deltawi/Qwen-1.5-1.8B-SQL', is fine-tuned on SQL generation based on questions and context. It's designed to generate SQL queries from natural language descriptions, leveraging the Qwen 1.5 - 1.8B model. ## Installation To use this model, you need to install the 'transformers' library from Hugging Face. You can do this using pip: ## Usage ## More details - Base Model: Qwen 1.5-1.8B - Fine-tuned for: SQL Query Generation - Fine-tuning using LoRA: r=64 - Training Data: b-mc2/sql-create-context ### Framework versions - PEFT 0.8.2
[ "# Qwen-1.5-1.8B-SQL Model", "## Description\nThis model, 'deltawi/Qwen-1.5-1.8B-SQL', is fine-tuned on SQL generation based on questions and context. It's designed to generate SQL queries from natural language descriptions, leveraging the Qwen 1.5 - 1.8B model.", "## Installation\nTo use this model, you need to install the 'transformers' library from Hugging Face. You can do this using pip:", "## Usage", "## More details\n\n- Base Model: Qwen 1.5-1.8B\n- Fine-tuned for: SQL Query Generation\n- Fine-tuning using LoRA: r=64\n- Training Data: b-mc2/sql-create-context", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #safetensors #base_model-Qwen/Qwen1.5-1.8B-Chat #region-us \n", "# Qwen-1.5-1.8B-SQL Model", "## Description\nThis model, 'deltawi/Qwen-1.5-1.8B-SQL', is fine-tuned on SQL generation based on questions and context. It's designed to generate SQL queries from natural language descriptions, leveraging the Qwen 1.5 - 1.8B model.", "## Installation\nTo use this model, you need to install the 'transformers' library from Hugging Face. You can do this using pip:", "## Usage", "## More details\n\n- Base Model: Qwen 1.5-1.8B\n- Fine-tuned for: SQL Query Generation\n- Fine-tuning using LoRA: r=64\n- Training Data: b-mc2/sql-create-context", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 30, 11, 60, 31, 3, 53, 11 ]
[ "passage: TAGS\n#peft #safetensors #base_model-Qwen/Qwen1.5-1.8B-Chat #region-us \n# Qwen-1.5-1.8B-SQL Model## Description\nThis model, 'deltawi/Qwen-1.5-1.8B-SQL', is fine-tuned on SQL generation based on questions and context. It's designed to generate SQL queries from natural language descriptions, leveraging the Qwen 1.5 - 1.8B model.## Installation\nTo use this model, you need to install the 'transformers' library from Hugging Face. You can do this using pip:## Usage## More details\n\n- Base Model: Qwen 1.5-1.8B\n- Fine-tuned for: SQL Query Generation\n- Fine-tuning using LoRA: r=64\n- Training Data: b-mc2/sql-create-context### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
transformers
<!-- 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. --> # BERT2BERT_try This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0150 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2457 | 0.08 | 50 | 0.0349 | | 0.0445 | 0.16 | 100 | 0.0297 | | 0.0339 | 0.24 | 150 | 0.0214 | | 0.0234 | 0.31 | 200 | 0.0221 | | 0.0193 | 0.39 | 250 | 0.0211 | | 0.027 | 0.47 | 300 | 0.0172 | | 0.0199 | 0.55 | 350 | 0.0176 | | 0.021 | 0.63 | 400 | 0.0162 | | 0.0231 | 0.71 | 450 | 0.0164 | | 0.021 | 0.79 | 500 | 0.0152 | | 0.0235 | 0.86 | 550 | 0.0149 | | 0.0178 | 0.94 | 600 | 0.0150 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
{"tags": ["generated_from_trainer"], "model-index": [{"name": "BERT2BERT_try", "results": []}]}
text2text-generation
houdini001/BERT2BERT_try
[ "transformers", "tensorboard", "safetensors", "encoder-decoder", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T13:33:34+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us
BERT2BERT\_try ============== This model is a fine-tuned version of [](URL on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0150 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #tensorboard #safetensors #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
[ 54, 113, 4, 33 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #encoder-decoder #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.2" ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
null
pogpog/tiiuae-falcon-rw-1b-conversation-summary
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T13:36:11+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# [MaziyarPanahi/OGNO-7B-GGUF](https://huggingface.co/MaziyarPanahi/OGNO-7B-GGUF) - Model creator: [paulml](https://huggingface.co/paulml) - Original model: [paulml/OGNO-7B](https://huggingface.co/paulml/OGNO-7B) ## Description [MaziyarPanahi/OGNO-7B-GGUF](https://huggingface.co/MaziyarPanahi/OGNO-7B-GGUF) contains GGUF format model files for [paulml/OGNO-7B](https://huggingface.co/paulml/OGNO-7B). ## How to use Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: [MaziyarPanahi/OGNO-7B-GGUF](https://huggingface.co/MaziyarPanahi/OGNO-7B-GGUF) and below it, a specific filename to download, such as: OGNO-7B-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download MaziyarPanahi/OGNO-7B-GGUF OGNO-7B-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download [MaziyarPanahi/OGNO-7B-GGUF](https://huggingface.co/MaziyarPanahi/OGNO-7B-GGUF) --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MaziyarPanahi/OGNO-7B-GGUF OGNO-7B-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m OGNO-7B-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./OGNO-7B-GGUF.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./OGNO-7B-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
{"tags": ["quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/Omningotex-7b-slerp", "eren23/dpo-binarized-NeutrixOmnibe-7B", "base_model:liminerity/Omningotex-7b-slerp", "base_model:eren23/dpo-binarized-NeutrixOmnibe-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us"], "model_name": "OGNO-7B-GGUF", "base_model": "paulml/OGNO-7B", "inference": false, "model_creator": "paulml", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"}
text-generation
MaziyarPanahi/OGNO-7B-GGUF
[ "transformers", "gguf", "mistral", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "safetensors", "text-generation", "merge", "mergekit", "lazymergekit", "liminerity/Omningotex-7b-slerp", "eren23/dpo-binarized-NeutrixOmnibe-7B", "base_model:liminerity/Omningotex-7b-slerp", "base_model:eren23/dpo-binarized-NeutrixOmnibe-7B", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "base_model:paulml/OGNO-7B" ]
2024-02-14T13:37:16+00:00
[]
[]
TAGS #transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #safetensors #text-generation #merge #mergekit #lazymergekit #liminerity/Omningotex-7b-slerp #eren23/dpo-binarized-NeutrixOmnibe-7B #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/dpo-binarized-NeutrixOmnibe-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #base_model-paulml/OGNO-7B
# MaziyarPanahi/OGNO-7B-GGUF - Model creator: paulml - Original model: paulml/OGNO-7B ## Description MaziyarPanahi/OGNO-7B-GGUF contains GGUF format model files for paulml/OGNO-7B. ## How to use Thanks to TheBloke for preparing an amazing README on how to use GGUF models: ### About GGUF GGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL. Here is an incomplete list of clients and libraries that are known to support GGUF: * URL. The source project for GGUF. Offers a CLI and a server option. * text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection. * URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use. * ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ### Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * URL ### In 'text-generation-webui' Under Download Model, you can enter the model repo: MaziyarPanahi/OGNO-7B-GGUF and below it, a specific filename to download, such as: OGNO-7B-GGUF.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the 'huggingface-hub' Python library: Then you can download any individual model file to the current directory, at high speed, with a command like this: </details> <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI. To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer': And set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1': Windows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command. </details> ## Example 'URL' command Make sure you are using 'URL' from commit d0cee0d or later. Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins' For other parameters and how to use them, please refer to the URL documentation ## How to run in 'text-generation-webui' Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL. ## How to run from Python code You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: llama-cpp-python docs. #### First install the package Run one of the following commands, according to your system: #### Simple llama-cpp-python example code ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * LangChain + llama-cpp-python * LangChain + ctransformers
[ "# MaziyarPanahi/OGNO-7B-GGUF\n- Model creator: paulml\n- Original model: paulml/OGNO-7B", "## Description\nMaziyarPanahi/OGNO-7B-GGUF contains GGUF format model files for paulml/OGNO-7B.", "## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.", "### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw", "## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL", "### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/OGNO-7B-GGUF and below it, a specific filename to download, such as: OGNO-7B-GGUF.Q4_K_M.gguf.\n\nThen click Download.", "### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n</details>\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>", "## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation", "## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.", "## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code", "## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers" ]
[ "TAGS\n#transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #safetensors #text-generation #merge #mergekit #lazymergekit #liminerity/Omningotex-7b-slerp #eren23/dpo-binarized-NeutrixOmnibe-7B #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/dpo-binarized-NeutrixOmnibe-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #base_model-paulml/OGNO-7B \n", "# MaziyarPanahi/OGNO-7B-GGUF\n- Model creator: paulml\n- Original model: paulml/OGNO-7B", "## Description\nMaziyarPanahi/OGNO-7B-GGUF contains GGUF format model files for paulml/OGNO-7B.", "## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.", "### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw", "## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL", "### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/OGNO-7B-GGUF and below it, a specific filename to download, such as: OGNO-7B-GGUF.Q4_K_M.gguf.\n\nThen click Download.", "### On the command line, including multiple files at once\n\nI recommend using the 'huggingface-hub' Python library:\n\n\n\nThen you can download any individual model file to the current directory, at high speed, with a command like this:\n\n\n</details>\n<details>\n <summary>More advanced huggingface-cli download usage (click to read)</summary>\n\nYou can also download multiple files at once with a pattern:\n\n\n\nFor more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI.\n\nTo accelerate downloads on fast connections (1Gbit/s or higher), install 'hf_transfer':\n\n\n\nAnd set environment variable 'HF_HUB_ENABLE_HF_TRANSFER' to '1':\n\n\n\nWindows Command Line users: You can set the environment variable by running 'set HF_HUB_ENABLE_HF_TRANSFER=1' before the download command.\n</details>", "## Example 'URL' command\n\nMake sure you are using 'URL' from commit d0cee0d or later.\n\n\n\nChange '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.\n\nChange '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.\n\nIf you want to have a chat-style conversation, replace the '-p <PROMPT>' argument with '-i -ins'\n\nFor other parameters and how to use them, please refer to the URL documentation", "## How to run in 'text-generation-webui'\n\nFurther instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL.", "## How to run from Python code\n\nYou can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.", "### How to load this model in Python code, using llama-cpp-python\n\nFor full documentation, please see: llama-cpp-python docs.", "#### First install the package\n\nRun one of the following commands, according to your system:", "#### Simple llama-cpp-python example code", "## How to use with LangChain\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers" ]
[ 187, 32, 32, 26, 401, 323, 84, 71, 218, 182, 49, 77, 36, 19, 12, 50 ]
[ "passage: TAGS\n#transformers #gguf #mistral #quantized #2-bit #3-bit #4-bit #5-bit #6-bit #8-bit #GGUF #safetensors #text-generation #merge #mergekit #lazymergekit #liminerity/Omningotex-7b-slerp #eren23/dpo-binarized-NeutrixOmnibe-7B #base_model-liminerity/Omningotex-7b-slerp #base_model-eren23/dpo-binarized-NeutrixOmnibe-7B #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #base_model-paulml/OGNO-7B \n# MaziyarPanahi/OGNO-7B-GGUF\n- Model creator: paulml\n- Original model: paulml/OGNO-7B## Description\nMaziyarPanahi/OGNO-7B-GGUF contains GGUF format model files for paulml/OGNO-7B.## How to use\nThanks to TheBloke for preparing an amazing README on how to use GGUF models:", "passage: ### About GGUF\n\nGGUF is a new format introduced by the URL team on August 21st 2023. It is a replacement for GGML, which is no longer supported by URL.\n\nHere is an incomplete list of clients and libraries that are known to support GGUF:\n\n* URL. The source project for GGUF. Offers a CLI and a server option.\n* text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.\n* KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.\n* GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.\n* LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.\n* LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.\n* URL, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.\n* llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.\n* candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.\n* ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.### Explanation of quantisation methods\n\n<details>\n <summary>Click to see details</summary>\n\nThe new methods available are:\n\n* GGML_TYPE_Q2_K - \"type-1\" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)\n* GGML_TYPE_Q3_K - \"type-0\" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.\n* GGML_TYPE_Q4_K - \"type-1\" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.\n* GGML_TYPE_Q5_K - \"type-1\" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw\n* GGML_TYPE_Q6_K - \"type-0\" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw## How to download GGUF files\n\nNote for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.\n\nThe following clients/libraries will automatically download models for you, providing a list of available models to choose from:\n\n* LM Studio\n* LoLLMS Web UI\n* URL### In 'text-generation-webui'\n\nUnder Download Model, you can enter the model repo: MaziyarPanahi/OGNO-7B-GGUF and below it, a specific filename to download, such as: OGNO-7B-GGUF.Q4_K_M.gguf.\n\nThen click Download." ]
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/IclpTHDxA6a7xc7TZ5vth.png) # Made using Cultrix's Model, which can be found [here](https://huggingface.co/CultriX/NeuralTrix-7B-dpo). [Join our Discord!](https://discord.gg/XfUWdT9D) [BASE MODEL HERE](https://huggingface.co/Kquant03/NeuralTrix-7B-dpo-laser) NeuralTrix-7B-v1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [mlabonne/OmniBeagle-7B](https://huggingface.co/mlabonne/OmniBeagle-7B) * [flemmingmiguel/MBX-7B-v3](https://huggingface.co/flemmingmiguel/MBX-7B-v3) * [AiMavenAi/AiMaven-Prometheus](https://huggingface.co/AiMavenAi/AiMaven-Prometheus) It was then trained with DPO by Cultrix using: * https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1 I performed laser_snr_math on it afterwards to see if I could improve it. ## 🧩 Configuration ```yaml models: - model: mistralai/Mistral-7B-v0.1 # no parameters necessary for base model - model: mlabonne/OmniBeagle-7B parameters: density: 0.65 weight: 0.4 - model: flemmingmiguel/MBX-7B-v3 parameters: density: 0.6 weight: 0.35 - model: AiMavenAi/AiMaven-Prometheus parameters: density: 0.6 weight: 0.35 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "CultriX/NeuralTrix-7B-v1" 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"]) ```
{"language": ["en"], "license": "apache-2.0"}
null
Kquant03/NeuralTrix-7B-dpo-laser-GGUF
[ "gguf", "en", "license:apache-2.0", "region:us" ]
2024-02-14T13:37:51+00:00
[]
[ "en" ]
TAGS #gguf #en #license-apache-2.0 #region-us
!image/png # Made using Cultrix's Model, which can be found here. Join our Discord! BASE MODEL HERE NeuralTrix-7B-v1 is a merge of the following models using LazyMergekit: * mlabonne/OmniBeagle-7B * flemmingmiguel/MBX-7B-v3 * AiMavenAi/AiMaven-Prometheus It was then trained with DPO by Cultrix using: * URL I performed laser_snr_math on it afterwards to see if I could improve it. ## Configuration ## Usage
[ "# Made using Cultrix's Model, which can be found here.\nJoin our Discord!\n\nBASE MODEL HERE\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus\n\nIt was then trained with DPO by Cultrix using: \n* URL\n\nI performed laser_snr_math on it afterwards to see if I could improve it.", "## Configuration", "## Usage" ]
[ "TAGS\n#gguf #en #license-apache-2.0 #region-us \n", "# Made using Cultrix's Model, which can be found here.\nJoin our Discord!\n\nBASE MODEL HERE\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus\n\nIt was then trained with DPO by Cultrix using: \n* URL\n\nI performed laser_snr_math on it afterwards to see if I could improve it.", "## Configuration", "## Usage" ]
[ 19, 123, 4, 3 ]
[ "passage: TAGS\n#gguf #en #license-apache-2.0 #region-us \n# Made using Cultrix's Model, which can be found here.\nJoin our Discord!\n\nBASE MODEL HERE\n\nNeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:\n* mlabonne/OmniBeagle-7B\n* flemmingmiguel/MBX-7B-v3\n* AiMavenAi/AiMaven-Prometheus\n\nIt was then trained with DPO by Cultrix using: \n* URL\n\nI performed laser_snr_math on it afterwards to see if I could improve it.## Configuration## Usage" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
automatic-speech-recognition
spsither/wav2vec2_run9.560
[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T13:43:12+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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. --> # output This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.7883 - Wer: 1.0199 ## 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: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.92 | 0.95 | 400 | 2.9522 | 1.0026 | | 1.0435 | 1.89 | 800 | 0.8608 | 1.0552 | | 0.5354 | 2.84 | 1200 | 0.7762 | 1.0169 | | 0.404 | 3.79 | 1600 | 0.6984 | 1.0293 | | 0.3301 | 4.73 | 2000 | 0.6811 | 1.0217 | | 0.2745 | 5.68 | 2400 | 0.7027 | 1.0308 | | 0.2346 | 6.63 | 2800 | 0.7296 | 1.0185 | | 0.2096 | 7.57 | 3200 | 0.7148 | 1.0294 | | 0.1912 | 8.52 | 3600 | 0.7109 | 1.0335 | | 0.172 | 9.47 | 4000 | 0.7894 | 1.0252 | | 0.1567 | 10.41 | 4400 | 0.7592 | 1.0219 | | 0.1457 | 11.36 | 4800 | 0.8030 | 1.0141 | | 0.1337 | 12.31 | 5200 | 0.7811 | 1.0237 | | 0.1288 | 13.25 | 5600 | 0.7703 | 1.0188 | | 0.1165 | 14.2 | 6000 | 0.7728 | 1.0199 | | 0.105 | 15.15 | 6400 | 0.7934 | 1.0206 | | 0.1028 | 16.09 | 6800 | 0.7978 | 1.0185 | | 0.092 | 17.04 | 7200 | 0.8276 | 1.0289 | | 0.0901 | 17.99 | 7600 | 0.7881 | 1.0202 | | 0.0818 | 18.93 | 8000 | 0.7847 | 1.0162 | | 0.0801 | 19.88 | 8400 | 0.8142 | 1.0230 | | 0.0768 | 20.83 | 8800 | 0.7735 | 1.0215 | | 0.0721 | 21.78 | 9200 | 0.7941 | 1.0227 | | 0.0658 | 22.72 | 9600 | 0.8100 | 1.0219 | | 0.0627 | 23.67 | 10000 | 0.7592 | 1.0196 | | 0.0591 | 24.62 | 10400 | 0.8028 | 1.0210 | | 0.0537 | 25.56 | 10800 | 0.8019 | 1.0253 | | 0.0507 | 26.51 | 11200 | 0.7951 | 1.0212 | | 0.0495 | 27.46 | 11600 | 0.7893 | 1.0207 | | 0.0466 | 28.4 | 12000 | 0.7854 | 1.0188 | | 0.0431 | 29.35 | 12400 | 0.7883 | 1.0199 | ### Framework versions - Transformers 4.32.1 - Pytorch 2.2.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice_13_0"], "metrics": ["wer"], "base_model": "facebook/wav2vec2-xls-r-300m", "model-index": [{"name": "output", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "common_voice_13_0", "type": "common_voice_13_0", "config": "hi", "split": "test", "args": "hi"}, "metrics": [{"type": "wer", "value": 1.019918009027289, "name": "Wer"}]}]}]}
automatic-speech-recognition
surchand/output
[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_13_0", "base_model:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]
2024-02-14T13:45:15+00:00
[]
[]
TAGS #transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us
output ====== This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common\_voice\_13\_0 dataset. It achieves the following results on the evaluation set: * Loss: 0.7883 * Wer: 1.0199 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: 0.0003 * train\_batch\_size: 2 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 30 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.2.0+cu121 * Datasets 2.12.0 * Tokenizers 0.13.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.2.0+cu121\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
[ "TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.2.0+cu121\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
[ 87, 143, 4, 33 ]
[ "passage: TAGS\n#transformers #pytorch #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 30### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.2.0+cu121\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]
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null
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# CroissantLLMChat (190k steps + Chat) This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens and a final Chat finetuning phase. https://arxiv.org/abs/2402.00786 For best performance, it should be used with a temperature of 0.3 or more, and with the exact template described below: ```python chat = [ {"role": "user", "content": "Que puis-je faire à Marseille en hiver?"}, ] chat_input = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` corresponding to: ```python chat_input = """<|im_start|>user {USER QUERY}<|im_end|> <|im_start|>assistant\n""" ``` ## Abstract We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models. ## Citation Our work can be cited as: ```bash @misc{faysse2024croissantllm, title={CroissantLLM: A Truly Bilingual French-English Language Model}, author={Manuel Faysse and Patrick Fernandes and Nuno M. Guerreiro and António Loison and Duarte M. Alves and Caio Corro and Nicolas Boizard and João Alves and Ricardo Rei and Pedro H. Martins and Antoni Bigata Casademunt and François Yvon and André F. T. Martins and Gautier Viaud and Céline Hudelot and Pierre Colombo}, year={2024}, eprint={2402.00786}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Usage This model is a Chat model, that is, it is finetuned for Chat function and works best with the provided template. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "croissantllm/CroissantLLMChat-v0.1" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto") chat = [ {"role": "user", "content": "Que puis-je faire à Marseille en hiver?"}, ] chat_input = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) inputs = tokenizer(chat_input, return_tensors="pt", add_special_tokens=True).to(model.device) tokens = model.generate(**inputs, max_new_tokens=150, do_sample=True, top_p=0.95, top_k=60, temperature=0.3) print(tokenizer.decode(tokens[0])) ``` ## Model limitations Evaluation results indicate the model is strong in its size category, and offers decent performances on writing-based tasks and internal knowledge, and very strong performance on translation tasks. The small size of the CroissantLLM model however hinders its capacity to perform more complex reasoning-based tasks, at least in a zero or few-shot manner in its generalist base or chat-model versions. This is aligned with other models of size and underlines the importance of scale for more abstract tasks. #### Knowledge Cutoff The model training dataset has a data cutoff date corresponding to the November 2023 Wikipedia dump. This is the de facto knowledge cutoff date for our base model, although a lot of information dates back further. Updated versions can be trained through continued pre-training or subsequent fine-tuning. #### Multilingual performance. CroissantLLM is mostly a French and English model. Code performance is relatively limited, and although some amount of data from other languages is included within the SlimPajama training set, out-of-the-box performance in other languages is not to be expected, although some European languages do work quite well. #### Hallucinations. CroissantLLM can hallucinate and output factually incorrect data, especially regarding complex topics. This is to be expected given the small model size, and hallucination rates seem inferior to most models of the same size category although no quantitative assessments have been conducted outside of MT-Bench experiments. *** Quantization of Model [croissantllm/CroissantLLMChat-v0.1](https://huggingface.co/croissantllm/CroissantLLMChat-v0.1). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline
{"language": ["fr", "en"], "license": "mit", "tags": ["legal", "code", "text-generation-inference", "art"], "datasets": ["croissantllm/croissant_dataset", "croissantllm/CroissantLLM-2201-sft", "cerebras/SlimPajama-627B", "uonlp/CulturaX", "pg19", "bigcode/starcoderdata"], "pipeline_tag": "text2text-generation"}
text2text-generation
nold/CroissantLLMChat-v0.1-GGUF
[ "gguf", "legal", "code", "text-generation-inference", "art", "text2text-generation", "fr", "en", "dataset:croissantllm/croissant_dataset", "dataset:croissantllm/CroissantLLM-2201-sft", "dataset:cerebras/SlimPajama-627B", "dataset:uonlp/CulturaX", "dataset:pg19", "dataset:bigcode/starcoderdata", "arxiv:2402.00786", "license:mit", "region:us" ]
2024-02-14T13:45:16+00:00
[ "2402.00786" ]
[ "fr", "en" ]
TAGS #gguf #legal #code #text-generation-inference #art #text2text-generation #fr #en #dataset-croissantllm/croissant_dataset #dataset-croissantllm/CroissantLLM-2201-sft #dataset-cerebras/SlimPajama-627B #dataset-uonlp/CulturaX #dataset-pg19 #dataset-bigcode/starcoderdata #arxiv-2402.00786 #license-mit #region-us
# CroissantLLMChat (190k steps + Chat) This model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens and a final Chat finetuning phase. URL For best performance, it should be used with a temperature of 0.3 or more, and with the exact template described below: corresponding to: ## Abstract We introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware. To that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources. To assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives. This work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models. Our work can be cited as: ## Usage This model is a Chat model, that is, it is finetuned for Chat function and works best with the provided template. ## Model limitations Evaluation results indicate the model is strong in its size category, and offers decent performances on writing-based tasks and internal knowledge, and very strong performance on translation tasks. The small size of the CroissantLLM model however hinders its capacity to perform more complex reasoning-based tasks, at least in a zero or few-shot manner in its generalist base or chat-model versions. This is aligned with other models of size and underlines the importance of scale for more abstract tasks. #### Knowledge Cutoff The model training dataset has a data cutoff date corresponding to the November 2023 Wikipedia dump. This is the de facto knowledge cutoff date for our base model, although a lot of information dates back further. Updated versions can be trained through continued pre-training or subsequent fine-tuning. #### Multilingual performance. CroissantLLM is mostly a French and English model. Code performance is relatively limited, and although some amount of data from other languages is included within the SlimPajama training set, out-of-the-box performance in other languages is not to be expected, although some European languages do work quite well. #### Hallucinations. CroissantLLM can hallucinate and output factually incorrect data, especially regarding complex topics. This is to be expected given the small model size, and hallucination rates seem inferior to most models of the same size category although no quantitative assessments have been conducted outside of MT-Bench experiments. * Quantization of Model croissantllm/CroissantLLMChat-v0.1. Created using llm-quantizer Pipeline
[ "# CroissantLLMChat (190k steps + Chat)\n\nThis model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens and a final Chat finetuning phase.\n\nURL\n\nFor best performance, it should be used with a temperature of 0.3 or more, and with the exact template described below:\n\n\n\ncorresponding to:", "## Abstract\nWe introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.\nTo that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.\nTo assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.\nThis work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.\n\nOur work can be cited as:", "## Usage\n\nThis model is a Chat model, that is, it is finetuned for Chat function and works best with the provided template.", "## Model limitations\n\nEvaluation results indicate the model is strong in its size category, and offers decent performances on writing-based tasks and internal knowledge, and very strong performance on translation tasks. The small size of the CroissantLLM model however hinders its capacity to perform more complex reasoning-based tasks, at least in a zero or few-shot manner in its generalist base or chat-model versions. This is aligned with other models of size and underlines the importance of scale for more abstract tasks.", "#### Knowledge Cutoff \nThe model training dataset has a data cutoff date corresponding to the November 2023 Wikipedia dump. This is the de facto knowledge cutoff date for our base model, although a lot of information dates back further. Updated versions can be trained through continued pre-training or subsequent fine-tuning.", "#### Multilingual performance.\nCroissantLLM is mostly a French and English model. Code performance is relatively limited, and although some amount of data from other languages is included within the SlimPajama training set, out-of-the-box performance in other languages is not to be expected, although some European languages do work quite well.", "#### Hallucinations.\nCroissantLLM can hallucinate and output factually incorrect data, especially regarding complex topics. This is to be expected given the small model size, and hallucination rates seem inferior to most models of the same size category although no quantitative assessments have been conducted outside of MT-Bench experiments.\n\n\n\n*\n\nQuantization of Model croissantllm/CroissantLLMChat-v0.1.\nCreated using llm-quantizer Pipeline" ]
[ "TAGS\n#gguf #legal #code #text-generation-inference #art #text2text-generation #fr #en #dataset-croissantllm/croissant_dataset #dataset-croissantllm/CroissantLLM-2201-sft #dataset-cerebras/SlimPajama-627B #dataset-uonlp/CulturaX #dataset-pg19 #dataset-bigcode/starcoderdata #arxiv-2402.00786 #license-mit #region-us \n", "# CroissantLLMChat (190k steps + Chat)\n\nThis model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens and a final Chat finetuning phase.\n\nURL\n\nFor best performance, it should be used with a temperature of 0.3 or more, and with the exact template described below:\n\n\n\ncorresponding to:", "## Abstract\nWe introduce CroissantLLM, a 1.3B language model pretrained on a set of 3T English and French tokens, to bring to the research and industrial community a high-performance, fully open-sourced bilingual model that runs swiftly on consumer-grade local hardware.\nTo that end, we pioneer the approach of training an intrinsically bilingual model with a 1:1 English-to-French pretraining data ratio, a custom tokenizer, and bilingual finetuning datasets. We release the training dataset, notably containing a French split with manually curated, high-quality, and varied data sources.\nTo assess performance outside of English, we craft a novel benchmark, FrenchBench, consisting of an array of classification and generation tasks, covering various orthogonal aspects of model performance in the French Language. Additionally, rooted in transparency and to foster further Large Language Model research, we release codebases, and dozens of checkpoints across various model sizes, training data distributions, and training steps, as well as fine-tuned Chat models, and strong translation models. We evaluate our model through the FMTI framework, and validate 81% of the transparency criteria, far beyond the scores of even most open initiatives.\nThis work enriches the NLP landscape, breaking away from previous English-centric work in order to strengthen our understanding of multilinguality in language models.\n\nOur work can be cited as:", "## Usage\n\nThis model is a Chat model, that is, it is finetuned for Chat function and works best with the provided template.", "## Model limitations\n\nEvaluation results indicate the model is strong in its size category, and offers decent performances on writing-based tasks and internal knowledge, and very strong performance on translation tasks. The small size of the CroissantLLM model however hinders its capacity to perform more complex reasoning-based tasks, at least in a zero or few-shot manner in its generalist base or chat-model versions. This is aligned with other models of size and underlines the importance of scale for more abstract tasks.", "#### Knowledge Cutoff \nThe model training dataset has a data cutoff date corresponding to the November 2023 Wikipedia dump. This is the de facto knowledge cutoff date for our base model, although a lot of information dates back further. Updated versions can be trained through continued pre-training or subsequent fine-tuning.", "#### Multilingual performance.\nCroissantLLM is mostly a French and English model. Code performance is relatively limited, and although some amount of data from other languages is included within the SlimPajama training set, out-of-the-box performance in other languages is not to be expected, although some European languages do work quite well.", "#### Hallucinations.\nCroissantLLM can hallucinate and output factually incorrect data, especially regarding complex topics. This is to be expected given the small model size, and hallucination rates seem inferior to most models of the same size category although no quantitative assessments have been conducted outside of MT-Bench experiments.\n\n\n\n*\n\nQuantization of Model croissantllm/CroissantLLMChat-v0.1.\nCreated using llm-quantizer Pipeline" ]
[ 125, 81, 336, 29, 112, 70, 74, 102 ]
[ "passage: TAGS\n#gguf #legal #code #text-generation-inference #art #text2text-generation #fr #en #dataset-croissantllm/croissant_dataset #dataset-croissantllm/CroissantLLM-2201-sft #dataset-cerebras/SlimPajama-627B #dataset-uonlp/CulturaX #dataset-pg19 #dataset-bigcode/starcoderdata #arxiv-2402.00786 #license-mit #region-us \n# CroissantLLMChat (190k steps + Chat)\n\nThis model is part of the CroissantLLM initiative, and corresponds to the checkpoint after 190k steps (2.99 T) tokens and a final Chat finetuning phase.\n\nURL\n\nFor best performance, it should be used with a temperature of 0.3 or more, and with the exact template described below:\n\n\n\ncorresponding to:" ]
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null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "bigscience/bloom-560m"}
null
KapitalK/bloom-something
[ "peft", "arxiv:1910.09700", "base_model:bigscience/bloom-560m", "region:us" ]
2024-02-14T13:46:02+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-bigscience/bloom-560m #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloom-560m #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 31, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloom-560m #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
transformers
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{"library_name": "transformers", "tags": []}
text-generation
h-xw/mistral_b_finance_finetuned_test
[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]
2024-02-14T13:48:37+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# NeuralTrixlaser-bf16 NeuralTrixlaser-bf16 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [bardsai/jaskier-7b-dpo-v3.3](https://huggingface.co/bardsai/jaskier-7b-dpo-v3.3) * [Kquant03/NeuralTrix-7B-dpo-laser](https://huggingface.co/Kquant03/NeuralTrix-7B-dpo-laser) * [CultriX/NeuralTrix-v4-bf16](https://huggingface.co/CultriX/NeuralTrix-v4-bf16) * [CultriX/NeuralTrix-V2](https://huggingface.co/CultriX/NeuralTrix-V2) ## 🧩 Configuration ```yaml models: - model: eren23/dpo-binarized-NeuralTrix-7B # no parameters necessary for base model - model: bardsai/jaskier-7b-dpo-v3.3 parameters: density: 0.65 weight: 0.4 - model: Kquant03/NeuralTrix-7B-dpo-laser parameters: density: 0.6 weight: 0.35 - model: CultriX/NeuralTrix-v4-bf16 parameters: density: 0.55 weight: 0.15 - model: CultriX/NeuralTrix-V2 parameters: density: 0.55 weight: 0.15 merge_method: dare_ties base_model: eren23/dpo-binarized-NeuralTrix-7B parameters: int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "CultriX/" 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"]) ```
{"tags": ["merge", "mergekit", "lazymergekit", "bardsai/jaskier-7b-dpo-v3.3", "Kquant03/NeuralTrix-7B-dpo-laser", "CultriX/NeuralTrix-v4-bf16", "CultriX/NeuralTrix-V2"], "base_model": ["bardsai/jaskier-7b-dpo-v3.3", "Kquant03/NeuralTrix-7B-dpo-laser", "CultriX/NeuralTrix-v4-bf16", "CultriX/NeuralTrix-V2"]}
text-generation
CultriX/NeuralTrixlaser-bf16
[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "bardsai/jaskier-7b-dpo-v3.3", "Kquant03/NeuralTrix-7B-dpo-laser", "CultriX/NeuralTrix-v4-bf16", "CultriX/NeuralTrix-V2", "base_model:bardsai/jaskier-7b-dpo-v3.3", "base_model:Kquant03/NeuralTrix-7B-dpo-laser", "base_model:CultriX/NeuralTrix-v4-bf16", "base_model:CultriX/NeuralTrix-V2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-14T13:49:03+00:00
[]
[]
TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #Kquant03/NeuralTrix-7B-dpo-laser #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-V2 #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-Kquant03/NeuralTrix-7B-dpo-laser #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-V2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# NeuralTrixlaser-bf16 NeuralTrixlaser-bf16 is a merge of the following models using LazyMergekit: * bardsai/jaskier-7b-dpo-v3.3 * Kquant03/NeuralTrix-7B-dpo-laser * CultriX/NeuralTrix-v4-bf16 * CultriX/NeuralTrix-V2 ## Configuration ## Usage
[ "# NeuralTrixlaser-bf16\n\nNeuralTrixlaser-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* Kquant03/NeuralTrix-7B-dpo-laser\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-V2", "## Configuration", "## Usage" ]
[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #Kquant03/NeuralTrix-7B-dpo-laser #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-V2 #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-Kquant03/NeuralTrix-7B-dpo-laser #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-V2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# NeuralTrixlaser-bf16\n\nNeuralTrixlaser-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* Kquant03/NeuralTrix-7B-dpo-laser\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-V2", "## Configuration", "## Usage" ]
[ 198, 92, 4, 3 ]
[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #bardsai/jaskier-7b-dpo-v3.3 #Kquant03/NeuralTrix-7B-dpo-laser #CultriX/NeuralTrix-v4-bf16 #CultriX/NeuralTrix-V2 #base_model-bardsai/jaskier-7b-dpo-v3.3 #base_model-Kquant03/NeuralTrix-7B-dpo-laser #base_model-CultriX/NeuralTrix-v4-bf16 #base_model-CultriX/NeuralTrix-V2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# NeuralTrixlaser-bf16\n\nNeuralTrixlaser-bf16 is a merge of the following models using LazyMergekit:\n* bardsai/jaskier-7b-dpo-v3.3\n* Kquant03/NeuralTrix-7B-dpo-laser\n* CultriX/NeuralTrix-v4-bf16\n* CultriX/NeuralTrix-V2## Configuration## Usage" ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This fine-tuned [Microsoft Phi-2](https://hf.co/microsoft/phi-2) is a Transformer with **2.7 billion** parameters. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
{"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["nlp", "code"], "datasets": ["MH0386/napoleon_bonaparte"]}
text-generation
MH0386/phi-2-napoleon-bonaparte
[ "transformers", "safetensors", "phi", "text-generation", "nlp", "code", "custom_code", "en", "dataset:MH0386/napoleon_bonaparte", "arxiv:1910.09700", "license:mit", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]
2024-02-14T13:49:24+00:00
[ "1910.09700" ]
[ "en" ]
TAGS #transformers #safetensors #phi #text-generation #nlp #code #custom_code #en #dataset-MH0386/napoleon_bonaparte #arxiv-1910.09700 #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us
# Model Card for Model ID This fine-tuned Microsoft Phi-2 is a Transformer with 2.7 billion parameters. ## 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID\n\n\nThis fine-tuned Microsoft Phi-2 is a Transformer with 2.7 billion parameters.", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #phi #text-generation #nlp #code #custom_code #en #dataset-MH0386/napoleon_bonaparte #arxiv-1910.09700 #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us \n", "# Model Card for Model ID\n\n\nThis fine-tuned Microsoft Phi-2 is a Transformer with 2.7 billion parameters.", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 80, 25, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #safetensors #phi #text-generation #nlp #code #custom_code #en #dataset-MH0386/napoleon_bonaparte #arxiv-1910.09700 #license-mit #autotrain_compatible #endpoints_compatible #4-bit #region-us \n# Model Card for Model ID\n\n\nThis fine-tuned Microsoft Phi-2 is a Transformer with 2.7 billion parameters.## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]" ]
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null
null
transformers
# Uploaded model - **Developed by:** oliverbob - **License:** apache-2.0 - **Finetuned from model :** tla v1 chat BIBLE AI --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - tla architecture base_model: tla # Trained from [OpenBible Dataset](https://huggingface.co/datasets/oliverbob/openbible) - **Developed by:** oliverbob - **License:** apache-2.0 - **Date:** Day of hearts, 2024 - - ❤️ God is love and God is good! 😄 Enjoy!! This will hold the model for /bibleai. See generated gguf at /biblegpt.
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "tla"}
null
oliverbob/openbible
[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:tla", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-14T13:50:07+00:00
[]
[ "en" ]
TAGS #transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-tla #license-apache-2.0 #endpoints_compatible #region-us
# Uploaded model - Developed by: oliverbob - License: apache-2.0 - Finetuned from model : tla v1 chat BIBLE AI --- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - tla architecture base_model: tla # Trained from OpenBible Dataset - Developed by: oliverbob - License: apache-2.0 - Date: Day of hearts, 2024 - - ️ God is love and God is good! Enjoy!! This will hold the model for /bibleai. See generated gguf at /biblegpt.
[ "# Uploaded model\n\n- Developed by: oliverbob\n- License: apache-2.0\n- Finetuned from model : tla v1 chat\n\nBIBLE AI\n---\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- tla architecture\nbase_model: tla", "# Trained from OpenBible Dataset\n\n- Developed by: oliverbob\n- License: apache-2.0\n- Date: Day of hearts, 2024\n-\n- ️ God is love and God is good! \n\nEnjoy!!\n\nThis will hold the model for /bibleai.\nSee generated gguf at /biblegpt." ]
[ "TAGS\n#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-tla #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: oliverbob\n- License: apache-2.0\n- Finetuned from model : tla v1 chat\n\nBIBLE AI\n---\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- tla architecture\nbase_model: tla", "# Trained from OpenBible Dataset\n\n- Developed by: oliverbob\n- License: apache-2.0\n- Date: Day of hearts, 2024\n-\n- ️ God is love and God is good! \n\nEnjoy!!\n\nThis will hold the model for /bibleai.\nSee generated gguf at /biblegpt." ]
[ 52, 74, 71 ]
[ "passage: TAGS\n#transformers #gguf #llama #text-generation-inference #unsloth #en #base_model-tla #license-apache-2.0 #endpoints_compatible #region-us \n# Uploaded model\n\n- Developed by: oliverbob\n- License: apache-2.0\n- Finetuned from model : tla v1 chat\n\nBIBLE AI\n---\nlanguage:\n- en\nlicense: apache-2.0\ntags:\n- text-generation-inference\n- transformers\n- unsloth\n- tla architecture\nbase_model: tla# Trained from OpenBible Dataset\n\n- Developed by: oliverbob\n- License: apache-2.0\n- Date: Day of hearts, 2024\n-\n- ️ God is love and God is good! \n\nEnjoy!!\n\nThis will hold the model for /bibleai.\nSee generated gguf at /biblegpt." ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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{"library_name": "transformers", "tags": []}
null
Bucki17/bert-base-uncased-2022-habana
[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T13:50:21+00:00
[ "1910.09700" ]
[]
TAGS #transformers #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ 26, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]
[ "passage: TAGS\n#transformers #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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{"library_name": "transformers", "tags": []}
null
avemio-digital/unsloth_mistral_4bit_adapter_max1000
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T13:51:36+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
peft
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [swap-uniba/LLaMAntino-2-7b-hf-ITA](https://huggingface.co/swap-uniba/LLaMAntino-2-7b-hf-ITA) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.4095 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.6551 | 1.0 | 90 | 1.4257 | | 1.1957 | 2.0 | 180 | 1.3750 | | 0.8459 | 3.0 | 270 | 1.4095 | ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.2
{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "swap-uniba/LLaMAntino-2-7b-hf-ITA", "model-index": [{"name": "results", "results": []}]}
null
lvcalucioli/results
[ "peft", "tensorboard", "safetensors", "t5", "trl", "sft", "generated_from_trainer", "base_model:swap-uniba/LLaMAntino-2-7b-hf-ITA", "license:llama2", "region:us" ]
2024-02-14T13:54:54+00:00
[]
[]
TAGS #peft #tensorboard #safetensors #t5 #trl #sft #generated_from_trainer #base_model-swap-uniba/LLaMAntino-2-7b-hf-ITA #license-llama2 #region-us
results ======= This model is a fine-tuned version of swap-uniba/LLaMAntino-2-7b-hf-ITA on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.4095 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 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * lr\_scheduler\_warmup\_ratio: 0.03 * num\_epochs: 3 ### Training results ### Framework versions * PEFT 0.8.2 * Transformers 4.38.0.dev0 * Pytorch 2.0.1+cu117 * Datasets 2.16.1 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.2" ]
[ "TAGS\n#peft #tensorboard #safetensors #t5 #trl #sft #generated_from_trainer #base_model-swap-uniba/LLaMAntino-2-7b-hf-ITA #license-llama2 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.2" ]
[ 65, 116, 4, 44 ]
[ "passage: TAGS\n#peft #tensorboard #safetensors #t5 #trl #sft #generated_from_trainer #base_model-swap-uniba/LLaMAntino-2-7b-hf-ITA #license-llama2 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* lr\\_scheduler\\_warmup\\_ratio: 0.03\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* PEFT 0.8.2\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.2" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "bigscience/bloomz-560m"}
null
KapitalK/bloom-something2
[ "peft", "arxiv:1910.09700", "base_model:bigscience/bloomz-560m", "region:us" ]
2024-02-14T13:55:23+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 32, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
transformers
## Exllama v2 Quantizations of aisak-assistant Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/mandelakori/aisak-assistant | Branch | Bits | lm_head bits | VRAM (4k) | VRAM (16k) | VRAM (32k) | Description | | ----- | ---- | ------- | ------ | ------ | ------ | ------------ | | [8_0](https://huggingface.co/bartowski/aisak-assistant-exl2/tree/8_0) | 8.0 | 8.0 | 8.4 GB | 9.8 GB | 11.8 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. | | [6_5](https://huggingface.co/bartowski/aisak-assistant-exl2/tree/6_5) | 6.5 | 8.0 | 7.2 GB | 8.6 GB | 10.6 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. | | [5_0](https://huggingface.co/bartowski/aisak-assistant-exl2/tree/5_0) | 5.0 | 6.0 | 6.0 GB | 7.4 GB | 9.4 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. | | [4_25](https://huggingface.co/bartowski/aisak-assistant-exl2/tree/4_25) | 4.25 | 6.0 | 5.3 GB | 6.7 GB | 8.7 GB | GPTQ equivalent bits per weight, slightly higher quality. | | [3_5](https://huggingface.co/bartowski/aisak-assistant-exl2/tree/3_5) | 3.5 | 6.0 | 4.7 GB | 6.1 GB | 8.1 GB | Lower quality, only use if you have to. | ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/aisak-assistant-exl2 aisak-assistant-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `aisak-assistant-exl2`: ```shell mkdir aisak-assistant-exl2 huggingface-cli download bartowski/aisak-assistant-exl2 --local-dir aisak-assistant-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir aisak-assistant-exl2-6_5 huggingface-cli download bartowski/aisak-assistant-exl2 --revision 6_5 --local-dir aisak-assistant-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir aisak-assistant-exl2-6.5 huggingface-cli download bartowski/aisak-assistant-exl2 --revision 6_5 --local-dir aisak-assistant-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
{"library_name": "transformers", "quantized_by": "bartowski", "pipeline_tag": "text-generation"}
text-generation
bartowski/aisak-assistant-exl2
[ "transformers", "text-generation", "endpoints_compatible", "region:us" ]
2024-02-14T13:55:44+00:00
[]
[]
TAGS #transformers #text-generation #endpoints_compatible #region-us
Exllama v2 Quantizations of aisak-assistant ------------------------------------------- Using <a href="URL ExLlamaV2 v0.0.13 for quantization. **The "main" branch only contains the URL, download one of the other branches for the model (see below)** Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions. Original model: URL Download instructions --------------------- With git: With huggingface hub (credit to TheBloke for instructions): To download the 'main' (only useful if you only care about URL) branch to a folder called 'aisak-assistant-exl2': To download from a different branch, add the '--revision' parameter: Linux: Windows (which apparently doesn't like \_ in folders sometimes?): Want to support my work? Visit my ko-fi page here: URL
[]
[ "TAGS\n#transformers #text-generation #endpoints_compatible #region-us \n" ]
[ 22 ]
[ "passage: TAGS\n#transformers #text-generation #endpoints_compatible #region-us \n" ]
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null
null
transformers
<!-- 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_new_ner_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: 0.7201 - Precision: 0.3051 - Recall: 0.2802 - F1: 0.2921 - Accuracy: 0.8555 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 117 | 0.8489 | 0.2502 | 0.1629 | 0.1973 | 0.8340 | | No log | 2.0 | 234 | 0.7201 | 0.3051 | 0.2802 | 0.2921 | 0.8555 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.16.1 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "my_new_ner_model", "results": []}]}
token-classification
veronica1608/my_new_ner_model
[ "transformers", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T13:56:19+00:00
[]
[]
TAGS #transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
my\_new\_ner\_model =================== This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.7201 * Precision: 0.3051 * Recall: 0.2802 * F1: 0.2921 * Accuracy: 0.8555 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0 * Datasets 2.16.1 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
[ 69, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #safetensors #distilbert #token-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]
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null
null
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4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Dolphin 2.6 Phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2).
{"license": "mit"}
null
numen-tech/dolphin-2_6-phi-2-w4a16g128asym
[ "arxiv:2308.13137", "license:mit", "region:us" ]
2024-02-14T13:58:09+00:00
[ "2308.13137" ]
[]
TAGS #arxiv-2308.13137 #license-mit #region-us
4-bit OmniQuant quantized version of Dolphin 2.6 Phi-2.
[]
[ "TAGS\n#arxiv-2308.13137 #license-mit #region-us \n" ]
[ 19 ]
[ "passage: TAGS\n#arxiv-2308.13137 #license-mit #region-us \n" ]
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null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "bigscience/bloomz-560m"}
null
KapitalK/bloom-something3
[ "peft", "arxiv:1910.09700", "base_model:bigscience/bloomz-560m", "region:us" ]
2024-02-14T14:01:34+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 32, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-pubmed-1024 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.2410 - Rouge1: 43.6037 - Rouge2: 17.2895 - Rougel: 25.6916 - Rougelsum: 38.819 - Gen Len: 207.62 ## 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: 0.0008 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 4.8142 | 0.27 | 500 | 4.7781 | 37.4249 | 13.3533 | 21.8304 | 33.5429 | 167.98 | | 4.7227 | 0.55 | 1000 | 4.6067 | 40.4166 | 14.7121 | 23.5203 | 36.1746 | 187.26 | | 4.6406 | 0.82 | 1500 | 4.5968 | 40.7033 | 15.1399 | 23.7701 | 36.3048 | 187.96 | | 4.5179 | 1.09 | 2000 | 4.4875 | 41.2297 | 15.7839 | 23.797 | 36.6246 | 189.1 | | 4.5044 | 1.36 | 2500 | 4.4398 | 41.7532 | 15.7797 | 24.5182 | 37.5172 | 203.19 | | 4.4599 | 1.64 | 3000 | 4.4042 | 42.9839 | 16.5654 | 25.0308 | 38.1967 | 210.62 | | 4.4092 | 1.91 | 3500 | 4.3640 | 42.2944 | 16.3717 | 24.6831 | 37.5064 | 211.33 | | 4.3226 | 2.18 | 4000 | 4.3496 | 42.6501 | 16.4452 | 24.7418 | 38.2741 | 225.19 | | 4.3078 | 2.46 | 4500 | 4.3160 | 42.7482 | 16.9222 | 25.4787 | 38.5397 | 207.54 | | 4.2834 | 2.73 | 5000 | 4.2992 | 42.6235 | 16.9886 | 25.3069 | 38.5346 | 205.73 | | 4.2535 | 3.0 | 5500 | 4.2865 | 42.8731 | 16.8583 | 25.6184 | 38.498 | 203.19 | | 4.1865 | 3.28 | 6000 | 4.2658 | 43.2303 | 17.154 | 25.7881 | 38.7525 | 215.33 | | 4.165 | 3.55 | 6500 | 4.2536 | 44.1507 | 17.211 | 26.02 | 39.5668 | 206.67 | | 4.155 | 3.82 | 7000 | 4.2410 | 43.6037 | 17.2895 | 25.6916 | 38.819 | 207.62 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.15.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-pubmed-1024", "results": []}]}
text2text-generation
mtc/bart-base-pubmed-1024
[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T14:03:08+00:00
[]
[]
TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bart-base-pubmed-1024 ===================== This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 4.2410 * Rouge1: 43.6037 * Rouge2: 17.2895 * Rougel: 25.6916 * Rougelsum: 38.819 * Gen Len: 207.62 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: 0.0008 * train\_batch\_size: 16 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: polynomial * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 4 * mixed\_precision\_training: Native AMP * label\_smoothing\_factor: 0.2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.0.1+cu117 * Datasets 2.14.4 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
[ 64, 172, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
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null
null
null
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Phi-2 Orange](https://huggingface.co/rhysjones/phi-2-orange).
{"license": "mit"}
null
numen-tech/phi-2-orange-w4a16g128asym
[ "arxiv:2308.13137", "license:mit", "region:us" ]
2024-02-14T14:03:26+00:00
[ "2308.13137" ]
[]
TAGS #arxiv-2308.13137 #license-mit #region-us
4-bit OmniQuant quantized version of Phi-2 Orange.
[]
[ "TAGS\n#arxiv-2308.13137 #license-mit #region-us \n" ]
[ 19 ]
[ "passage: TAGS\n#arxiv-2308.13137 #license-mit #region-us \n" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-arxiv-1024 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.4204 - Rouge1: 42.7148 - Rouge2: 14.9393 - Rougel: 23.8135 - Rougelsum: 38.2094 - Gen Len: 152.94 ## 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: 0.0008 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 5.0132 | 0.16 | 500 | 4.9526 | 36.6006 | 11.6516 | 21.2812 | 32.3855 | 102.5 | | 4.9026 | 0.32 | 1000 | 4.8487 | 37.0279 | 11.9575 | 21.6566 | 33.3527 | 98.72 | | 4.8134 | 0.47 | 1500 | 4.8093 | 38.6789 | 12.0964 | 21.5679 | 34.4042 | 131.17 | | 4.7615 | 0.63 | 2000 | 4.7357 | 38.9948 | 12.6214 | 21.5771 | 34.6947 | 120.84 | | 4.7316 | 0.79 | 2500 | 4.6984 | 39.0043 | 13.692 | 22.4767 | 34.7406 | 114.67 | | 4.6984 | 0.95 | 3000 | 4.6661 | 37.7638 | 13.0795 | 21.9015 | 34.1801 | 112.11 | | 4.6423 | 1.1 | 3500 | 4.6413 | 40.2953 | 13.9014 | 22.6632 | 35.9141 | 127.43 | | 4.6166 | 1.26 | 4000 | 4.6175 | 40.99 | 14.3428 | 23.4201 | 36.6002 | 133.73 | | 4.5878 | 1.42 | 4500 | 4.6042 | 40.8889 | 14.0993 | 23.0454 | 36.9924 | 141.88 | | 4.5874 | 1.58 | 5000 | 4.5846 | 39.9072 | 14.2083 | 22.8314 | 35.9495 | 123.82 | | 4.5642 | 1.73 | 5500 | 4.5687 | 40.4716 | 14.1263 | 22.6271 | 36.2139 | 137.2 | | 4.555 | 1.89 | 6000 | 4.5551 | 41.3314 | 14.232 | 22.8318 | 37.1038 | 148.78 | | 4.4763 | 2.05 | 6500 | 4.5433 | 41.7555 | 14.6625 | 23.7076 | 37.705 | 142.12 | | 4.4687 | 2.21 | 7000 | 4.5232 | 41.226 | 14.6976 | 23.0482 | 36.7016 | 133.7 | | 4.4737 | 2.37 | 7500 | 4.5128 | 40.0649 | 13.9868 | 23.1803 | 35.9016 | 122.17 | | 4.4634 | 2.52 | 8000 | 4.4999 | 42.5774 | 15.4706 | 23.4321 | 38.212 | 137.87 | | 4.4443 | 2.68 | 8500 | 4.4829 | 41.7603 | 15.1096 | 23.5735 | 37.5121 | 147.78 | | 4.4409 | 2.84 | 9000 | 4.4757 | 41.9056 | 14.7477 | 23.1478 | 37.6321 | 142.64 | | 4.4271 | 3.0 | 9500 | 4.4642 | 41.7456 | 15.1452 | 23.4016 | 37.6441 | 138.98 | | 4.3629 | 3.15 | 10000 | 4.4569 | 41.5637 | 15.0198 | 23.0226 | 37.22 | 148.2 | | 4.3489 | 3.31 | 10500 | 4.4502 | 42.0897 | 14.7576 | 23.1048 | 37.6046 | 142.48 | | 4.3377 | 3.47 | 11000 | 4.4403 | 43.3032 | 15.4076 | 23.665 | 39.0579 | 156.0 | | 4.339 | 3.63 | 11500 | 4.4329 | 42.6232 | 15.0481 | 23.6074 | 37.637 | 151.36 | | 4.3423 | 3.78 | 12000 | 4.4272 | 42.565 | 14.9409 | 23.2332 | 38.1214 | 154.21 | | 4.3269 | 3.94 | 12500 | 4.4204 | 42.7148 | 14.9393 | 23.8135 | 38.2094 | 152.94 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.15.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-arxiv-1024", "results": []}]}
text2text-generation
mtc/bart-base-arxiv-1024
[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T14:05:18+00:00
[]
[]
TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bart-base-arxiv-1024 ==================== This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 4.4204 * Rouge1: 42.7148 * Rouge2: 14.9393 * Rougel: 23.8135 * Rougelsum: 38.2094 * Gen Len: 152.94 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: 0.0008 * train\_batch\_size: 16 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: polynomial * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 4 * mixed\_precision\_training: Native AMP * label\_smoothing\_factor: 0.2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.0.1+cu117 * Datasets 2.14.4 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
[ 64, 172, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
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transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
text-generation
amichalski2/tinyllama-email-model-full
[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-14T14:05:23+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-govreport-1024 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.5326 - Rouge1: 47.2014 - Rouge2: 14.3537 - Rougel: 20.357 - Rougelsum: 45.2941 - Gen Len: 748.32 ## 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: 0.0008 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 500 - num_epochs: 4 - mixed_precision_training: Native AMP - label_smoothing_factor: 0.2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 4.7807 | 1.83 | 1000 | 4.7100 | 44.714 | 12.3085 | 18.8368 | 42.8279 | 749.72 | | 4.4195 | 3.65 | 2000 | 4.5326 | 47.2014 | 14.3537 | 20.357 | 45.2941 | 748.32 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.14.4 - Tokenizers 0.15.2
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-base", "model-index": [{"name": "bart-base-govreport-1024", "results": []}]}
text2text-generation
mtc/bart-base-govreport-1024
[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:facebook/bart-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T14:09:13+00:00
[]
[]
TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
bart-base-govreport-1024 ======================== This model is a fine-tuned version of facebook/bart-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 4.5326 * Rouge1: 47.2014 * Rouge2: 14.3537 * Rougel: 20.357 * Rougelsum: 45.2941 * Gen Len: 748.32 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: 0.0008 * train\_batch\_size: 8 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: polynomial * lr\_scheduler\_warmup\_steps: 500 * num\_epochs: 4 * mixed\_precision\_training: Native AMP * label\_smoothing\_factor: 0.2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.0.1+cu117 * Datasets 2.14.4 * Tokenizers 0.15.2
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
[ "TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
[ 64, 172, 4, 33 ]
[ "passage: TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #base_model-facebook/bart-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0008\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: polynomial\n* lr\\_scheduler\\_warmup\\_steps: 500\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP\n* label\\_smoothing\\_factor: 0.2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.14.4\n* Tokenizers 0.15.2" ]
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Mistral-7B-v0.1-finetuned-tweets-dataset-new This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3357 - Accuracy: 0.731 - F1: 0.6934 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.8685 | 1.0 | 1000 | 1.3357 | 0.731 | 0.6934 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "Mistral-7B-v0.1-finetuned-tweets-dataset-new", "results": []}]}
null
mama12/Mistral-7B-v0.1-finetuned-tweets-dataset-new
[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]
2024-02-14T14:09:29+00:00
[]
[]
TAGS #tensorboard #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
Mistral-7B-v0.1-finetuned-tweets-dataset-new ============================================ This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.3357 * Accuracy: 0.731 * F1: 0.6934 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 46, 98, 4, 33 ]
[ "passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
null
transformers
# Roberta-Base-CoNLL2003 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset. ## Model Usage We made and used the original tokenizer with [BPE-Dropout](https://aclanthology.org/2020.acl-main.170/). So, you can't use AutoTokenizer but if subword normalization is not used, original RobertaTokenizer can be substituted. Example and Tokenizer Repository: [github](https://github.com/4ldk/CoNLL2003_Choices) ```python from transformers import RobertaTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = RobertaTokenizer.from_pretrained("4ldk/Roberta-Base-CoNLL2003") model = AutoModelForTokenClassification.from_pretrained("4ldk/Roberta-Base-CoNLL2003") nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True) example = "My name is Philipp and live in Germany" nlp(example) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: AdamW with betas=(0.9,0.999), epsilon=1e-08, and weight decay=0.01 - lr_scheduler_type: linear with warmup rate = 0.1 - num_epochs: 20 - subword regularization p = 0.0 (= trained without subword regularization) And we add the sentences following the input sentence in the original dataset. Therefore, it cannot be reproduced from the dataset published on huggingface. ### Training results #### CoNNL2003 It achieves the following results on the evaluation set: - Precision: 0.9707 - Recall: 0.9636 - F1: 0.9671 It achieves the following results on the test set: - Precision: 0.9352 - Recall: 0.9218 - F1: 0.9285 #### CoNNLpp(2023) [Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023]( https://aclanthology.org/2023.acl-long.459.pdf) ([github](https://github.com/ShuhengL/acl2023_conllpp)) - Precision: 0.9244 - Recall: 0.9225 - F1: 0.9235 #### CoNLLpp(CrossWeigh) [CrossWeigh: Training Named Entity Tagger from Imperfect Annotations](https://aclanthology.org/D19-1519/) ([github](https://github.com/ZihanWangKi/CrossWeigh)) - Precision: 0.9449 - Recall: 0.9403 - F1: 0.9426 ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu117
{"language": ["en"], "license": "apache-2.0", "tags": ["token-classification"], "datasets": ["conll2003", "conllpp"], "metrics": [{"f1": 92.85}, {"f1(valid)": 96.71}, {"f1(CoNLLpp(2023))": 92.35}, {"f1(CoNLLpp(CrossWeigh))": 94.26}]}
token-classification
4ldk/Roberta-Base-CoNLL2003
[ "transformers", "safetensors", "roberta", "token-classification", "en", "dataset:conll2003", "dataset:conllpp", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T14:13:41+00:00
[]
[ "en" ]
TAGS #transformers #safetensors #roberta #token-classification #en #dataset-conll2003 #dataset-conllpp #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
# Roberta-Base-CoNLL2003 This model is a fine-tuned version of roberta-base on the conll2003 dataset. ## Model Usage We made and used the original tokenizer with BPE-Dropout. So, you can't use AutoTokenizer but if subword normalization is not used, original RobertaTokenizer can be substituted. Example and Tokenizer Repository: github ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-5 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: AdamW with betas=(0.9,0.999), epsilon=1e-08, and weight decay=0.01 - lr_scheduler_type: linear with warmup rate = 0.1 - num_epochs: 20 - subword regularization p = 0.0 (= trained without subword regularization) And we add the sentences following the input sentence in the original dataset. Therefore, it cannot be reproduced from the dataset published on huggingface. ### Training results #### CoNNL2003 It achieves the following results on the evaluation set: - Precision: 0.9707 - Recall: 0.9636 - F1: 0.9671 It achieves the following results on the test set: - Precision: 0.9352 - Recall: 0.9218 - F1: 0.9285 #### CoNNLpp(2023) Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023 (github) - Precision: 0.9244 - Recall: 0.9225 - F1: 0.9235 #### CoNLLpp(CrossWeigh) CrossWeigh: Training Named Entity Tagger from Imperfect Annotations (github) - Precision: 0.9449 - Recall: 0.9403 - F1: 0.9426 ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu117
[ "# Roberta-Base-CoNLL2003\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.", "## Model Usage\n\nWe made and used the original tokenizer with BPE-Dropout. \nSo, you can't use AutoTokenizer but if subword normalization is not used, original RobertaTokenizer can be substituted.\n\nExample and Tokenizer Repository: github", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-5\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: AdamW with betas=(0.9,0.999), epsilon=1e-08, and weight decay=0.01\n- lr_scheduler_type: linear with warmup rate = 0.1\n- num_epochs: 20\n- subword regularization p = 0.0 (= trained without subword regularization)\n\nAnd we add the sentences following the input sentence in the original dataset. Therefore, it cannot be reproduced from the dataset published on huggingface.", "### Training results", "#### CoNNL2003\n\nIt achieves the following results on the evaluation set:\n- Precision: 0.9707\n- Recall: 0.9636\n- F1: 0.9671\n\nIt achieves the following results on the test set:\n- Precision: 0.9352 \n- Recall: 0.9218\n- F1: 0.9285", "#### CoNNLpp(2023)\n\nDo CoNLL-2003 Named Entity Taggers Still Work Well in 2023\n(github)\n\n- Precision: 0.9244\n- Recall: 0.9225\n- F1: 0.9235", "#### CoNLLpp(CrossWeigh)\n\nCrossWeigh: Training Named Entity Tagger from Imperfect Annotations\n(github)\n\n- Precision: 0.9449\n- Recall: 0.9403\n- F1: 0.9426", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.0.1+cu117" ]
[ "TAGS\n#transformers #safetensors #roberta #token-classification #en #dataset-conll2003 #dataset-conllpp #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Roberta-Base-CoNLL2003\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.", "## Model Usage\n\nWe made and used the original tokenizer with BPE-Dropout. \nSo, you can't use AutoTokenizer but if subword normalization is not used, original RobertaTokenizer can be substituted.\n\nExample and Tokenizer Repository: github", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-5\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: AdamW with betas=(0.9,0.999), epsilon=1e-08, and weight decay=0.01\n- lr_scheduler_type: linear with warmup rate = 0.1\n- num_epochs: 20\n- subword regularization p = 0.0 (= trained without subword regularization)\n\nAnd we add the sentences following the input sentence in the original dataset. Therefore, it cannot be reproduced from the dataset published on huggingface.", "### Training results", "#### CoNNL2003\n\nIt achieves the following results on the evaluation set:\n- Precision: 0.9707\n- Recall: 0.9636\n- F1: 0.9671\n\nIt achieves the following results on the test set:\n- Precision: 0.9352 \n- Recall: 0.9218\n- F1: 0.9285", "#### CoNNLpp(2023)\n\nDo CoNLL-2003 Named Entity Taggers Still Work Well in 2023\n(github)\n\n- Precision: 0.9244\n- Recall: 0.9225\n- F1: 0.9235", "#### CoNLLpp(CrossWeigh)\n\nCrossWeigh: Training Named Entity Tagger from Imperfect Annotations\n(github)\n\n- Precision: 0.9449\n- Recall: 0.9403\n- F1: 0.9426", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.0.1+cu117" ]
[ 63, 33, 65, 3, 155, 4, 71, 51, 55, 20 ]
[ "passage: TAGS\n#transformers #safetensors #roberta #token-classification #en #dataset-conll2003 #dataset-conllpp #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Roberta-Base-CoNLL2003\n\nThis model is a fine-tuned version of roberta-base on the conll2003 dataset.## Model Usage\n\nWe made and used the original tokenizer with BPE-Dropout. \nSo, you can't use AutoTokenizer but if subword normalization is not used, original RobertaTokenizer can be substituted.\n\nExample and Tokenizer Repository: github## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-5\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: AdamW with betas=(0.9,0.999), epsilon=1e-08, and weight decay=0.01\n- lr_scheduler_type: linear with warmup rate = 0.1\n- num_epochs: 20\n- subword regularization p = 0.0 (= trained without subword regularization)\n\nAnd we add the sentences following the input sentence in the original dataset. Therefore, it cannot be reproduced from the dataset published on huggingface.### Training results#### CoNNL2003\n\nIt achieves the following results on the evaluation set:\n- Precision: 0.9707\n- Recall: 0.9636\n- F1: 0.9671\n\nIt achieves the following results on the test set:\n- Precision: 0.9352 \n- Recall: 0.9218\n- F1: 0.9285#### CoNNLpp(2023)\n\nDo CoNLL-2003 Named Entity Taggers Still Work Well in 2023\n(github)\n\n- Precision: 0.9244\n- Recall: 0.9225\n- F1: 0.9235#### CoNLLpp(CrossWeigh)\n\nCrossWeigh: Training Named Entity Tagger from Imperfect Annotations\n(github)\n\n- Precision: 0.9449\n- Recall: 0.9403\n- F1: 0.9426" ]
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transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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{"library_name": "transformers", "tags": []}
null
engrzulqarnain/pine_script_model
[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]
2024-02-14T14:13:48+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
transformers
<!-- 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 tiny Albanian Test This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16 Albanian dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0015 - eval_wer: 51.0387 - eval_runtime: 134.3539 - eval_samples_per_second: 2.858 - eval_steps_per_second: 0.357 - epoch: 37.04 - step: 2000 ## 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 ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
{"language": ["sq"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_Albanian"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "Whisper tiny Albanian Test", "results": []}]}
automatic-speech-recognition
rishabhjain16/whisper-small_to_cv_albanian
[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "sq", "dataset:mozilla-foundation/common_voice_16_Albanian", "base_model:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]
2024-02-14T14:14:08+00:00
[]
[ "sq" ]
TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #sq #dataset-mozilla-foundation/common_voice_16_Albanian #base_model-openai/whisper-tiny #license-apache-2.0 #endpoints_compatible #region-us
# Whisper tiny Albanian Test This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16 Albanian dataset. It achieves the following results on the evaluation set: - eval_loss: 1.0015 - eval_wer: 51.0387 - eval_runtime: 134.3539 - eval_samples_per_second: 2.858 - eval_steps_per_second: 0.357 - epoch: 37.04 - step: 2000 ## 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 ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1
[ "# Whisper tiny Albanian Test\n\nThis model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16 Albanian dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.0015\n- eval_wer: 51.0387\n- eval_runtime: 134.3539\n- eval_samples_per_second: 2.858\n- eval_steps_per_second: 0.357\n- epoch: 37.04\n- step: 2000", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 4000", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #sq #dataset-mozilla-foundation/common_voice_16_Albanian #base_model-openai/whisper-tiny #license-apache-2.0 #endpoints_compatible #region-us \n", "# Whisper tiny Albanian Test\n\nThis model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16 Albanian dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.0015\n- eval_wer: 51.0387\n- eval_runtime: 134.3539\n- eval_samples_per_second: 2.858\n- eval_steps_per_second: 0.357\n- epoch: 37.04\n- step: 2000", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 4000", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
[ 101, 115, 6, 12, 8, 3, 104, 38 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #sq #dataset-mozilla-foundation/common_voice_16_Albanian #base_model-openai/whisper-tiny #license-apache-2.0 #endpoints_compatible #region-us \n# Whisper tiny Albanian Test\n\nThis model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16 Albanian dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 1.0015\n- eval_wer: 51.0387\n- eval_runtime: 134.3539\n- eval_samples_per_second: 2.858\n- eval_steps_per_second: 0.357\n- epoch: 37.04\n- step: 2000## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 4000### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]
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null
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transformers
<!-- 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. --> # ocr8_distilbert_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.2417 - Accuracy: 0.7427 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 52 | 1.4624 | 0.6796 | | No log | 2.0 | 104 | 1.2417 | 0.7427 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "ocr8_distilbert_model", "results": []}]}
text-classification
sebastiencormier/ocr8_distilbert_model
[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T14:14:45+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
ocr8\_distilbert\_model ======================= This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.2417 * Accuracy: 0.7427 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 72, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
null
gguf
GGUF importance matrix (imatrix) quants for https://huggingface.co/LargeWorldModel/LWM-Text-Chat-128K The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw. * The imatrix Q4-K quant fits with 32K context on 24GB and gives me ~100 t/s inference on a 3090. * With IQ3_XXS it seems to fit ~37K context on 24GB (and it is even faster than Q4-K). * With either quant on a 3090 it seems to decode context at well over 2000 t/s. * Using Q8 K-cache (instead of F16) you can fit up to 43-44K context but inference speed goes down a little bit. * Also for some reason I need to use 1.0 penalty to avoid the response being cut-off. | Layers | Context | Template | | --- | --- | --- | | <pre>32</pre> | <pre>131072</pre> | <pre>You are a helpful assistant.<br>USER:<br>{context}<br>{question}<br>Don't give information outside the document or repeat your findings. Keep your response short and direct.<br>ASSISTANT:<br>{response}</pre> |
{"license": "llama2", "library_name": "gguf", "pipeline_tag": "text-generation"}
text-generation
dranger003/LWM-Text-Chat-128K-iMat.GGUF
[ "gguf", "text-generation", "license:llama2", "region:us" ]
2024-02-14T14:14:59+00:00
[]
[]
TAGS #gguf #text-generation #license-llama2 #region-us
GGUF importance matrix (imatrix) quants for URL The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using URL. * The imatrix Q4-K quant fits with 32K context on 24GB and gives me ~100 t/s inference on a 3090. * With IQ3\_XXS it seems to fit ~37K context on 24GB (and it is even faster than Q4-K). * With either quant on a 3090 it seems to decode context at well over 2000 t/s. * Using Q8 K-cache (instead of F16) you can fit up to 43-44K context but inference speed goes down a little bit. * Also for some reason I need to use 1.0 penalty to avoid the response being cut-off. Layers: ``` 32 ``` , Context: ``` 131072 ``` , Template: ``` You are a helpful assistant. USER: {context} {question} Don't give information outside the document or repeat your findings. Keep your response short and direct. ASSISTANT: {response} ```
[]
[ "TAGS\n#gguf #text-generation #license-llama2 #region-us \n" ]
[ 21 ]
[ "passage: TAGS\n#gguf #text-generation #license-llama2 #region-us \n" ]
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null
null
transformers
<!-- 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. --> # ocr8_bert_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0803 - Accuracy: 0.7476 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 52 | 1.3205 | 0.6650 | | No log | 2.0 | 104 | 1.0803 | 0.7476 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.1
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-uncased", "model-index": [{"name": "ocr8_bert_model", "results": []}]}
text-classification
sebastiencormier/ocr8_bert_model
[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]
2024-02-14T14:15:28+00:00
[]
[]
TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
ocr8\_bert\_model ================= This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.0803 * Accuracy: 0.7476 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0 * Datasets 2.17.0 * Tokenizers 0.15.1
[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
[ 68, 98, 4, 30 ]
[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]
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null
null
fastai
# Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
{"language": ["et"], "tags": ["fastai"]}
null
saied/Estonian-ULMFIT
[ "fastai", "et", "region:us" ]
2024-02-14T14:18:35+00:00
[]
[ "et" ]
TAGS #fastai #et #region-us
# Amazing! Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the documentation here)! 2. Create a demo in Gradio or Streamlit using Spaces (documentation here). 3. Join the fastai community on the Fastai Discord! Greetings fellow fastlearner ! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
[ "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ "TAGS\n#fastai #et #region-us \n", "# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!", "# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---", "# Model card", "## Model description\nMore information needed", "## Intended uses & limitations\nMore information needed", "## Training and evaluation data\nMore information needed" ]
[ 11, 20, 79, 3, 6, 12, 8 ]
[ "passage: TAGS\n#fastai #et #region-us \n# Amazing!\n\n Congratulations on hosting your fastai model on the Hugging Face Hub!# Some next steps\n1. Fill out this model card with more information (see the template below and the documentation here)!\n\n2. Create a demo in Gradio or Streamlit using Spaces (documentation here).\n\n3. Join the fastai community on the Fastai Discord!\n\nGreetings fellow fastlearner ! Don't forget to delete this content from your model card.\n\n\n---# Model card## Model description\nMore information needed## Intended uses & limitations\nMore information needed## Training and evaluation data\nMore information needed" ]
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null
null
transformers
# VITS model Text to Speech Russian Example Text to Speech ```python from transformers import VitsModel, AutoTokenizer import torch import scipy model = VitsModel.from_pretrained("joefox/tts_vits_ru_hf") tokenizer = AutoTokenizer.from_pretrained("joefox/tts_vits_ru_hf") text = "Привет, как дел+а? Всё +очень хорош+о! А у тебя как?" inputs = tokenizer(text, return_tensors="pt") inputs['speaker_id'] = 3 with torch.no_grad(): output = model(**inputs).waveform scipy.io.wavfile.write("techno.wav", rate=model.config.sampling_rate, data=output[0].cpu().numpy()) ``` For displayed in a Jupyter Notebook / Google Colab: ```python from IPython.display import Audio Audio(output, rate=model.config.sampling_rate) ``` ## ## Languages covered Russian (ru_RU)
{"language": ["ru"], "license": "cc-by-nc-4.0", "tags": ["vits"], "pipeline_tag": "text-to-speech", "widget": [{"example_title": "text to speech", "text": "\u041f\u0440\u0438\u0432+\u0435\u0442, \u043a\u0430\u043a \u0434\u0435\u043b+\u0430? \u0412\u0441\u0451 +\u043e\u0447\u0435\u043d\u044c \u0445\u043e\u0440\u043e\u0448+\u043e! \u0410 \u0443 \u0442\u0435\u0431\u044f \u043a\u0430\u043a?"}]}
text-to-speech
joefox/tts_vits_ru_hf
[ "transformers", "safetensors", "vits", "text-to-audio", "text-to-speech", "ru", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]
2024-02-14T14:20:51+00:00
[]
[ "ru" ]
TAGS #transformers #safetensors #vits #text-to-audio #text-to-speech #ru #license-cc-by-nc-4.0 #endpoints_compatible #region-us
# VITS model Text to Speech Russian Example Text to Speech For displayed in a Jupyter Notebook / Google Colab: ## ## Languages covered Russian (ru_RU)
[ "# VITS model Text to Speech Russian\n\nExample Text to Speech\n\n\n\n\n\nFor displayed in a Jupyter Notebook / Google Colab:", "##", "## Languages covered\n\nRussian (ru_RU)" ]
[ "TAGS\n#transformers #safetensors #vits #text-to-audio #text-to-speech #ru #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n", "# VITS model Text to Speech Russian\n\nExample Text to Speech\n\n\n\n\n\nFor displayed in a Jupyter Notebook / Google Colab:", "##", "## Languages covered\n\nRussian (ru_RU)" ]
[ 52, 27, 1, 10 ]
[ "passage: TAGS\n#transformers #safetensors #vits #text-to-audio #text-to-speech #ru #license-cc-by-nc-4.0 #endpoints_compatible #region-us \n# VITS model Text to Speech Russian\n\nExample Text to Speech\n\n\n\n\n\nFor displayed in a Jupyter Notebook / Google Colab:#### Languages covered\n\nRussian (ru_RU)" ]
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null
null
transformers
# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}
image-classification
not-lain/testrepo
[ "transformers", "safetensors", "MobileNetV1", "image-classification", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]
2024-02-14T14:21:41+00:00
[ "1910.09700" ]
[]
TAGS #transformers #safetensors #MobileNetV1 #image-classification #custom_code #arxiv-1910.09700 #autotrain_compatible #region-us
# 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: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
[ "TAGS\n#transformers #safetensors #MobileNetV1 #image-classification #custom_code #arxiv-1910.09700 #autotrain_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]
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[ "passage: TAGS\n#transformers #safetensors #MobileNetV1 #image-classification #custom_code #arxiv-1910.09700 #autotrain_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]
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null
null
peft
# 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 0.8.2
{"library_name": "peft", "base_model": "bigscience/bloomz-560m"}
null
KapitalK/bloom-something4
[ "peft", "arxiv:1910.09700", "base_model:bigscience/bloomz-560m", "region:us" ]
2024-02-14T14:26:25+00:00
[ "1910.09700" ]
[]
TAGS #peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us
# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2
[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ "TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]
[ 32, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]
[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-bigscience/bloomz-560m #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]
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null
null
transformers
# Brunhilde-13b-v1 Brunhilde-13b-v1 is a merge of the following models * [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b) * [Undi95/ReMM-SLERP-L2-13B](https://huggingface.co/Undi95/ReMM-SLERP-L2-13B) ## Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "arlineka/Brunhilde-13b-v1" 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"]) ```
{"license": "cc-by-nc-4.0", "tags": ["merge"]}
text-generation
arlineka/Brunhilde-13b-v1
[ "transformers", "safetensors", "llama", "text-generation", "merge", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]
2024-02-14T14:27:02+00:00
[]
[]
TAGS #transformers #safetensors #llama #text-generation #merge #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
# Brunhilde-13b-v1 Brunhilde-13b-v1 is a merge of the following models * Gryphe/MythoMax-L2-13b * Undi95/ReMM-SLERP-L2-13B ## Usage
[ "# Brunhilde-13b-v1\n\nBrunhilde-13b-v1 is a merge of the following models\n* Gryphe/MythoMax-L2-13b\n* Undi95/ReMM-SLERP-L2-13B", "## Usage" ]
[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Brunhilde-13b-v1\n\nBrunhilde-13b-v1 is a merge of the following models\n* Gryphe/MythoMax-L2-13b\n* Undi95/ReMM-SLERP-L2-13B", "## Usage" ]
[ 61, 52, 3 ]
[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Brunhilde-13b-v1\n\nBrunhilde-13b-v1 is a merge of the following models\n* Gryphe/MythoMax-L2-13b\n* Undi95/ReMM-SLERP-L2-13B## Usage" ]
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