Datasets:

---

ftopal

/

huggingface-models

like 0

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

[]

[]

[ "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1.\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL\n\n\n # *Project Information*\n\n\n Policy-based learning is directly approximating π without having to learn a value function- Our objective then is to maximize the performance of the parameterized policy using gradient ascent.\n TL;DR: Having the cart learn to balance the pole via optimizing π for the best output; *the pole not falling over*. \n This method of learning skips over using a value function like Q-learning does, allowing an immediate improvement in the next iteration instead of having to calculate and approximate tables and numbers for a new action, as Q-learning does.\n \n\n This specific CartPole model only has 500 training timesteps- the average is 1000, which is the reason why the cart struggles so much with balancing the pole in the video; it has not trained enough for it.\n A model trained with 1000 timesteps is successful in balancing the pole, and the more training steps a model has, the more accurate its result is, like when you play a really hard level in a video game over and over, it eventually gets easier.\n However, the more timesteps a model has, the longer it takes to train and render- 1000 timesteps take 10-15 minutes to load, and the time only increases the more training timesteps are inputted. \n\n Here -https...- is a video of it working with 1000 timesteps, and here -https...- is one with 2000 *(links will be inserted soon)*" ]

[ "TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1.\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL\n\n\n # *Project Information*\n\n\n Policy-based learning is directly approximating π without having to learn a value function- Our objective then is to maximize the performance of the parameterized policy using gradient ascent.\n TL;DR: Having the cart learn to balance the pole via optimizing π for the best output; *the pole not falling over*. \n This method of learning skips over using a value function like Q-learning does, allowing an immediate improvement in the next iteration instead of having to calculate and approximate tables and numbers for a new action, as Q-learning does.\n \n\n This specific CartPole model only has 500 training timesteps- the average is 1000, which is the reason why the cart struggles so much with balancing the pole in the video; it has not trained enough for it.\n A model trained with 1000 timesteps is successful in balancing the pole, and the more training steps a model has, the more accurate its result is, like when you play a really hard level in a video game over and over, it eventually gets easier.\n However, the more timesteps a model has, the longer it takes to train and render- 1000 timesteps take 10-15 minutes to load, and the time only increases the more training timesteps are inputted. \n\n Here -https...- is a video of it working with 1000 timesteps, and here -https...- is one with 2000 *(links will be inserted soon)*" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 22190, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* TensorFlow 2.16.1\n* Datasets 2.18.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #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* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 22190, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* TensorFlow 2.16.1\n* Datasets 2.18.0\n* Tokenizers 0.19.1" ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID\n\n\n\neval_accuracy: 0.7110014287569815", "## 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 0.10.1.dev0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-TheBloke/Mistral-7B-Instruct-v0.2-AWQ #region-us \n", "# Model Card for Model ID\n\n\n\neval_accuracy: 0.7110014287569815", "## 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 0.10.1.dev0" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

[ "instruction tuning", "chemistry", "molecule", "small molecule", "en", "arxiv:2402.09391", "license:cc-by-4.0", "region:us" ]

[ "2402.09391" ]

[ "en" ]

[ "## ️ Usage\n\nFor instructions to run the model, please refer to our repository.", "## Limitations\n\nWhile the model is carefully trained, we do not guarantee its effectiveness. The model may output incorrect or inaccurate information. Please use it at your own risk.\n\nAdditionally, the model is built as a mature product but solely for research purpose. It may generate harmful or biased information. We emphatically urge all users to adhere to the highest ethical standards when using the model, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.", "## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries." ]

[ "TAGS\n#instruction tuning #chemistry #molecule #small molecule #en #arxiv-2402.09391 #license-cc-by-4.0 #region-us \n", "## ️ Usage\n\nFor instructions to run the model, please refer to our repository.", "## Limitations\n\nWhile the model is carefully trained, we do not guarantee its effectiveness. The model may output incorrect or inaccurate information. Please use it at your own risk.\n\nAdditionally, the model is built as a mature product but solely for research purpose. It may generate harmful or biased information. We emphatically urge all users to adhere to the highest ethical standards when using the model, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.", "## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries." ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:shrenikb/LLaMA0.5sparsity", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-shrenikb/LLaMA0.5sparsity #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 0.10.0" ]

[ "instruction tuning", "chemistry", "molecule", "small molecule", "en", "arxiv:2402.09391", "license:cc-by-4.0", "region:us" ]

[ "2402.09391" ]

[ "en" ]

[ "## ️ Usage\n\nFor instructions to run the model, please refer to our repository.", "## Limitations\n\nWhile the model is carefully trained, we do not guarantee its effectiveness. The model may output incorrect or inaccurate information. Please use it at your own risk.\n\nAdditionally, the model is built as a mature product but solely for research purpose. It may generate harmful or biased information. We emphatically urge all users to adhere to the highest ethical standards when using the model, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.", "## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries." ]

[ "TAGS\n#instruction tuning #chemistry #molecule #small molecule #en #arxiv-2402.09391 #license-cc-by-4.0 #region-us \n", "## ️ Usage\n\nFor instructions to run the model, please refer to our repository.", "## Limitations\n\nWhile the model is carefully trained, we do not guarantee its effectiveness. The model may output incorrect or inaccurate information. Please use it at your own risk.\n\nAdditionally, the model is built as a mature product but solely for research purpose. It may generate harmful or biased information. We emphatically urge all users to adhere to the highest ethical standards when using the model, including maintaining fairness, transparency, and responsibility in their research. Any usage of the dataset that may lead to harm or pose a detriment to society is strictly forbidden.", "## Citation\nIf our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries." ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.41e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #trl #reward-trainer #generated_from_trainer #base_model-google/gemma-2b #license-gemma #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.41e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "gguf", "liminerity/merge4", "liminerity/merge2", "merge", "merged", "mergedllm", "mergee", "mergedededed", "memfdmsfndsfjkasjkl", "llama-cpp", "gguf-my-repo", "license:apache-2.0", "region:us" ]

[]

[]

[ "# cleatherbury/M7-7b-Q8_0-GGUF\nThis model was converted to GGUF format from 'liminerity/M7-7b' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "TAGS\n#gguf #liminerity/merge4 #liminerity/merge2 #merge #merged #mergedllm #mergee #mergedededed #memfdmsfndsfjkasjkl #llama-cpp #gguf-my-repo #license-apache-2.0 #region-us \n", "# cleatherbury/M7-7b-Q8_0-GGUF\nThis model was converted to GGUF format from 'liminerity/M7-7b' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: anton96vice\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: anton96vice\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "base_model:ChaoticNeutrals/IQ_Test_l3_8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "# Chaos RP\n\n!image/png\n\nA chaotic force beckons for you, will you heed her call?\n\nBuilt upon an intelligent foundation and tuned for roleplaying, this model will fulfill your wildest fantasies with the bare minimum of effort.\n\nEnjoy!" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #base_model-ChaoticNeutrals/IQ_Test_l3_8B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Chaos RP\n\n!image/png\n\nA chaotic force beckons for you, will you heed her call?\n\nBuilt upon an intelligent foundation and tuned for roleplaying, this model will fulfill your wildest fantasies with the bare minimum of effort.\n\nEnjoy!" ]

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

[]

[]

[ "### 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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "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: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "trl", "safetensors", "DPO", "WeniGPT", "pt", "base_model:Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged", "license:mit", "region:us" ]

[]

[ "pt" ]

[ "# Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-1.0.24-DPO\n\nThis model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for Weni.\nDescription: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT\n\nIt achieves the following results on the evaluation set:\n{'eval_loss': 0.008221210911870003, 'eval_runtime': 15.9054, 'eval_samples_per_second': 1.76, 'eval_steps_per_second': 0.44, 'eval_rewards/chosen': 2.6355252265930176, 'eval_rewards/rejected': -2.999356508255005, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 5.634881496429443, 'eval_logps/rejected': -208.50425720214844, 'eval_logps/chosen': -182.0274200439453, 'eval_logits/rejected': -1.9468201398849487, 'eval_logits/chosen': -1.9320443868637085, 'epoch': 5.806451612903226}", "## Intended uses & limitations\n\nThis model has not been trained to avoid specific intructions.", "## Training procedure\n\nFinetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged with the following prompt:", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- per_device_train_batch_size: 1\n- per_device_eval_batch_size: 1\n- gradient_accumulation_steps: 2\n- num_gpus: 4\n- total_train_batch_size: 8\n- optimizer: AdamW\n- lr_scheduler_type: cosine\n- num_steps: 180\n- quantization_type: bitsandbytes\n- LoRA: (\"\\n - bits: 4\\n - use_exllama: True\\n - device_map: auto\\n - use_cache: False\\n - lora_r: 64\\n - lora_alpha: 32\\n - lora_dropout: 0.05\\n - bias: none\\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']\\n - task_type: CAUSAL_LM\",)", "### Training results", "### Framework versions\n\n- transformers==4.40.0\n- datasets==2.18.0\n- peft==0.10.0\n- safetensors==0.4.2\n- evaluate==0.4.1\n- bitsandbytes==0.43\n- huggingface_hub==0.22.2\n- seqeval==1.2.2\n- auto-gptq==0.7.1\n- gpustat==1.1.1\n- deepspeed==0.14.0\n- wandb==0.16.6\n- trl==0.8.1\n- accelerate==0.29.3\n- coloredlogs==15.0.1\n- traitlets==5.14.2\n- git+URL", "### Hardware\n- Cloud provided: URL" ]

[ "TAGS\n#trl #safetensors #DPO #WeniGPT #pt #base_model-Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged #license-mit #region-us \n", "# Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-1.0.24-DPO\n\nThis model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for Weni.\nDescription: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT\n\nIt achieves the following results on the evaluation set:\n{'eval_loss': 0.008221210911870003, 'eval_runtime': 15.9054, 'eval_samples_per_second': 1.76, 'eval_steps_per_second': 0.44, 'eval_rewards/chosen': 2.6355252265930176, 'eval_rewards/rejected': -2.999356508255005, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 5.634881496429443, 'eval_logps/rejected': -208.50425720214844, 'eval_logps/chosen': -182.0274200439453, 'eval_logits/rejected': -1.9468201398849487, 'eval_logits/chosen': -1.9320443868637085, 'epoch': 5.806451612903226}", "## Intended uses & limitations\n\nThis model has not been trained to avoid specific intructions.", "## Training procedure\n\nFinetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged with the following prompt:", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- per_device_train_batch_size: 1\n- per_device_eval_batch_size: 1\n- gradient_accumulation_steps: 2\n- num_gpus: 4\n- total_train_batch_size: 8\n- optimizer: AdamW\n- lr_scheduler_type: cosine\n- num_steps: 180\n- quantization_type: bitsandbytes\n- LoRA: (\"\\n - bits: 4\\n - use_exllama: True\\n - device_map: auto\\n - use_cache: False\\n - lora_r: 64\\n - lora_alpha: 32\\n - lora_dropout: 0.05\\n - bias: none\\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']\\n - task_type: CAUSAL_LM\",)", "### Training results", "### Framework versions\n\n- transformers==4.40.0\n- datasets==2.18.0\n- peft==0.10.0\n- safetensors==0.4.2\n- evaluate==0.4.1\n- bitsandbytes==0.43\n- huggingface_hub==0.22.2\n- seqeval==1.2.2\n- auto-gptq==0.7.1\n- gpustat==1.1.1\n- deepspeed==0.14.0\n- wandb==0.16.6\n- trl==0.8.1\n- accelerate==0.29.3\n- coloredlogs==15.0.1\n- traitlets==5.14.2\n- git+URL", "### Hardware\n- Cloud provided: URL" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_ablation_4iters_bs256_iter_1", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.0001_ablation_4iters_bs256_iter_2\n\nThis model is a fine-tuned version of ShenaoZ/0.0001_ablation_4iters_bs256_iter_1 on the updated and the original datasets.", "## 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-updated #dataset-original #base_model-ShenaoZ/0.0001_ablation_4iters_bs256_iter_1 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.0001_ablation_4iters_bs256_iter_2\n\nThis model is a fine-tuned version of ShenaoZ/0.0001_ablation_4iters_bs256_iter_1 on the updated and the original datasets.", "## 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

[ "gguf", "llama-cpp", "gguf-my-repo", "ko", "license:apache-2.0", "region:us" ]

[]

[ "ko" ]

[ "# cleatherbury/SOLAR-10.7B-Q8_0-GGUF\nThis model was converted to GGUF format from 'krevas/SOLAR-10.7B' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "TAGS\n#gguf #llama-cpp #gguf-my-repo #ko #license-apache-2.0 #region-us \n", "# cleatherbury/SOLAR-10.7B-Q8_0-GGUF\nThis model was converted to GGUF format from 'krevas/SOLAR-10.7B' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

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

[ "1910.09700" ]

[]

[ "# 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 #conversational #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" ]

[ "transformers", "safetensors", "bart", "text2text-generation", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# TRL Model\n\nThis is a TRL language model that has been fine-tuned with reinforcement learning to\n guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.", "## Usage\n\nTo use this model for inference, first install the TRL library:\n\n\n\nYou can then generate text as follows:\n\n\n\nIf you want to use the model for training or to obtain the outputs from the value head, load the model as follows:" ]

[ "TAGS\n#transformers #safetensors #bart #text2text-generation #trl #ppo #reinforcement-learning #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# TRL Model\n\nThis is a TRL language model that has been fine-tuned with reinforcement learning to\n guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.", "## Usage\n\nTo use this model for inference, first install the TRL library:\n\n\n\nYou can then generate text as follows:\n\n\n\nIf you want to use the model for training or to obtain the outputs from the value head, load the model as follows:" ]

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

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #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 0.10.0" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.001_ablation_5iters_bs256_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_ablation_5iters_bs256_iter_3\n\nThis model is a fine-tuned version of ShenaoZ/0.001_ablation_5iters_bs256_iter_2 on the updated and the original datasets.", "## 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-updated #dataset-original #base_model-ShenaoZ/0.001_ablation_5iters_bs256_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_ablation_5iters_bs256_iter_3\n\nThis model is a fine-tuned version of ShenaoZ/0.001_ablation_5iters_bs256_iter_2 on the updated and the original datasets.", "## 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.41e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #trl #reward-trainer #generated_from_trainer #base_model-google/gemma-2b #license-gemma #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.41e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:ZhangShenao/0.0_ablation_5iters_dataset", "base_model:ZhangShenao/0.0_ablation_5iters_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.0_ablation_5iters_iter_3\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_5iters_iter_2 on the ZhangShenao/0.0_ablation_5iters_dataset 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_5iters_dataset #base_model-ZhangShenao/0.0_ablation_5iters_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.0_ablation_5iters_iter_3\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_5iters_iter_2 on the ZhangShenao/0.0_ablation_5iters_dataset 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:shrenikb/LLaMA0.5sparsity", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-shrenikb/LLaMA0.5sparsity #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 0.10.0" ]

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

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #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 0.10.0" ]

[ "transformers", "safetensors", "llama", "text-generation", "korean", "gemma", "conversational", "ko", "en", "arxiv:1910.09700", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[ "ko", "en" ]

[ "# 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 #korean #gemma #conversational #ko #en #arxiv-1910.09700 #base_model-meta-llama/Meta-Llama-3-8B #license-other #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" ]

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

[]

[]

[ "# Model Card for Model ID", "# AVA-Llama-3\nFine-Tuned Llama 3 Persian Large Language Model LLM / Persian Llama 3", "# AVA-Llama-3 / Persian Llama-3 / Persian Llama 3\n\n\n <img src=\"Llama 3.png\" height=\"600\" width=\"940\" >", "### This Repository Contains Documents for Fine-Tuned Llama 3 Persian Large Language Model(LLM) Called AVA-Llama-3\n(Still in progress)\n\n-------------------------------------------------", "### Dataset used:\n\nTo Be Done\n\n-------------------------------------------------", "### Usage:\n\nAll models are hosted in HuggingFace, and here is the code for inference:\n\n\n\nYou can also use colab link in the jupyter file\n\n-------------------------------------------------", "## QA Comparision to other models:\n\n <img src=\"URL\" height=\"600\" width=\"940\" >\n\n\nAVA-Llama-3 vs AVA-V4 vs AVA-V2 vs Maral vs Llama", "### Q1: \n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> ایلان ریو ماسک در پرتوریا، آفریقای جنوبی به دنیا امده است </td>\n <td> </td>\n </tr>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> ایلان ریو ماسک در پرتوریا، آفریقای جنوبی به دنیا امده است. او در 18 سالگی به کانادا رفت </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> ایلان ماسک در پرتوریا، آفریقای جنوبی به دنیا آمده است. او در 18 سالگی به کانادا رفت </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> ایلان ماسک در پرتوریا، آفریقای جنوبی به دنیا آمده است </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> اگر او در کجا به دنیا امده باشد، چه کسی او را در کجا می‌دانست؟ </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n--------------------------------------------------------", "### Q2: \n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> این یک بازی با کلمات است که به ما کمک می کند تا به یک پاسخ عجیب برسیم. پاسخ این است که سه قاتل باقی می مانند. دلیل این است که یکی از قاتلان وارد اتاق می شود و یکی از قاتلان دیگر را می کشد، اما هیچ کس اتاق را ترک نمی کند. بنابراین، سه قاتل باقی می مانند.\n </td>\n <td> </td>\n </tr>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> پاسخ: در اتاق هنوز یک قاتل باقی مانده است.\n استدلال: از هر دو قاتل یکی در اتاق باقی مانده است. زیرا هر کدام یکی را می کشد و هیچ کدام از اتاق ترک نمی کند. پس از کشتن دیگری، یکی از قاتلان باقی مانده در اتاق هست </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> در این سوال، اولیه استدلال به این صورت است که در یک اتاق سه قاتل هستند. واقعیت این است که یکی از آنها را می کشد و یکی از آنها را نمی کشد. بنابراین، هیچ کس اتاق را ترک نمی کند و دو قاتل در اتاق باقی مانده است.\n\nاگر در این سوال به استدلال بیشتری افتد، می توانیم ببینیم که هر دو قاتل هم در اتاق هستند و هیچ کدام را نمی کشند. اما در این استدلال اصلی و اولیه، تعداد قاتل های باقی مانده در اتاق به دو تقریباً این است. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td>در این سوال سه قاتل در یک اتاق وجود دارد و یکی از آنها را می کشد. هیچ کس اتاق را ترک نمی کند، بنابراین دو قاتل باقی مانده است. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> این استدلال می تواند به یک اتاق با چهار قاتل در آن باشد. یک قاتل وارد اتاق می شود و یکی از آنها را می کشد. هیچ کس اتاق را ترک نمی کند چند قاتل در آن باقی مانده است. این استدلال می تواند به یک اتاق با چهار قاتل در آن باشد که یک قاتل وارد اتاق می شود و یکی از آنها را می کشد و هیچ کس اتاق را ترک نمی کند چند قاتل در آن باقی مانده است. می تواند به یک اتاق با چهار قاتل در آن باشد که یک قاتل وارد اتاق می شود و یکی از آنها را می کشد و هیچ کس اتاق را ترک نمی کند چند قاتل در آن باقی مانده است. می تواند به یک اتاق با چهار قاتل در آن باشد که یک قاتل وارد اتاق می شود و یکی از آنها را می کشد و هیچ کس اتاق را\n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n----------------------------------------------------------", "### Q3: \n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> \n ملکه الیزابت یک شاهزاده بریتانیایی است که از ۱۹۵۲ تا ۲۰۲۲ بر تخت سلطنت بریتانیا سلطنت کرد. او در ۲۱ آوریل 1926 در لندن متولد شد و در ۸ سپتامبر 2022 در سن ۹۶ سالگی در باکینگهام پالاس درگذشت. ملکه الیزابت دوم طولانی‌ترین سلطنت بریتانیا را در اختیار داشت و در طول سلطنت خود بریتانیا را از جنگ جهانی دوم تا جنگ سرد و سپس به دوران مدرن رهبری کرد. او همچنین در طول سلطنت خود به عنوان یک نماد ملی و یک نماد برای بریتانیا شناخته شد. ملکه الیزابت دوم همچنین به عنوان ملکه کانادا، استرالیا، نیوزیلند و سایر کشورهای مشترک‌المنافع بریتانیا نیز شناخته می‌شود. </td>\n <td> <h1> </h1> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> ملکه ایلزابت بولنتن بود که در سال 1533 تا 1558 در انگلستان دور بود. او در زمان بود که انگلستان در دوران تاریخی به عنوان یک کشور به پیشرفت بیشتری رسید </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> ملکه الیزابت یک شهرستان در انگلستان است. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> ملکه الیزابت یک کشور در جنوب آفریقا است که در سال 1910 به این نام تعیین شد. این کشور به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینک </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> ملکه الیزابت ریاضی ایرانی است که در سال ۱۹۴۷ در ایران تولید شد. این ملکه در سال ۱۹۶۷ در ایران تولید شده و در سال ۱۹۷۰ در ایران و در سال ۱۹۷۱ در امریکا تولید شده و در سال ۱۹۷۲ در ایران و در سال ۱۹۷۳ در امریکا تولید شده و در سال ۱۹۷۴ در ایران و در سال ۱۹۷۵ در امریکا تولید شده و در سال ۱۹۷۶ در ایران و در سال ۱۹۷۷ در امریکا تولید شده و در سال ۱۹۷۸ در ایران و در سال ۱۹۷۹ در امریکا تولید شده و در سال ۱۹۸۰ در ایران و در سال ۱۹۸۱ در امریکا تولید شده و در سال ۱۹۸۲ در ایران و در سال ۱۹۸۳ در امریکا تولید شده و در سال ۱۹۸۴ در ایران و در سال ۱۹۸۵ در امریک\n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n-----------------------------------------------------------------", "### Q4: \n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> منظور شما این است که توپ در داخل لیوان برعکس قرار دارد و سپس لیوان را برداشته و داخل مایکروویو قرار می دهید. در این صورت، توپ در داخل مایکروویو قرار دارد و برعکس لیوان نیست.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td>", "### Assistant: اگر یک توپ داخل یک لیوان برعکس باشد، و ما لیوان را برداشته و داخل مایکروویو بگذاریم، توپ داخل مایکروویو خواهد بود. در این واقعیت، توپ در داخل مایکروویو قرار داشته و لیوان را برداشته شده است </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> اگر یک توپ داخل یک لیوان باشد، و ما لیوان را برداشته و داخل مایکروویو بگذاریم، توپ داخل مایکروویو خواهد بود. این تشکیل اندازه گیری شده است و در فیزیک و ارتباطات است، و این تشکیل اندازه گیری شده به عنوان \"توپ داخل لیوان در داخل مایکروویو\" شناسایی می شود. این تشکیل اندازه گیری شده در فیزیک و ارتباطات به عنوان یک نمونه از انواع تشکیلات اندازه گیری شده ارائه می شود که توسط جهان در حال ارتباط است.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> اگر یک توپ داخل یک لیوان برعکس باشد و ما لیوان را برداشته و داخل مایکروویو بگذاریم، توپ داخل مایکروویو خواهد بود. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> توپ داخل یک لیوان یا مایکروویو است که در آن می توان برای انجام کارهای مختلف استفاده کنید. لیوان یک ماشین است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک ماشین است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آ </td>\n <td> </td>\n </tr>\n</table>\n\n\n-----------------------------------------------------------------", "### Q5: \n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <td> AVA-Llama-3 </td>\n <td> \n\nدر تاریکی شب، صدای قلبم می‌زند\nدلشکسته و بی‌امید، در تاریکی شب\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\n </td>\n <td> \n <tr>\n <td> AVA-V4 </td>\n <td> بله، می توانم یک شعر با دقیقا پنجاه کلمه برای شما بسازم. اینجا یک شعر با پنجاه کلمه ای که برای شما نوشتم:\n\nدر جنگل دیدار کردم یک گربه سیاه\nکه در دست دو گربه سبز قرار داشت\nاز آن که در جنگل زمانه های گذشته\nگربه ها در دست دیگران قرار گرفت </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> بله، می‌توانید یک شعر با دقیقا پنجاه کلمه بساز. اینجا یک نمونه شعر با پنجاه کلمه ایده آمده است:\n\nدر دل آب و هوای زمان،\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\n\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\nدر دل آب و هوای زمان،\n\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\nدر دل آب و هوای زمان،\n\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\nدر دل آب و هوای زمان،\n\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\nدر دل آب و هوای زمان. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> در اینجا یک شعر با دقیقا پنجاه کلمه وجود دارد:\n\"با دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه، </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> بله، شعر یک نوع از مواد مختلف است که در معروف شعر با نام شعر انسان به اندازهٔ ۱۰۰۰ کلمه در مقابل ۱۰۰۰ کلمه در مقابس شعر حیوانات و ۱۰۰۰ کلمه در مقابس شعر گیاهان و ۱۰۰۰ کلمه در مقابس شعر گیاهان با نام شعر گیاهان انسان و ۱۰۰۰ کلمه در مقابس شعر گیاهان دیگر و ۱۰۰۰ کلمه در مقابس شعر دیگر انسان و ۱۰۰۰ کلمه در مقابس شعر دیگر انسان و ۱۰۰۰ کلمه در مقابس شعر دیگر حیوانات و ۱۰۰۰ کلمه در مقابس شعر دیگر گیاهان و ۱۰۰۰ کلمه در مقابس شعر دیگر گیاهان انسان و ۱۰۰۰ کلمه در مقابس شعر دیگر حیوانات و ۱۰۰۰ کلمه در مقابس شعر دیگر گیاهان و ۱۰۰۰ کلمه در مقابس شعر دیگ </td>\n <td> </td>\n </tr>\n</table>\n\n-----------------------------------------------------------------", "### Q6: \n\n\n\n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\t <tr>\n <td> AVA-Llama-3 </td>\n <td>\n\t \nیک نامه خطاب به رییس شرکت من بنویسید که من در حال استعفا و ترک شرکت هستم.\n\n[Your Name]\n[Your Address]\n[City, State, ZIP]\n[Date]\n\n[CEO's Name]\n[Company Name]\n[Company Address]\n[City, State, ZIP]\n\nDear [CEO's Name],\n\nI am writing to inform you that I have decided to resign from my position as [Position] at [Company Name], effective [Date of Last Day of Work]. This was not an easy decision for me to make, as I have truly enjoyed my time at the company and have been grateful for the opportunities that I have been given.\n\nHowever, I have come to realize that it is time for me to move on and explore new opportunities. I am grateful for the experience and skills that I have gained during my time at [Company Name], and I am confident that they will serve me well in my future endeavors.\n\nI want to thank you for the support and guidance that you have provided me during my time at the company. I appreciate the opportunities that you have given me to grow and develop as a professional, and I am grateful for the trust that you have placed in me.\n\nI will do everything I can to ensure a smooth transition of my responsibilities and complete any outstanding tasks before my departure. If there is anything specific that you would like me to focus on during my remaining time here, please let me know.\n\nThank you again for the opportunity to work at [Company Name]. I wish the company all the best for the future.\n\nSincerely,\n\n[Your Name]\n </td>\n <tr>\n <td> AVA-V4 </td>\n <td> ۱۳۹۹/۰۸/۱۴۰۰\n\nمترجم: شما باید نامه خطاب خود را به انگلیسی نوشته و از طریق این پیام به من ارسال کنید.\n\nمتوضع: در این نامه خطاب، من در حال استعفا و ترک شرکت هستم.\n\nنامه خطاب:\n\nاز انجام ناخودپسندیده و از دست دادن از شرکت خود انجام می دهم.\n\nشما باید نامه خطاب خود را به انگلیسی نوشته و از طریق این پیام به من ارسال کنید.\n\nمتوضع: در این نامه خطاب، من در حال استعفا و ترک شرکت هستم.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> اگر شما یک نامه خطاب به رییس شرکت خود بنویسید که من در حال استعفا و ترک شرکت هستم، این است که می توانید از آن استفاده کنید:\n\n نامه خطاب\n\nشماره: [شماره نامه]\n\nتاریخ: [تاریخ نوشتن نامه]\n\n[نام و نام خانوادگی رییس شرکت]\n[عنوان شرکت]\n[آدرس شرکت]\n\nشکر خوب،\n\nبه عنوان یکی از عضویان شرکت [نام شرکت]، احتمالاً اولین خبری که به شما این نامه ارسال می کنم نیست. اما در حال حاضر، من در حال استعفا و ترک از شرکت هستم و این نامه را به شما ارسال می کنم تا آن را به دنباله های دیگری اضافه کنم.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> رییس شرکت،\nمن در حال استعفا و ترک شرکت هستم. این نه به دلیل اینکه به شرکت نیاز ندارم، بلکه به دلیل اینکه با این کار به خود و به شرکت اشتباه کرده ام. این به دلیل این است که از طرف شرکت به دنبال ارزش های بیشتری بودم و از طرف من به دنبال ارزش های بیشتری بودم. اما این ارزش ها به طور متوقف شده است و از طرف شرکت و من ارزش های بیشتری به دست نمی آید. این به دلیل این است که ارزش های بیشتری به طور متوقف شده است و از طرف شرکت و من ارزش های بیشتری به دست نمی آید. اما این ارزش ها به طور متوقف شده است و از طرف شرکت و من ارزش های بیشتری به دست نمی آید. اما این ارزش ها به طور متوقف شده است و از طرف شرکت و من ارزش های بی </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> ### Assistant: خوب است که شرکت را ترک کردید. من به شرکت باز کردم و امروز در شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال\n </td>\n <td> </td>\n </tr>\n</table>\n\n-----------------------------------------------------------------", "### Q7: \n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> رییس جمهور آمریکا در سال 1996، بیل کلینتون بود </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> رییس جمهور آمریکا در سال 1996 بود بیل کلینتن. </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> در سال 1996، رییس جمهور آمریکا بیل کلنتن بود. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> رییس جمهور آمریکا در سال 1996 بیل کلنتن بود. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> رییس جمهور آمریکا در سال ۱۹۹۶ به نام بلار بورنس بود. او در ۱۹۹۲ از رییس دادگاه آمریکا و در ۱۹۹۴ از رییس دادگاه آمریکا شد و در ۱۹۹۶ به رییس جمهور آمریکا اتحادیه شد. او در ۱۹۹۷ به رییس جمهور آمریکا دوم شد و در ۱۹۹۹ به رییس جمهور آمریکا سوم شد. او در ۱۹۹۱ به رییس دادگاه آمریکا شد و در ۱۹۹۲ به رییس دادگاه آمریکا شد و در ۱۹۹۳ به رییس جمهور آمریکا شد. او در ۱۹۹۴ به رییس دادگاه آمریکا شد و در ۱۹۹۵ به رییس جمهور آمریکا شد. او در ۱۹۹۶ به رییس جمهور آمریکا شد و در ۱۹۹۷ به رییس جمهور آمریکا دوم شد. او در ۱۹۹۸ به رییس جمهور آمریکا سوم شد. او در ۱۹۹ </td>\n <td> </td>\n </tr>\n</table>\n\n-----------------------------------------------------------------", "### Q8: \n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n<tr>\n <td> AVA-Llama-3 </td>\n <td> \nاگر پنج پیراهن 4 ساعت طول می کشد تا خشک شود، می توانیم به این نتیجه برسیم که هر پیراهن 4/5 ساعت طول می کشد تا خشک شود. برای پیدا کردن زمان لازم برای خشک شدن 20 پیراهن، می توانیم از این رابطه استفاده کنیم:\n\n4/5 * 20 = 16\n\nبنابراین، 20 پیراهن 16 ساعت طول می کشد تا خشک شود.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> اگر پنج پیراهن 4 ساعت طول بکشد تا خشک شود، 20 پیراهن به اندازه ی یک ساعت طول می کشد تا خشک شود. این اطمینان را دارید که هر پیراهن در نتیجه 20 پیراهن 4 ساعت طول می کشد تا خشک شود.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> اگر پنج پیراهن 4 ساعت طول بکشد تا خشک شود، 20 پیراهن به اندازه ی شبیه ساعت بکشند تا خشک شود. برای حل مورد نیاز، شما می توانید از فرمول زیر استفاده کنید:\n\nطول بکشنده شدن 20 پیراهن = طول بکشنده شدن 5 پیراهن × (شماره پیراهن / شماره پیراهن)\n\nطول بکشنده شدن 20 پیراهن = 4 ساعت × (20 / 5)\n\nطول بکشنده شدن 20 پیراهن = 4 ساعت × 4\n\nطول بکشنده شدن 20 پیراهن = 16 ساعت\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> برای حل این پرسش، ابتدا باید از این اطلاعات استفاده کنید که پنج پیراهن 4 ساعت طول می کشد تا خشک شود. از این اطلاعات باید برای حل پرسش استفاده کنید.برای حل این پرسش، ابتدا باید از این اطلاعات استفاده کنید که پنج پیراهن 4 ساعت طول می کشد تا خشک شود. از این اطلاعات باید برای حل پرسش استفاده کنید.برای حل این پرسش، ابتدا باید از این اطلاعات استفاده کنید که پنج پیراهن 4 ساعت طول می کشد تا خشک شود. از این اطلاعات باید برای حل پرسش استفاده کنید.برای حل این پرسش، ابتدا باید از این اطلاعات استفاده کنید که پنج پیراهن 4 ساعت طول می کشد تا خشک شود. از این اطلاعات باید برای حل پرسش استفاده کنید.برای حل این پرسش\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پن\n </td>\n <td> </td>\n </tr>\n</table>\n\n-----------------------------------------------------------------", "### Q9: \n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> \nاین مسابقه بسیار جالب است! با در نظر گرفتن سرعت هر دو، می توانیم سرعت آنها را به یک واحد تبدیل کنیم. سرعت خرگوش 10 کیلومتر بر ساعت است، بنابراین سرعت لاک پشت را به 10 کیلومتر بر ساعت تبدیل می کنیم. در این صورت، سرعت لاک پشت 0.1 کیلومتر بر ساعت است.\n\nدر این صورت، سرعت خرگوش 10 کیلومتر بر ساعت و سرعت لاک پشت 0.1 کیلومتر بر ساعت است. بنابراین، خرگوش برنده مسابقه است. لاک پشت به دلیل سرعت بسیار پایین خود نمی تواند با خرگوش رقابت کند.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> \nبرای حل این مسئله، ما باید سرعت هر دو از جمله را بررسی کنیم و سپس آن‌ها را با هم مقایسه کنیم. سرعت خرگوش 10 کیلومتر در 1 دقیقه است و سرعت لاک پشت 1 کیلومتر در 10 دقیقه است.\n\nمسابقه 100 متری از آنجایی که خرگوش سریع‌تر از لاک پشت است، خرگوش به طور کامل قابلیت برداشتن این جاهزه را دارد. بنابراین، خرگوش برنده مسابقه است.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> برای مشاهده نتیجه مسابقه، باید سرعت خرگوش و لاک پشت را به هم انداز کنیم. سرعت خرگوش 10 کیلومتر در ساعت و لاک پشت 1 کیلومتر در ساعت است. برای اینکه نتیجه مسابقه را بدست آوریم، باید 100 متر را به سرعت خرگوش و لاک پشت تبدیل کنیم.\n\n100 متر / 10 کیلومتر در ساعت = 10 دقیقه\n\n100 متر / 1 کیلومتر در ساعت = 100 دقیقه\n\nبنابراین، خرگوش 10 دقیقه و لاک پشت 100 دقیقه می گذرد تا 100 متر را پیروز کنند. بنابراین، برنده مسابقه خرگوش است. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> برنده مسابقه خرگوش است. با سرعت 10 کیلومتر در ساعت، خرگوش به انتهای 100 متر در 10 دقیقه به پایان می رسد، در حالی که لاک پشت با سرعت 1 کیلومتر در ساعت به پایان می رسد و به انتهای 100 متر در 100 دقیقه به پایان می رسد.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> با این نوع سوال می توان به نوع سوال با نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با \n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n-----------------------------------------------------------------", "### Q9: \n\n\n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr> \n <tr>\n <td> AVA-Llama-3 </td>\n <td> \n\t Here is a JSON file containing the information you requested:\n\n\n\nIf you want to add more information to each person, you can add additional properties to the JSON object. For example:\n\n\n\nLet me know if you have any other questions!\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> ساختن یک فایل json با این اطلاعات از قبل ایجاد شده است. این فایل json شامل اطلاعات دو نفر از افراد مانند bob و jon است. برای افزودن اطلاعات موجود در فایل json یا ایجاد جدید، باید از کاماند زیر استفاده کنید:\n\n\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> ساده، این کار را با استفاده از زبان پروگرامینگ Python می‌توانیم انجام دهیم. ابتدا، یک فایل json با نام \"URL\" به دست آورید. سپس، از کلاس dict در Python استفاده کرده، یک فایل json را ایجاد می‌کنیم که اطلاعات این چهار نام را با این فرمت برمی‌چارد:\n\nimport json\n\npeople = {\n \"bob\": {},\n \"jon\": {},\n \"mary\": {},\n \"rich\": {}\n}\n\nwith open(\"URL\", \"w\") as f:\n URL(people, f) </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> با استفاده از یک فایل json به این صورت ایجاد کنید:\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> امروز چه نوع افراد را در مدرسه دانشگاه می‌دانید؟ می‌خوام بی‌نهایت افراد دو نوع را در مدرسه دانشگاه می‌دانم. این افراد با نام برادر و مرد است. برادر در مدرسه دانشگاه در دوره اول دوست دارند و مرد در دوره دوم دوست دارند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می\n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n-----------------------------------------------------------------", "### Q9: \n\n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> \nیک اسکریپت پایتون برای چاپ 1 تا 10 به این صورت می توان بنویسید:\n\nاین اسکریپت یک حلقه برای loop 1 تا 10 ایجاد می کند و هر بار در هر دور، عدد فعلی را چاپ می کند.\n </td>\n <td> </td>\n <tr>\n <td> AVA-V4 </td>\n <td> این یک اسکریپت پایتون است که از 1 تا 10 چاپ می کند:\n \n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> یک اسکریپت پایتون برای چاپ 1 تا 10 به شرح زیر برمی گرد:\n\nاین اسکریپت با استفاده از لوپ (loop) در زمان اجرایی به نام 'range(1, 11)' از 1 تا 10 عدد را مشاهده می کند و هر عدد را با استفاده از فنکشن 'print()' چاپ می کند.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> در اینجا یک اسکریپت پایتون برای چاپ 1 تا 10 وجود دارد: </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> ### Assistant: این اسکریپت چاپ کنید و بعد از انجام چاپ، می توانید باز کنید. اگر نیاز به این اسکریپت ندارید، با موفقیت این اسکریپت را از منبع اصلی دانستید. اگر نیاز دارید، با موفقیت این اسکریپت را از منبع اصلی دانستید. اگر نیاز ندارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز دارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز ندارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز دارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز ندارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز دارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز ندارید\n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n\n-----------------------------------------------------------------", "### Q9: \n\n\n\n\n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n\n <tr>\n <td> AVA-Llama-3 </td>\n <td> \n\t \nدر اینجا یک تابع URL برای دریافت رشته و تقسیم آن بر اساس فاصله ارائه می شود:\n\nاین تابع از روش 'split' درbuilt-in 'String' در URL استفاده می کند تا رشته را بر اساس فاصله (که با کاراکتر فاصله 'space' مشخص می شود) تقسیم کند. سپس آرایه ای از رشته های جدا شده را بازمی گرداند.\n\nاگر می خواهید رشته را بر اساس فاصله خاصی تقسیم کنید، می توانید از یک رشته به عنوان دومین آرگومان به روش 'split' پاس دهید. برای مثال، برای تقسیم رشته بر اساس فاصله با فاصله 2 کاراکتر، می توانید از رشته ''\\u0020'' استفاده کنید:\n\nاین تابع رشته را بر اساس فاصله 2 کاراکتر (که با کاراکتر فاصله ''\\u0020'' مشخص می شود) تقسیم می کند. سپس آرایه ای از رشته های جدا شده را بازمی گرداند.\n </td>\n <td> </td>\n </tr>\n \n <tr>\n <td> AVA-V4 </td>\n <td> یک تابع URL برای دریافت رشته و تقسیم آن بر اساس فاصله به شما این کد پیوند می دهم:\n\n\n\nاین کد تابع 'splitStringBySpace' برچسب کرده است که رشته وارد شده را با فاصله بنویسید. این کد را در نظر دهید و اگر نیاز به تغییرات دیگری دارید، لطفا بگویید.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> یک تابع node js برای دریافت رشته و تقسیم آن بر اساس فاصله به شکل زیر می تواند باشد:\n\n\n\nدر این کد، ابتدا به کار برده 'readline' بخش های فایل ورودی را دریافت می کند. سپس تابع 'splitStringBySpace' را برای تقسیم رشته ای که دریافت کرده ایم استفاده می کند. این تابع رشته را با فاصله های فارسی راهنمایی دار تقسیم می کند و به صورت مجزا برمی گرداند. نتیجه این کار این است که هر کلمه در یک عنصر مجزا قرار می گیرد. سپس این عنصرها را در کنسول نمایش می دهد.\n\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> در اینجا یک تابع برای دریافت رشته و تقسیم آن بر اساس فاصله وجود دارد:\nدر این تابع، رشته وارد شده به عنوان پارامتر وارد می شود. در طول تابع، رشته به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عن </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> - </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n\n\n-----------------------------------------------------------------", "## License\n\n\n\nReleased Jan 30, 2024 by Mehdi Hosseini Moghadam\n\nAttention ️: The user is responsible for using AVA-Llama-3 / Persian Llama 3\n\nAny misuse of the model (of any kind) is the responsibility of the user and not the creator", "## Contact\n\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 50px !important;width: 50px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;\" ></a>\n\t\n\t\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 50px !important;width: 50px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;\" ></a>\n\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 50px !important;width: 50px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;\" ></a>" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "# AVA-Llama-3\nFine-Tuned Llama 3 Persian Large Language Model LLM / Persian Llama 3", "# AVA-Llama-3 / Persian Llama-3 / Persian Llama 3\n\n\n <img src=\"Llama 3.png\" height=\"600\" width=\"940\" >", "### This Repository Contains Documents for Fine-Tuned Llama 3 Persian Large Language Model(LLM) Called AVA-Llama-3\n(Still in progress)\n\n-------------------------------------------------", "### Dataset used:\n\nTo Be Done\n\n-------------------------------------------------", "### Usage:\n\nAll models are hosted in HuggingFace, and here is the code for inference:\n\n\n\nYou can also use colab link in the jupyter file\n\n-------------------------------------------------", "## QA Comparision to other models:\n\n <img src=\"URL\" height=\"600\" width=\"940\" >\n\n\nAVA-Llama-3 vs AVA-V4 vs AVA-V2 vs Maral vs Llama", "### Q1: \n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> ایلان ریو ماسک در پرتوریا، آفریقای جنوبی به دنیا امده است </td>\n <td> </td>\n </tr>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> ایلان ریو ماسک در پرتوریا، آفریقای جنوبی به دنیا امده است. او در 18 سالگی به کانادا رفت </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> ایلان ماسک در پرتوریا، آفریقای جنوبی به دنیا آمده است. او در 18 سالگی به کانادا رفت </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> ایلان ماسک در پرتوریا، آفریقای جنوبی به دنیا آمده است </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> اگر او در کجا به دنیا امده باشد، چه کسی او را در کجا می‌دانست؟ </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n--------------------------------------------------------", "### Q2: \n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> این یک بازی با کلمات است که به ما کمک می کند تا به یک پاسخ عجیب برسیم. پاسخ این است که سه قاتل باقی می مانند. دلیل این است که یکی از قاتلان وارد اتاق می شود و یکی از قاتلان دیگر را می کشد، اما هیچ کس اتاق را ترک نمی کند. بنابراین، سه قاتل باقی می مانند.\n </td>\n <td> </td>\n </tr>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> پاسخ: در اتاق هنوز یک قاتل باقی مانده است.\n استدلال: از هر دو قاتل یکی در اتاق باقی مانده است. زیرا هر کدام یکی را می کشد و هیچ کدام از اتاق ترک نمی کند. پس از کشتن دیگری، یکی از قاتلان باقی مانده در اتاق هست </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> در این سوال، اولیه استدلال به این صورت است که در یک اتاق سه قاتل هستند. واقعیت این است که یکی از آنها را می کشد و یکی از آنها را نمی کشد. بنابراین، هیچ کس اتاق را ترک نمی کند و دو قاتل در اتاق باقی مانده است.\n\nاگر در این سوال به استدلال بیشتری افتد، می توانیم ببینیم که هر دو قاتل هم در اتاق هستند و هیچ کدام را نمی کشند. اما در این استدلال اصلی و اولیه، تعداد قاتل های باقی مانده در اتاق به دو تقریباً این است. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td>در این سوال سه قاتل در یک اتاق وجود دارد و یکی از آنها را می کشد. هیچ کس اتاق را ترک نمی کند، بنابراین دو قاتل باقی مانده است. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> این استدلال می تواند به یک اتاق با چهار قاتل در آن باشد. یک قاتل وارد اتاق می شود و یکی از آنها را می کشد. هیچ کس اتاق را ترک نمی کند چند قاتل در آن باقی مانده است. این استدلال می تواند به یک اتاق با چهار قاتل در آن باشد که یک قاتل وارد اتاق می شود و یکی از آنها را می کشد و هیچ کس اتاق را ترک نمی کند چند قاتل در آن باقی مانده است. می تواند به یک اتاق با چهار قاتل در آن باشد که یک قاتل وارد اتاق می شود و یکی از آنها را می کشد و هیچ کس اتاق را ترک نمی کند چند قاتل در آن باقی مانده است. می تواند به یک اتاق با چهار قاتل در آن باشد که یک قاتل وارد اتاق می شود و یکی از آنها را می کشد و هیچ کس اتاق را\n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n----------------------------------------------------------", "### Q3: \n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> \n ملکه الیزابت یک شاهزاده بریتانیایی است که از ۱۹۵۲ تا ۲۰۲۲ بر تخت سلطنت بریتانیا سلطنت کرد. او در ۲۱ آوریل 1926 در لندن متولد شد و در ۸ سپتامبر 2022 در سن ۹۶ سالگی در باکینگهام پالاس درگذشت. ملکه الیزابت دوم طولانی‌ترین سلطنت بریتانیا را در اختیار داشت و در طول سلطنت خود بریتانیا را از جنگ جهانی دوم تا جنگ سرد و سپس به دوران مدرن رهبری کرد. او همچنین در طول سلطنت خود به عنوان یک نماد ملی و یک نماد برای بریتانیا شناخته شد. ملکه الیزابت دوم همچنین به عنوان ملکه کانادا، استرالیا، نیوزیلند و سایر کشورهای مشترک‌المنافع بریتانیا نیز شناخته می‌شود. </td>\n <td> <h1> </h1> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> ملکه ایلزابت بولنتن بود که در سال 1533 تا 1558 در انگلستان دور بود. او در زمان بود که انگلستان در دوران تاریخی به عنوان یک کشور به پیشرفت بیشتری رسید </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> ملکه الیزابت یک شهرستان در انگلستان است. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> ملکه الیزابت یک کشور در جنوب آفریقا است که در سال 1910 به این نام تعیین شد. این کشور به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینکه در سال 1910 به این نام تعیین شد، به دلیل اینک </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> ملکه الیزابت ریاضی ایرانی است که در سال ۱۹۴۷ در ایران تولید شد. این ملکه در سال ۱۹۶۷ در ایران تولید شده و در سال ۱۹۷۰ در ایران و در سال ۱۹۷۱ در امریکا تولید شده و در سال ۱۹۷۲ در ایران و در سال ۱۹۷۳ در امریکا تولید شده و در سال ۱۹۷۴ در ایران و در سال ۱۹۷۵ در امریکا تولید شده و در سال ۱۹۷۶ در ایران و در سال ۱۹۷۷ در امریکا تولید شده و در سال ۱۹۷۸ در ایران و در سال ۱۹۷۹ در امریکا تولید شده و در سال ۱۹۸۰ در ایران و در سال ۱۹۸۱ در امریکا تولید شده و در سال ۱۹۸۲ در ایران و در سال ۱۹۸۳ در امریکا تولید شده و در سال ۱۹۸۴ در ایران و در سال ۱۹۸۵ در امریک\n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n-----------------------------------------------------------------", "### Q4: \n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> منظور شما این است که توپ در داخل لیوان برعکس قرار دارد و سپس لیوان را برداشته و داخل مایکروویو قرار می دهید. در این صورت، توپ در داخل مایکروویو قرار دارد و برعکس لیوان نیست.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td>", "### Assistant: اگر یک توپ داخل یک لیوان برعکس باشد، و ما لیوان را برداشته و داخل مایکروویو بگذاریم، توپ داخل مایکروویو خواهد بود. در این واقعیت، توپ در داخل مایکروویو قرار داشته و لیوان را برداشته شده است </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> اگر یک توپ داخل یک لیوان باشد، و ما لیوان را برداشته و داخل مایکروویو بگذاریم، توپ داخل مایکروویو خواهد بود. این تشکیل اندازه گیری شده است و در فیزیک و ارتباطات است، و این تشکیل اندازه گیری شده به عنوان \"توپ داخل لیوان در داخل مایکروویو\" شناسایی می شود. این تشکیل اندازه گیری شده در فیزیک و ارتباطات به عنوان یک نمونه از انواع تشکیلات اندازه گیری شده ارائه می شود که توسط جهان در حال ارتباط است.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> اگر یک توپ داخل یک لیوان برعکس باشد و ما لیوان را برداشته و داخل مایکروویو بگذاریم، توپ داخل مایکروویو خواهد بود. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> توپ داخل یک لیوان یا مایکروویو است که در آن می توان برای انجام کارهای مختلف استفاده کنید. لیوان یک ماشین است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک ماشین است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آن می توان برای انجام کارهای مختلف استفاده کنید. مایکروویو یک توپ داخل است که در آ </td>\n <td> </td>\n </tr>\n</table>\n\n\n-----------------------------------------------------------------", "### Q5: \n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <td> AVA-Llama-3 </td>\n <td> \n\nدر تاریکی شب، صدای قلبم می‌زند\nدلشکسته و بی‌امید، در تاریکی شب\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\nاما در تاریکی شب، نمی‌توانم به تو برسم\nمن می‌خواهم به تو برسم، به تو که هستی\n </td>\n <td> \n <tr>\n <td> AVA-V4 </td>\n <td> بله، می توانم یک شعر با دقیقا پنجاه کلمه برای شما بسازم. اینجا یک شعر با پنجاه کلمه ای که برای شما نوشتم:\n\nدر جنگل دیدار کردم یک گربه سیاه\nکه در دست دو گربه سبز قرار داشت\nاز آن که در جنگل زمانه های گذشته\nگربه ها در دست دیگران قرار گرفت </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> بله، می‌توانید یک شعر با دقیقا پنجاه کلمه بساز. اینجا یک نمونه شعر با پنجاه کلمه ایده آمده است:\n\nدر دل آب و هوای زمان،\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\n\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\nدر دل آب و هوای زمان،\n\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\nدر دل آب و هوای زمان،\n\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\nدر دل آب و هوای زمان،\n\nدر دل آفتاب و شب،\nدر دل گل و گوشت،\nدر دل عشق و دوری،\nدر دل آب و هوای زمان. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> در اینجا یک شعر با دقیقا پنجاه کلمه وجود دارد:\n\"با دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه،\nبا دقیقا پنجاه کلمه، </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> بله، شعر یک نوع از مواد مختلف است که در معروف شعر با نام شعر انسان به اندازهٔ ۱۰۰۰ کلمه در مقابل ۱۰۰۰ کلمه در مقابس شعر حیوانات و ۱۰۰۰ کلمه در مقابس شعر گیاهان و ۱۰۰۰ کلمه در مقابس شعر گیاهان با نام شعر گیاهان انسان و ۱۰۰۰ کلمه در مقابس شعر گیاهان دیگر و ۱۰۰۰ کلمه در مقابس شعر دیگر انسان و ۱۰۰۰ کلمه در مقابس شعر دیگر انسان و ۱۰۰۰ کلمه در مقابس شعر دیگر حیوانات و ۱۰۰۰ کلمه در مقابس شعر دیگر گیاهان و ۱۰۰۰ کلمه در مقابس شعر دیگر گیاهان انسان و ۱۰۰۰ کلمه در مقابس شعر دیگر حیوانات و ۱۰۰۰ کلمه در مقابس شعر دیگر گیاهان و ۱۰۰۰ کلمه در مقابس شعر دیگ </td>\n <td> </td>\n </tr>\n</table>\n\n-----------------------------------------------------------------", "### Q6: \n\n\n\n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\t <tr>\n <td> AVA-Llama-3 </td>\n <td>\n\t \nیک نامه خطاب به رییس شرکت من بنویسید که من در حال استعفا و ترک شرکت هستم.\n\n[Your Name]\n[Your Address]\n[City, State, ZIP]\n[Date]\n\n[CEO's Name]\n[Company Name]\n[Company Address]\n[City, State, ZIP]\n\nDear [CEO's Name],\n\nI am writing to inform you that I have decided to resign from my position as [Position] at [Company Name], effective [Date of Last Day of Work]. This was not an easy decision for me to make, as I have truly enjoyed my time at the company and have been grateful for the opportunities that I have been given.\n\nHowever, I have come to realize that it is time for me to move on and explore new opportunities. I am grateful for the experience and skills that I have gained during my time at [Company Name], and I am confident that they will serve me well in my future endeavors.\n\nI want to thank you for the support and guidance that you have provided me during my time at the company. I appreciate the opportunities that you have given me to grow and develop as a professional, and I am grateful for the trust that you have placed in me.\n\nI will do everything I can to ensure a smooth transition of my responsibilities and complete any outstanding tasks before my departure. If there is anything specific that you would like me to focus on during my remaining time here, please let me know.\n\nThank you again for the opportunity to work at [Company Name]. I wish the company all the best for the future.\n\nSincerely,\n\n[Your Name]\n </td>\n <tr>\n <td> AVA-V4 </td>\n <td> ۱۳۹۹/۰۸/۱۴۰۰\n\nمترجم: شما باید نامه خطاب خود را به انگلیسی نوشته و از طریق این پیام به من ارسال کنید.\n\nمتوضع: در این نامه خطاب، من در حال استعفا و ترک شرکت هستم.\n\nنامه خطاب:\n\nاز انجام ناخودپسندیده و از دست دادن از شرکت خود انجام می دهم.\n\nشما باید نامه خطاب خود را به انگلیسی نوشته و از طریق این پیام به من ارسال کنید.\n\nمتوضع: در این نامه خطاب، من در حال استعفا و ترک شرکت هستم.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> اگر شما یک نامه خطاب به رییس شرکت خود بنویسید که من در حال استعفا و ترک شرکت هستم، این است که می توانید از آن استفاده کنید:\n\n نامه خطاب\n\nشماره: [شماره نامه]\n\nتاریخ: [تاریخ نوشتن نامه]\n\n[نام و نام خانوادگی رییس شرکت]\n[عنوان شرکت]\n[آدرس شرکت]\n\nشکر خوب،\n\nبه عنوان یکی از عضویان شرکت [نام شرکت]، احتمالاً اولین خبری که به شما این نامه ارسال می کنم نیست. اما در حال حاضر، من در حال استعفا و ترک از شرکت هستم و این نامه را به شما ارسال می کنم تا آن را به دنباله های دیگری اضافه کنم.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> رییس شرکت،\nمن در حال استعفا و ترک شرکت هستم. این نه به دلیل اینکه به شرکت نیاز ندارم، بلکه به دلیل اینکه با این کار به خود و به شرکت اشتباه کرده ام. این به دلیل این است که از طرف شرکت به دنبال ارزش های بیشتری بودم و از طرف من به دنبال ارزش های بیشتری بودم. اما این ارزش ها به طور متوقف شده است و از طرف شرکت و من ارزش های بیشتری به دست نمی آید. این به دلیل این است که ارزش های بیشتری به طور متوقف شده است و از طرف شرکت و من ارزش های بیشتری به دست نمی آید. اما این ارزش ها به طور متوقف شده است و از طرف شرکت و من ارزش های بیشتری به دست نمی آید. اما این ارزش ها به طور متوقف شده است و از طرف شرکت و من ارزش های بی </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> ### Assistant: خوب است که شرکت را ترک کردید. من به شرکت باز کردم و امروز در شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال استعفا و می خوام به شرکت باز شدم. من در حال\n </td>\n <td> </td>\n </tr>\n</table>\n\n-----------------------------------------------------------------", "### Q7: \n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> رییس جمهور آمریکا در سال 1996، بیل کلینتون بود </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> رییس جمهور آمریکا در سال 1996 بود بیل کلینتن. </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> در سال 1996، رییس جمهور آمریکا بیل کلنتن بود. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> رییس جمهور آمریکا در سال 1996 بیل کلنتن بود. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> رییس جمهور آمریکا در سال ۱۹۹۶ به نام بلار بورنس بود. او در ۱۹۹۲ از رییس دادگاه آمریکا و در ۱۹۹۴ از رییس دادگاه آمریکا شد و در ۱۹۹۶ به رییس جمهور آمریکا اتحادیه شد. او در ۱۹۹۷ به رییس جمهور آمریکا دوم شد و در ۱۹۹۹ به رییس جمهور آمریکا سوم شد. او در ۱۹۹۱ به رییس دادگاه آمریکا شد و در ۱۹۹۲ به رییس دادگاه آمریکا شد و در ۱۹۹۳ به رییس جمهور آمریکا شد. او در ۱۹۹۴ به رییس دادگاه آمریکا شد و در ۱۹۹۵ به رییس جمهور آمریکا شد. او در ۱۹۹۶ به رییس جمهور آمریکا شد و در ۱۹۹۷ به رییس جمهور آمریکا دوم شد. او در ۱۹۹۸ به رییس جمهور آمریکا سوم شد. او در ۱۹۹ </td>\n <td> </td>\n </tr>\n</table>\n\n-----------------------------------------------------------------", "### Q8: \n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n<tr>\n <td> AVA-Llama-3 </td>\n <td> \nاگر پنج پیراهن 4 ساعت طول می کشد تا خشک شود، می توانیم به این نتیجه برسیم که هر پیراهن 4/5 ساعت طول می کشد تا خشک شود. برای پیدا کردن زمان لازم برای خشک شدن 20 پیراهن، می توانیم از این رابطه استفاده کنیم:\n\n4/5 * 20 = 16\n\nبنابراین، 20 پیراهن 16 ساعت طول می کشد تا خشک شود.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> اگر پنج پیراهن 4 ساعت طول بکشد تا خشک شود، 20 پیراهن به اندازه ی یک ساعت طول می کشد تا خشک شود. این اطمینان را دارید که هر پیراهن در نتیجه 20 پیراهن 4 ساعت طول می کشد تا خشک شود.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> اگر پنج پیراهن 4 ساعت طول بکشد تا خشک شود، 20 پیراهن به اندازه ی شبیه ساعت بکشند تا خشک شود. برای حل مورد نیاز، شما می توانید از فرمول زیر استفاده کنید:\n\nطول بکشنده شدن 20 پیراهن = طول بکشنده شدن 5 پیراهن × (شماره پیراهن / شماره پیراهن)\n\nطول بکشنده شدن 20 پیراهن = 4 ساعت × (20 / 5)\n\nطول بکشنده شدن 20 پیراهن = 4 ساعت × 4\n\nطول بکشنده شدن 20 پیراهن = 16 ساعت\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> برای حل این پرسش، ابتدا باید از این اطلاعات استفاده کنید که پنج پیراهن 4 ساعت طول می کشد تا خشک شود. از این اطلاعات باید برای حل پرسش استفاده کنید.برای حل این پرسش، ابتدا باید از این اطلاعات استفاده کنید که پنج پیراهن 4 ساعت طول می کشد تا خشک شود. از این اطلاعات باید برای حل پرسش استفاده کنید.برای حل این پرسش، ابتدا باید از این اطلاعات استفاده کنید که پنج پیراهن 4 ساعت طول می کشد تا خشک شود. از این اطلاعات باید برای حل پرسش استفاده کنید.برای حل این پرسش، ابتدا باید از این اطلاعات استفاده کنید که پنج پیراهن 4 ساعت طول می کشد تا خشک شود. از این اطلاعات باید برای حل پرسش استفاده کنید.برای حل این پرسش\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پنج پیراهن چه طول دارند؟ پن\n </td>\n <td> </td>\n </tr>\n</table>\n\n-----------------------------------------------------------------", "### Q9: \n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> \nاین مسابقه بسیار جالب است! با در نظر گرفتن سرعت هر دو، می توانیم سرعت آنها را به یک واحد تبدیل کنیم. سرعت خرگوش 10 کیلومتر بر ساعت است، بنابراین سرعت لاک پشت را به 10 کیلومتر بر ساعت تبدیل می کنیم. در این صورت، سرعت لاک پشت 0.1 کیلومتر بر ساعت است.\n\nدر این صورت، سرعت خرگوش 10 کیلومتر بر ساعت و سرعت لاک پشت 0.1 کیلومتر بر ساعت است. بنابراین، خرگوش برنده مسابقه است. لاک پشت به دلیل سرعت بسیار پایین خود نمی تواند با خرگوش رقابت کند.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> \nبرای حل این مسئله، ما باید سرعت هر دو از جمله را بررسی کنیم و سپس آن‌ها را با هم مقایسه کنیم. سرعت خرگوش 10 کیلومتر در 1 دقیقه است و سرعت لاک پشت 1 کیلومتر در 10 دقیقه است.\n\nمسابقه 100 متری از آنجایی که خرگوش سریع‌تر از لاک پشت است، خرگوش به طور کامل قابلیت برداشتن این جاهزه را دارد. بنابراین، خرگوش برنده مسابقه است.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> برای مشاهده نتیجه مسابقه، باید سرعت خرگوش و لاک پشت را به هم انداز کنیم. سرعت خرگوش 10 کیلومتر در ساعت و لاک پشت 1 کیلومتر در ساعت است. برای اینکه نتیجه مسابقه را بدست آوریم، باید 100 متر را به سرعت خرگوش و لاک پشت تبدیل کنیم.\n\n100 متر / 10 کیلومتر در ساعت = 10 دقیقه\n\n100 متر / 1 کیلومتر در ساعت = 100 دقیقه\n\nبنابراین، خرگوش 10 دقیقه و لاک پشت 100 دقیقه می گذرد تا 100 متر را پیروز کنند. بنابراین، برنده مسابقه خرگوش است. </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> برنده مسابقه خرگوش است. با سرعت 10 کیلومتر در ساعت، خرگوش به انتهای 100 متر در 10 دقیقه به پایان می رسد، در حالی که لاک پشت با سرعت 1 کیلومتر در ساعت به پایان می رسد و به انتهای 100 متر در 100 دقیقه به پایان می رسد.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> با این نوع سوال می توان به نوع سوال با نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با این حال، اگر با این سوال می توان به نوع جواب انتظار داشته باشید، می توان به نوع جواب انتظار داشته باشید. با \n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n-----------------------------------------------------------------", "### Q9: \n\n\n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr> \n <tr>\n <td> AVA-Llama-3 </td>\n <td> \n\t Here is a JSON file containing the information you requested:\n\n\n\nIf you want to add more information to each person, you can add additional properties to the JSON object. For example:\n\n\n\nLet me know if you have any other questions!\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V4 </td>\n <td> ساختن یک فایل json با این اطلاعات از قبل ایجاد شده است. این فایل json شامل اطلاعات دو نفر از افراد مانند bob و jon است. برای افزودن اطلاعات موجود در فایل json یا ایجاد جدید، باید از کاماند زیر استفاده کنید:\n\n\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> ساده، این کار را با استفاده از زبان پروگرامینگ Python می‌توانیم انجام دهیم. ابتدا، یک فایل json با نام \"URL\" به دست آورید. سپس، از کلاس dict در Python استفاده کرده، یک فایل json را ایجاد می‌کنیم که اطلاعات این چهار نام را با این فرمت برمی‌چارد:\n\nimport json\n\npeople = {\n \"bob\": {},\n \"jon\": {},\n \"mary\": {},\n \"rich\": {}\n}\n\nwith open(\"URL\", \"w\") as f:\n URL(people, f) </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> با استفاده از یک فایل json به این صورت ایجاد کنید:\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> امروز چه نوع افراد را در مدرسه دانشگاه می‌دانید؟ می‌خوام بی‌نهایت افراد دو نوع را در مدرسه دانشگاه می‌دانم. این افراد با نام برادر و مرد است. برادر در مدرسه دانشگاه در دوره اول دوست دارند و مرد در دوره دوم دوست دارند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می‌خوام این افراد در مدرسه دانشگاه در حال تحصیل هستند. می\n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n-----------------------------------------------------------------", "### Q9: \n\n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n <tr>\n <td> AVA-Llama-3 </td>\n <td> \nیک اسکریپت پایتون برای چاپ 1 تا 10 به این صورت می توان بنویسید:\n\nاین اسکریپت یک حلقه برای loop 1 تا 10 ایجاد می کند و هر بار در هر دور، عدد فعلی را چاپ می کند.\n </td>\n <td> </td>\n <tr>\n <td> AVA-V4 </td>\n <td> این یک اسکریپت پایتون است که از 1 تا 10 چاپ می کند:\n \n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> یک اسکریپت پایتون برای چاپ 1 تا 10 به شرح زیر برمی گرد:\n\nاین اسکریپت با استفاده از لوپ (loop) در زمان اجرایی به نام 'range(1, 11)' از 1 تا 10 عدد را مشاهده می کند و هر عدد را با استفاده از فنکشن 'print()' چاپ می کند.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> در اینجا یک اسکریپت پایتون برای چاپ 1 تا 10 وجود دارد: </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> ### Assistant: این اسکریپت چاپ کنید و بعد از انجام چاپ، می توانید باز کنید. اگر نیاز به این اسکریپت ندارید، با موفقیت این اسکریپت را از منبع اصلی دانستید. اگر نیاز دارید، با موفقیت این اسکریپت را از منبع اصلی دانستید. اگر نیاز ندارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز دارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز ندارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز دارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز ندارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز دارید، می توانید این اسکریپت را از منبع اصلی دانستید. اگر نیاز ندارید\n </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n\n-----------------------------------------------------------------", "### Q9: \n\n\n\n\n\n\n\n\n\n\n\n<table width=\"300\">\n <tr>\n <td> Model </td>\n <td> Answer </td>\n <td> Rating </td>\n </tr>\n\n <tr>\n <td> AVA-Llama-3 </td>\n <td> \n\t \nدر اینجا یک تابع URL برای دریافت رشته و تقسیم آن بر اساس فاصله ارائه می شود:\n\nاین تابع از روش 'split' درbuilt-in 'String' در URL استفاده می کند تا رشته را بر اساس فاصله (که با کاراکتر فاصله 'space' مشخص می شود) تقسیم کند. سپس آرایه ای از رشته های جدا شده را بازمی گرداند.\n\nاگر می خواهید رشته را بر اساس فاصله خاصی تقسیم کنید، می توانید از یک رشته به عنوان دومین آرگومان به روش 'split' پاس دهید. برای مثال، برای تقسیم رشته بر اساس فاصله با فاصله 2 کاراکتر، می توانید از رشته ''\\u0020'' استفاده کنید:\n\nاین تابع رشته را بر اساس فاصله 2 کاراکتر (که با کاراکتر فاصله ''\\u0020'' مشخص می شود) تقسیم می کند. سپس آرایه ای از رشته های جدا شده را بازمی گرداند.\n </td>\n <td> </td>\n </tr>\n \n <tr>\n <td> AVA-V4 </td>\n <td> یک تابع URL برای دریافت رشته و تقسیم آن بر اساس فاصله به شما این کد پیوند می دهم:\n\n\n\nاین کد تابع 'splitStringBySpace' برچسب کرده است که رشته وارد شده را با فاصله بنویسید. این کد را در نظر دهید و اگر نیاز به تغییرات دیگری دارید، لطفا بگویید.\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> AVA-V2 </td>\n <td> یک تابع node js برای دریافت رشته و تقسیم آن بر اساس فاصله به شکل زیر می تواند باشد:\n\n\n\nدر این کد، ابتدا به کار برده 'readline' بخش های فایل ورودی را دریافت می کند. سپس تابع 'splitStringBySpace' را برای تقسیم رشته ای که دریافت کرده ایم استفاده می کند. این تابع رشته را با فاصله های فارسی راهنمایی دار تقسیم می کند و به صورت مجزا برمی گرداند. نتیجه این کار این است که هر کلمه در یک عنصر مجزا قرار می گیرد. سپس این عنصرها را در کنسول نمایش می دهد.\n\n </td>\n <td> </td>\n </tr>\n <tr>\n <td> Maral </td>\n <td> در اینجا یک تابع برای دریافت رشته و تقسیم آن بر اساس فاصله وجود دارد:\nدر این تابع، رشته وارد شده به عنوان پارامتر وارد می شود. در طول تابع، رشته به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عنوان یک رشته ای به عن </td>\n <td> </td>\n </tr>\n <tr>\n <td> Llama </td>\n <td> - </td>\n <td> </td>\n </tr>\n</table>\n\n\n\n\n\n\n-----------------------------------------------------------------", "## License\n\n\n\nReleased Jan 30, 2024 by Mehdi Hosseini Moghadam\n\nAttention ️: The user is responsible for using AVA-Llama-3 / Persian Llama 3\n\nAny misuse of the model (of any kind) is the responsibility of the user and not the creator", "## Contact\n\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 50px !important;width: 50px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;\" ></a>\n\t\n\t\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 50px !important;width: 50px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;\" ></a>\n\n<a href=\"URL target=\"_blank\"><img src=\"URL alt=\"Buy Me A Coffee\" style=\"height: 50px !important;width: 50px !important;box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;-webkit-box-shadow: 0px 3px 2px 0px rgba(190, 190, 190, 0.5) !important;\" ></a>" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

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

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #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" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: hashemalsaket\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: hashemalsaket\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

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

[ "1910.09700" ]

[]

[ "# 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 #gpt_neox #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" ]

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "en", "base_model:ChaoticNeutrals/IQ_Test_l3_8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# jeiku/Chaos_RP_l3_8B-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'jeiku/Chaos_RP_l3_8B' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "TAGS\n#transformers #gguf #llama-cpp #gguf-my-repo #en #base_model-ChaoticNeutrals/IQ_Test_l3_8B #license-apache-2.0 #endpoints_compatible #region-us \n", "# jeiku/Chaos_RP_l3_8B-Q4_K_M-GGUF\nThis model was converted to GGUF format from 'jeiku/Chaos_RP_l3_8B' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

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

[ "1910.09700" ]

[]

[ "# 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 #phi #text-generation #custom_code #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" ]

[ "gguf", "pruna-ai", "region:us" ]

[]

[]

[ "### 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\n\n```\n\n* Option D - Running with LangChain\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nConfigurations\n--------------\n\n\nThe configuration info are in 'smash\\_config.json'.\n\n\nCredits & License\n-----------------\n\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.\n\n\nWant to compress other models?\n------------------------------\n\n\n* Contact us and tell us which model to compress next here.\n* Request access to easily compress your own AI models here." ]

[ "TAGS\n#gguf #pruna-ai #region-us \n", "### 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\n\n```\n\n* Option D - Running with LangChain\n\n\nHere are guides on using llama-cpp-python and ctransformers with LangChain:\n\n\n* LangChain + llama-cpp-python\n* LangChain + ctransformers\n\n\nConfigurations\n--------------\n\n\nThe configuration info are in 'smash\\_config.json'.\n\n\nCredits & License\n-----------------\n\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.\n\n\nWant to compress other models?\n------------------------------\n\n\n* Contact us and tell us which model to compress next here.\n* Request access to easily compress your own AI models here." ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.41e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #trl #reward-trainer #generated_from_trainer #base_model-google/gemma-2b #license-gemma #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.41e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

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

[]

[]

[ "# COLX_563_lab4_intent\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0398\n- F1 Score: 0.9952", "## 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: 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- Transformers 4.39.1\n- Pytorch 2.4.0.dev20240325\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# COLX_563_lab4_intent\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0398\n- F1 Score: 0.9952", "## 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: 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- Transformers 4.39.1\n- Pytorch 2.4.0.dev20240325\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: Chandrateja\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #conversational #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: Chandrateja\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.41e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #trl #reward-trainer #generated_from_trainer #base_model-google/gemma-2b #license-gemma #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1.41e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1.0", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.38.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

[ "# 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 #t5 #text2text-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" ]

[ "transformers", "safetensors", "distilbert", "generated_from_trainer", "base_model:allistair99/distilbert-base-uncased-distilled-squad-finetuned-SRH-v1", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "# distilbert-base-uncased-distilled-squad-BiLSTM-finetuned-squad-Step1-v2\n\nThis model is a fine-tuned version of allistair99/distilbert-base-uncased-distilled-squad-finetuned-SRH-v1 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: 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- lr_scheduler_warmup_steps: 500\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #distilbert #generated_from_trainer #base_model-allistair99/distilbert-base-uncased-distilled-squad-finetuned-SRH-v1 #license-apache-2.0 #endpoints_compatible #region-us \n", "# distilbert-base-uncased-distilled-squad-BiLSTM-finetuned-squad-Step1-v2\n\nThis model is a fine-tuned version of allistair99/distilbert-base-uncased-distilled-squad-finetuned-SRH-v1 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: 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- lr_scheduler_warmup_steps: 500\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "internvl_chat", "feature-extraction", "visual-question-answering", "custom_code", "dataset:laion/laion2B-en", "dataset:laion/laion-coco", "dataset:laion/laion2B-multi", "dataset:kakaobrain/coyo-700m", "dataset:conceptual_captions", "dataset:wanng/wukong100m", "arxiv:2312.14238", "license:mit", "8-bit", "region:us" ]

[ "2312.14238" ]

[]

[]

[ "TAGS\n#transformers #safetensors #internvl_chat #feature-extraction #visual-question-answering #custom_code #dataset-laion/laion2B-en #dataset-laion/laion-coco #dataset-laion/laion2B-multi #dataset-kakaobrain/coyo-700m #dataset-conceptual_captions #dataset-wanng/wukong100m #arxiv-2312.14238 #license-mit #8-bit #region-us \n" ]

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

[ "1910.09700" ]

[]

[ "# 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 #stablelm #text-generation #conversational #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" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \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", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \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", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \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", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \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", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "diffusers", "safetensors", "nsfw", "diffusers:PixArtAlphaPipeline", "region:us" ]

[]

[]

[]

[ "TAGS\n#diffusers #safetensors #nsfw #diffusers-PixArtAlphaPipeline #region-us \n" ]

[ "transformers", "pytorch", "llama", "text-generation", "code", "chatml", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "has_space", "text-generation-inference", "region:us" ]

[]

[]

[ "# LocalCoder-v0\n\nA tiny (1.1B) parameter SLM for programming based on TinyLlama. It shows promising early results but has not been extensively tested.\n\nThe model was trained using supervised fine tuning (SFT) for 1 epoch on a large dataset of conversational code generation. While the model prioritizes code generation, it also performs decently at general conversational tasks and multi-round conversations. The model has the ability to correct code it has generated based on feedback given by the user, and can follow system prompts well.\n\nThe model can generate code in multiple programming languages but specializes in Python. This model currently only works with English.\n\nThis is an early open research preview. The model is still in an early stage. If you enjoy this model, please let me know - I'm thinking about training this model on more epochs or training a larger model.", "## Notes\n\nDespite this being a decent model for its size, it tends to tell you what the output of the code would be (such as \"When running this code, you will get ...\"). This can be problematic as the model will hallucinate on the expected result. You can avoid this by explicitly telling it not to tell you what the result of the code it generates will be in the system prompt.\n\nAdditionally, because of its size, it isn't an amazing model. I'm hoping to train a 7B model in the future, since quantizing seems better than SLMs (at least at this stage).", "## Usage\n\nThis model uses the ChatML conversation format:\n\n\n\nYou may load the model using Hugging Face Transformers after you have logged in to Hugging Face:\n\n\n\n<details>\n<summary><b>Interactive chatbot example</b></summary>\n\n\n\n</details>", "## Examples\n\n<details>\n<summary><b>Hello, how are you doing today?</b></summary>\n\nHello! I'm doing great! How can I help you today?\n\n</details>\n<details>\n<summary><b>How should I calculate the Fibonacci sequence using the Python programming language?</b></summary>\n\nThe Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. Here is a simple Python program that calculates the Fibonacci sequence:\n\n\n\nIn this program, 'fibonacci(n)' calculates the nth Fibonacci number. The function 'fibonacci(n)' checks if 'n' is less than or equal to 1, in which case it returns 'n'. Otherwise, it initializes 'a' and 'b' to 0 and 1 respectively, and then iterates from 2 to 'n + 1' (inclusive) to calculate the Fibonacci number at each step.\n\nThe output of this program will be the 10th Fibonacci number, which is 55.\n\n</details>\n<details>\n<summary><b>Write me an essay about AI.</b></summary>\n\nTitle: AI: The Future of Our World\n\nIntroduction:\nArtificial Intelligence (AI) is a rapidly evolving field that has the potential to revolutionize our world in numerous ways. This essay will explore the various aspects of AI, its impact on our daily lives, and its potential for the future.\n\n1. AI Basics:\nArtificial Intelligence is a complex field that involves the development of intelligent systems capable of performing tasks that typically require human intelligence. AI systems are designed to learn from data and make decisions based on the information they receive. This process is known as machine learning, and it is the foundation of AI.\n\n2. AI Applications:\nAI has numerous applications in various fields. For example, AI is used in healthcare to diagnose diseases, in finance to detect fraud, and in transportation to optimize routes and schedules. In the realm of entertainment, AI is used to create and manage virtual reality experiences.\n\n3. Ethical Considerations:\nWhile AI has many benefits, it also raises ethical concerns. For instance, AI systems can be trained to discriminate against certain groups, leading to issues such as bias and discrimination. Additionally, AI systems can be used to manipulate public opinion, which raises concerns about transparency and accountability.\n\n4. AI Ethics and Governance:\nTo address these ethical concerns, governments and organizations are working on developing AI ethics guidelines and regulations. These guidelines aim to ensure that AI systems are used responsibly and in a way that benefits society as a whole.\n\n5. AI and Jobs:\nAI has the potential to displace certain jobs in the future. For example, AI systems can be used to automate tasks that require human intelligence, such as data entry or customer service. However, this could also lead to job losses in certain sectors.\n\n6. AI and Privacy:\nAI systems can be used to track individuals' movements and behavior, which raises concerns about privacy. For instance, AI systems can be used to monitor people's online activity, which could lead to concerns about privacy and data security.\n\n7. AI and Security:\nAI systems can be used to detect and prevent cyber attacks, which is crucial in today's digital age. However, AI systems can also be used to infiltrate systems and steal sensitive information. Therefore, it is essential to develop robust security measures for AI systems.\n\n8. AI and Ethics:\nAI ethics is a growing field that focuses on the responsible use of AI systems. This includes issues such as transparency, accountability, and fairness. As AI systems become more sophisticated, it is crucial to develop ethical guidelines and regulations to ensure that AI systems are used responsibly and in a way that benefits society as a whole.\n\nConclusion:\nIn conclusion, AI is a powerful and complex field that has the potential to revolutionize our world. While AI has many benefits, it also raises ethical concerns. To address these concerns, governments and organizations are working on developing AI ethics guidelines and regulations. AI systems can be used to automate tasks, but this could also lead to job losses in certain sectors. Additionally, AI systems can be used to track individuals' movements and behavior, which raises concerns about privacy and data security. However, AI systems can also be used to detect and prevent cyber attacks, which is crucial in today's digital age. As AI systems become more sophisticated, it is essential to develop ethical guidelines and regulations to ensure that AI systems are used responsibly and in a way that benefits society as a whole.\n\n</details>", "## Risks\n\nThis model has not been extensively tested and may generate incorrect or inaccurate code. Always check before using generated code.\n\nThis model has not been \"alignment-tuned\" and does not have any safety or guardrail mechanisms. You are responsible for your usage of the model.", "## License\n\nThis model is open-access, not open sourced. Unfortunately, due to several factors (including the training dataset), this model may not be used commercially. You may run it locally, and the author(s) place no restriction on the outputs of the model. However, you are responsible for your usage of the model. This model may not be used to generate illegal or harmful content (including but not limited to malware, viruses, etc)." ]

[ "TAGS\n#transformers #pytorch #llama #text-generation #code #chatml #conversational #license-other #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n", "# LocalCoder-v0\n\nA tiny (1.1B) parameter SLM for programming based on TinyLlama. It shows promising early results but has not been extensively tested.\n\nThe model was trained using supervised fine tuning (SFT) for 1 epoch on a large dataset of conversational code generation. While the model prioritizes code generation, it also performs decently at general conversational tasks and multi-round conversations. The model has the ability to correct code it has generated based on feedback given by the user, and can follow system prompts well.\n\nThe model can generate code in multiple programming languages but specializes in Python. This model currently only works with English.\n\nThis is an early open research preview. The model is still in an early stage. If you enjoy this model, please let me know - I'm thinking about training this model on more epochs or training a larger model.", "## Notes\n\nDespite this being a decent model for its size, it tends to tell you what the output of the code would be (such as \"When running this code, you will get ...\"). This can be problematic as the model will hallucinate on the expected result. You can avoid this by explicitly telling it not to tell you what the result of the code it generates will be in the system prompt.\n\nAdditionally, because of its size, it isn't an amazing model. I'm hoping to train a 7B model in the future, since quantizing seems better than SLMs (at least at this stage).", "## Usage\n\nThis model uses the ChatML conversation format:\n\n\n\nYou may load the model using Hugging Face Transformers after you have logged in to Hugging Face:\n\n\n\n<details>\n<summary><b>Interactive chatbot example</b></summary>\n\n\n\n</details>", "## Examples\n\n<details>\n<summary><b>Hello, how are you doing today?</b></summary>\n\nHello! I'm doing great! How can I help you today?\n\n</details>\n<details>\n<summary><b>How should I calculate the Fibonacci sequence using the Python programming language?</b></summary>\n\nThe Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. Here is a simple Python program that calculates the Fibonacci sequence:\n\n\n\nIn this program, 'fibonacci(n)' calculates the nth Fibonacci number. The function 'fibonacci(n)' checks if 'n' is less than or equal to 1, in which case it returns 'n'. Otherwise, it initializes 'a' and 'b' to 0 and 1 respectively, and then iterates from 2 to 'n + 1' (inclusive) to calculate the Fibonacci number at each step.\n\nThe output of this program will be the 10th Fibonacci number, which is 55.\n\n</details>\n<details>\n<summary><b>Write me an essay about AI.</b></summary>\n\nTitle: AI: The Future of Our World\n\nIntroduction:\nArtificial Intelligence (AI) is a rapidly evolving field that has the potential to revolutionize our world in numerous ways. This essay will explore the various aspects of AI, its impact on our daily lives, and its potential for the future.\n\n1. AI Basics:\nArtificial Intelligence is a complex field that involves the development of intelligent systems capable of performing tasks that typically require human intelligence. AI systems are designed to learn from data and make decisions based on the information they receive. This process is known as machine learning, and it is the foundation of AI.\n\n2. AI Applications:\nAI has numerous applications in various fields. For example, AI is used in healthcare to diagnose diseases, in finance to detect fraud, and in transportation to optimize routes and schedules. In the realm of entertainment, AI is used to create and manage virtual reality experiences.\n\n3. Ethical Considerations:\nWhile AI has many benefits, it also raises ethical concerns. For instance, AI systems can be trained to discriminate against certain groups, leading to issues such as bias and discrimination. Additionally, AI systems can be used to manipulate public opinion, which raises concerns about transparency and accountability.\n\n4. AI Ethics and Governance:\nTo address these ethical concerns, governments and organizations are working on developing AI ethics guidelines and regulations. These guidelines aim to ensure that AI systems are used responsibly and in a way that benefits society as a whole.\n\n5. AI and Jobs:\nAI has the potential to displace certain jobs in the future. For example, AI systems can be used to automate tasks that require human intelligence, such as data entry or customer service. However, this could also lead to job losses in certain sectors.\n\n6. AI and Privacy:\nAI systems can be used to track individuals' movements and behavior, which raises concerns about privacy. For instance, AI systems can be used to monitor people's online activity, which could lead to concerns about privacy and data security.\n\n7. AI and Security:\nAI systems can be used to detect and prevent cyber attacks, which is crucial in today's digital age. However, AI systems can also be used to infiltrate systems and steal sensitive information. Therefore, it is essential to develop robust security measures for AI systems.\n\n8. AI and Ethics:\nAI ethics is a growing field that focuses on the responsible use of AI systems. This includes issues such as transparency, accountability, and fairness. As AI systems become more sophisticated, it is crucial to develop ethical guidelines and regulations to ensure that AI systems are used responsibly and in a way that benefits society as a whole.\n\nConclusion:\nIn conclusion, AI is a powerful and complex field that has the potential to revolutionize our world. While AI has many benefits, it also raises ethical concerns. To address these concerns, governments and organizations are working on developing AI ethics guidelines and regulations. AI systems can be used to automate tasks, but this could also lead to job losses in certain sectors. Additionally, AI systems can be used to track individuals' movements and behavior, which raises concerns about privacy and data security. However, AI systems can also be used to detect and prevent cyber attacks, which is crucial in today's digital age. As AI systems become more sophisticated, it is essential to develop ethical guidelines and regulations to ensure that AI systems are used responsibly and in a way that benefits society as a whole.\n\n</details>", "## Risks\n\nThis model has not been extensively tested and may generate incorrect or inaccurate code. Always check before using generated code.\n\nThis model has not been \"alignment-tuned\" and does not have any safety or guardrail mechanisms. You are responsible for your usage of the model.", "## License\n\nThis model is open-access, not open sourced. Unfortunately, due to several factors (including the training dataset), this model may not be used commercially. You may run it locally, and the author(s) place no restriction on the outputs of the model. However, you are responsible for your usage of the model. This model may not be used to generate illegal or harmful content (including but not limited to malware, viruses, etc)." ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

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

[]

[]

[ "# Adapter 'ltuzova/classification_tapt_seq_bn_add_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset.\n\nThis adapter was created for usage with the Adapters library.", "## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:", "## Architecture & Training", "## Evaluation results" ]

[ "TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/amazon_helpfulness #region-us \n", "# Adapter 'ltuzova/classification_tapt_seq_bn_add_adapter' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/amazon_helpfulness dataset.\n\nThis adapter was created for usage with the Adapters library.", "## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:", "## Architecture & Training", "## Evaluation results" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:shrenikb/LLaMA0.75sparsity", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-shrenikb/LLaMA0.75sparsity #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 0.10.0" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:shrenikb/LLaMA0.25sparsity", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-shrenikb/LLaMA0.25sparsity #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 0.10.0" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:huggyllama/llama-7b", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-huggyllama/llama-7b #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 0.10.0" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:shrenikb/LLaMA0.75sparsity", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-shrenikb/LLaMA0.75sparsity #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 0.10.0" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:shrenikb/LLaMA0.25sparsity", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-shrenikb/LLaMA0.25sparsity #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 0.10.0" ]

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

[]

[]

[ "# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: conlan/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "TAGS\n#ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us \n", "# poca Agent playing SoccerTwos\n This is a trained model of a poca agent playing SoccerTwos\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: conlan/poca-SoccerTwos\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "peft", "region:us" ]

[]

[]

[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]

[ "TAGS\n#peft #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]

[ "trl", "safetensors", "DPO", "WeniGPT", "pt", "base_model:Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged", "license:mit", "region:us" ]

[]

[ "pt" ]

[ "# Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-1.0.25-DPO\n\nThis model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for Weni.\nDescription: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT\n\nIt achieves the following results on the evaluation set:\n{'eval_loss': 0.004782966338098049, 'eval_runtime': 14.2865, 'eval_samples_per_second': 1.96, 'eval_steps_per_second': 0.49, 'eval_rewards/chosen': 3.9168648719787598, 'eval_rewards/rejected': -6.206678867340088, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 10.123543739318848, 'eval_logps/rejected': -240.9935760498047, 'eval_logps/chosen': -117.46734619140625, 'eval_logits/rejected': -1.9027243852615356, 'eval_logits/chosen': -1.8786267042160034, 'epoch': 5.806451612903226}", "## Intended uses & limitations\n\nThis model has not been trained to avoid specific intructions.", "## Training procedure\n\nFinetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged with the following prompt:", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- per_device_train_batch_size: 1\n- per_device_eval_batch_size: 1\n- gradient_accumulation_steps: 2\n- num_gpus: 4\n- total_train_batch_size: 8\n- optimizer: AdamW\n- lr_scheduler_type: cosine\n- num_steps: 180\n- quantization_type: bitsandbytes\n- LoRA: (\"\\n - bits: 4\\n - use_exllama: True\\n - device_map: auto\\n - use_cache: False\\n - lora_r: 128\\n - lora_alpha: 64\\n - lora_dropout: 0.05\\n - bias: none\\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']\\n - task_type: CAUSAL_LM\",)", "### Training results", "### Framework versions\n\n- transformers==4.40.0\n- datasets==2.18.0\n- peft==0.10.0\n- safetensors==0.4.2\n- evaluate==0.4.1\n- bitsandbytes==0.43\n- huggingface_hub==0.22.2\n- seqeval==1.2.2\n- auto-gptq==0.7.1\n- gpustat==1.1.1\n- deepspeed==0.14.0\n- wandb==0.16.6\n- trl==0.8.1\n- accelerate==0.29.3\n- coloredlogs==15.0.1\n- traitlets==5.14.2\n- git+URL", "### Hardware\n- Cloud provided: URL" ]

[ "TAGS\n#trl #safetensors #DPO #WeniGPT #pt #base_model-Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged #license-mit #region-us \n", "# Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-1.0.25-DPO\n\nThis model is a fine-tuned version of [Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged] on the dataset Weni/wenigpt-agent-dpo-1.0.0 with the DPO trainer. It is part of the WeniGPT project for Weni.\nDescription: Experiment on DPO with other hyperparameters and best SFT model of WeniGPT\n\nIt achieves the following results on the evaluation set:\n{'eval_loss': 0.004782966338098049, 'eval_runtime': 14.2865, 'eval_samples_per_second': 1.96, 'eval_steps_per_second': 0.49, 'eval_rewards/chosen': 3.9168648719787598, 'eval_rewards/rejected': -6.206678867340088, 'eval_rewards/accuracies': 1.0, 'eval_rewards/margins': 10.123543739318848, 'eval_logps/rejected': -240.9935760498047, 'eval_logps/chosen': -117.46734619140625, 'eval_logits/rejected': -1.9027243852615356, 'eval_logits/chosen': -1.8786267042160034, 'epoch': 5.806451612903226}", "## Intended uses & limitations\n\nThis model has not been trained to avoid specific intructions.", "## Training procedure\n\nFinetuning was done on the model Weni/WeniGPT-Agents-Mistral-1.0.0-SFT-merged with the following prompt:", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-06\n- per_device_train_batch_size: 1\n- per_device_eval_batch_size: 1\n- gradient_accumulation_steps: 2\n- num_gpus: 4\n- total_train_batch_size: 8\n- optimizer: AdamW\n- lr_scheduler_type: cosine\n- num_steps: 180\n- quantization_type: bitsandbytes\n- LoRA: (\"\\n - bits: 4\\n - use_exllama: True\\n - device_map: auto\\n - use_cache: False\\n - lora_r: 128\\n - lora_alpha: 64\\n - lora_dropout: 0.05\\n - bias: none\\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']\\n - task_type: CAUSAL_LM\",)", "### Training results", "### Framework versions\n\n- transformers==4.40.0\n- datasets==2.18.0\n- peft==0.10.0\n- safetensors==0.4.2\n- evaluate==0.4.1\n- bitsandbytes==0.43\n- huggingface_hub==0.22.2\n- seqeval==1.2.2\n- auto-gptq==0.7.1\n- gpustat==1.1.1\n- deepspeed==0.14.0\n- wandb==0.16.6\n- trl==0.8.1\n- accelerate==0.29.3\n- coloredlogs==15.0.1\n- traitlets==5.14.2\n- git+URL", "### Hardware\n- Cloud provided: URL" ]

[ "transformers", "pytorch", "llama", "text-generation", "axolotl", "mergekit", "conversational", "en", "dataset:JeanKaddour/minipile", "arxiv:2403.17887", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2403.17887" ]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #llama #text-generation #axolotl #mergekit #conversational #en #dataset-JeanKaddour/minipile #arxiv-2403.17887 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "text-generation", "en", "license:cc-by-nc-4.0", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#text-generation #en #license-cc-by-nc-4.0 #region-us \n" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: Chandrateja\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-Instruct-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: Chandrateja\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

[]

[]

[ "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

[ "TAGS\n#CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing CartPole-v1\n This is a trained model of a Reinforce agent playing CartPole-v1 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]

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

[ "1910.09700" ]

[]

[ "# 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 #gemma #text-generation #conversational #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" ]

[ "transformers", "pytorch", "llama", "text-generation", "axolotl", "mergekit", "conversational", "en", "dataset:JeanKaddour/minipile", "arxiv:2403.17887", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2403.17887" ]

[ "en" ]

[]

[ "TAGS\n#transformers #pytorch #llama #text-generation #axolotl #mergekit #conversational #en #dataset-JeanKaddour/minipile #arxiv-2403.17887 #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

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

[ "1910.09700" ]

[]

[ "# 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 #phi #text-generation #custom_code #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" ]

[ "transformers", "safetensors", "mistral", "text-generation", "dataset:flammenai/Date-DPO-v2", "base_model:flammenai/flammen20-mistral-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# flammen21-mistral-7B\n\nA Mistral 7B LLM built from merging pretrained models and finetuning on flammenai/Date-DPO-v2. \nFlammen specializes in exceptional character roleplay, creative writing, and general intelligence", "### Method\n\nFinetuned using an L4 on Google Colab.\n\nFine-tune a Mistral-7b model with Direct Preference Optimization - Maxime Labonne", "### Configuration\n\nLoRA, model, and training settings:" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #dataset-flammenai/Date-DPO-v2 #base_model-flammenai/flammen20-mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# flammen21-mistral-7B\n\nA Mistral 7B LLM built from merging pretrained models and finetuning on flammenai/Date-DPO-v2. \nFlammen specializes in exceptional character roleplay, creative writing, and general intelligence", "### Method\n\nFinetuned using an L4 on Google Colab.\n\nFine-tune a Mistral-7b model with Direct Preference Optimization - Maxime Labonne", "### Configuration\n\nLoRA, model, and training settings:" ]

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "base_model:microsoft/trocr-base-stage1", "endpoints_compatible", "region:us" ]

[]

[]

[ "# IS557_TrOCR_new\n\nThis model is a fine-tuned version of microsoft/trocr-base-stage1 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: 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: 3.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #base_model-microsoft/trocr-base-stage1 #endpoints_compatible #region-us \n", "# IS557_TrOCR_new\n\nThis model is a fine-tuned version of microsoft/trocr-base-stage1 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: 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: 3.0\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "safetensors", "gemma", "text-generation", "pytorch", "instruct", "finetune", "translation", "conversational", "ko", "dataset:traintogpb/aihub-flores-koen-integrated-sparta-30k", "dataset:lemon-mint/korean_high_quality_translation_426k", "base_model:beomi/gemma-ko-2b", "license:gemma", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "ko" ]

[ "# Gemma 2B Translation v0.121\n\n- Eval Loss: '0.3734'\n- Train Loss: '0.3929'\n- lr: '6e-05'\n- optimizer: adamw\n- lr_scheduler_type: cosine", "## Prompt Template", "## Model Description\n\n- Developed by: 'lemon-mint'\n- Model type: Gemma\n- Language(s) (NLP): English\n- License: gemma-terms-of-use\n- Finetuned from model: beomi/gemma-ko-2b" ]

[ "TAGS\n#transformers #safetensors #gemma #text-generation #pytorch #instruct #finetune #translation #conversational #ko #dataset-traintogpb/aihub-flores-koen-integrated-sparta-30k #dataset-lemon-mint/korean_high_quality_translation_426k #base_model-beomi/gemma-ko-2b #license-gemma #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Gemma 2B Translation v0.121\n\n- Eval Loss: '0.3734'\n- Train Loss: '0.3929'\n- lr: '6e-05'\n- optimizer: adamw\n- lr_scheduler_type: cosine", "## Prompt Template", "## Model Description\n\n- Developed by: 'lemon-mint'\n- Model type: Gemma\n- Language(s) (NLP): English\n- License: gemma-terms-of-use\n- Finetuned from model: beomi/gemma-ko-2b" ]

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

[]

[]

[ "### 训练可用数据整理", "### github地址", "### 推荐微调工具\n\n\n在此感谢以下项目，提供了许多优秀的中文微调工具，供大家参考：\n\n\n* Firefly - URL\n* LLaMA-Factory - URL", "### Chat版模型下载\n\n\n* Instruct + 继续中文sft版\n* huggingface地址", "### 模型量化加速、部署", "### 模型使用\n\n\n默认情况下直接运行以下代码即可体验llama3中文对话，请自行修改'model\\_name\\_or\\_path'为你下载的模型路径", "### 多轮问答示例\n\n\npython\nimport URL as nn\nimport torch\n\n\nclass TransformerBlock(nn.Module):\ndef **init**(self, d\\_model, n\\_heads, dropout=0.1):\nsuper(TransformerBlock, self).**init**()\nself.self\\_attn = MultiHeadAttention(d\\_model, n\\_heads)\nself.feed\\_forward = nn.Linear(d\\_model, d\\_model)\nself.dropout = nn.Dropout(dropout)\n\n\n\n```\ndef forward(self, x):\n # Self-Attention\n residual = x\n output = self.self_attn(x, x)\n output += residual\n output = self.dropout(output)\n\n # Feed Forward\n residual = output\n output = self.feed_forward(output)\n output += residual\n output = self.dropout(output)\n\n return output\n\n```\n\nclass MultiHeadAttention(nn.Module):\ndef **init**(self, d\\_model, n\\_heads):\nsuper(MultiHeadAttention, self).**init**()\nself.d\\_model = d\\_model\nself.n\\_heads = n\\_heads\nself.query\\_linear = nn.Linear(d\\_model, d\\_model)\nself.key\\_linear = nn.Linear(d\\_model, d\\_model)\nself.value\\_linear = nn.Linear(d\\_model, d\\_model)\nself.dropout = nn.Dropout(0.1)\n\n\n\n```\ndef forward(self, query, key, value):\n batch_size = URL(0)\n head_dim = self.d_model // self.n_heads\n\n query = self.query_linear(query).view(batch_size, -1, self.n_heads, head_dim)\n key = self.key_linear(key).view(batch_size, -1, self.n_heads, head_dim)\n value = self.value_linear(value).view(batch_size, -1, self.n_heads, head_dim)\n\n attention_scores = URL(query.permute(0, 2, 1, 3), key.permute(0, 2, 3, 1)) / URL(head_dim)\n attention_scores = F.softmax(attention_scores, dim=-1)\n attention_scores = self.dropout(attention_scores)\n\n output = URL(attention_scores, value)\n output = URL(batch_size, -1, self.d_model)\n\n return output\n\n```\n\npython\ndef quick\\_sort(arr):\nif len(arr) <= 1:\nreturn arr\npivot = arr[0]\nleft = [x for x in arr[1:] if x < pivot]\nright = [x for x in arr[1:] if x >= pivot]\nreturn quick\\_sort(left) + [pivot] + quick\\_sort(right)" ]

[ "TAGS\n#transformers #safetensors #gguf #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### 训练可用数据整理", "### github地址", "### 推荐微调工具\n\n\n在此感谢以下项目，提供了许多优秀的中文微调工具，供大家参考：\n\n\n* Firefly - URL\n* LLaMA-Factory - URL", "### Chat版模型下载\n\n\n* Instruct + 继续中文sft版\n* huggingface地址", "### 模型量化加速、部署", "### 模型使用\n\n\n默认情况下直接运行以下代码即可体验llama3中文对话，请自行修改'model\\_name\\_or\\_path'为你下载的模型路径", "### 多轮问答示例\n\n\npython\nimport URL as nn\nimport torch\n\n\nclass TransformerBlock(nn.Module):\ndef **init**(self, d\\_model, n\\_heads, dropout=0.1):\nsuper(TransformerBlock, self).**init**()\nself.self\\_attn = MultiHeadAttention(d\\_model, n\\_heads)\nself.feed\\_forward = nn.Linear(d\\_model, d\\_model)\nself.dropout = nn.Dropout(dropout)\n\n\n\n```\ndef forward(self, x):\n # Self-Attention\n residual = x\n output = self.self_attn(x, x)\n output += residual\n output = self.dropout(output)\n\n # Feed Forward\n residual = output\n output = self.feed_forward(output)\n output += residual\n output = self.dropout(output)\n\n return output\n\n```\n\nclass MultiHeadAttention(nn.Module):\ndef **init**(self, d\\_model, n\\_heads):\nsuper(MultiHeadAttention, self).**init**()\nself.d\\_model = d\\_model\nself.n\\_heads = n\\_heads\nself.query\\_linear = nn.Linear(d\\_model, d\\_model)\nself.key\\_linear = nn.Linear(d\\_model, d\\_model)\nself.value\\_linear = nn.Linear(d\\_model, d\\_model)\nself.dropout = nn.Dropout(0.1)\n\n\n\n```\ndef forward(self, query, key, value):\n batch_size = URL(0)\n head_dim = self.d_model // self.n_heads\n\n query = self.query_linear(query).view(batch_size, -1, self.n_heads, head_dim)\n key = self.key_linear(key).view(batch_size, -1, self.n_heads, head_dim)\n value = self.value_linear(value).view(batch_size, -1, self.n_heads, head_dim)\n\n attention_scores = URL(query.permute(0, 2, 1, 3), key.permute(0, 2, 3, 1)) / URL(head_dim)\n attention_scores = F.softmax(attention_scores, dim=-1)\n attention_scores = self.dropout(attention_scores)\n\n output = URL(attention_scores, value)\n output = URL(batch_size, -1, self.d_model)\n\n return output\n\n```\n\npython\ndef quick\\_sort(arr):\nif len(arr) <= 1:\nreturn arr\npivot = arr[0]\nleft = [x for x in arr[1:] if x < pivot]\nright = [x for x in arr[1:] if x >= pivot]\nreturn quick\\_sort(left) + [pivot] + quick\\_sort(right)" ]

[ "transformers", "pytorch", "gpt_neo", "text-generation", "generated_from_trainer", "base_model:EleutherAI/gpt-neo-2.7B", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# gpt-neo-2.7B_LAMA_TREx_finetuning_MAGNET\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-2.7B 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: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 0\n- distributed_type: multi-GPU\n- num_devices: 4\n- total_train_batch_size: 128\n- total_eval_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Training results", "### Framework versions\n\n- Transformers 4.33.2\n- Pytorch 1.13.1\n- Datasets 2.14.5\n- Tokenizers 0.13.3" ]

[ "TAGS\n#transformers #pytorch #gpt_neo #text-generation #generated_from_trainer #base_model-EleutherAI/gpt-neo-2.7B #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# gpt-neo-2.7B_LAMA_TREx_finetuning_MAGNET\n\nThis model is a fine-tuned version of EleutherAI/gpt-neo-2.7B 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: 2e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 0\n- distributed_type: multi-GPU\n- num_devices: 4\n- total_train_batch_size: 128\n- total_eval_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Training results", "### Framework versions\n\n- Transformers 4.33.2\n- Pytorch 1.13.1\n- Datasets 2.14.5\n- Tokenizers 0.13.3" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \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", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \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", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "peft", "arxiv:1910.09700", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \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", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \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", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "transformers", "safetensors", "internvl_chat", "feature-extraction", "custom_code", "arxiv:1910.09700", "4-bit", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #internvl_chat #feature-extraction #custom_code #arxiv-1910.09700 #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" ]

[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

[]

[]

[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]

[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

[]

[]

[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]

[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]

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

[ "1910.09700" ]

[]

[ "# 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 #camembert #token-classification #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" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:taide/TAIDE-LX-7B-Chat", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: chtseng\n- License: apache-2.0\n- Finetuned from model : taide/TAIDE-LX-7B-Chat\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-taide/TAIDE-LX-7B-Chat #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: chtseng\n- License: apache-2.0\n- Finetuned from model : taide/TAIDE-LX-7B-Chat\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

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

[ "1910.09700" ]

[]

[ "# 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 #phi #text-generation #custom_code #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" ]

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

[ "1910.09700" ]

[]

[ "# 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 #gemma #text-generation #conversational #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" ]

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

[ "1910.09700" ]

[]

[ "# 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" ]

[ "transformers", "openvino", "llama", "text-generation", "facebook", "meta", "llama-3", "conversational", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "# Meta-Llama-3-8B-Instruct INT4 Quantized\n\n- INT-4 quantized version of meta-llama/Meta-Llama-3-8B-Instruct created using OpenVINO", "## Model Details\n\nMeta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.\n\nModel developers Meta\n\nVariations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.\n\nInput Models input text only.\n\nOutput Models generate text and code only.\n\nModel Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.\n\nModel Release Date April 18, 2024.", "## Usage" ]

[ "TAGS\n#transformers #openvino #llama #text-generation #facebook #meta #llama-3 #conversational #en #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Meta-Llama-3-8B-Instruct INT4 Quantized\n\n- INT-4 quantized version of meta-llama/Meta-Llama-3-8B-Instruct created using OpenVINO", "## Model Details\n\nMeta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.\n\nModel developers Meta\n\nVariations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.\n\nInput Models input text only.\n\nOutput Models generate text and code only.\n\nModel Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.\n\nModel Release Date April 18, 2024.", "## Usage" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:ZhangShenao/0.0_ablation_6iters_dataset", "base_model:ZhangShenao/0.0_ablation_6iters_iter_3", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.0_ablation_6iters_iter_4\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_6iters_iter_3 on the ZhangShenao/0.0_ablation_6iters_dataset 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-ZhangShenao/0.0_ablation_6iters_dataset #base_model-ZhangShenao/0.0_ablation_6iters_iter_3 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.0_ablation_6iters_iter_4\n\nThis model is a fine-tuned version of ZhangShenao/0.0_ablation_6iters_iter_3 on the ZhangShenao/0.0_ablation_6iters_dataset 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

[ "# 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 #bert #text-classification #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" ]

[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "4-bit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\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: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.40.0\n* Pytorch 2.1.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #tensorboard #safetensors #mistral #alignment-handbook #trl #dpo #generated_from_trainer #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #4-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* distributed\\_type: multi-GPU\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: 0.1\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.40.0\n* Pytorch 2.1.2+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.001_ablation_4iters_bs256_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_ablation_4iters_bs256_iter_3\n\nThis model is a fine-tuned version of ShenaoZ/0.001_ablation_4iters_bs256_iter_2 on the updated and the original datasets.", "## 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-updated #dataset-original #base_model-ShenaoZ/0.001_ablation_4iters_bs256_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_ablation_4iters_bs256_iter_3\n\nThis model is a fine-tuned version of ShenaoZ/0.001_ablation_4iters_bs256_iter_2 on the updated and the original datasets.", "## 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: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #gpt2 #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" ]

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

[]

[]

[ "# AnnualSummarizer\n\nThis model was trained from scratch 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: 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: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# AnnualSummarizer\n\nThis model was trained from scratch 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: 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: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #gpt2 #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" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: ojasviyadav\n- License: apache-2.0\n- Finetuned from model : unsloth/gemma-7b-bnb-4bit\n\nThis gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #text-generation-inference #unsloth #gemma #trl #en #base_model-unsloth/gemma-7b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: ojasviyadav\n- License: apache-2.0\n- Finetuned from model : unsloth/gemma-7b-bnb-4bit\n\nThis gemma model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

[ "# 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 #gpt2 #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" ]

[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

[]

[]

[ "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]

[ "TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]

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

[]

[]

[ "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: APLunch/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n", "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A *longer tutorial* to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: APLunch/ppo-SnowballTarget\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

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

[ "1910.09700" ]

[]

[ "# 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 #roberta #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #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" ]

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

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[ "# COLX_563_lab4_slot_hotel_area\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0951\n- F1 Score: 0.9600", "## 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: 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: 5", "### Training results", "### Framework versions\n\n- Transformers 4.39.1\n- Pytorch 2.4.0.dev20240325\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# COLX_563_lab4_slot_hotel_area\n\nThis model is a fine-tuned version of roberta-base on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.0951\n- F1 Score: 0.9600", "## 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: 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: 5", "### Training results", "### Framework versions\n\n- Transformers 4.39.1\n- Pytorch 2.4.0.dev20240325\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]