Datasets:

---

ftopal

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huggingface-models

like 0

[ "transformers", "pytorch", "mt5", "text2text-generation", "generated_from_trainer", "base_model:samzirbo/mT5.en-es.pretrained", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* training\\_steps: 30000", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #base_model-samzirbo/mT5.en-es.pretrained #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* training\\_steps: 30000", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "llama", "text-generation", "pruna-ai", "conversational", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Hermes-2-Pro-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Hermes-2-Pro-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\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#transformers #llama #text-generation #pruna-ai #conversational #base_model-NousResearch/Hermes-2-Pro-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Hermes-2-Pro-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Hermes-2-Pro-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

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

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\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: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "llama", "text-generation", "pruna-ai", "conversational", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Hermes-2-Pro-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Hermes-2-Pro-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\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#transformers #llama #text-generation #pruna-ai #conversational #base_model-NousResearch/Hermes-2-Pro-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Hermes-2-Pro-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Hermes-2-Pro-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "llama", "text-generation", "pruna-ai", "conversational", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Hermes-2-Pro-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Hermes-2-Pro-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\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#transformers #llama #text-generation #pruna-ai #conversational #base_model-NousResearch/Hermes-2-Pro-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with hqq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Hermes-2-Pro-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Hermes-2-Pro-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "transformers", "safetensors", "llama", "text-generation", "pruna-ai", "conversational", "base_model:NousResearch/Hermes-2-Pro-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

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[ "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Hermes-2-Pro-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Hermes-2-Pro-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\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#transformers #safetensors #llama #text-generation #pruna-ai #conversational #base_model-NousResearch/Hermes-2-Pro-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Simply make AI models cheaper, smaller, faster, and greener!\n\n- Give a thumbs up if you like this model!\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your *own* AI models here.\n- Read the documentations to know more here\n- Join Pruna AI community on Discord here to share feedback/suggestions or get help.", "## Results\n\n!image info\n\nFrequently Asked Questions\n- *How does the compression work?* The model is compressed with awq.\n- *How does the model quality change?* The quality of the model output might vary compared to the base model.\n- *How is the model efficiency evaluated?* These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in 'model/smash_config.json' and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.\n- *What is the model format?* We use safetensors.\n- *What calibration data has been used?* If needed by the compression method, we used WikiText as the calibration data.\n- *What is the naming convention for Pruna Huggingface models?* We take the original model name and append \"turbo\", \"tiny\", or \"green\" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.\n- *How to compress my own models?* You can request premium access to more compression methods and tech support for your specific use-cases here.\n- *What are \"first\" metrics?* Results mentioning \"first\" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.\n- *What are \"Sync\" and \"Async\" metrics?* \"Sync\" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. \"Async\" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.", "## Setup\n\nYou can run the smashed model with these steps:\n\n0. Check requirements from the original repo NousResearch/Hermes-2-Pro-Llama-3-8B installed. In particular, check python, cuda, and transformers versions.\n1. Make sure that you have installed quantization related packages.\n \n2. Load & run the model.", "## Configurations\n\nThe configuration info are in 'smash_config.json'.", "## Credits & License\n\nThe license of the smashed model follows the license of the original model. Please check the license of the original model NousResearch/Hermes-2-Pro-Llama-3-8B before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi.", "## Want to compress other models?\n\n- Contact us and tell us which model to compress next here.\n- Request access to easily compress your own AI models here." ]

[ "setfit", "safetensors", "mpnet", "absa", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]

[ "2209.11055" ]

[]

[ "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: zeroix07/setfit-absa-model-aspect\n* SetFitABSA Polarity Model: zeroix07/setfit-absa-model-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 2 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (4, 4)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.7.0\n* spaCy: 3.7.4\n* Transformers: 4.39.3\n* PyTorch: 2.1.2\n* Datasets: 2.18.0\n* Tokenizers: 0.15.2", "### BibTeX" ]

[ "TAGS\n#setfit #safetensors #mpnet #absa #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #model-index #region-us \n", "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: zeroix07/setfit-absa-model-aspect\n* SetFitABSA Polarity Model: zeroix07/setfit-absa-model-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 2 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (4, 4)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.7.0\n* spaCy: 3.7.4\n* Transformers: 4.39.3\n* PyTorch: 2.1.2\n* Datasets: 2.18.0\n* Tokenizers: 0.15.2", "### BibTeX" ]

[ "setfit", "safetensors", "mpnet", "absa", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]

[ "2209.11055" ]

[]

[ "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: zeroix07/setfit-absa-model-aspect\n* SetFitABSA Polarity Model: zeroix07/setfit-absa-model-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 3 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (4, 4)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.7.0\n* spaCy: 3.7.4\n* Transformers: 4.39.3\n* PyTorch: 2.1.2\n* Datasets: 2.18.0\n* Tokenizers: 0.15.2", "### BibTeX" ]

[ "TAGS\n#setfit #safetensors #mpnet #absa #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-sentence-transformers/paraphrase-mpnet-base-v2 #model-index #region-us \n", "### Model Description\n\n\n* Model Type: SetFit\n* Sentence Transformer body: sentence-transformers/paraphrase-mpnet-base-v2\n* Classification head: a LogisticRegression instance\n* spaCy Model: en\\_core\\_web\\_lg\n* SetFitABSA Aspect Model: zeroix07/setfit-absa-model-aspect\n* SetFitABSA Polarity Model: zeroix07/setfit-absa-model-polarity\n* Maximum Sequence Length: 512 tokens\n* Number of Classes: 3 classes", "### Model Sources\n\n\n* Repository: SetFit on GitHub\n* Paper: Efficient Few-Shot Learning Without Prompts\n* Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts", "### Model Labels\n\n\n\nEvaluation\n----------", "### Metrics\n\n\n\nUses\n----", "### Direct Use for Inference\n\n\nFirst install the SetFit library:\n\n\nThen you can load this model and run inference.\n\n\nTraining Details\n----------------", "### Training Set Metrics", "### Training Hyperparameters\n\n\n* batch\\_size: (4, 4)\n* num\\_epochs: (1, 1)\n* max\\_steps: -1\n* sampling\\_strategy: oversampling\n* num\\_iterations: 20\n* body\\_learning\\_rate: (2e-05, 1e-05)\n* head\\_learning\\_rate: 0.01\n* loss: CosineSimilarityLoss\n* distance\\_metric: cosine\\_distance\n* margin: 0.25\n* end\\_to\\_end: False\n* use\\_amp: False\n* warmup\\_proportion: 0.1\n* seed: 42\n* eval\\_max\\_steps: -1\n* load\\_best\\_model\\_at\\_end: False", "### Training Results", "### Framework Versions\n\n\n* Python: 3.10.13\n* SetFit: 1.0.3\n* Sentence Transformers: 2.7.0\n* spaCy: 3.7.4\n* Transformers: 4.39.3\n* PyTorch: 2.1.2\n* Datasets: 2.18.0\n* Tokenizers: 0.15.2", "### BibTeX" ]

[ "transformers", "safetensors", "roberta", "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 #roberta #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" ]

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

[]

[]

[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "transformers", "safetensors", "bert", "feature-extraction", "custom_code", "region:us" ]

[]

[]

[ "# fine-tuned/jina-embeddings-v2-base-en-02052024-2a6pbxm4b-webapp_8647177611", "## Model Description\n\nfine-tuned/jina-embeddings-v2-base-en-02052024-2a6pbxm4b-webapp_8647177611 is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for a specific domain.", "## Use Case\nThis model is designed to support various applications in natural language processing and understanding.", "## Associated Dataset\n\nThis the dataset for this model can be found here.", "## How to Use\n\nThis model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:" ]

[ "TAGS\n#transformers #safetensors #bert #feature-extraction #custom_code #region-us \n", "# fine-tuned/jina-embeddings-v2-base-en-02052024-2a6pbxm4b-webapp_8647177611", "## Model Description\n\nfine-tuned/jina-embeddings-v2-base-en-02052024-2a6pbxm4b-webapp_8647177611 is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for a specific domain.", "## Use Case\nThis model is designed to support various applications in natural language processing and understanding.", "## Associated Dataset\n\nThis the dataset for this model can be found here.", "## How to Use\n\nThis model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:" ]

[ "peft", "safetensors", "mistral", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

[]

[]

[ "# nils3.0\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 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- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.1.dev0\n- Transformers 4.36.2\n- Pytorch 2.3.0+cu121\n- Datasets 2.16.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#peft #safetensors #mistral #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "# nils3.0\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 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- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.1.dev0\n- Transformers 4.36.2\n- Pytorch 2.3.0+cu121\n- Datasets 2.16.0\n- Tokenizers 0.15.2" ]

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

[]

[]

[ "# swin-tiny-patch4-window7-224-finetuned-eurosat\n\nThis model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 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: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3", "### Framework versions\n\n- Transformers 4.40.1\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# swin-tiny-patch4-window7-224-finetuned-eurosat\n\nThis model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 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: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3", "### Framework versions\n\n- Transformers 4.40.1\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

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

[]

[ "en" ]

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

[ "region:us" ]

[]

[]

[]

[ "TAGS\n#region-us \n" ]

[ "gguf", "region:us" ]

[]

[]

[ "### 1. Introduction of Deepseek Coder\n\n\nDeepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.\n\n\n* Massive Training Data: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.\n* Highly Flexible & Scalable: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.\n* Superior Model Performance: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.\n* Advanced Code Completion Capabilities: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.", "### 2. Model Summary\n\n\ndeepseek-coder-33b-instruct is a 33B parameter model initialized from deepseek-coder-33b-base and fine-tuned on 2B tokens of instruction data.\n\n\n* Home Page: DeepSeek\n* Repository: deepseek-ai/deepseek-coder\n* Chat With DeepSeek Coder: DeepSeek-Coder", "### 3. How to Use\n\n\nHere give some examples of how to use our model.", "#### Chat Model Inference", "### 4. License\n\n\nThis code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.\n\n\nSee the LICENSE-MODEL for more details.", "### 5. Contact\n\n\nIf you have any questions, please raise an issue or contact us at agi\\_code@URL." ]

[ "TAGS\n#gguf #region-us \n", "### 1. Introduction of Deepseek Coder\n\n\nDeepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.\n\n\n* Massive Training Data: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.\n* Highly Flexible & Scalable: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.\n* Superior Model Performance: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.\n* Advanced Code Completion Capabilities: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.", "### 2. Model Summary\n\n\ndeepseek-coder-33b-instruct is a 33B parameter model initialized from deepseek-coder-33b-base and fine-tuned on 2B tokens of instruction data.\n\n\n* Home Page: DeepSeek\n* Repository: deepseek-ai/deepseek-coder\n* Chat With DeepSeek Coder: DeepSeek-Coder", "### 3. How to Use\n\n\nHere give some examples of how to use our model.", "#### Chat Model Inference", "### 4. License\n\n\nThis code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.\n\n\nSee the LICENSE-MODEL for more details.", "### 5. Contact\n\n\nIf you have any questions, please raise an issue or contact us at agi\\_code@URL." ]

[ "sentence-transformers", "safetensors", "bert", "sentence-similarity", "feature-extraction", "loss:SoftmaxLoss", "loss:CosineSimilarityLoss", "en", "arxiv:1908.10084", "base_model:google-bert/bert-base-uncased", "model-index", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

[ "1908.10084" ]

[ "en" ]

[ "### Model Description\n\n\n* Model Type: Sentence Transformer\n* Base model: google-bert/bert-base-uncased\n* Maximum Sequence Length: 512 tokens\n* Output Dimensionality: 768 tokens\n* Similarity Function: Cosine Similarity\n* Training Datasets:\n\t+ all-nli\n\t+ sts\n* Language: en", "### Model Sources\n\n\n* Documentation: Sentence Transformers Documentation\n* Repository: Sentence Transformers on GitHub\n* Hugging Face: Sentence Transformers on Hugging Face", "### Full Model Architecture\n\n\nUsage\n-----", "### Direct Usage (Sentence Transformers)\n\n\nFirst install the Sentence Transformers library:\n\n\nThen you can load this model and run inference.\n\n\nEvaluation\n----------", "### Metrics", "#### Semantic Similarity\n\n\n* Dataset: 'sts-dev'\n* Evaluated with `EmbeddingSimilarityEvaluator`", "#### Semantic Similarity\n\n\n* Dataset: 'sts-test'\n* Evaluated with `EmbeddingSimilarityEvaluator`\n\n\n\nTraining Details\n----------------", "### Training Datasets", "#### all-nli\n\n\n* Dataset: all-nli at cc6c526\n* Size: 942,069 training samples\n* Columns: `premise`, `hypothesis`, and `label`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `SoftmaxLoss`", "#### sts\n\n\n* Dataset: sts at ab7a5ac\n* Size: 5,749 training samples\n* Columns: `sentence1`, `sentence2`, and `score`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `CosineSimilarityLoss` with these parameters:", "### Evaluation Datasets", "#### all-nli\n\n\n* Dataset: all-nli at cc6c526\n* Size: 1,000 evaluation samples\n* Columns: `premise`, `hypothesis`, and `label`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `SoftmaxLoss`", "#### sts\n\n\n* Dataset: sts at ab7a5ac\n* Size: 1,500 evaluation samples\n* Columns: `sentence1`, `sentence2`, and `score`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `CosineSimilarityLoss` with these parameters:", "### Training Hyperparameters", "#### Non-Default Hyperparameters\n\n\n* 'eval\\_strategy': steps\n* 'per\\_device\\_train\\_batch\\_size': 16\n* 'per\\_device\\_eval\\_batch\\_size': 16\n* 'num\\_train\\_epochs': 1\n* 'warmup\\_ratio': 0.1\n* 'fp16': True\n* 'multi\\_dataset\\_batch\\_sampler': round\\_robin", "#### All Hyperparameters\n\n\nClick to expand\n* 'overwrite\\_output\\_dir': False\n* 'do\\_predict': False\n* 'eval\\_strategy': steps\n* 'prediction\\_loss\\_only': False\n* 'per\\_device\\_train\\_batch\\_size': 16\n* 'per\\_device\\_eval\\_batch\\_size': 16\n* 'per\\_gpu\\_train\\_batch\\_size': None\n* 'per\\_gpu\\_eval\\_batch\\_size': None\n* 'gradient\\_accumulation\\_steps': 1\n* 'eval\\_accumulation\\_steps': None\n* 'learning\\_rate': 5e-05\n* 'weight\\_decay': 0.0\n* 'adam\\_beta1': 0.9\n* 'adam\\_beta2': 0.999\n* 'adam\\_epsilon': 1e-08\n* 'max\\_grad\\_norm': 1.0\n* 'num\\_train\\_epochs': 1\n* 'max\\_steps': -1\n* 'lr\\_scheduler\\_type': linear\n* 'lr\\_scheduler\\_kwargs': {}\n* 'warmup\\_ratio': 0.1\n* 'warmup\\_steps': 0\n* 'log\\_level': passive\n* 'log\\_level\\_replica': warning\n* 'log\\_on\\_each\\_node': True\n* 'logging\\_nan\\_inf\\_filter': True\n* 'save\\_safetensors': True\n* 'save\\_on\\_each\\_node': False\n* 'save\\_only\\_model': False\n* 'no\\_cuda': False\n* 'use\\_cpu': False\n* 'use\\_mps\\_device': False\n* 'seed': 42\n* 'data\\_seed': None\n* 'jit\\_mode\\_eval': False\n* 'use\\_ipex': False\n* 'bf16': False\n* 'fp16': True\n* 'fp16\\_opt\\_level': O1\n* 'half\\_precision\\_backend': auto\n* 'bf16\\_full\\_eval': False\n* 'fp16\\_full\\_eval': False\n* 'tf32': None\n* 'local\\_rank': 0\n* 'ddp\\_backend': None\n* 'tpu\\_num\\_cores': None\n* 'tpu\\_metrics\\_debug': False\n* 'debug': []\n* 'dataloader\\_drop\\_last': False\n* 'dataloader\\_num\\_workers': 0\n* 'dataloader\\_prefetch\\_factor': None\n* 'past\\_index': -1\n* 'disable\\_tqdm': False\n* 'remove\\_unused\\_columns': True\n* 'label\\_names': None\n* 'load\\_best\\_model\\_at\\_end': False\n* 'ignore\\_data\\_skip': False\n* 'fsdp': []\n* 'fsdp\\_min\\_num\\_params': 0\n* 'fsdp\\_config': {'min\\_num\\_params': 0, 'xla': False, 'xla\\_fsdp\\_v2': False, 'xla\\_fsdp\\_grad\\_ckpt': False}\n* 'fsdp\\_transformer\\_layer\\_cls\\_to\\_wrap': None\n* 'accelerator\\_config': {'split\\_batches': False, 'dispatch\\_batches': None, 'even\\_batches': True, 'use\\_seedable\\_sampler': True, 'non\\_blocking': False, 'gradient\\_accumulation\\_kwargs': None}\n* 'deepspeed': None\n* 'label\\_smoothing\\_factor': 0.0\n* 'optim': adamw\\_torch\n* 'optim\\_args': None\n* 'adafactor': False\n* 'group\\_by\\_length': False\n* 'length\\_column\\_name': length\n* 'ddp\\_find\\_unused\\_parameters': None\n* 'ddp\\_bucket\\_cap\\_mb': None\n* 'ddp\\_broadcast\\_buffers': None\n* 'dataloader\\_pin\\_memory': True\n* 'dataloader\\_persistent\\_workers': False\n* 'skip\\_memory\\_metrics': True\n* 'use\\_legacy\\_prediction\\_loop': False\n* 'push\\_to\\_hub': False\n* 'resume\\_from\\_checkpoint': None\n* 'hub\\_model\\_id': None\n* 'hub\\_strategy': every\\_save\n* 'hub\\_private\\_repo': False\n* 'hub\\_always\\_push': False\n* 'gradient\\_checkpointing': False\n* 'gradient\\_checkpointing\\_kwargs': None\n* 'include\\_inputs\\_for\\_metrics': False\n* 'eval\\_do\\_concat\\_batches': True\n* 'fp16\\_backend': auto\n* 'push\\_to\\_hub\\_model\\_id': None\n* 'push\\_to\\_hub\\_organization': None\n* 'mp\\_parameters':\n* 'auto\\_find\\_batch\\_size': False\n* 'full\\_determinism': False\n* 'torchdynamo': None\n* 'ray\\_scope': last\n* 'ddp\\_timeout': 1800\n* 'torch\\_compile': False\n* 'torch\\_compile\\_backend': None\n* 'torch\\_compile\\_mode': None\n* 'dispatch\\_batches': None\n* 'split\\_batches': None\n* 'include\\_tokens\\_per\\_second': False\n* 'include\\_num\\_input\\_tokens\\_seen': False\n* 'neftune\\_noise\\_alpha': None\n* 'optim\\_target\\_modules': None\n* 'batch\\_sampler': batch\\_sampler\n* 'multi\\_dataset\\_batch\\_sampler': round\\_robin", "### Training Logs", "### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Energy Consumed: 0.017 kWh\n* Carbon Emitted: 0.006 kg of CO2\n* Hours Used: 0.097 hours", "### Training Hardware\n\n\n* On Cloud: No\n* GPU Model: 1 x NVIDIA GeForce RTX 3090\n* CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K\n* RAM Size: 31.78 GB", "### Framework Versions\n\n\n* Python: 3.11.6\n* Sentence Transformers: 3.0.0.dev0\n* Transformers: 4.41.0.dev0\n* PyTorch: 2.3.0+cu121\n* Accelerate: 0.26.1\n* Datasets: 2.18.0\n* Tokenizers: 0.19.1", "### BibTeX", "#### Sentence Transformers and SoftmaxLoss" ]

[ "TAGS\n#sentence-transformers #safetensors #bert #sentence-similarity #feature-extraction #loss-SoftmaxLoss #loss-CosineSimilarityLoss #en #arxiv-1908.10084 #base_model-google-bert/bert-base-uncased #model-index #co2_eq_emissions #endpoints_compatible #region-us \n", "### Model Description\n\n\n* Model Type: Sentence Transformer\n* Base model: google-bert/bert-base-uncased\n* Maximum Sequence Length: 512 tokens\n* Output Dimensionality: 768 tokens\n* Similarity Function: Cosine Similarity\n* Training Datasets:\n\t+ all-nli\n\t+ sts\n* Language: en", "### Model Sources\n\n\n* Documentation: Sentence Transformers Documentation\n* Repository: Sentence Transformers on GitHub\n* Hugging Face: Sentence Transformers on Hugging Face", "### Full Model Architecture\n\n\nUsage\n-----", "### Direct Usage (Sentence Transformers)\n\n\nFirst install the Sentence Transformers library:\n\n\nThen you can load this model and run inference.\n\n\nEvaluation\n----------", "### Metrics", "#### Semantic Similarity\n\n\n* Dataset: 'sts-dev'\n* Evaluated with `EmbeddingSimilarityEvaluator`", "#### Semantic Similarity\n\n\n* Dataset: 'sts-test'\n* Evaluated with `EmbeddingSimilarityEvaluator`\n\n\n\nTraining Details\n----------------", "### Training Datasets", "#### all-nli\n\n\n* Dataset: all-nli at cc6c526\n* Size: 942,069 training samples\n* Columns: `premise`, `hypothesis`, and `label`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `SoftmaxLoss`", "#### sts\n\n\n* Dataset: sts at ab7a5ac\n* Size: 5,749 training samples\n* Columns: `sentence1`, `sentence2`, and `score`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `CosineSimilarityLoss` with these parameters:", "### Evaluation Datasets", "#### all-nli\n\n\n* Dataset: all-nli at cc6c526\n* Size: 1,000 evaluation samples\n* Columns: `premise`, `hypothesis`, and `label`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `SoftmaxLoss`", "#### sts\n\n\n* Dataset: sts at ab7a5ac\n* Size: 1,500 evaluation samples\n* Columns: `sentence1`, `sentence2`, and `score`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `CosineSimilarityLoss` with these parameters:", "### Training Hyperparameters", "#### Non-Default Hyperparameters\n\n\n* 'eval\\_strategy': steps\n* 'per\\_device\\_train\\_batch\\_size': 16\n* 'per\\_device\\_eval\\_batch\\_size': 16\n* 'num\\_train\\_epochs': 1\n* 'warmup\\_ratio': 0.1\n* 'fp16': True\n* 'multi\\_dataset\\_batch\\_sampler': round\\_robin", "#### All Hyperparameters\n\n\nClick to expand\n* 'overwrite\\_output\\_dir': False\n* 'do\\_predict': False\n* 'eval\\_strategy': steps\n* 'prediction\\_loss\\_only': False\n* 'per\\_device\\_train\\_batch\\_size': 16\n* 'per\\_device\\_eval\\_batch\\_size': 16\n* 'per\\_gpu\\_train\\_batch\\_size': None\n* 'per\\_gpu\\_eval\\_batch\\_size': None\n* 'gradient\\_accumulation\\_steps': 1\n* 'eval\\_accumulation\\_steps': None\n* 'learning\\_rate': 5e-05\n* 'weight\\_decay': 0.0\n* 'adam\\_beta1': 0.9\n* 'adam\\_beta2': 0.999\n* 'adam\\_epsilon': 1e-08\n* 'max\\_grad\\_norm': 1.0\n* 'num\\_train\\_epochs': 1\n* 'max\\_steps': -1\n* 'lr\\_scheduler\\_type': linear\n* 'lr\\_scheduler\\_kwargs': {}\n* 'warmup\\_ratio': 0.1\n* 'warmup\\_steps': 0\n* 'log\\_level': passive\n* 'log\\_level\\_replica': warning\n* 'log\\_on\\_each\\_node': True\n* 'logging\\_nan\\_inf\\_filter': True\n* 'save\\_safetensors': True\n* 'save\\_on\\_each\\_node': False\n* 'save\\_only\\_model': False\n* 'no\\_cuda': False\n* 'use\\_cpu': False\n* 'use\\_mps\\_device': False\n* 'seed': 42\n* 'data\\_seed': None\n* 'jit\\_mode\\_eval': False\n* 'use\\_ipex': False\n* 'bf16': False\n* 'fp16': True\n* 'fp16\\_opt\\_level': O1\n* 'half\\_precision\\_backend': auto\n* 'bf16\\_full\\_eval': False\n* 'fp16\\_full\\_eval': False\n* 'tf32': None\n* 'local\\_rank': 0\n* 'ddp\\_backend': None\n* 'tpu\\_num\\_cores': None\n* 'tpu\\_metrics\\_debug': False\n* 'debug': []\n* 'dataloader\\_drop\\_last': False\n* 'dataloader\\_num\\_workers': 0\n* 'dataloader\\_prefetch\\_factor': None\n* 'past\\_index': -1\n* 'disable\\_tqdm': False\n* 'remove\\_unused\\_columns': True\n* 'label\\_names': None\n* 'load\\_best\\_model\\_at\\_end': False\n* 'ignore\\_data\\_skip': False\n* 'fsdp': []\n* 'fsdp\\_min\\_num\\_params': 0\n* 'fsdp\\_config': {'min\\_num\\_params': 0, 'xla': False, 'xla\\_fsdp\\_v2': False, 'xla\\_fsdp\\_grad\\_ckpt': False}\n* 'fsdp\\_transformer\\_layer\\_cls\\_to\\_wrap': None\n* 'accelerator\\_config': {'split\\_batches': False, 'dispatch\\_batches': None, 'even\\_batches': True, 'use\\_seedable\\_sampler': True, 'non\\_blocking': False, 'gradient\\_accumulation\\_kwargs': None}\n* 'deepspeed': None\n* 'label\\_smoothing\\_factor': 0.0\n* 'optim': adamw\\_torch\n* 'optim\\_args': None\n* 'adafactor': False\n* 'group\\_by\\_length': False\n* 'length\\_column\\_name': length\n* 'ddp\\_find\\_unused\\_parameters': None\n* 'ddp\\_bucket\\_cap\\_mb': None\n* 'ddp\\_broadcast\\_buffers': None\n* 'dataloader\\_pin\\_memory': True\n* 'dataloader\\_persistent\\_workers': False\n* 'skip\\_memory\\_metrics': True\n* 'use\\_legacy\\_prediction\\_loop': False\n* 'push\\_to\\_hub': False\n* 'resume\\_from\\_checkpoint': None\n* 'hub\\_model\\_id': None\n* 'hub\\_strategy': every\\_save\n* 'hub\\_private\\_repo': False\n* 'hub\\_always\\_push': False\n* 'gradient\\_checkpointing': False\n* 'gradient\\_checkpointing\\_kwargs': None\n* 'include\\_inputs\\_for\\_metrics': False\n* 'eval\\_do\\_concat\\_batches': True\n* 'fp16\\_backend': auto\n* 'push\\_to\\_hub\\_model\\_id': None\n* 'push\\_to\\_hub\\_organization': None\n* 'mp\\_parameters':\n* 'auto\\_find\\_batch\\_size': False\n* 'full\\_determinism': False\n* 'torchdynamo': None\n* 'ray\\_scope': last\n* 'ddp\\_timeout': 1800\n* 'torch\\_compile': False\n* 'torch\\_compile\\_backend': None\n* 'torch\\_compile\\_mode': None\n* 'dispatch\\_batches': None\n* 'split\\_batches': None\n* 'include\\_tokens\\_per\\_second': False\n* 'include\\_num\\_input\\_tokens\\_seen': False\n* 'neftune\\_noise\\_alpha': None\n* 'optim\\_target\\_modules': None\n* 'batch\\_sampler': batch\\_sampler\n* 'multi\\_dataset\\_batch\\_sampler': round\\_robin", "### Training Logs", "### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Energy Consumed: 0.017 kWh\n* Carbon Emitted: 0.006 kg of CO2\n* Hours Used: 0.097 hours", "### Training Hardware\n\n\n* On Cloud: No\n* GPU Model: 1 x NVIDIA GeForce RTX 3090\n* CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K\n* RAM Size: 31.78 GB", "### Framework Versions\n\n\n* Python: 3.11.6\n* Sentence Transformers: 3.0.0.dev0\n* Transformers: 4.41.0.dev0\n* PyTorch: 2.3.0+cu121\n* Accelerate: 0.26.1\n* Datasets: 2.18.0\n* Tokenizers: 0.19.1", "### BibTeX", "#### Sentence Transformers and SoftmaxLoss" ]

[ "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", "pytorch", "mistral", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "## License\n\nThis model is available under the Apache 2.0 License.", "## Discord Server\n\nJoin our Discord server here.", "## Feeling Generous? \n\nEager to buy me a cup of 2$ coffe or iced tea? Sure, here is the link: URL Please add a note on which one you want me to drink?" ]

[ "TAGS\n#transformers #pytorch #mistral #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## License\n\nThis model is available under the Apache 2.0 License.", "## Discord Server\n\nJoin our Discord server here.", "## Feeling Generous? \n\nEager to buy me a cup of 2$ coffe or iced tea? Sure, here is the link: URL Please add a note on which one you want me to drink?" ]

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

[]

[ "en" ]

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

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-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: 0.0001\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[]

[ "en" ]

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

[ "peft", "tensorboard", "safetensors", "transformer", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]

[]

[]

[ "# outputs\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\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- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.36.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.16.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#peft #tensorboard #safetensors #transformer #generated_from_trainer #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "# outputs\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\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- lr_scheduler_warmup_steps: 2\n- training_steps: 10\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.10.0\n- Transformers 4.36.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.16.0\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "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 #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "gguf", "mistral", "en", "ja", "dataset:TFMC/imatrix-dataset-for-japanese-llm", "license:apache-2.0", "region:us" ]

[]

[ "en", "ja" ]

[ "# tokyotech-llm-Swallow-MS-7b-instruct-v0.1-gguf\ntokyotech-llmさんが公開しているSwallow-MS-7b-instruct-v0.1のggufフォーマット変換版です。 \n\nimatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。", "## 他のモデル\nmistral \nmmnga/tokyotech-llm-Swallow-MS-7b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-7b-plus-hf-gguf \nmmnga/tokyotech-llm-Swallow-MS-7b-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf \n\nllama2 \nmmnga/tokyotech-llm-Swallow-7b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-13b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-70b-instruct-v0.1-gguf", "## Usage" ]

[ "TAGS\n#gguf #mistral #en #ja #dataset-TFMC/imatrix-dataset-for-japanese-llm #license-apache-2.0 #region-us \n", "# tokyotech-llm-Swallow-MS-7b-instruct-v0.1-gguf\ntokyotech-llmさんが公開しているSwallow-MS-7b-instruct-v0.1のggufフォーマット変換版です。 \n\nimatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。", "## 他のモデル\nmistral \nmmnga/tokyotech-llm-Swallow-MS-7b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-7b-plus-hf-gguf \nmmnga/tokyotech-llm-Swallow-MS-7b-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf \n\nllama2 \nmmnga/tokyotech-llm-Swallow-7b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-13b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-70b-instruct-v0.1-gguf", "## Usage" ]

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "autotrain", "dataset:V16/autotrain-data", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# Model Trained Using AutoTrain\n\n- Problem type: Text Classification", "## Validation Metrics\nloss: 0.4838660955429077\n\nf1_macro: 0.762273830650919\n\nf1_micro: 0.7968253968253968\n\nf1_weighted: 0.7910936557475937\n\nprecision_macro: 0.8108958879749956\n\nprecision_micro: 0.7968253968253968\n\nprecision_weighted: 0.79479940517321\n\nrecall_macro: 0.728675645342312\n\nrecall_micro: 0.7968253968253968\n\nrecall_weighted: 0.7968253968253968\n\naccuracy: 0.7968253968253968" ]

[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #autotrain #dataset-V16/autotrain-data #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\n- Problem type: Text Classification", "## Validation Metrics\nloss: 0.4838660955429077\n\nf1_macro: 0.762273830650919\n\nf1_micro: 0.7968253968253968\n\nf1_weighted: 0.7910936557475937\n\nprecision_macro: 0.8108958879749956\n\nprecision_micro: 0.7968253968253968\n\nprecision_weighted: 0.79479940517321\n\nrecall_macro: 0.728675645342312\n\nrecall_micro: 0.7968253968253968\n\nrecall_weighted: 0.7968253968253968\n\naccuracy: 0.7968253968253968" ]

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

[]

[ "en" ]

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

[ "transformers", "safetensors", "bert", "feature-extraction", "custom_code", "region:us" ]

[]

[]

[ "# fine-tuned/jina-embeddings-v2-base-en-02052024-pmvv-webapp_8647177611", "## Model Description\n\nfine-tuned/jina-embeddings-v2-base-en-02052024-pmvv-webapp_8647177611 is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for a specific domain.", "## Use Case\nThis model is designed to support various applications in natural language processing and understanding.", "## Associated Dataset\n\nThis the dataset for this model can be found here.", "## How to Use\n\nThis model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:" ]

[ "TAGS\n#transformers #safetensors #bert #feature-extraction #custom_code #region-us \n", "# fine-tuned/jina-embeddings-v2-base-en-02052024-pmvv-webapp_8647177611", "## Model Description\n\nfine-tuned/jina-embeddings-v2-base-en-02052024-pmvv-webapp_8647177611 is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for a specific domain.", "## Use Case\nThis model is designed to support various applications in natural language processing and understanding.", "## Associated Dataset\n\nThis the dataset for this model can be found here.", "## How to Use\n\nThis model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:" ]

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

[]

[]

[ "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\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: Max87152/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "TAGS\n#ml-agents #tensorboard #onnx #Huggy #deep-reinforcement-learning #reinforcement-learning #ML-Agents-Huggy #region-us \n", "# ppo Agent playing Huggy\n This is a trained model of a ppo agent playing Huggy\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: Max87152/ppo-Huggy\n 3. Step 2: Select your *.nn /*.onnx file\n 4. Click on Watch the agent play" ]

[ "sentence-transformers", "safetensors", "distilbert", "sentence-similarity", "feature-extraction", "loss:TripletLoss", "en", "arxiv:1908.10084", "arxiv:1703.07737", "base_model:distilbert/distilbert-base-uncased", "model-index", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

[ "1908.10084", "1703.07737" ]

[ "en" ]

[ "### Model Description\n\n\n* Model Type: Sentence Transformer\n* Base model: distilbert/distilbert-base-uncased\n* Maximum Sequence Length: 512 tokens\n* Output Dimensionality: 768 tokens\n* Similarity Function: Cosine Similarity\n* Training Dataset:\n\t+ sentence-transformers/wikipedia-sections\n* Language: en", "### Model Sources\n\n\n* Documentation: Sentence Transformers Documentation\n* Repository: Sentence Transformers on GitHub\n* Hugging Face: Sentence Transformers on Hugging Face", "### Full Model Architecture\n\n\nUsage\n-----", "### Direct Usage (Sentence Transformers)\n\n\nFirst install the Sentence Transformers library:\n\n\nThen you can load this model and run inference.\n\n\nEvaluation\n----------", "### Metrics", "#### Triplet\n\n\n* Dataset: 'wikipedia-sections-dev'\n* Evaluated with `TripletEvaluator`", "#### Triplet\n\n\n* Dataset: 'wikipedia-sections-test'\n* Evaluated with `TripletEvaluator`\n\n\n\nTraining Details\n----------------", "### Training Dataset", "#### sentence-transformers/wikipedia-sections\n\n\n* Dataset: sentence-transformers/wikipedia-sections at 576bb61\n* Size: 10,000 training samples\n* Columns: `anchor`, `positive`, and `negative`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `TripletLoss` with these parameters:", "### Evaluation Dataset", "#### sentence-transformers/wikipedia-sections\n\n\n* Dataset: sentence-transformers/wikipedia-sections at 576bb61\n* Size: 1,000 evaluation samples\n* Columns: `anchor`, `positive`, and `negative`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `TripletLoss` with these parameters:", "### Training Hyperparameters", "#### Non-Default Hyperparameters\n\n\n* 'eval\\_strategy': steps\n* 'per\\_device\\_train\\_batch\\_size': 16\n* 'per\\_device\\_eval\\_batch\\_size': 16\n* 'num\\_train\\_epochs': 1\n* 'warmup\\_ratio': 0.1\n* 'fp16': True", "#### All Hyperparameters\n\n\nClick to expand\n* 'overwrite\\_output\\_dir': False\n* 'do\\_predict': False\n* 'eval\\_strategy': steps\n* 'prediction\\_loss\\_only': False\n* 'per\\_device\\_train\\_batch\\_size': 16\n* 'per\\_device\\_eval\\_batch\\_size': 16\n* 'per\\_gpu\\_train\\_batch\\_size': None\n* 'per\\_gpu\\_eval\\_batch\\_size': None\n* 'gradient\\_accumulation\\_steps': 1\n* 'eval\\_accumulation\\_steps': None\n* 'learning\\_rate': 5e-05\n* 'weight\\_decay': 0.0\n* 'adam\\_beta1': 0.9\n* 'adam\\_beta2': 0.999\n* 'adam\\_epsilon': 1e-08\n* 'max\\_grad\\_norm': 1.0\n* 'num\\_train\\_epochs': 1\n* 'max\\_steps': -1\n* 'lr\\_scheduler\\_type': linear\n* 'lr\\_scheduler\\_kwargs': {}\n* 'warmup\\_ratio': 0.1\n* 'warmup\\_steps': 0\n* 'log\\_level': passive\n* 'log\\_level\\_replica': warning\n* 'log\\_on\\_each\\_node': True\n* 'logging\\_nan\\_inf\\_filter': True\n* 'save\\_safetensors': True\n* 'save\\_on\\_each\\_node': False\n* 'save\\_only\\_model': False\n* 'no\\_cuda': False\n* 'use\\_cpu': False\n* 'use\\_mps\\_device': False\n* 'seed': 42\n* 'data\\_seed': None\n* 'jit\\_mode\\_eval': False\n* 'use\\_ipex': False\n* 'bf16': False\n* 'fp16': True\n* 'fp16\\_opt\\_level': O1\n* 'half\\_precision\\_backend': auto\n* 'bf16\\_full\\_eval': False\n* 'fp16\\_full\\_eval': False\n* 'tf32': None\n* 'local\\_rank': 0\n* 'ddp\\_backend': None\n* 'tpu\\_num\\_cores': None\n* 'tpu\\_metrics\\_debug': False\n* 'debug': []\n* 'dataloader\\_drop\\_last': False\n* 'dataloader\\_num\\_workers': 0\n* 'dataloader\\_prefetch\\_factor': None\n* 'past\\_index': -1\n* 'disable\\_tqdm': False\n* 'remove\\_unused\\_columns': True\n* 'label\\_names': None\n* 'load\\_best\\_model\\_at\\_end': False\n* 'ignore\\_data\\_skip': False\n* 'fsdp': []\n* 'fsdp\\_min\\_num\\_params': 0\n* 'fsdp\\_config': {'min\\_num\\_params': 0, 'xla': False, 'xla\\_fsdp\\_v2': False, 'xla\\_fsdp\\_grad\\_ckpt': False}\n* 'fsdp\\_transformer\\_layer\\_cls\\_to\\_wrap': None\n* 'accelerator\\_config': {'split\\_batches': False, 'dispatch\\_batches': None, 'even\\_batches': True, 'use\\_seedable\\_sampler': True, 'non\\_blocking': False, 'gradient\\_accumulation\\_kwargs': None}\n* 'deepspeed': None\n* 'label\\_smoothing\\_factor': 0.0\n* 'optim': adamw\\_torch\n* 'optim\\_args': None\n* 'adafactor': False\n* 'group\\_by\\_length': False\n* 'length\\_column\\_name': length\n* 'ddp\\_find\\_unused\\_parameters': None\n* 'ddp\\_bucket\\_cap\\_mb': None\n* 'ddp\\_broadcast\\_buffers': None\n* 'dataloader\\_pin\\_memory': True\n* 'dataloader\\_persistent\\_workers': False\n* 'skip\\_memory\\_metrics': True\n* 'use\\_legacy\\_prediction\\_loop': False\n* 'push\\_to\\_hub': False\n* 'resume\\_from\\_checkpoint': None\n* 'hub\\_model\\_id': None\n* 'hub\\_strategy': every\\_save\n* 'hub\\_private\\_repo': False\n* 'hub\\_always\\_push': False\n* 'gradient\\_checkpointing': False\n* 'gradient\\_checkpointing\\_kwargs': None\n* 'include\\_inputs\\_for\\_metrics': False\n* 'eval\\_do\\_concat\\_batches': True\n* 'fp16\\_backend': auto\n* 'push\\_to\\_hub\\_model\\_id': None\n* 'push\\_to\\_hub\\_organization': None\n* 'mp\\_parameters':\n* 'auto\\_find\\_batch\\_size': False\n* 'full\\_determinism': False\n* 'torchdynamo': None\n* 'ray\\_scope': last\n* 'ddp\\_timeout': 1800\n* 'torch\\_compile': False\n* 'torch\\_compile\\_backend': None\n* 'torch\\_compile\\_mode': None\n* 'dispatch\\_batches': None\n* 'split\\_batches': None\n* 'include\\_tokens\\_per\\_second': False\n* 'include\\_num\\_input\\_tokens\\_seen': False\n* 'neftune\\_noise\\_alpha': None\n* 'optim\\_target\\_modules': None\n* 'batch\\_sampler': batch\\_sampler\n* 'multi\\_dataset\\_batch\\_sampler': proportional", "### Training Logs", "### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Energy Consumed: 0.009 kWh\n* Carbon Emitted: 0.003 kg of CO2\n* Hours Used: 0.045 hours", "### Training Hardware\n\n\n* On Cloud: No\n* GPU Model: 1 x NVIDIA GeForce RTX 3090\n* CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K\n* RAM Size: 31.78 GB", "### Framework Versions\n\n\n* Python: 3.11.6\n* Sentence Transformers: 3.0.0.dev0\n* Transformers: 4.41.0.dev0\n* PyTorch: 2.3.0+cu121\n* Accelerate: 0.26.1\n* Datasets: 2.18.0\n* Tokenizers: 0.19.1", "### BibTeX", "#### Sentence Transformers", "#### TripletLoss" ]

[ "TAGS\n#sentence-transformers #safetensors #distilbert #sentence-similarity #feature-extraction #loss-TripletLoss #en #arxiv-1908.10084 #arxiv-1703.07737 #base_model-distilbert/distilbert-base-uncased #model-index #co2_eq_emissions #endpoints_compatible #region-us \n", "### Model Description\n\n\n* Model Type: Sentence Transformer\n* Base model: distilbert/distilbert-base-uncased\n* Maximum Sequence Length: 512 tokens\n* Output Dimensionality: 768 tokens\n* Similarity Function: Cosine Similarity\n* Training Dataset:\n\t+ sentence-transformers/wikipedia-sections\n* Language: en", "### Model Sources\n\n\n* Documentation: Sentence Transformers Documentation\n* Repository: Sentence Transformers on GitHub\n* Hugging Face: Sentence Transformers on Hugging Face", "### Full Model Architecture\n\n\nUsage\n-----", "### Direct Usage (Sentence Transformers)\n\n\nFirst install the Sentence Transformers library:\n\n\nThen you can load this model and run inference.\n\n\nEvaluation\n----------", "### Metrics", "#### Triplet\n\n\n* Dataset: 'wikipedia-sections-dev'\n* Evaluated with `TripletEvaluator`", "#### Triplet\n\n\n* Dataset: 'wikipedia-sections-test'\n* Evaluated with `TripletEvaluator`\n\n\n\nTraining Details\n----------------", "### Training Dataset", "#### sentence-transformers/wikipedia-sections\n\n\n* Dataset: sentence-transformers/wikipedia-sections at 576bb61\n* Size: 10,000 training samples\n* Columns: `anchor`, `positive`, and `negative`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `TripletLoss` with these parameters:", "### Evaluation Dataset", "#### sentence-transformers/wikipedia-sections\n\n\n* Dataset: sentence-transformers/wikipedia-sections at 576bb61\n* Size: 1,000 evaluation samples\n* Columns: `anchor`, `positive`, and `negative`\n* Approximate statistics based on the first 1000 samples:\n* Samples:\n* Loss: `TripletLoss` with these parameters:", "### Training Hyperparameters", "#### Non-Default Hyperparameters\n\n\n* 'eval\\_strategy': steps\n* 'per\\_device\\_train\\_batch\\_size': 16\n* 'per\\_device\\_eval\\_batch\\_size': 16\n* 'num\\_train\\_epochs': 1\n* 'warmup\\_ratio': 0.1\n* 'fp16': True", "#### All Hyperparameters\n\n\nClick to expand\n* 'overwrite\\_output\\_dir': False\n* 'do\\_predict': False\n* 'eval\\_strategy': steps\n* 'prediction\\_loss\\_only': False\n* 'per\\_device\\_train\\_batch\\_size': 16\n* 'per\\_device\\_eval\\_batch\\_size': 16\n* 'per\\_gpu\\_train\\_batch\\_size': None\n* 'per\\_gpu\\_eval\\_batch\\_size': None\n* 'gradient\\_accumulation\\_steps': 1\n* 'eval\\_accumulation\\_steps': None\n* 'learning\\_rate': 5e-05\n* 'weight\\_decay': 0.0\n* 'adam\\_beta1': 0.9\n* 'adam\\_beta2': 0.999\n* 'adam\\_epsilon': 1e-08\n* 'max\\_grad\\_norm': 1.0\n* 'num\\_train\\_epochs': 1\n* 'max\\_steps': -1\n* 'lr\\_scheduler\\_type': linear\n* 'lr\\_scheduler\\_kwargs': {}\n* 'warmup\\_ratio': 0.1\n* 'warmup\\_steps': 0\n* 'log\\_level': passive\n* 'log\\_level\\_replica': warning\n* 'log\\_on\\_each\\_node': True\n* 'logging\\_nan\\_inf\\_filter': True\n* 'save\\_safetensors': True\n* 'save\\_on\\_each\\_node': False\n* 'save\\_only\\_model': False\n* 'no\\_cuda': False\n* 'use\\_cpu': False\n* 'use\\_mps\\_device': False\n* 'seed': 42\n* 'data\\_seed': None\n* 'jit\\_mode\\_eval': False\n* 'use\\_ipex': False\n* 'bf16': False\n* 'fp16': True\n* 'fp16\\_opt\\_level': O1\n* 'half\\_precision\\_backend': auto\n* 'bf16\\_full\\_eval': False\n* 'fp16\\_full\\_eval': False\n* 'tf32': None\n* 'local\\_rank': 0\n* 'ddp\\_backend': None\n* 'tpu\\_num\\_cores': None\n* 'tpu\\_metrics\\_debug': False\n* 'debug': []\n* 'dataloader\\_drop\\_last': False\n* 'dataloader\\_num\\_workers': 0\n* 'dataloader\\_prefetch\\_factor': None\n* 'past\\_index': -1\n* 'disable\\_tqdm': False\n* 'remove\\_unused\\_columns': True\n* 'label\\_names': None\n* 'load\\_best\\_model\\_at\\_end': False\n* 'ignore\\_data\\_skip': False\n* 'fsdp': []\n* 'fsdp\\_min\\_num\\_params': 0\n* 'fsdp\\_config': {'min\\_num\\_params': 0, 'xla': False, 'xla\\_fsdp\\_v2': False, 'xla\\_fsdp\\_grad\\_ckpt': False}\n* 'fsdp\\_transformer\\_layer\\_cls\\_to\\_wrap': None\n* 'accelerator\\_config': {'split\\_batches': False, 'dispatch\\_batches': None, 'even\\_batches': True, 'use\\_seedable\\_sampler': True, 'non\\_blocking': False, 'gradient\\_accumulation\\_kwargs': None}\n* 'deepspeed': None\n* 'label\\_smoothing\\_factor': 0.0\n* 'optim': adamw\\_torch\n* 'optim\\_args': None\n* 'adafactor': False\n* 'group\\_by\\_length': False\n* 'length\\_column\\_name': length\n* 'ddp\\_find\\_unused\\_parameters': None\n* 'ddp\\_bucket\\_cap\\_mb': None\n* 'ddp\\_broadcast\\_buffers': None\n* 'dataloader\\_pin\\_memory': True\n* 'dataloader\\_persistent\\_workers': False\n* 'skip\\_memory\\_metrics': True\n* 'use\\_legacy\\_prediction\\_loop': False\n* 'push\\_to\\_hub': False\n* 'resume\\_from\\_checkpoint': None\n* 'hub\\_model\\_id': None\n* 'hub\\_strategy': every\\_save\n* 'hub\\_private\\_repo': False\n* 'hub\\_always\\_push': False\n* 'gradient\\_checkpointing': False\n* 'gradient\\_checkpointing\\_kwargs': None\n* 'include\\_inputs\\_for\\_metrics': False\n* 'eval\\_do\\_concat\\_batches': True\n* 'fp16\\_backend': auto\n* 'push\\_to\\_hub\\_model\\_id': None\n* 'push\\_to\\_hub\\_organization': None\n* 'mp\\_parameters':\n* 'auto\\_find\\_batch\\_size': False\n* 'full\\_determinism': False\n* 'torchdynamo': None\n* 'ray\\_scope': last\n* 'ddp\\_timeout': 1800\n* 'torch\\_compile': False\n* 'torch\\_compile\\_backend': None\n* 'torch\\_compile\\_mode': None\n* 'dispatch\\_batches': None\n* 'split\\_batches': None\n* 'include\\_tokens\\_per\\_second': False\n* 'include\\_num\\_input\\_tokens\\_seen': False\n* 'neftune\\_noise\\_alpha': None\n* 'optim\\_target\\_modules': None\n* 'batch\\_sampler': batch\\_sampler\n* 'multi\\_dataset\\_batch\\_sampler': proportional", "### Training Logs", "### Environmental Impact\n\n\nCarbon emissions were measured using CodeCarbon.\n\n\n* Energy Consumed: 0.009 kWh\n* Carbon Emitted: 0.003 kg of CO2\n* Hours Used: 0.045 hours", "### Training Hardware\n\n\n* On Cloud: No\n* GPU Model: 1 x NVIDIA GeForce RTX 3090\n* CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K\n* RAM Size: 31.78 GB", "### Framework Versions\n\n\n* Python: 3.11.6\n* Sentence Transformers: 3.0.0.dev0\n* Transformers: 4.41.0.dev0\n* PyTorch: 2.3.0+cu121\n* Accelerate: 0.26.1\n* Datasets: 2.18.0\n* Tokenizers: 0.19.1", "### BibTeX", "#### Sentence Transformers", "#### TripletLoss" ]

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

[]

[ "en" ]

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

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-google/vit-base-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "tf", "roberta", "question-answering", "generated_from_keras_callback", "base_model:FacebookAI/roberta-base", "license:mit", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7588, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* TensorFlow 2.15.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tf #roberta #question-answering #generated_from_keras_callback #base_model-FacebookAI/roberta-base #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'Adam', 'weight\\_decay': None, 'clipnorm': None, 'global\\_clipnorm': None, 'clipvalue': None, 'use\\_ema': False, 'ema\\_momentum': 0.99, 'ema\\_overwrite\\_frequency': None, 'jit\\_compile': True, 'is\\_legacy\\_optimizer': False, 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 2e-05, 'decay\\_steps': 7588, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* TensorFlow 2.15.0\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-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 #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-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" ]

[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "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 #mistral #text-generation #trl #sft #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "gpt2", "text-generation", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us", "has_space" ]

[]

[ "ru" ]

[]

[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us #has_space \n" ]

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

[]

[ "en" ]

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

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:dominguesm/mambarim-110m", "license:cc-by-4.0", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-dominguesm/mambarim-110m #license-cc-by-4.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-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: 0.0001\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral 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 #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "transformers", "safetensors", "mistral", "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 #mistral #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", "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" ]

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

[]

[]

[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dmis-lab/biobert-base-cased-v1.2", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.2 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 6", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[]

[ "en" ]

[ "# Yoga LLaMA-3 8B Instruct v0.1\n\nThe Yoga LLaMa is an instruction-tuned version of the LLaMA-3 8B model on a custom Yoga dataset. This model is capable of doing question-answering on basic Yoga theory.", "### 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- Model type: An 8B parameter LLaMA-3 model fine-tuned on a custom Yoga dataset.\n- Language(s) (NLP): English\n- License: Apache 2.0\n- Finetuned from model: LLaMA-3", "### Model Sources [optional]\n\n- Repository: URL", "## Uses\n\nIt's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Metrics", "### Results", "## Model Card Authors\n\nVijayasri Iyer", "## Model Card Contact\n\nPlease contact at thisisvij98@URL" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #trl #sft #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Yoga LLaMA-3 8B Instruct v0.1\n\nThe Yoga LLaMa is an instruction-tuned version of the LLaMA-3 8B model on a custom Yoga dataset. This model is capable of doing question-answering on basic Yoga theory.", "### 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- Model type: An 8B parameter LLaMA-3 model fine-tuned on a custom Yoga dataset.\n- Language(s) (NLP): English\n- License: Apache 2.0\n- Finetuned from model: LLaMA-3", "### Model Sources [optional]\n\n- Repository: URL", "## Uses\n\nIt's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Metrics", "### Results", "## Model Card Authors\n\nVijayasri Iyer", "## Model Card Contact\n\nPlease contact at thisisvij98@URL" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral 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 #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "merge", "mergekit", "lazymergekit", "automerger", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:mlabonne/OrpoLlama-3-8B", "license:apache-2.0", "region:us" ]

[]

[]

[ "# LlamaAqua-7B\n\nLlamaAqua-7B is an automated merge created by Maxime Labonne using the following configuration.\n* NousResearch/Meta-Llama-3-8B-Instruct\n* mlabonne/OrpoLlama-3-8B", "## Configuration", "## Usage" ]

[ "TAGS\n#merge #mergekit #lazymergekit #automerger #base_model-NousResearch/Meta-Llama-3-8B-Instruct #base_model-mlabonne/OrpoLlama-3-8B #license-apache-2.0 #region-us \n", "# LlamaAqua-7B\n\nLlamaAqua-7B is an automated merge created by Maxime Labonne using the following configuration.\n* NousResearch/Meta-Llama-3-8B-Instruct\n* mlabonne/OrpoLlama-3-8B", "## Configuration", "## Usage" ]

[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "license:apache-2.0", "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\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 #qwen2 #text-generation #conversational #arxiv-1910.09700 #license-apache-2.0 #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\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", "facebook", "meta", "pytorch", "llama", "llama-3", "en", "base_model:NousResearch/Meta-Llama-3-70B-Instruct", "license:other", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #facebook #meta #pytorch #llama #llama-3 #en #base_model-NousResearch/Meta-Llama-3-70B-Instruct #license-other #endpoints_compatible #region-us \n" ]

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]

[]

[ "en" ]

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

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

[ "diffusers", "safetensors", "pytorch", "unconditional-image-generation", "diffusion-models-class", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

[]

[]

[ "# Example Fine-Tuned Model for Unit 2 of the Diffusion Models Class \r\n\r\nDescribe your model here", "## Usage\r\n\r\n'''python\r\nfrom diffusers import DDPMPipeline\r\n\r\npipeline = DDPMPipeline.from_pretrained('ljw20180420/ddpm-celebahq-finetuned-butterflies-2epochs')\r\nimage = pipeline().images[0]\r\nimage" ]

[ "TAGS\n#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us \n", "# Example Fine-Tuned Model for Unit 2 of the Diffusion Models Class \r\n\r\nDescribe your model here", "## Usage\r\n\r\n'''python\r\nfrom diffusers import DDPMPipeline\r\n\r\npipeline = DDPMPipeline.from_pretrained('ljw20180420/ddpm-celebahq-finetuned-butterflies-2epochs')\r\nimage = pipeline().images[0]\r\nimage" ]

[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]

[]

[]

[ "# PolizzeDonut-SoloGDMarks-5Epochs\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder 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: 2\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\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us \n", "# PolizzeDonut-SoloGDMarks-5Epochs\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder 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: 2\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\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-13b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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-2-13b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

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

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral 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 #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

[]

[]

[ "# DreamBooth - SidXXD/poison-dog-1\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]

[ "TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# DreamBooth - SidXXD/poison-dog-1\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us" ]

[]

[]

[ "# e-palmisano/Phi-3-ITA-mini-128k-instruct-2-Q8_0-GGUF\nThis model was converted to GGUF format from 'e-palmisano/Phi-3-ITA-mini-128k-instruct-2' 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 #endpoints_compatible #region-us \n", "# e-palmisano/Phi-3-ITA-mini-128k-instruct-2-Q8_0-GGUF\nThis model was converted to GGUF format from 'e-palmisano/Phi-3-ITA-mini-128k-instruct-2' 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-2-13b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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-2-13b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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", "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", "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: tingting\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: tingting\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", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral 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 #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "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: tingting\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: tingting\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", "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", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral 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 #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 12\n* total\\_eval\\_batch\\_size: 6\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 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #tensorboard #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #dpo #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 3\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 12\n* total\\_eval\\_batch\\_size: 6\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 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-13b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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-2-13b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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", "roberta", "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 #roberta #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" ]

[ "transformers", "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "en", "base_model:NousResearch/Meta-Llama-3-8B", "license:other", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #facebook #meta #pytorch #llama #llama-3 #en #base_model-NousResearch/Meta-Llama-3-8B #license-other #endpoints_compatible #region-us \n" ]

[ "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", "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: tingting\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: tingting\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\"/>" ]

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

[]

[]

[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral 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 #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-13b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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-2-13b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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", "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", "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: tingting\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: tingting\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", "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: AdilSayedCivility\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: AdilSayedCivility\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", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.4.0.dev20240427\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #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: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.4.0.dev20240427\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "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" ]

[ "gguf", "en", "ja", "dataset:TFMC/imatrix-dataset-for-japanese-llm", "license:llama2", "region:us" ]

[]

[ "en", "ja" ]

[ "# tokyotech-llm-Swallow-13b-instruct-v0.1-gguf\ntokyotech-llmさんが公開しているSwallow-13b-instruct-v0.1のggufフォーマット変換版です。 \n\nimatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。", "## 他のモデル\nmistral \nmmnga/tokyotech-llm-Swallow-MS-7b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-7b-plus-hf-gguf \nmmnga/tokyotech-llm-Swallow-MS-7b-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf \n\nllama2 \nmmnga/tokyotech-llm-Swallow-7b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-13b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-70b-instruct-v0.1-gguf", "## Usage" ]

[ "TAGS\n#gguf #en #ja #dataset-TFMC/imatrix-dataset-for-japanese-llm #license-llama2 #region-us \n", "# tokyotech-llm-Swallow-13b-instruct-v0.1-gguf\ntokyotech-llmさんが公開しているSwallow-13b-instruct-v0.1のggufフォーマット変換版です。 \n\nimatrixのデータはTFMC/imatrix-dataset-for-japanese-llmを使用して作成しました。", "## 他のモデル\nmistral \nmmnga/tokyotech-llm-Swallow-MS-7b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-7b-plus-hf-gguf \nmmnga/tokyotech-llm-Swallow-MS-7b-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-MX-8x7b-NVE-v0.1-gguf \n\nllama2 \nmmnga/tokyotech-llm-Swallow-7b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-13b-instruct-v0.1-gguf \nmmnga/tokyotech-llm-Swallow-70b-instruct-v0.1-gguf", "## Usage" ]

[ "transformers", "safetensors", "mpnet", "feature-extraction", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# phospho-small\n\nThis is a SetFit model that can be used for Text Classification on CPU.\n\nThe model has been trained using an efficient few-shot learning technique.", "## Usage", "## References\n\nThis work was possible thanks to the SetFit library and the work of:\n\nTunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren (2022). Efficient Few-Shot Learning Without Prompts. \n\nArXiv: URL" ]

[ "TAGS\n#transformers #safetensors #mpnet #feature-extraction #en #license-apache-2.0 #endpoints_compatible #region-us \n", "# phospho-small\n\nThis is a SetFit model that can be used for Text Classification on CPU.\n\nThe model has been trained using an efficient few-shot learning technique.", "## Usage", "## References\n\nThis work was possible thanks to the SetFit library and the work of:\n\nTunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren (2022). Efficient Few-Shot Learning Without Prompts. \n\nArXiv: URL" ]

[ "transformers", "safetensors", "unsloth", "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 #unsloth #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-2", "sft", "en", "dataset:LDJnr/Puffin", "base_model:NousResearch/Nous-Puffin-70B", "license:mit", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #llama-2 #sft #en #dataset-LDJnr/Puffin #base_model-NousResearch/Nous-Puffin-70B #license-mit #endpoints_compatible #region-us \n" ]

[ "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", "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", "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: tingting\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: tingting\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", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral 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 #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch", "text-generation", "en", "license:llama3", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#nvidia #chatqa-1.5 #chatqa #llama-3 #pytorch #text-generation #en #license-llama3 #region-us \n" ]

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

[]

[]

[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]

[ "TAGS\n#transformers #tensorboard #safetensors #autotrain #text-generation-inference #text-generation #peft #conversational #license-other #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]

[ "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", "safetensors", "phi3", "text-generation", "text-generation-inference", "unsloth", "mistral", "trl", "conversational", "custom_code", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

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

[ "TAGS\n#transformers #safetensors #phi3 #text-generation #text-generation-inference #unsloth #mistral #trl #conversational #custom_code #en #base_model-unsloth/Phi-3-mini-4k-instruct-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: Phimabri\n- License: apache-2.0\n- Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "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", "mistral", "text-generation", "en", "license:cc-by-nc-4.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "### Precautions to Take\n\n\nUse with Caution: Be aware that the model's output may contain factual inaccuracies or biases.\n\n\nVerify Output: Always verify the model's output with other sources to ensure its accuracy.\n\n\nReport Issues: If you encounter any issues or biases in the model's output, please report them so that they can be addressed in future updates.\n\n\nAvoid Sensitive Applications: Do not use the model for applications where accuracy and reliability are critical, such as medical or financial decision-making.\n\n\nBy understanding the experimental nature of this model and taking the necessary precautions, you can help ensure that it is used responsibly and effectively\n\n\nLicense:\nThis model is strictly non-commercial (cc-by-nc-4.0) use only. The \"Model\" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.\n\n\nDisclaimer: By Downloading And/Or using the model, you fully agree to the license (cc-by-nc-4.0) and its commercial-use restrictions.\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #en #license-cc-by-nc-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Precautions to Take\n\n\nUse with Caution: Be aware that the model's output may contain factual inaccuracies or biases.\n\n\nVerify Output: Always verify the model's output with other sources to ensure its accuracy.\n\n\nReport Issues: If you encounter any issues or biases in the model's output, please report them so that they can be addressed in future updates.\n\n\nAvoid Sensitive Applications: Do not use the model for applications where accuracy and reliability are critical, such as medical or financial decision-making.\n\n\nBy understanding the experimental nature of this model and taking the necessary precautions, you can help ensure that it is used responsibly and effectively\n\n\nLicense:\nThis model is strictly non-commercial (cc-by-nc-4.0) use only. The \"Model\" is completely free (ie. base model, derivates, merges/mixes) to use for non-commercial purposes as long as the the included cc-by-nc-4.0 license in any parent repository, and the non-commercial use statute remains, regardless of other models' licences. The licence can be changed after new model released. If you are to use this model for commercial purpose, Contact me.\n\n\nDisclaimer: By Downloading And/Or using the model, you fully agree to the license (cc-by-nc-4.0) and its commercial-use restrictions.\n\n\nOpen LLM Leaderboard Evaluation Results\n=======================================\n\n\nDetailed results can be found here" ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* total\\_eval\\_batch\\_size: 2\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5.5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 2\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 8\n* total\\_eval\\_batch\\_size: 2\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 200\n* num\\_epochs: 5.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-2-13b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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-2-13b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: tingting\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-2-13b-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", "openvino", "llama", "text-generation", "nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch", "conversational", "en", "arxiv:2401.10225", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "2401.10225" ]

[ "en" ]

[ "### take the whole document as context\n\n\nThis can be applied to the scenario where the whole document can be fitted into the model, so that there is no need to run retrieval over the document.", "### run retrieval to get top-n chunks as context\n\n\nThis can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our Dragon-multiturn retriever which can handle conversatinoal query. In addition, we provide a few documents for users to play with.\n\n\nCorrespondence to\n-----------------\n\n\nZihan Liu (zihanl@URL), Wei Ping (wping@URL)\n\n\n\n```\n\n@article{liu2024chatqa,\n title={ChatQA: Building GPT-4 Level Conversational QA Models},\n author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},\n journal={arXiv preprint arXiv:2401.10225},\n year={2024}}\n\n```\n\nLicense\n-------\n\n\nThe use of this model is governed by the META LLAMA 3 COMMUNITY LICENSE AGREEMENT" ]

[ "TAGS\n#transformers #openvino #llama #text-generation #nvidia #chatqa-1.5 #chatqa #llama-3 #pytorch #conversational #en #arxiv-2401.10225 #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### take the whole document as context\n\n\nThis can be applied to the scenario where the whole document can be fitted into the model, so that there is no need to run retrieval over the document.", "### run retrieval to get top-n chunks as context\n\n\nThis can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our Dragon-multiturn retriever which can handle conversatinoal query. In addition, we provide a few documents for users to play with.\n\n\nCorrespondence to\n-----------------\n\n\nZihan Liu (zihanl@URL), Wei Ping (wping@URL)\n\n\n\n```\n\n@article{liu2024chatqa,\n title={ChatQA: Building GPT-4 Level Conversational QA Models},\n author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan},\n journal={arXiv preprint arXiv:2401.10225},\n year={2024}}\n\n```\n\nLicense\n-------\n\n\nThe use of this model is governed by the META LLAMA 3 COMMUNITY LICENSE AGREEMENT" ]

[ "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", "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", "bert", "text-classification", "generated_from_trainer", "base_model:bert-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: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 1\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 1.13.1\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 1\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 1.13.1\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: HadjYahia\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 #pytorch #gemma #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/gemma-7b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: HadjYahia\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\"/>" ]