Datasets:

---

ftopal

/

huggingface-models

like 0

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "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: 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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #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: 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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:AstraliteHeart/pony-diffusion-v6", "license:creativeml-openrail-m", "region:us" ]

[]

[]

[ "# Vshojo - Matara Kan\n\n<Gallery />", "## Model description \n\nMatara Kan From VShojo!\n\nTrained on 2 outfits, every outfit has a trigger word corresponding to the appearance of the character and suggested prompts that summons related clothes and accesories.\n\nWorks well with 0.7-1.0 weight", "## Trigger words\n\nDebut Outfit: 'matarakandef, extra arms, antennae, cleavage, cleavage cutout, white dress, navel, thighhighs'\n\nSweater Outfit: 'matarakanswt, arthropod girl, necklace, ribbed sweater, purple sweater, cleavage, cleavage cutout, grey pants, black belt, glasses'", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "### License\n\nThis LoRA model is provided under the CreativeML Open RAIL-M license.", "## Restrictions:\n\n- Usage in Generation Services: You are not allowed to use the model in any generation services without proper permission from the original creator.\n\n- Commercial Usage: The sale of the model or any commercial usage is strictly prohibited without explicit written permission from the original creator." ]

[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-AstraliteHeart/pony-diffusion-v6 #license-creativeml-openrail-m #region-us \n", "# Vshojo - Matara Kan\n\n<Gallery />", "## Model description \n\nMatara Kan From VShojo!\n\nTrained on 2 outfits, every outfit has a trigger word corresponding to the appearance of the character and suggested prompts that summons related clothes and accesories.\n\nWorks well with 0.7-1.0 weight", "## Trigger words\n\nDebut Outfit: 'matarakandef, extra arms, antennae, cleavage, cleavage cutout, white dress, navel, thighhighs'\n\nSweater Outfit: 'matarakanswt, arthropod girl, necklace, ribbed sweater, purple sweater, cleavage, cleavage cutout, grey pants, black belt, glasses'", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "### License\n\nThis LoRA model is provided under the CreativeML Open RAIL-M license.", "## Restrictions:\n\n- Usage in Generation Services: You are not allowed to use the model in any generation services without proper permission from the original creator.\n\n- Commercial Usage: The sale of the model or any commercial usage is strictly prohibited without explicit written permission from the original creator." ]

[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "conversational", "en", "dataset:argilla/distilabel-capybara-dpo-7k-binarized", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #safetensors #mixtral #text-generation #moe #conversational #en #dataset-argilla/distilabel-capybara-dpo-7k-binarized #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "pytorch", "bert", "text-classification", "veterinary", "pets", "classification", "vetbert", "BERT", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# VetBERT Disease Syndrome Classifier\n\nThis is a finetuned version of the VetBERT model, designed to classify the disease syndrome within a veterinary clinical note.\n\n\nThis pretrained model is designed for performing NLP tasks related to veterinary clinical notes. The Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes (Hur et al., BioNLP 2020) paper introduced VetBERT model: an initialized Bert Model with ClinicalBERT (Bio+Clinical BERT) and further pretrained on the VetCompass Australia corpus for performing tasks specific to veterinary medicine.", "## Pretraining Data\n\nThe VetBERT model was initialized from Bio_ClinicalBERT model, which was initialized from BERT. The VetBERT model was trained on over 15 million veterinary clincal Records and 1.3 Billion tokens.", "## Pretraining Hyperparameters\n\nDuring the pretraining phase for VetBERT, we used a batch size of 32, a maximum sequence length of 512, and a learning rate of 5 · 10−5. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15 and max predictions per sequence = 20).", "## VetBERT Finetuning\n\nVetBERT was further finetuned on a set of 5002 annotated clinical notes to classifiy the disease syndrome associated with the clinical notes as outlined in the paper: Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes", "## How to use the model\n\nLoad the model via the transformers library:\n\n\n\nPlease cite this article: Brian Hur, Timothy Baldwin, Karin Verspoor, Laura Hardefeldt, and James Gilkerson. 2020. Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 156–166, Online. Association for Computational Linguistics." ]

[ "TAGS\n#transformers #pytorch #bert #text-classification #veterinary #pets #classification #vetbert #BERT #en #autotrain_compatible #endpoints_compatible #region-us \n", "# VetBERT Disease Syndrome Classifier\n\nThis is a finetuned version of the VetBERT model, designed to classify the disease syndrome within a veterinary clinical note.\n\n\nThis pretrained model is designed for performing NLP tasks related to veterinary clinical notes. The Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes (Hur et al., BioNLP 2020) paper introduced VetBERT model: an initialized Bert Model with ClinicalBERT (Bio+Clinical BERT) and further pretrained on the VetCompass Australia corpus for performing tasks specific to veterinary medicine.", "## Pretraining Data\n\nThe VetBERT model was initialized from Bio_ClinicalBERT model, which was initialized from BERT. The VetBERT model was trained on over 15 million veterinary clincal Records and 1.3 Billion tokens.", "## Pretraining Hyperparameters\n\nDuring the pretraining phase for VetBERT, we used a batch size of 32, a maximum sequence length of 512, and a learning rate of 5 · 10−5. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15 and max predictions per sequence = 20).", "## VetBERT Finetuning\n\nVetBERT was further finetuned on a set of 5002 annotated clinical notes to classifiy the disease syndrome associated with the clinical notes as outlined in the paper: Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes", "## How to use the model\n\nLoad the model via the transformers library:\n\n\n\nPlease cite this article: Brian Hur, Timothy Baldwin, Karin Verspoor, Laura Hardefeldt, and James Gilkerson. 2020. Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 156–166, Online. Association for Computational Linguistics." ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID\n\n\n\njmodel/gemma-2b-frozen-mlp.gate_proj-zh__checkpoint-25000", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #gemma #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID\n\n\n\njmodel/gemma-2b-frozen-mlp.gate_proj-zh__checkpoint-25000", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.1\n* Pytorch 2.2.1\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 12\n* eval\\_batch\\_size: 12\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.40.1\n* Pytorch 2.2.1\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "arxiv:2403.15377", "license:apache-2.0", "region:us" ]

[ "2403.15377" ]

[]

[ "# Model Card for InternVideo2\n\nThis modelcard aims to give the model info of 'InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding'.", "## Model Details", "### Model Sources\n\n- Repository: InternVideo2\n- Paper: 2403.15377\n- Point of Contact: mailto:InternVideo Group\n\nIf you find this work useful for your research, please consider citing InternVideo2. Your acknowledgement would greatly help us in continuing to contribute resources to the research community." ]

[ "TAGS\n#arxiv-2403.15377 #license-apache-2.0 #region-us \n", "# Model Card for InternVideo2\n\nThis modelcard aims to give the model info of 'InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding'.", "## Model Details", "### Model Sources\n\n- Repository: InternVideo2\n- Paper: 2403.15377\n- Point of Contact: mailto:InternVideo Group\n\nIf you find this work useful for your research, please consider citing InternVideo2. Your acknowledgement would greatly help us in continuing to contribute resources to the research community." ]

[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

[ "1910.09700" ]

[]

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

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "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" ]

[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

[]

[]

[ "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nUsing Unit 6 Deep Reinforcement Learning, this project was aimed at using an Advantage Actor-Critic (A2C) agent to train a robotic arm \nto move to the right position. The aim is to get a result of >= -3.5 (the result = mean_reward - std of reward). My result was \nMean reward = -0.14 +/- 0.09 (ie. -0.23 to -0.05). I used the A2C Robotic Arm code as a baseline to improve the learning results. There were \n2 main things that I changed: hyperparameters and the implementation of a callback function. \n\n1) Hyperparameters:\n Using URL I was able to determine the hyperparameters that I wanted to tune:\n gamma, gae_lambda, learning_rate and verbose)\n I then used a sample code: URL to find the values\n of each of these hyperparameters. I kept learning_rate and gamma the same. Changing the verbose just gives more information about the tuning\n process (doesn't change much of the training process). As for gae_lambda, setting it slightly lower than 1 allowed for reduced bias and a\n faster learning rate (to get a specific number from 0.9 to 0.99, I worked with AI to find the best parameter).\n\n3) Callback Function:\n A callback function can greatly improve results especially when training for a longer period of time. I used a callback function that checked\n the mean reward every 1000 steps. Using the code provided on this website,\n (URL I wrote a basic code and worked with AI to modify it for the\n intended purpose. Once I gained a more nuanced code, I was able to monitor the training progress throughout and ensure that the model was\n improving with each iteration. This allowed me to modify the code and parameters if needed before the training ended to fine-tune a better\n model overall." ]

[ "TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nUsing Unit 6 Deep Reinforcement Learning, this project was aimed at using an Advantage Actor-Critic (A2C) agent to train a robotic arm \nto move to the right position. The aim is to get a result of >= -3.5 (the result = mean_reward - std of reward). My result was \nMean reward = -0.14 +/- 0.09 (ie. -0.23 to -0.05). I used the A2C Robotic Arm code as a baseline to improve the learning results. There were \n2 main things that I changed: hyperparameters and the implementation of a callback function. \n\n1) Hyperparameters:\n Using URL I was able to determine the hyperparameters that I wanted to tune:\n gamma, gae_lambda, learning_rate and verbose)\n I then used a sample code: URL to find the values\n of each of these hyperparameters. I kept learning_rate and gamma the same. Changing the verbose just gives more information about the tuning\n process (doesn't change much of the training process). As for gae_lambda, setting it slightly lower than 1 allowed for reduced bias and a\n faster learning rate (to get a specific number from 0.9 to 0.99, I worked with AI to find the best parameter).\n\n3) Callback Function:\n A callback function can greatly improve results especially when training for a longer period of time. I used a callback function that checked\n the mean reward every 1000 steps. Using the code provided on this website,\n (URL I wrote a basic code and worked with AI to modify it for the\n intended purpose. Once I gained a more nuanced code, I was able to monitor the training progress throughout and ensure that the model was\n improving with each iteration. This allowed me to modify the code and parameters if needed before the training ended to fine-tune a better\n model overall." ]

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:cagliostrolab/animagine-xl-3.1", "region:us" ]

[]

[]

[ "# HyeraXL\n\n<Gallery />", "## Trigger words\n\nYou should use 'Hyera' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]

[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-cagliostrolab/animagine-xl-3.1 #region-us \n", "# HyeraXL\n\n<Gallery />", "## Trigger words\n\nYou should use 'Hyera' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "diffusers-training", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

[]

[]

[ "# Textual inversion text2image fine-tuning - Nekodigi/textual_inversion_cat\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]

[ "TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #diffusers-training #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# Textual inversion text2image fine-tuning - Nekodigi/textual_inversion_cat\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID\n\n\n\n\nOriginal Name: jmodel/gemma-2b-frozen-mlp-zh__checkpoint-25000", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #gemma #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID\n\n\n\n\nOriginal Name: jmodel/gemma-2b-frozen-mlp-zh__checkpoint-25000", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID\n\n\n\nOriginal Name: jmodel/gemma-2b-full-sft-zh-cosine__checkpoint-25000", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "TAGS\n#transformers #safetensors #gemma #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID\n\n\n\nOriginal Name: jmodel/gemma-2b-full-sft-zh-cosine__checkpoint-25000", "## 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" ]

[ "arxiv:2403.15377", "license:apache-2.0", "region:us" ]

[ "2403.15377" ]

[]

[ "# Model Card for InternVideo2\n\nThis modelcard aims to give the model info of 'InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding'.", "## Model Details", "### Model Sources\n\n- Repository: InternVideo2\n- Paper: 2403.15377\n- Point of Contact: mailto:InternVideo Group\n\nIf you find this work useful for your research, please consider citing InternVideo2. Your acknowledgement would greatly help us in continuing to contribute resources to the research community." ]

[ "TAGS\n#arxiv-2403.15377 #license-apache-2.0 #region-us \n", "# Model Card for InternVideo2\n\nThis modelcard aims to give the model info of 'InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding'.", "## Model Details", "### Model Sources\n\n- Repository: InternVideo2\n- Paper: 2403.15377\n- Point of Contact: mailto:InternVideo Group\n\nIf you find this work useful for your research, please consider citing InternVideo2. Your acknowledgement would greatly help us in continuing to contribute resources to the research community." ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "dataset:mlabonne/guanaco-llama2-1k", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[ "en" ]

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

[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #dataset-mlabonne/guanaco-llama2-1k #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" ]

[ "diffusers", "tensorboard", "safetensors", "arxiv:1910.09700", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers 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#diffusers #tensorboard #safetensors #arxiv-1910.09700 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers 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", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[]

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

[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "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: jurieyel\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: jurieyel\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", "llama", "text-generation", "not-for-all-audiences", "conversational", "en", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "en" ]

[ "# miqu-evil-dpo", "# Model Details", "## Description\nmiqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.\n\nIt is trained with evil-tune method applied.\n\n!image/png", "## Prompt template: Mistral Inst", "## Disclaimer\nThe AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use." ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# miqu-evil-dpo", "# Model Details", "## Description\nmiqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.\n\nIt is trained with evil-tune method applied.\n\n!image/png", "## Prompt template: Mistral Inst", "## Disclaimer\nThe AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use." ]

[ "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", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]

[]

[]

[ "# Taiwan-inquiry_7B_v2.1-awq\n- Model creator: Joseph (Chen-Wei) Li\n- Original model: Taiwan-inquiry_7B_2.1", "## Reference\n- LLM量化實作-AWQ量化" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Taiwan-inquiry_7B_v2.1-awq\n- Model creator: Joseph (Chen-Wei) Li\n- Original model: Taiwan-inquiry_7B_2.1", "## Reference\n- LLM量化實作-AWQ量化" ]

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

[]

[ "en" ]

[ "# Uploaded model\n\n- Developed by: gnokit\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-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 #gguf #llama #text-generation-inference #unsloth #trl #en #base_model-unsloth/tinyllama-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n", "# Uploaded model\n\n- Developed by: gnokit\n- License: apache-2.0\n- Finetuned from model : unsloth/tinyllama-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", "gguf", "merge", "mergekit", "lazymergekit", "DreadPoor/Ceasg-7B-ties", "ResplendentAI/Datura_7B", "en", "base_model:DreadPoor/Dryad-7B-ties", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #merge #mergekit #lazymergekit #DreadPoor/Ceasg-7B-ties #ResplendentAI/Datura_7B #en #base_model-DreadPoor/Dryad-7B-ties #endpoints_compatible #region-us \n" ]

[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "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 #wav2vec2 #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.12.0\n* Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #base_model-facebook/wav2vec2-base #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.12.0\n* Tokenizers 0.15.1" ]

[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "diffusers-training", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

[]

[]

[ "# SDXL LoRA DreamBooth - HoangDuyICT/dress-in-dsdress\n\n<Gallery />", "## Model description", "### These are HoangDuyICT/dress-in-dsdress LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.", "## Download model", "### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'dress-in-dsdress.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:dress-in-dsdress:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'dress-in-dsdress_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'dress-in-dsdress_emb' to your prompt. For example, 'DSDRESS, a woman wearing a white dress with long sleeves'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)", "## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers", "## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt", "## Details\nAll Files & versions.\n\nThe weights were trained using diffusers Advanced Dreambooth Training Script.\n\nLoRA for the text encoder was enabled. False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix." ]

[ "TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #diffusers-training #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n", "# SDXL LoRA DreamBooth - HoangDuyICT/dress-in-dsdress\n\n<Gallery />", "## Model description", "### These are HoangDuyICT/dress-in-dsdress LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.", "## Download model", "### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke\n\n- LoRA: download 'dress-in-dsdress.safetensors' here .\n - Place it on your 'models/Lora' folder.\n - On AUTOMATIC1111, load the LoRA by adding '<lora:dress-in-dsdress:1>' to your prompt. On ComfyUI just load it as a regular LoRA.\n- *Embeddings*: download 'dress-in-dsdress_emb.safetensors' here .\n - Place it on it on your 'embeddings' folder\n - Use it by adding 'dress-in-dsdress_emb' to your prompt. For example, 'DSDRESS, a woman wearing a white dress with long sleeves'\n (you need both the LoRA and the embeddings as they were trained together for this LoRA)", "## Use it with the diffusers library\n\n\n\nFor more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers", "## Trigger words\n\nTo trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:\n\nto trigger concept 'TOK' → use '<s0><s1>' in your prompt", "## Details\nAll Files & versions.\n\nThe weights were trained using diffusers Advanced Dreambooth Training Script.\n\nLoRA for the text encoder was enabled. False.\n\nPivotal tuning was enabled: True.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix." ]

[ "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", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "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: 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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #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: 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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "arxiv:2403.15377", "license:apache-2.0", "region:us" ]

[ "2403.15377" ]

[]

[ "# Model Card for InternVideo2\n\nThis modelcard aims to give the model info of 'InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding'.", "## Model Details", "### Model Sources\n\n- Repository: InternVideo2\n- Paper: 2403.15377\n- Point of Contact: mailto:InternVideo Group\n\nIf you find this work useful for your research, please consider citing InternVideo2. Your acknowledgement would greatly help us in continuing to contribute resources to the research community." ]

[ "TAGS\n#arxiv-2403.15377 #license-apache-2.0 #region-us \n", "# Model Card for InternVideo2\n\nThis modelcard aims to give the model info of 'InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding'.", "## Model Details", "### Model Sources\n\n- Repository: InternVideo2\n- Paper: 2403.15377\n- Point of Contact: mailto:InternVideo Group\n\nIf you find this work useful for your research, please consider citing InternVideo2. Your acknowledgement would greatly help us in continuing to contribute resources to the research community." ]

[ "transformers", "safetensors", "llama", "text-generation", "llama-3", "facebook", "meta", "astronomer", "pretrained", "finetuned", "autotrain_compatible", "endpoints_compatible", "base_model:meta-llama/Meta-Llama-3-70B", "license:other", "text-generation-inference", "region:us" ]

[]

[]

[ "# Llama-3-70B-Special-Tokens-Adjusted\n- Ideal and stable Llama-3-70B for fine-tuning.\n- Original Model creator: Meta\n- Original model: meta-llama/Meta-Llama-3-70B\n- The usage of this model must abide by the Llama 3 Community License. \n- Built with Meta Llama 3\n- Created by David Xue from Astronomer", "## Description\nThis is the exact same model (meta-llama/Meta-Llama-3-70B) with the weights for the input and output embeddings from lm head and embedding matrix adjusted using the mean of the trained tokens for certain tokens that were untrained, which caused widespread issues for people attempting to fine-tune this base model with either adding their own tokens or using existing special tokens.", "## Why We Made This Model\n\nThe Llama 3 base (non-instruct) model, while powerful, came with a significant oversight that some special tokens for instruction following within its architecture were left untrained, potentially derailing further fine-tuning processes. This was first noted by Daniel Han on X, highlighting a critical but fixable flaw in a widely used model.\n\n<img src=\"URL alt=\"graph\" width=\"400\"/>\n\nThe primary goal of releasing a patched version of this model was to address this issue so that the community can utilize the Llama 3 model without facing training instabilities, such as sudden gradient explosions or 'NaN' gradients, or having to go through complicated processes to fix the model themselves before fine-tuning. \n\nNote: specifically for the 70B model, the untrained special tokens did not have all zero values for the embedding weights. So the significance of this problem may not be as severe as it is on the base 8B model. This model was made anyway by the request of the community, though in theory directly fine-tuning should be ok.", "## Details of the Adjustment\n\nThe meta-llama/Meta-Llama-3-70B model was pulled directly from HuggingFace and loaded using transformers. Then, the input embedding and output embedding values are retrieved using 'model.get_input_embeddings().URL' and 'model.get_output_embeddings().URL'. These 2 matrics are identical in shape, with each row representing a token id, and each column representing an embedding feature.\n\nThe special (untrained & problematic) tokens can be found by locating the rows where the entire row of the embedding values are ~~~all zeros~~~ less than 9e-7 (for the 70B model, no row had all zeros, so thresholding using 9e-7 was done to fine under-trained tokens), which imply they were not trained during the pretraining phase of the model from Meta. Such untrained tokens could lead to heavy computational issues, like gradient explosions or 'NaN' gradients, during downstream fine-tuning on specific tasks.\n\n\n<details>\n <summary>See here for a list of the tokens we found that has fit the \"untrained\" profile described:</summary>\n['À',\n 'Á',\n 'õ',\n 'ö',\n '÷',\n 'ø',\n 'ù',\n 'ú',\n 'û',\n 'ü',\n 'ý',\n 'þ',\n 'ÿ',\n '\">ččĊ',\n ';čččĊ',\n 'ĉTokenNameIdentifier',\n 'ĠForCanBeConverted',\n 'ĠForCanBeConvertedToF',\n 'PostalCodesNL',\n '$PostalCodesNL',\n 'useRalative',\n 'Û±Û',\n 'аÑĢакÑĤ',\n 'аÑĤиÑģÑı',\n 'иÑĤиÑģÑı',\n 'ávajÃŃcÃŃ',\n 'Ä°TESÄ°',\n 'илакÑĤи',\n 'илаÑģÑı',\n 'ÑĭÑŁN',\n 'ÐİÑĭÑŁN',\n 'ılmaktadır',\n 'ÐİÑĭÑŁNÐİÑĭÑŁN',\n 'ıldıģında',\n '<|reserved_special_token_0|>',\n '<|reserved_special_token_1|>',\n '<|reserved_special_token_2|>',\n '<|reserved_special_token_3|>',\n '<|start_header_id|>',\n '<|end_header_id|>',\n '<|reserved_special_token_4|>',\n '<|eot_id|>',\n '<|reserved_special_token_5|>',\n '<|reserved_special_token_6|>',\n '<|reserved_special_token_7|>',\n '<|reserved_special_token_8|>',\n '<|reserved_special_token_9|>',\n '<|reserved_special_token_10|>',\n '<|reserved_special_token_11|>',\n '<|reserved_special_token_12|>',\n '<|reserved_special_token_13|>',\n '<|reserved_special_token_14|>',\n '<|reserved_special_token_15|>',\n '<|reserved_special_token_16|>',\n '<|reserved_special_token_17|>',\n '<|reserved_special_token_18|>',\n '<|reserved_special_token_19|>',\n '<|reserved_special_token_20|>',\n '<|reserved_special_token_21|>',\n '<|reserved_special_token_22|>',\n '<|reserved_special_token_23|>',\n '<|reserved_special_token_24|>',\n '<|reserved_special_token_25|>',\n '<|reserved_special_token_26|>',\n '<|reserved_special_token_27|>',\n '<|reserved_special_token_28|>',\n '<|reserved_special_token_29|>',\n '<|reserved_special_token_30|>',\n '<|reserved_special_token_31|>',\n '<|reserved_special_token_32|>',\n '<|reserved_special_token_33|>',\n '<|reserved_special_token_34|>',\n '<|reserved_special_token_35|>',\n '<|reserved_special_token_36|>',\n '<|reserved_special_token_37|>',\n '<|reserved_special_token_38|>',\n '<|reserved_special_token_39|>',\n '<|reserved_special_token_40|>',\n '<|reserved_special_token_41|>',\n '<|reserved_special_token_42|>',\n '<|reserved_special_token_43|>',\n '<|reserved_special_token_44|>',\n '<|reserved_special_token_45|>',\n '<|reserved_special_token_46|>',\n '<|reserved_special_token_47|>',\n '<|reserved_special_token_48|>',\n '<|reserved_special_token_49|>',\n '<|reserved_special_token_50|>',\n '<|reserved_special_token_51|>',\n '<|reserved_special_token_52|>',\n '<|reserved_special_token_53|>',\n '<|reserved_special_token_54|>',\n '<|reserved_special_token_55|>',\n '<|reserved_special_token_56|>',\n '<|reserved_special_token_57|>',\n '<|reserved_special_token_58|>',\n '<|reserved_special_token_59|>',\n '<|reserved_special_token_60|>',\n '<|reserved_special_token_61|>',\n '<|reserved_special_token_62|>',\n '<|reserved_special_token_63|>',\n '<|reserved_special_token_64|>',\n '<|reserved_special_token_65|>',\n '<|reserved_special_token_66|>',\n '<|reserved_special_token_67|>',\n '<|reserved_special_token_68|>',\n '<|reserved_special_token_69|>',\n '<|reserved_special_token_70|>',\n '<|reserved_special_token_71|>',\n '<|reserved_special_token_72|>',\n '<|reserved_special_token_73|>',\n '<|reserved_special_token_74|>',\n '<|reserved_special_token_75|>',\n '<|reserved_special_token_76|>',\n '<|reserved_special_token_77|>',\n '<|reserved_special_token_78|>',\n '<|reserved_special_token_79|>',\n '<|reserved_special_token_80|>',\n '<|reserved_special_token_81|>',\n '<|reserved_special_token_82|>',\n '<|reserved_special_token_83|>',\n '<|reserved_special_token_84|>',\n '<|reserved_special_token_85|>',\n '<|reserved_special_token_86|>',\n '<|reserved_special_token_87|>',\n '<|reserved_special_token_88|>',\n '<|reserved_special_token_89|>',\n '<|reserved_special_token_90|>',\n '<|reserved_special_token_91|>',\n '<|reserved_special_token_92|>',\n '<|reserved_special_token_93|>',\n '<|reserved_special_token_94|>',\n '<|reserved_special_token_95|>',\n '<|reserved_special_token_96|>',\n '<|reserved_special_token_97|>',\n '<|reserved_special_token_98|>',\n '<|reserved_special_token_99|>',\n '<|reserved_special_token_100|>',\n '<|reserved_special_token_101|>',\n '<|reserved_special_token_102|>',\n '<|reserved_special_token_103|>',\n '<|reserved_special_token_104|>',\n '<|reserved_special_token_105|>',\n '<|reserved_special_token_106|>',\n '<|reserved_special_token_107|>',\n '<|reserved_special_token_108|>',\n '<|reserved_special_token_109|>',\n '<|reserved_special_token_110|>',\n '<|reserved_special_token_111|>',\n '<|reserved_special_token_112|>',\n '<|reserved_special_token_113|>',\n '<|reserved_special_token_114|>',\n '<|reserved_special_token_115|>',\n '<|reserved_special_token_116|>',\n '<|reserved_special_token_117|>',\n '<|reserved_special_token_118|>',\n '<|reserved_special_token_119|>',\n '<|reserved_special_token_120|>',\n '<|reserved_special_token_121|>',\n '<|reserved_special_token_122|>',\n '<|reserved_special_token_123|>',\n '<|reserved_special_token_124|>',\n '<|reserved_special_token_125|>',\n '<|reserved_special_token_126|>',\n '<|reserved_special_token_127|>',\n '<|reserved_special_token_128|>',\n '<|reserved_special_token_129|>',\n '<|reserved_special_token_130|>',\n '<|reserved_special_token_131|>',\n '<|reserved_special_token_132|>',\n '<|reserved_special_token_133|>',\n '<|reserved_special_token_134|>',\n '<|reserved_special_token_135|>',\n '<|reserved_special_token_136|>',\n '<|reserved_special_token_137|>',\n '<|reserved_special_token_138|>',\n '<|reserved_special_token_139|>',\n '<|reserved_special_token_140|>',\n '<|reserved_special_token_141|>',\n '<|reserved_special_token_142|>',\n '<|reserved_special_token_143|>',\n '<|reserved_special_token_144|>',\n '<|reserved_special_token_145|>',\n '<|reserved_special_token_146|>',\n '<|reserved_special_token_147|>',\n '<|reserved_special_token_148|>',\n '<|reserved_special_token_149|>',\n '<|reserved_special_token_150|>',\n '<|reserved_special_token_151|>',\n '<|reserved_special_token_152|>',\n '<|reserved_special_token_153|>',\n '<|reserved_special_token_154|>',\n '<|reserved_special_token_155|>',\n '<|reserved_special_token_156|>',\n '<|reserved_special_token_157|>',\n '<|reserved_special_token_158|>',\n '<|reserved_special_token_159|>',\n '<|reserved_special_token_160|>',\n '<|reserved_special_token_161|>',\n '<|reserved_special_token_162|>',\n '<|reserved_special_token_163|>',\n '<|reserved_special_token_164|>',\n '<|reserved_special_token_165|>',\n '<|reserved_special_token_166|>',\n '<|reserved_special_token_167|>',\n '<|reserved_special_token_168|>',\n '<|reserved_special_token_169|>',\n '<|reserved_special_token_170|>',\n '<|reserved_special_token_171|>',\n '<|reserved_special_token_172|>',\n '<|reserved_special_token_173|>',\n '<|reserved_special_token_174|>',\n '<|reserved_special_token_175|>',\n '<|reserved_special_token_176|>',\n '<|reserved_special_token_177|>',\n '<|reserved_special_token_178|>',\n '<|reserved_special_token_179|>',\n '<|reserved_special_token_180|>',\n '<|reserved_special_token_181|>',\n '<|reserved_special_token_182|>',\n '<|reserved_special_token_183|>',\n '<|reserved_special_token_184|>',\n '<|reserved_special_token_185|>',\n '<|reserved_special_token_186|>',\n '<|reserved_special_token_187|>',\n '<|reserved_special_token_188|>',\n '<|reserved_special_token_189|>',\n '<|reserved_special_token_190|>',\n '<|reserved_special_token_191|>',\n '<|reserved_special_token_192|>',\n '<|reserved_special_token_193|>',\n '<|reserved_special_token_194|>',\n '<|reserved_special_token_195|>',\n '<|reserved_special_token_196|>',\n '<|reserved_special_token_197|>',\n '<|reserved_special_token_198|>',\n '<|reserved_special_token_199|>',\n '<|reserved_special_token_200|>',\n '<|reserved_special_token_201|>',\n '<|reserved_special_token_202|>',\n '<|reserved_special_token_203|>',\n '<|reserved_special_token_204|>',\n '<|reserved_special_token_205|>',\n '<|reserved_special_token_206|>',\n '<|reserved_special_token_207|>',\n '<|reserved_special_token_208|>',\n '<|reserved_special_token_209|>',\n '<|reserved_special_token_210|>',\n '<|reserved_special_token_211|>',\n '<|reserved_special_token_212|>',\n '<|reserved_special_token_213|>',\n '<|reserved_special_token_214|>',\n '<|reserved_special_token_215|>',\n '<|reserved_special_token_216|>',\n '<|reserved_special_token_217|>',\n '<|reserved_special_token_218|>',\n '<|reserved_special_token_219|>',\n '<|reserved_special_token_220|>',\n '<|reserved_special_token_221|>',\n '<|reserved_special_token_222|>',\n '<|reserved_special_token_223|>',\n '<|reserved_special_token_224|>',\n '<|reserved_special_token_225|>',\n '<|reserved_special_token_226|>',\n '<|reserved_special_token_227|>',\n '<|reserved_special_token_228|>',\n '<|reserved_special_token_229|>',\n '<|reserved_special_token_230|>',\n '<|reserved_special_token_231|>',\n '<|reserved_special_token_232|>',\n '<|reserved_special_token_233|>',\n '<|reserved_special_token_234|>',\n '<|reserved_special_token_235|>',\n '<|reserved_special_token_236|>',\n '<|reserved_special_token_237|>',\n '<|reserved_special_token_238|>',\n '<|reserved_special_token_239|>',\n '<|reserved_special_token_240|>',\n '<|reserved_special_token_241|>',\n '<|reserved_special_token_242|>',\n '<|reserved_special_token_243|>',\n '<|reserved_special_token_244|>',\n '<|reserved_special_token_245|>',\n '<|reserved_special_token_246|>',\n '<|reserved_special_token_247|>',\n '<|reserved_special_token_248|>',\n '<|reserved_special_token_249|>',\n '<|reserved_special_token_250|>']\n</details>\n\n\nOnce these untrained tokens are identified, the average of trained tokens can be calculated by using the sums of embedding values of trained tokens for each feature/column and divided by the number of trained. This is done for both input and output matrices.\n\nLastly, the problematic token's rows in the 2 embedding matrics are set to the computed mean, thus completing the adjustment.", "## Contributors\n- David Xue, Machine Learning Engineer from Astronomer" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #llama-3 #facebook #meta #astronomer #pretrained #finetuned #autotrain_compatible #endpoints_compatible #base_model-meta-llama/Meta-Llama-3-70B #license-other #text-generation-inference #region-us \n", "# Llama-3-70B-Special-Tokens-Adjusted\n- Ideal and stable Llama-3-70B for fine-tuning.\n- Original Model creator: Meta\n- Original model: meta-llama/Meta-Llama-3-70B\n- The usage of this model must abide by the Llama 3 Community License. \n- Built with Meta Llama 3\n- Created by David Xue from Astronomer", "## Description\nThis is the exact same model (meta-llama/Meta-Llama-3-70B) with the weights for the input and output embeddings from lm head and embedding matrix adjusted using the mean of the trained tokens for certain tokens that were untrained, which caused widespread issues for people attempting to fine-tune this base model with either adding their own tokens or using existing special tokens.", "## Why We Made This Model\n\nThe Llama 3 base (non-instruct) model, while powerful, came with a significant oversight that some special tokens for instruction following within its architecture were left untrained, potentially derailing further fine-tuning processes. This was first noted by Daniel Han on X, highlighting a critical but fixable flaw in a widely used model.\n\n<img src=\"URL alt=\"graph\" width=\"400\"/>\n\nThe primary goal of releasing a patched version of this model was to address this issue so that the community can utilize the Llama 3 model without facing training instabilities, such as sudden gradient explosions or 'NaN' gradients, or having to go through complicated processes to fix the model themselves before fine-tuning. \n\nNote: specifically for the 70B model, the untrained special tokens did not have all zero values for the embedding weights. So the significance of this problem may not be as severe as it is on the base 8B model. This model was made anyway by the request of the community, though in theory directly fine-tuning should be ok.", "## Details of the Adjustment\n\nThe meta-llama/Meta-Llama-3-70B model was pulled directly from HuggingFace and loaded using transformers. Then, the input embedding and output embedding values are retrieved using 'model.get_input_embeddings().URL' and 'model.get_output_embeddings().URL'. These 2 matrics are identical in shape, with each row representing a token id, and each column representing an embedding feature.\n\nThe special (untrained & problematic) tokens can be found by locating the rows where the entire row of the embedding values are ~~~all zeros~~~ less than 9e-7 (for the 70B model, no row had all zeros, so thresholding using 9e-7 was done to fine under-trained tokens), which imply they were not trained during the pretraining phase of the model from Meta. Such untrained tokens could lead to heavy computational issues, like gradient explosions or 'NaN' gradients, during downstream fine-tuning on specific tasks.\n\n\n<details>\n <summary>See here for a list of the tokens we found that has fit the \"untrained\" profile described:</summary>\n['À',\n 'Á',\n 'õ',\n 'ö',\n '÷',\n 'ø',\n 'ù',\n 'ú',\n 'û',\n 'ü',\n 'ý',\n 'þ',\n 'ÿ',\n '\">ččĊ',\n ';čččĊ',\n 'ĉTokenNameIdentifier',\n 'ĠForCanBeConverted',\n 'ĠForCanBeConvertedToF',\n 'PostalCodesNL',\n '$PostalCodesNL',\n 'useRalative',\n 'Û±Û',\n 'аÑĢакÑĤ',\n 'аÑĤиÑģÑı',\n 'иÑĤиÑģÑı',\n 'ávajÃŃcÃŃ',\n 'Ä°TESÄ°',\n 'илакÑĤи',\n 'илаÑģÑı',\n 'ÑĭÑŁN',\n 'ÐİÑĭÑŁN',\n 'ılmaktadır',\n 'ÐİÑĭÑŁNÐİÑĭÑŁN',\n 'ıldıģında',\n '<|reserved_special_token_0|>',\n '<|reserved_special_token_1|>',\n '<|reserved_special_token_2|>',\n '<|reserved_special_token_3|>',\n '<|start_header_id|>',\n '<|end_header_id|>',\n '<|reserved_special_token_4|>',\n '<|eot_id|>',\n '<|reserved_special_token_5|>',\n '<|reserved_special_token_6|>',\n '<|reserved_special_token_7|>',\n '<|reserved_special_token_8|>',\n '<|reserved_special_token_9|>',\n '<|reserved_special_token_10|>',\n '<|reserved_special_token_11|>',\n '<|reserved_special_token_12|>',\n '<|reserved_special_token_13|>',\n '<|reserved_special_token_14|>',\n '<|reserved_special_token_15|>',\n '<|reserved_special_token_16|>',\n '<|reserved_special_token_17|>',\n '<|reserved_special_token_18|>',\n '<|reserved_special_token_19|>',\n '<|reserved_special_token_20|>',\n '<|reserved_special_token_21|>',\n '<|reserved_special_token_22|>',\n '<|reserved_special_token_23|>',\n '<|reserved_special_token_24|>',\n '<|reserved_special_token_25|>',\n '<|reserved_special_token_26|>',\n '<|reserved_special_token_27|>',\n '<|reserved_special_token_28|>',\n '<|reserved_special_token_29|>',\n '<|reserved_special_token_30|>',\n '<|reserved_special_token_31|>',\n '<|reserved_special_token_32|>',\n '<|reserved_special_token_33|>',\n '<|reserved_special_token_34|>',\n '<|reserved_special_token_35|>',\n '<|reserved_special_token_36|>',\n '<|reserved_special_token_37|>',\n '<|reserved_special_token_38|>',\n '<|reserved_special_token_39|>',\n '<|reserved_special_token_40|>',\n '<|reserved_special_token_41|>',\n '<|reserved_special_token_42|>',\n '<|reserved_special_token_43|>',\n '<|reserved_special_token_44|>',\n '<|reserved_special_token_45|>',\n '<|reserved_special_token_46|>',\n '<|reserved_special_token_47|>',\n '<|reserved_special_token_48|>',\n '<|reserved_special_token_49|>',\n '<|reserved_special_token_50|>',\n '<|reserved_special_token_51|>',\n '<|reserved_special_token_52|>',\n '<|reserved_special_token_53|>',\n '<|reserved_special_token_54|>',\n '<|reserved_special_token_55|>',\n '<|reserved_special_token_56|>',\n '<|reserved_special_token_57|>',\n '<|reserved_special_token_58|>',\n '<|reserved_special_token_59|>',\n '<|reserved_special_token_60|>',\n '<|reserved_special_token_61|>',\n '<|reserved_special_token_62|>',\n '<|reserved_special_token_63|>',\n '<|reserved_special_token_64|>',\n '<|reserved_special_token_65|>',\n '<|reserved_special_token_66|>',\n '<|reserved_special_token_67|>',\n '<|reserved_special_token_68|>',\n '<|reserved_special_token_69|>',\n '<|reserved_special_token_70|>',\n '<|reserved_special_token_71|>',\n '<|reserved_special_token_72|>',\n '<|reserved_special_token_73|>',\n '<|reserved_special_token_74|>',\n '<|reserved_special_token_75|>',\n '<|reserved_special_token_76|>',\n '<|reserved_special_token_77|>',\n '<|reserved_special_token_78|>',\n '<|reserved_special_token_79|>',\n '<|reserved_special_token_80|>',\n '<|reserved_special_token_81|>',\n '<|reserved_special_token_82|>',\n '<|reserved_special_token_83|>',\n '<|reserved_special_token_84|>',\n '<|reserved_special_token_85|>',\n '<|reserved_special_token_86|>',\n '<|reserved_special_token_87|>',\n '<|reserved_special_token_88|>',\n '<|reserved_special_token_89|>',\n '<|reserved_special_token_90|>',\n '<|reserved_special_token_91|>',\n '<|reserved_special_token_92|>',\n '<|reserved_special_token_93|>',\n '<|reserved_special_token_94|>',\n '<|reserved_special_token_95|>',\n '<|reserved_special_token_96|>',\n '<|reserved_special_token_97|>',\n '<|reserved_special_token_98|>',\n '<|reserved_special_token_99|>',\n '<|reserved_special_token_100|>',\n '<|reserved_special_token_101|>',\n '<|reserved_special_token_102|>',\n '<|reserved_special_token_103|>',\n '<|reserved_special_token_104|>',\n '<|reserved_special_token_105|>',\n '<|reserved_special_token_106|>',\n '<|reserved_special_token_107|>',\n '<|reserved_special_token_108|>',\n '<|reserved_special_token_109|>',\n '<|reserved_special_token_110|>',\n '<|reserved_special_token_111|>',\n '<|reserved_special_token_112|>',\n '<|reserved_special_token_113|>',\n '<|reserved_special_token_114|>',\n '<|reserved_special_token_115|>',\n '<|reserved_special_token_116|>',\n '<|reserved_special_token_117|>',\n '<|reserved_special_token_118|>',\n '<|reserved_special_token_119|>',\n '<|reserved_special_token_120|>',\n '<|reserved_special_token_121|>',\n '<|reserved_special_token_122|>',\n '<|reserved_special_token_123|>',\n '<|reserved_special_token_124|>',\n '<|reserved_special_token_125|>',\n '<|reserved_special_token_126|>',\n '<|reserved_special_token_127|>',\n '<|reserved_special_token_128|>',\n '<|reserved_special_token_129|>',\n '<|reserved_special_token_130|>',\n '<|reserved_special_token_131|>',\n '<|reserved_special_token_132|>',\n '<|reserved_special_token_133|>',\n '<|reserved_special_token_134|>',\n '<|reserved_special_token_135|>',\n '<|reserved_special_token_136|>',\n '<|reserved_special_token_137|>',\n '<|reserved_special_token_138|>',\n '<|reserved_special_token_139|>',\n '<|reserved_special_token_140|>',\n '<|reserved_special_token_141|>',\n '<|reserved_special_token_142|>',\n '<|reserved_special_token_143|>',\n '<|reserved_special_token_144|>',\n '<|reserved_special_token_145|>',\n '<|reserved_special_token_146|>',\n '<|reserved_special_token_147|>',\n '<|reserved_special_token_148|>',\n '<|reserved_special_token_149|>',\n '<|reserved_special_token_150|>',\n '<|reserved_special_token_151|>',\n '<|reserved_special_token_152|>',\n '<|reserved_special_token_153|>',\n '<|reserved_special_token_154|>',\n '<|reserved_special_token_155|>',\n '<|reserved_special_token_156|>',\n '<|reserved_special_token_157|>',\n '<|reserved_special_token_158|>',\n '<|reserved_special_token_159|>',\n '<|reserved_special_token_160|>',\n '<|reserved_special_token_161|>',\n '<|reserved_special_token_162|>',\n '<|reserved_special_token_163|>',\n '<|reserved_special_token_164|>',\n '<|reserved_special_token_165|>',\n '<|reserved_special_token_166|>',\n '<|reserved_special_token_167|>',\n '<|reserved_special_token_168|>',\n '<|reserved_special_token_169|>',\n '<|reserved_special_token_170|>',\n '<|reserved_special_token_171|>',\n '<|reserved_special_token_172|>',\n '<|reserved_special_token_173|>',\n '<|reserved_special_token_174|>',\n '<|reserved_special_token_175|>',\n '<|reserved_special_token_176|>',\n '<|reserved_special_token_177|>',\n '<|reserved_special_token_178|>',\n '<|reserved_special_token_179|>',\n '<|reserved_special_token_180|>',\n '<|reserved_special_token_181|>',\n '<|reserved_special_token_182|>',\n '<|reserved_special_token_183|>',\n '<|reserved_special_token_184|>',\n '<|reserved_special_token_185|>',\n '<|reserved_special_token_186|>',\n '<|reserved_special_token_187|>',\n '<|reserved_special_token_188|>',\n '<|reserved_special_token_189|>',\n '<|reserved_special_token_190|>',\n '<|reserved_special_token_191|>',\n '<|reserved_special_token_192|>',\n '<|reserved_special_token_193|>',\n '<|reserved_special_token_194|>',\n '<|reserved_special_token_195|>',\n '<|reserved_special_token_196|>',\n '<|reserved_special_token_197|>',\n '<|reserved_special_token_198|>',\n '<|reserved_special_token_199|>',\n '<|reserved_special_token_200|>',\n '<|reserved_special_token_201|>',\n '<|reserved_special_token_202|>',\n '<|reserved_special_token_203|>',\n '<|reserved_special_token_204|>',\n '<|reserved_special_token_205|>',\n '<|reserved_special_token_206|>',\n '<|reserved_special_token_207|>',\n '<|reserved_special_token_208|>',\n '<|reserved_special_token_209|>',\n '<|reserved_special_token_210|>',\n '<|reserved_special_token_211|>',\n '<|reserved_special_token_212|>',\n '<|reserved_special_token_213|>',\n '<|reserved_special_token_214|>',\n '<|reserved_special_token_215|>',\n '<|reserved_special_token_216|>',\n '<|reserved_special_token_217|>',\n '<|reserved_special_token_218|>',\n '<|reserved_special_token_219|>',\n '<|reserved_special_token_220|>',\n '<|reserved_special_token_221|>',\n '<|reserved_special_token_222|>',\n '<|reserved_special_token_223|>',\n '<|reserved_special_token_224|>',\n '<|reserved_special_token_225|>',\n '<|reserved_special_token_226|>',\n '<|reserved_special_token_227|>',\n '<|reserved_special_token_228|>',\n '<|reserved_special_token_229|>',\n '<|reserved_special_token_230|>',\n '<|reserved_special_token_231|>',\n '<|reserved_special_token_232|>',\n '<|reserved_special_token_233|>',\n '<|reserved_special_token_234|>',\n '<|reserved_special_token_235|>',\n '<|reserved_special_token_236|>',\n '<|reserved_special_token_237|>',\n '<|reserved_special_token_238|>',\n '<|reserved_special_token_239|>',\n '<|reserved_special_token_240|>',\n '<|reserved_special_token_241|>',\n '<|reserved_special_token_242|>',\n '<|reserved_special_token_243|>',\n '<|reserved_special_token_244|>',\n '<|reserved_special_token_245|>',\n '<|reserved_special_token_246|>',\n '<|reserved_special_token_247|>',\n '<|reserved_special_token_248|>',\n '<|reserved_special_token_249|>',\n '<|reserved_special_token_250|>']\n</details>\n\n\nOnce these untrained tokens are identified, the average of trained tokens can be calculated by using the sums of embedding values of trained tokens for each feature/column and divided by the number of trained. This is done for both input and output matrices.\n\nLastly, the problematic token's rows in the 2 embedding matrics are set to the computed mean, thus completing the adjustment.", "## Contributors\n- David Xue, Machine Learning Engineer from Astronomer" ]

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

[]

[]

[ "# opt-350m_ft_test2\n\nThis model was trained from scratch on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# opt-350m_ft_test2\n\nThis model was trained from scratch on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #mobilebert #text-classification #generated_from_trainer #base_model-google/mobilebert-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 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "sentence-transformers", "safetensors", "roberta", "feature-extraction", "sentence-similarity", "transformers", "endpoints_compatible", "region:us" ]

[]

[]

[ "# approximatelylinear/distilroberta-base-nli-matryoshka\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 4403 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #safetensors #roberta #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us \n", "# approximatelylinear/distilroberta-base-nli-matryoshka\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Usage (HuggingFace Transformers)\nWithout sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader' of length 4403 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "base_model:ChaoticNeutrals/IQ_Test_l3_8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "6-bit", "region:us" ]

[]

[ "en" ]

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

[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #base_model-ChaoticNeutrals/IQ_Test_l3_8B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us \n", "# Chaos RP\n\n!image/png\n\nA chaotic force beckons for you, will you heed her call?\n\nBuilt upon an intelligent foundation and tuned for roleplaying, this model will fulfill your wildest fantasies with the bare minimum of effort.\n\nEnjoy!" ]

[ "transformers", "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" ]

[ "diffusers", "text-to-image", "diffusers-training", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

[]

[]

[ "# SDXL LoRA DreamBooth - krishna4244/lora-trained-xl\n\n<Gallery />", "## Model description\n\nThese are krishna4244/lora-trained-xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of sks bulilding to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]

[ "TAGS\n#diffusers #text-to-image #diffusers-training #lora #template-sd-lora #stable-diffusion-xl #stable-diffusion-xl-diffusers #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n", "# SDXL LoRA DreamBooth - krishna4244/lora-trained-xl\n\n<Gallery />", "## Model description\n\nThese are krishna4244/lora-trained-xl LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of sks bulilding to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]

[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "# finetuning-sentiment-model\n\nThis model is a fine-tuned version of GPT2 on an IMDb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3105\n- Accuracy: 0.92", "## 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: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.28.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.13.3" ]

[ "TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# finetuning-sentiment-model\n\nThis model is a fine-tuned version of GPT2 on an IMDb dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.3105\n- Accuracy: 0.92", "## 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: 1\n- eval_batch_size: 1\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.28.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.13.3" ]

[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "endpoints_compatible", "region:us" ]

[]

[]

[ "# AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 482 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "TAGS\n#sentence-transformers #safetensors #mpnet #feature-extraction #sentence-similarity #endpoints_compatible #region-us \n", "# AhmetAytar/all-mpnet-base-v2-fine-tuned_5_textbook\n\nThis is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.", "## Usage (Sentence-Transformers)\n\nUsing this model becomes easy when you have sentence-transformers installed:\n\n\n\nThen you can use the model like this:", "## Evaluation Results\n\n\n\nFor an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL", "## Training\nThe model was trained with the parameters:\n\nDataLoader:\n\n'URL.dataloader.DataLoader' of length 482 with parameters:\n\n\nLoss:\n\n'sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss' with parameters:\n \n\nParameters of the fit()-Method:", "## Full Model Architecture", "## Citing & Authors" ]

[ "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" ]

[ "gguf", "llama-cpp", "gguf-my-repo", "fr", "it", "de", "es", "en", "license:apache-2.0", "region:us" ]

[]

[ "fr", "it", "de", "es", "en" ]

[ "# kat33/Mixtral-8x7B-Instruct-v0.1-Q4_K_S-GGUF\nThis model was converted to GGUF format from 'mistralai/Mixtral-8x7B-Instruct-v0.1' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "TAGS\n#gguf #llama-cpp #gguf-my-repo #fr #it #de #es #en #license-apache-2.0 #region-us \n", "# kat33/Mixtral-8x7B-Instruct-v0.1-Q4_K_S-GGUF\nThis model was converted to GGUF format from 'mistralai/Mixtral-8x7B-Instruct-v0.1' 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", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# opt-350m_ft_test3\n\nThis model was trained from scratch on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# opt-350m_ft_test3\n\nThis model was trained from scratch on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "transformers", "gguf", "moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "allknowingroger/MultiverseEx26-7B-slerp", "OpenBuddy/openbuddy-mistral2-7b-v20.2-32k", "en", "base_model:allknowingroger/MultiverseBuddy-15B-MoE", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #moe #frankenmoe #merge #mergekit #lazymergekit #allknowingroger/MultiverseEx26-7B-slerp #OpenBuddy/openbuddy-mistral2-7b-v20.2-32k #en #base_model-allknowingroger/MultiverseBuddy-15B-MoE #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "transformers", "safetensors", "llama", "text-generation", "not-for-all-audiences", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# This model isn't particularly great. It's just an undercooked experiment.\n\nReleasing it anyways just in case it accidentally makes good merge meat.", "# It also has a tendency to produce mature content without warning. \n\nThis model is tuned off of the base Llama-3-8B model. \n\nI adapted the leaked Undi dataset into training samples for custom formatting. This model pretty much only functions properly in SillyTavern. \n\nThe formatting has two pairs of pseudotokens\n\n\n\nThe self attention modules were fine tuned separately on this dataset and the pseudotokens were chosen because they made logical sense with respect to the character giving a reply without allowing the model to 'connect the dots' during training and figure out that it is indeed an AI language model.\n\nAfter this was done all modules were then finetuned together on the dendrite dataset in order to connect the changes made to the attention modules.\n\nSo with regards to building a SillyTavern prompt template you basically want the entire story string and any additional stylistic instructions enclosed in the [EGO] tags and then the user messages enclosed in [SEEN] tags. \n\nIt doesn't give particularly verbose replies unless you're continueing a roleplay with verbose messages. Otherwise it's pretty bad. \n\nTraining was done using qlora-pipe\n\nGGUFs care of Qaunt Cartel\n\nexl2 rpcal care of Qaunt Cartel" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #not-for-all-audiences #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# This model isn't particularly great. It's just an undercooked experiment.\n\nReleasing it anyways just in case it accidentally makes good merge meat.", "# It also has a tendency to produce mature content without warning. \n\nThis model is tuned off of the base Llama-3-8B model. \n\nI adapted the leaked Undi dataset into training samples for custom formatting. This model pretty much only functions properly in SillyTavern. \n\nThe formatting has two pairs of pseudotokens\n\n\n\nThe self attention modules were fine tuned separately on this dataset and the pseudotokens were chosen because they made logical sense with respect to the character giving a reply without allowing the model to 'connect the dots' during training and figure out that it is indeed an AI language model.\n\nAfter this was done all modules were then finetuned together on the dendrite dataset in order to connect the changes made to the attention modules.\n\nSo with regards to building a SillyTavern prompt template you basically want the entire story string and any additional stylistic instructions enclosed in the [EGO] tags and then the user messages enclosed in [SEEN] tags. \n\nIt doesn't give particularly verbose replies unless you're continueing a roleplay with verbose messages. Otherwise it's pretty bad. \n\nTraining was done using qlora-pipe\n\nGGUFs care of Qaunt Cartel\n\nexl2 rpcal care of Qaunt Cartel" ]

[ "Animatediff", " motion", " lora", "feature-extraction", "license:unknown", "region:us" ]

[]

[]

[ "# Model Card for Model ID\nThis is an AnimateDiff Motion Lora model trained with 2000 steps\n2000_natural_animated_talk_r64_temporal_unet", "## Model Details\nThis is an AnimateDiff Motion Lora model trained with 2000 steps", "### Model Description\nOnly personal use is allowed, and forwarding or dissemination is not allowed.\n\nThe model used\nCheckpoint: \nURL\n\nAnimatediff motion: \nURL\n\n\nThe following example video generates prompt words:\nHe had a magnetic presence and an affable demeanor that instantly put people at ease.\n\n<video controls autoplay src=\"URL\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:" ]

[ "TAGS\n#Animatediff # motion # lora #feature-extraction #license-unknown #region-us \n", "# Model Card for Model ID\nThis is an AnimateDiff Motion Lora model trained with 2000 steps\n2000_natural_animated_talk_r64_temporal_unet", "## Model Details\nThis is an AnimateDiff Motion Lora model trained with 2000 steps", "### Model Description\nOnly personal use is allowed, and forwarding or dissemination is not allowed.\n\nThe model used\nCheckpoint: \nURL\n\nAnimatediff motion: \nURL\n\n\nThe following example video generates prompt words:\nHe had a magnetic presence and an affable demeanor that instantly put people at ease.\n\n<video controls autoplay src=\"URL\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:" ]

[ "arxiv:1910.09700", "license:apache-2.0", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for electric-turtle-0.1-FT\n \n Our First model of a finetuned LLM on high quality data.", "## Model Details", "### Model Description\n\nElectric-turtle-0.1-FT is our first public LLM. This model was finetuned on a dataset of high-quality data and was finetuned responsibly and with the environment in mind. To support open-source work, we have made this model open-source along with more planned open-source models coming soon.\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#arxiv-1910.09700 #license-apache-2.0 #region-us \n", "# Model Card for electric-turtle-0.1-FT\n \n Our First model of a finetuned LLM on high quality data.", "## Model Details", "### Model Description\n\nElectric-turtle-0.1-FT is our first public LLM. This model was finetuned on a dataset of high-quality data and was finetuned responsibly and with the environment in mind. To support open-source work, we have made this model open-source along with more planned open-source models coming soon.\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" ]

[ "safetensors", "license:apache-2.0", "region:us" ]

[]

[]

[]

[ "TAGS\n#safetensors #license-apache-2.0 #region-us \n" ]

[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Meta-Llama-3-8B-Instruct_fictional_Italian_v1\n\nThis model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator 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: 1\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 36", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #llama #text-generation #trl #sft #generated_from_trainer #conversational #dataset-generator #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Meta-Llama-3-8B-Instruct_fictional_Italian_v1\n\nThis model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the generator 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: 1\n- eval_batch_size: 2\n- seed: 42\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 36", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "region:us" ]

[]

[]

[ "# Cardiotensive sito ufficiale​, Cardiotensive farmaco​​, Cardiotensive recensioni oggi​​, Cardiotensive cosa contiene​, Cardiotensive opinioni negative​​, Cardiotensive altroconsumo​​, antiaggreganti nomi commerciali​​, farmaci cardiovascolari", "## Sito Ufficiale di Cardiotensive:\n\nBenvenuti nel sito ufficiale di Cardiotensive! Scoprite ora una soluzione innovativa per la gestione dell'ipertensione e il miglioramento della salute cardiovascolare. Cardiotensive rappresenta il vostro alleato per affrontare le sfide legate alla pressione sanguigna e promuovere il benessere del vostro cuore e dei vasi sanguigni. Visita il nostro sito ufficiale\n per approfittare delle offerte speciali e scoprire tutti i dettagli su questo integratore unico.\n\n __CLICCA QUI PER SAPERNE DI PIÙ__", "## Contenuto di Cardiotensive:\n\nCuriosi di sapere cosa contiene Cardiotensive? La nostra formula innovativa combina estratti naturali selezionati con cura per le loro comprovate proprietà benefiche sulla salute cardiovascolare. L'estratto di biancospino, di aglio, di foglie di ulivo e di ravanello sono solo alcuni degli ingredienti chiave che lavorano sinergicamente per supportare la funzione cardiaca, migliorare la circolazione e ridurre la pressione sanguigna. Scoprite di più sulle potenti proprietà di ogni ingrediente visitando il nostro sito ufficiale.", "## Recensioni di Cardiotensive Oggi:\n\nLe recensioni di Cardiotensive sono positive e incoraggianti. Gli utenti hanno condiviso esperienze soddisfacenti e miglioramenti significativi nella gestione della loro pressione sanguigna. Visita il nostro sito ufficiale per leggere le testimonianze degli utenti e scoprire come Cardiotensive possa fare la differenza nella tua vita.", "## Cardiotensive: Integratore o Farmaco?\n\nCardiotensive è un integratore alimentare formulato con ingredienti naturali e scientificamente validati per sostenere la salute cardiovascolare. Sebbene la sua efficacia sia supportata da evidenze scientifiche, è importante chiarire che Cardiotensive è classificato come integratore e non come farmaco. Consulta il nostro sito ufficiale per ulteriori informazioni sulla distinzione tra integratori e farmaci e su come Cardiotensive possa integrare il tuo regime di salute.\n\n __CLICCA QUI PER SAPERNE DI PIÙ__", "## Opinioni Negative su Cardiotensive:\n\nSiamo consapevoli che alcune opinioni negative su Cardiotensive possono sorgere da aspettative irrealistiche o da una comprensione limitata del prodotto. Tuttavia, accogliamo ogni feedback con apertura e siamo impegnati a garantire la massima soddisfazione dei nostri clienti. Visita il nostro sito ufficiale per avere una visione completa su Cardiotensive e per comprendere meglio come può supportare la tua salute cardiovascolare.", "## Cardiotensive e Altroconsumo:\n\nCardiotensive è stato sottoposto a rigorosi test di qualità e sicurezza per garantire la massima efficacia e affidabilità. Se desideri ulteriori informazioni su Cardiotensive e le sue prestazioni, ti invitiamo a visitare il nostro sito ufficiale e a consultare le recensioni degli esperti. Siamo fiduciosi che i risultati parleranno da soli.", "## Nomi Commerciali di Antiaggreganti e Farmaci Cardiovascolari:\n\nPer informazioni sui nomi commerciali di antiaggreganti e farmaci cardiovascolari, ti consigliamo di consultare un professionista sanitario qualificato. Per quanto riguarda Cardiotensive, visita il nostro sito ufficiale per conoscere tutti i dettagli sulla sua composizione e sui suoi benefici per la salute cardiovascolare. Siamo qui per supportarti nel tuo percorso verso una vita più sana e attiva.\n\n __CLICCA QUI PER SAPERNE DI PIÙ__" ]

[ "TAGS\n#region-us \n", "# Cardiotensive sito ufficiale​, Cardiotensive farmaco​​, Cardiotensive recensioni oggi​​, Cardiotensive cosa contiene​, Cardiotensive opinioni negative​​, Cardiotensive altroconsumo​​, antiaggreganti nomi commerciali​​, farmaci cardiovascolari", "## Sito Ufficiale di Cardiotensive:\n\nBenvenuti nel sito ufficiale di Cardiotensive! Scoprite ora una soluzione innovativa per la gestione dell'ipertensione e il miglioramento della salute cardiovascolare. Cardiotensive rappresenta il vostro alleato per affrontare le sfide legate alla pressione sanguigna e promuovere il benessere del vostro cuore e dei vasi sanguigni. Visita il nostro sito ufficiale\n per approfittare delle offerte speciali e scoprire tutti i dettagli su questo integratore unico.\n\n __CLICCA QUI PER SAPERNE DI PIÙ__", "## Contenuto di Cardiotensive:\n\nCuriosi di sapere cosa contiene Cardiotensive? La nostra formula innovativa combina estratti naturali selezionati con cura per le loro comprovate proprietà benefiche sulla salute cardiovascolare. L'estratto di biancospino, di aglio, di foglie di ulivo e di ravanello sono solo alcuni degli ingredienti chiave che lavorano sinergicamente per supportare la funzione cardiaca, migliorare la circolazione e ridurre la pressione sanguigna. Scoprite di più sulle potenti proprietà di ogni ingrediente visitando il nostro sito ufficiale.", "## Recensioni di Cardiotensive Oggi:\n\nLe recensioni di Cardiotensive sono positive e incoraggianti. Gli utenti hanno condiviso esperienze soddisfacenti e miglioramenti significativi nella gestione della loro pressione sanguigna. Visita il nostro sito ufficiale per leggere le testimonianze degli utenti e scoprire come Cardiotensive possa fare la differenza nella tua vita.", "## Cardiotensive: Integratore o Farmaco?\n\nCardiotensive è un integratore alimentare formulato con ingredienti naturali e scientificamente validati per sostenere la salute cardiovascolare. Sebbene la sua efficacia sia supportata da evidenze scientifiche, è importante chiarire che Cardiotensive è classificato come integratore e non come farmaco. Consulta il nostro sito ufficiale per ulteriori informazioni sulla distinzione tra integratori e farmaci e su come Cardiotensive possa integrare il tuo regime di salute.\n\n __CLICCA QUI PER SAPERNE DI PIÙ__", "## Opinioni Negative su Cardiotensive:\n\nSiamo consapevoli che alcune opinioni negative su Cardiotensive possono sorgere da aspettative irrealistiche o da una comprensione limitata del prodotto. Tuttavia, accogliamo ogni feedback con apertura e siamo impegnati a garantire la massima soddisfazione dei nostri clienti. Visita il nostro sito ufficiale per avere una visione completa su Cardiotensive e per comprendere meglio come può supportare la tua salute cardiovascolare.", "## Cardiotensive e Altroconsumo:\n\nCardiotensive è stato sottoposto a rigorosi test di qualità e sicurezza per garantire la massima efficacia e affidabilità. Se desideri ulteriori informazioni su Cardiotensive e le sue prestazioni, ti invitiamo a visitare il nostro sito ufficiale e a consultare le recensioni degli esperti. Siamo fiduciosi che i risultati parleranno da soli.", "## Nomi Commerciali di Antiaggreganti e Farmaci Cardiovascolari:\n\nPer informazioni sui nomi commerciali di antiaggreganti e farmaci cardiovascolari, ti consigliamo di consultare un professionista sanitario qualificato. Per quanto riguarda Cardiotensive, visita il nostro sito ufficiale per conoscere tutti i dettagli sulla sua composizione e sui suoi benefici per la salute cardiovascolare. Siamo qui per supportarti nel tuo percorso verso una vita più sana e attiva.\n\n __CLICCA QUI PER SAPERNE DI PIÙ__" ]

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ko", "dataset:vts_sample_data", "base_model:openai/whisper-large-v3", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

[]

[ "ko" ]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1\n* training\\_steps: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #ko #dataset-vts_sample_data #base_model-openai/whisper-large-v3 #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1\n* training\\_steps: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "diffusers", "text-to-image", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]

[]

[]

[ "# Critical Dream - cosmicBboy/stable-diffusion-xl-base-1.0-lora-dreambooth-critdream-v0.7.0\n\n<Gallery />", "## Model description\n\nThese are cosmicBboy/stable-diffusion-xl-base-1.0-lora-dreambooth-critdream-v0.7.0 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0, for the purposes of\ngenerating images for the Critical Dream\nproject.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: True.\n\nSpecial VAE used for training: stabilityai/sdxl-vae.", "## Trigger words\n\nYou should use a picture of [dm-matt-mercer], a dungeon master. background is a forest. fantasy art style, high quality, highly detailed, sharp focus\" to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Tracker run link\n\nURL", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]

[ "TAGS\n#diffusers #text-to-image #stable-diffusion-xl #stable-diffusion-xl-diffusers #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #region-us \n", "# Critical Dream - cosmicBboy/stable-diffusion-xl-base-1.0-lora-dreambooth-critdream-v0.7.0\n\n<Gallery />", "## Model description\n\nThese are cosmicBboy/stable-diffusion-xl-base-1.0-lora-dreambooth-critdream-v0.7.0 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0, for the purposes of\ngenerating images for the Critical Dream\nproject.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: True.\n\nSpecial VAE used for training: stabilityai/sdxl-vae.", "## Trigger words\n\nYou should use a picture of [dm-matt-mercer], a dungeon master. background is a forest. fantasy art style, high quality, highly detailed, sharp focus\" to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab.", "## Tracker run link\n\nURL", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "jeiku/Average_Normie_l3_v1_8B", "Kaoeiri/Keiana-L3-Test4.6-8B-2", "conversational", "base_model:jeiku/Average_Normie_l3_v1_8B", "base_model:Kaoeiri/Keiana-L3-Test4.6-8B-2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Keiana-L3-Test4.7-8B-3\n\nKeiana-L3-Test4.7-8B-3 is a merge of the following models using LazyMergekit:\n* jeiku/Average_Normie_l3_v1_8B\n* Kaoeiri/Keiana-L3-Test4.6-8B-2", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #jeiku/Average_Normie_l3_v1_8B #Kaoeiri/Keiana-L3-Test4.6-8B-2 #conversational #base_model-jeiku/Average_Normie_l3_v1_8B #base_model-Kaoeiri/Keiana-L3-Test4.6-8B-2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Keiana-L3-Test4.7-8B-3\n\nKeiana-L3-Test4.7-8B-3 is a merge of the following models using LazyMergekit:\n* jeiku/Average_Normie_l3_v1_8B\n* Kaoeiri/Keiana-L3-Test4.6-8B-2", "## Configuration", "## Usage" ]

[ "transformers", "safetensors", "gguf", "phi3", "text-generation", "nlp", "code", "conversational", "custom_code", "en", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "## Model Summary\n\nThe Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.\nThe model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.\n\nThe model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures.\nWhen assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters.\n\nResources and Technical Documentation:\n\n+ Phi-3 Microsoft Blog\n+ Phi-3 Technical Report\n+ Phi-3 on Azure AI Studio\n+ Phi-3 GGUF: 4K\n+ Phi-3 ONNX: 4K", "## Intended Uses\n\nPrimary use cases\n\nThe model is intended for commercial and research use in English. The model provides uses for applications which require:\n\n1) Memory/compute constrained environments\n2) Latency bound scenarios\n3) Strong reasoning (especially code, math and logic)\n\nOur model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. \n\nUse case considerations\n\nOur models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.\n\nNothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.", "## How to Use\n\nPhi-3 Mini-4K-Instruct has been integrated in the development version (4.40.0) of 'transformers'. Until the official version is released through 'pip', ensure that you are doing one of the following:\n\n* When loading the model, ensure that 'trust_remote_code=True' is passed as an argument of the 'from_pretrained()' function.\n\n* Update your local 'transformers' to the development version: 'pip uninstall -y transformers && pip install git+URL The previous command is an alternative to cloning and installing from the source.\n\nThe current 'transformers' version can be verified with: 'pip list | grep transformers'.\n\nPhi-3 Mini-4K-Instruct is also available in HuggingChat.", "### Chat Format\n\nGiven the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. \nYou can provide the prompt as a question with a generic template as follow:\n\nFor example:\n\n\nwhere the model generates the text after '<|assistant|>' . In case of few-shots prompt, the prompt can be formatted as the following:", "### Sample inference code\n\nThis code snippets show how to get quickly started with running the model on a GPU:", "## Responsible AI Considerations\n\nLike other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:\n\n+ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. \n+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. \n+ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. \n+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. \n+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as \"typing, math, random, collections, datetime, itertools\". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. \n\nDevelopers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:\n\n+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.\n+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. \n+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). \n+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. \n+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.", "## Training", "### Model\n\n* Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.\n* Inputs: Text. It is best suited for prompts using chat format.\n* Context length: 4K tokens\n* GPUs: 512 H100-80G\n* Training time: 7 days\n* Training data: 3.3T tokens\n* Outputs: Generated text in response to the input\n* Dates: Our models were trained between February and April 2024\n* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.", "### Datasets\n\nOur training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of \n1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; \n2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); \n3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.", "### Fine-tuning\n\nA basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided here.", "## Software\n\n* PyTorch\n* DeepSpeed\n* Transformers\n* Flash-Attention", "## Hardware\nNote that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:\n* NVIDIA A100\n* NVIDIA A6000\n* NVIDIA H100\n\nIf you want to run the model on:\n* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation=\"eager\"\n* CPU: use the GGUF quantized models 4K\n+ Optimized inference on GPU, CPU, and Mobile: use the ONNX models 4K", "## Cross Platform Support\n\nONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model here.\n\nOptimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. \nAlong with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile.\n\nHere are some of the optimized configurations we have added: \n\n1. ONNX models for int4 DML: Quantized to int4 via AWQ\n2. ONNX model for fp16 CUDA\n3. ONNX model for int4 CUDA: Quantized to int4 via RTN\n4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN", "## License\n\nThe model is licensed under the MIT license.", "## Trademarks\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies." ]

[ "TAGS\n#transformers #safetensors #gguf #phi3 #text-generation #nlp #code #conversational #custom_code #en #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "## Model Summary\n\nThe Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.\nThe model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.\n\nThe model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures.\nWhen assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters.\n\nResources and Technical Documentation:\n\n+ Phi-3 Microsoft Blog\n+ Phi-3 Technical Report\n+ Phi-3 on Azure AI Studio\n+ Phi-3 GGUF: 4K\n+ Phi-3 ONNX: 4K", "## Intended Uses\n\nPrimary use cases\n\nThe model is intended for commercial and research use in English. The model provides uses for applications which require:\n\n1) Memory/compute constrained environments\n2) Latency bound scenarios\n3) Strong reasoning (especially code, math and logic)\n\nOur model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features. \n\nUse case considerations\n\nOur models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.\n\nNothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.", "## How to Use\n\nPhi-3 Mini-4K-Instruct has been integrated in the development version (4.40.0) of 'transformers'. Until the official version is released through 'pip', ensure that you are doing one of the following:\n\n* When loading the model, ensure that 'trust_remote_code=True' is passed as an argument of the 'from_pretrained()' function.\n\n* Update your local 'transformers' to the development version: 'pip uninstall -y transformers && pip install git+URL The previous command is an alternative to cloning and installing from the source.\n\nThe current 'transformers' version can be verified with: 'pip list | grep transformers'.\n\nPhi-3 Mini-4K-Instruct is also available in HuggingChat.", "### Chat Format\n\nGiven the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows. \nYou can provide the prompt as a question with a generic template as follow:\n\nFor example:\n\n\nwhere the model generates the text after '<|assistant|>' . In case of few-shots prompt, the prompt can be formatted as the following:", "### Sample inference code\n\nThis code snippets show how to get quickly started with running the model on a GPU:", "## Responsible AI Considerations\n\nLike other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:\n\n+ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. \n+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases. \n+ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case. \n+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated. \n+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as \"typing, math, random, collections, datetime, itertools\". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses. \n\nDevelopers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:\n\n+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.\n+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context. \n+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG). \n+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case. \n+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.", "## Training", "### Model\n\n* Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.\n* Inputs: Text. It is best suited for prompts using chat format.\n* Context length: 4K tokens\n* GPUs: 512 H100-80G\n* Training time: 7 days\n* Training data: 3.3T tokens\n* Outputs: Generated text in response to the input\n* Dates: Our models were trained between February and April 2024\n* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.", "### Datasets\n\nOur training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of \n1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code; \n2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.); \n3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.", "### Fine-tuning\n\nA basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided here.", "## Software\n\n* PyTorch\n* DeepSpeed\n* Transformers\n* Flash-Attention", "## Hardware\nNote that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:\n* NVIDIA A100\n* NVIDIA A6000\n* NVIDIA H100\n\nIf you want to run the model on:\n* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation=\"eager\"\n* CPU: use the GGUF quantized models 4K\n+ Optimized inference on GPU, CPU, and Mobile: use the ONNX models 4K", "## Cross Platform Support\n\nONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model here.\n\nOptimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs. \nAlong with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile.\n\nHere are some of the optimized configurations we have added: \n\n1. ONNX models for int4 DML: Quantized to int4 via AWQ\n2. ONNX model for fp16 CUDA\n3. ONNX model for int4 CUDA: Quantized to int4 via RTN\n4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN", "## License\n\nThe model is licensed under the MIT license.", "## Trademarks\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies." ]

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "Roleplay", "roleplay", "en", "base_model:Sao10K/Fimbulvetr-11B-v2", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# About this model\n\nThis model can handle (limited) TSF content. If you Character Card have complex plot, maybe you should try other model (maybe bigger parameter?).\n\n- This model have overfit 1 time, I don't expect it too good, but it stable than v0.9. Version 0.9 is better in Roleplay, while this better in storytelling.\n\nDo you know TSF, TS, TG? A lot of model don't really know about that, so I do some experiment to finetune TSF dataset.\n\n- Finetuned with Chinese Novels dataset, to increase the accuracy in TSF theme, which is not quite popular.\n (R18 dataset). You should include chinese/japanese word about tag you want(search it in pixiv) in your character card to trigger it.\n This finetune idea is suitable for Chinese Roleplay than English (Becaue I could only find good Chinese datasets about it , it is nice that if you can open a discussion about English TSF datasets). But it still affect the models writing styles, so maybe less GPT-like response in both Chinese and English?.\n- Finetuned from model : Sao10K/Fimbulvetr-11B-v2 . Thank Sao10K a lot :)", "## 8k Context Length BTW, the original Fimbulvetr and Solar have only 4k context length, so I extended it .", "## GGUF version? here is it.", "## Dataset\nAll chinese novels dataset", "# Thank Unsloth for good finetuning tool. This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #Roleplay #roleplay #en #base_model-Sao10K/Fimbulvetr-11B-v2 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# About this model\n\nThis model can handle (limited) TSF content. If you Character Card have complex plot, maybe you should try other model (maybe bigger parameter?).\n\n- This model have overfit 1 time, I don't expect it too good, but it stable than v0.9. Version 0.9 is better in Roleplay, while this better in storytelling.\n\nDo you know TSF, TS, TG? A lot of model don't really know about that, so I do some experiment to finetune TSF dataset.\n\n- Finetuned with Chinese Novels dataset, to increase the accuracy in TSF theme, which is not quite popular.\n (R18 dataset). You should include chinese/japanese word about tag you want(search it in pixiv) in your character card to trigger it.\n This finetune idea is suitable for Chinese Roleplay than English (Becaue I could only find good Chinese datasets about it , it is nice that if you can open a discussion about English TSF datasets). But it still affect the models writing styles, so maybe less GPT-like response in both Chinese and English?.\n- Finetuned from model : Sao10K/Fimbulvetr-11B-v2 . Thank Sao10K a lot :)", "## 8k Context Length BTW, the original Fimbulvetr and Solar have only 4k context length, so I extended it .", "## GGUF version? here is it.", "## Dataset\nAll chinese novels dataset", "# Thank Unsloth for good finetuning tool. This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers 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#diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a diffusers 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", "opt", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# opt-350m_ft_test4\n\nThis model was trained from scratch on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# opt-350m_ft_test4\n\nThis model was trained from scratch on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "transformers", "style-bert-vits2", "style-bert-vits2-jp-extra", "tts", "childish", "childish voice", "japanese", "text2audio", "text-to-audio", "text to audio", "audio", "text-to-speech", "ja", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]

[]

[ "ja" ]

[ "# このモデルの長所は幼げなおっとりしたボイス生成を商用・非商用問わず無料で自由に使える点です。", "# The advantage of this model is that you can freely use the childish and unapologetic voice generation for free, both commercial and non-commercial.\n\nこのモデルはRikkaBotanのスイートバージョンです。\nセリフの読み上げに適しています。\nもしもっと硬く話してほしい場合は、coolバージョン\n英語で話してほしい場合はenglishバージョン\nささやき声で話してほしい場合はASMRバージョン\nを試してみてください。\n\nThis model is sweet version.\nIt is suitable for reading emotional text.\nIf you want them to speak more descriptively, try the cool version.\nIf you want them to speak in English, try the English version\nIf you want them to speak whisper voice, try the ASMR version.", "# モデルのサンプル音声/sample voice\n\nこのモデルのサンプル音声①です\n\n<audio controls src=\"URL\n\nこのモデルのサンプル音声②です。\n\n<audio controls src=\"URL", "# モデルの説明/model description\n\nこのモデルはTTS（text-to-speech）モデルである、\nstyle_bert_vits2_jp_extraを独自の音声データで学習させたモデルです。\nstyle_bert_vits2_jp_extraは日本語に特化した音声生成モデルであり、\nこれまでのモデルと比較して高精度かつ自然な音声生成が可能となっています。\n学習データはモデルを作成した研究者本人の音声のみであるため、\nライセンスはstyle_bert_vits2_jp_extraと同様に\n商用・非商用問わず、自由に無料でご使用いただけます。\n\nThis model is a TTS (text-to-speech) model.\nThis is a model that has trained style_bert_vits2_jp_extra with my own voice data.\nstyle_bert_vits2_jp_extra is a speech generation model specialized for Japanese.\nCompared to previous models, it is possible to generate highly accurate and natural speech.\nSince the training data is only the voice of the researcher who created the model,\nThe license is the same as style_bert_vits2_jp_extra\nYou can use it freely and free of charge, regardless of whether it is commercial or non-commercial.", "# モデルを使うときのお約束/limitation\n\n〇できること/What you can do\n\n成果物の加工 Processing of deliverables\n\n成果物の商用利用 Commercial use of deliverables\n\n成果物の学習素材としての利用 Use of deliverables as learning materials\n\nR-18、R-18G表現への利用（ただしゾーニングは必須です（小さなお友達のことをちゃんと考えてあげてね））\n\nUse for R-18 and R-18G expressions (but zoning is required (please think about your little friends))\n\n\n×できないこと/What you cannot do\n\n音声モデルの二次配布　Secondary distribution of voice models\n\n人を批判・攻撃すること　Criticizing or attacking others\n\n特定の政治的立場・宗教・思想への賛同または反対を呼びかけること　Calling for support or opposition to a particular political position, religion, or ideology\n\n刺激の強い表現をゾーニングなしで公開すること　Publishing R-18 voice without zoning\n\nなりすましなど、提供者に不利益をもたらすこと　detrimental to the provider", "# 商用利用可能なVRMも作りました。/ VRM(Vroid) model for commercial use\n\n\nAITuberや動画解説などに用いてください。/Please use this for AITuber and video creations\n\n\nVRM(Vroid)Model", "# できればやって欲しいこと/If you like\n\nX(Twitter)や説明文でこのモデルを使ったことを書いてもらえると作者が喜びます。(必須ではありません)\nIf you write that you are using this model, I will be glad!", "# モデルの使い方/how to use　（コードはgoogle colab用です。 For google colab）\n\n\n２通りの使用方法があります。必要に応じて選択してください。There are 2 ways to use model.\n\n\n１．style-bert-vits2のアプリを使ってボイスを生成する/to use style-bert-vits2 app\n\n\n①Style-Bert-VITS2 インストール先の Style-Bert-VITS2/model_assets/rikka_botan/ フォルダに URL, safetensors, style_vectors.npy の 3ファイルを置きます。\nPut 3 files on Style-Bert-VITS2/model_assets/rikka_botan/ folder\n以下のプログラムで自動的に保存できます。By using this program, we can save files.\n\n\n②以下のプログラムを実行します execute this program\n\n\n③public URLにアクセスします。access public url\n\n２．以下のコードを利用します。use this code", "# 謝辞/Acknowledgments\nstyle-bert-vits2-jp-extraを開発してくださったlitaginさんに感謝いたします。\nまた、本モデルは多くの研究者さんの努力の上にできています。先人たちの努力に深く感謝します。\nWe would like to thank Mr./Ms. litagin for developing style-bert-vits2-jp-extra.\nAdditionally, this model was created based on the efforts of many developers. We are deeply grateful for the efforts of our predecessors." ]

[ "TAGS\n#transformers #style-bert-vits2 #style-bert-vits2-jp-extra #tts #childish #childish voice #japanese #text2audio #text-to-audio #text to audio #audio #text-to-speech #ja #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n", "# このモデルの長所は幼げなおっとりしたボイス生成を商用・非商用問わず無料で自由に使える点です。", "# The advantage of this model is that you can freely use the childish and unapologetic voice generation for free, both commercial and non-commercial.\n\nこのモデルはRikkaBotanのスイートバージョンです。\nセリフの読み上げに適しています。\nもしもっと硬く話してほしい場合は、coolバージョン\n英語で話してほしい場合はenglishバージョン\nささやき声で話してほしい場合はASMRバージョン\nを試してみてください。\n\nThis model is sweet version.\nIt is suitable for reading emotional text.\nIf you want them to speak more descriptively, try the cool version.\nIf you want them to speak in English, try the English version\nIf you want them to speak whisper voice, try the ASMR version.", "# モデルのサンプル音声/sample voice\n\nこのモデルのサンプル音声①です\n\n<audio controls src=\"URL\n\nこのモデルのサンプル音声②です。\n\n<audio controls src=\"URL", "# モデルの説明/model description\n\nこのモデルはTTS（text-to-speech）モデルである、\nstyle_bert_vits2_jp_extraを独自の音声データで学習させたモデルです。\nstyle_bert_vits2_jp_extraは日本語に特化した音声生成モデルであり、\nこれまでのモデルと比較して高精度かつ自然な音声生成が可能となっています。\n学習データはモデルを作成した研究者本人の音声のみであるため、\nライセンスはstyle_bert_vits2_jp_extraと同様に\n商用・非商用問わず、自由に無料でご使用いただけます。\n\nThis model is a TTS (text-to-speech) model.\nThis is a model that has trained style_bert_vits2_jp_extra with my own voice data.\nstyle_bert_vits2_jp_extra is a speech generation model specialized for Japanese.\nCompared to previous models, it is possible to generate highly accurate and natural speech.\nSince the training data is only the voice of the researcher who created the model,\nThe license is the same as style_bert_vits2_jp_extra\nYou can use it freely and free of charge, regardless of whether it is commercial or non-commercial.", "# モデルを使うときのお約束/limitation\n\n〇できること/What you can do\n\n成果物の加工 Processing of deliverables\n\n成果物の商用利用 Commercial use of deliverables\n\n成果物の学習素材としての利用 Use of deliverables as learning materials\n\nR-18、R-18G表現への利用（ただしゾーニングは必須です（小さなお友達のことをちゃんと考えてあげてね））\n\nUse for R-18 and R-18G expressions (but zoning is required (please think about your little friends))\n\n\n×できないこと/What you cannot do\n\n音声モデルの二次配布　Secondary distribution of voice models\n\n人を批判・攻撃すること　Criticizing or attacking others\n\n特定の政治的立場・宗教・思想への賛同または反対を呼びかけること　Calling for support or opposition to a particular political position, religion, or ideology\n\n刺激の強い表現をゾーニングなしで公開すること　Publishing R-18 voice without zoning\n\nなりすましなど、提供者に不利益をもたらすこと　detrimental to the provider", "# 商用利用可能なVRMも作りました。/ VRM(Vroid) model for commercial use\n\n\nAITuberや動画解説などに用いてください。/Please use this for AITuber and video creations\n\n\nVRM(Vroid)Model", "# できればやって欲しいこと/If you like\n\nX(Twitter)や説明文でこのモデルを使ったことを書いてもらえると作者が喜びます。(必須ではありません)\nIf you write that you are using this model, I will be glad!", "# モデルの使い方/how to use　（コードはgoogle colab用です。 For google colab）\n\n\n２通りの使用方法があります。必要に応じて選択してください。There are 2 ways to use model.\n\n\n１．style-bert-vits2のアプリを使ってボイスを生成する/to use style-bert-vits2 app\n\n\n①Style-Bert-VITS2 インストール先の Style-Bert-VITS2/model_assets/rikka_botan/ フォルダに URL, safetensors, style_vectors.npy の 3ファイルを置きます。\nPut 3 files on Style-Bert-VITS2/model_assets/rikka_botan/ folder\n以下のプログラムで自動的に保存できます。By using this program, we can save files.\n\n\n②以下のプログラムを実行します execute this program\n\n\n③public URLにアクセスします。access public url\n\n２．以下のコードを利用します。use this code", "# 謝辞/Acknowledgments\nstyle-bert-vits2-jp-extraを開発してくださったlitaginさんに感謝いたします。\nまた、本モデルは多くの研究者さんの努力の上にできています。先人たちの努力に深く感謝します。\nWe would like to thank Mr./Ms. litagin for developing style-bert-vits2-jp-extra.\nAdditionally, this model was created based on the efforts of many developers. We are deeply grateful for the efforts of our predecessors." ]

[ "xtuner", "safetensors", "llava_llama", "image-text-to-text", "dataset:Lin-Chen/ShareGPT4V", "region:us" ]

[]

[]

[ "### Chat by LLaVA official library\n\n\n1. Install official LLaVA library\n2. Chat by below script\n\n\n\nURL", "### Reproduce\n\n\nPlease refer to docs." ]

[ "TAGS\n#xtuner #safetensors #llava_llama #image-text-to-text #dataset-Lin-Chen/ShareGPT4V #region-us \n", "### Chat by LLaVA official library\n\n\n1. Install official LLaVA library\n2. Chat by below script\n\n\n\nURL", "### Reproduce\n\n\nPlease refer to docs." ]

[ "transformers", "style-bert-vits2", "style-bert-vits2-jp-extra", "tts", "childish", "childish voice", "japanese", "text2audio", "text-to-audio", "text to audio", "audio", "text-to-speech", "ja", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]

[]

[ "ja" ]

[ "# このモデルの長所は幼げなおっとりしたボイス生成を商用・非商用問わず無料で自由に使える点です。", "# The advantage of this model is that you can freely use the childish and unapologetic voice generation for free, both commercial and non-commercial.\n\nこのモデルはRikkaBotanのクールバージョンです。\n説明文の読み上げに適しています。\nもしもっと感情的に話してほしい場合は、sweetバージョン\n英語で話してほしい場合は英語バージョン\nささやき声で話してほしい場合はASMRバージョン\nを試してみてください。\n\nThis model is cool version.\nIt is suitable for reading explanatory text.\nIf you want them to speak more emotionally, try the sweet version\nIf you want them to speak in English, try the English version\nIf you want them to speak whisper voice, try the ASMR version.", "# モデルのサンプル音声/sample voice\n\nこのモデルのサンプル音声①です\n\n<audio controls src=\"URL\n\nこのモデルのサンプル音声②です。\n\n<audio controls src=\"URL", "# モデルの説明/model description\n\nこのモデルはTTS（text-to-speech）モデルである、\nstyle_bert_vits2_jp_extraを独自の音声データで学習させたモデルです。\nstyle_bert_vits2_jp_extraは日本語に特化した音声生成モデルであり、\nこれまでのモデルと比較して高精度かつ自然な音声生成が可能となっています。\n学習データはモデルを作成した研究者本人の音声のみであるため、\nライセンスはstyle_bert_vits2_jp_extraと同様に\n商用・非商用問わず、自由に無料でご使用いただけます。\n\nThis model is a TTS (text-to-speech) model.\nThis is a model that has trained style_bert_vits2_jp_extra with my own voice data.\nstyle_bert_vits2_jp_extra is a speech generation model specialized for Japanese.\nCompared to previous models, it is possible to generate highly accurate and natural speech.\nSince the training data is only the voice of the researcher who created the model,\nThe license is the same as style_bert_vits2_jp_extra\nYou can use it freely and free of charge, regardless of whether it is commercial or non-commercial.", "# モデルを使うときのお約束/limitation\n\n〇できること/What you can do\n\n成果物の加工 Processing of deliverables\n\n成果物の商用利用 Commercial use of deliverables\n\n成果物の学習素材としての利用 Use of deliverables as learning materials\n\nR-18、R-18G表現への利用（ただしゾーニングは必須です（小さなお友達のことをちゃんと考えてあげてね））\n\nUse for R-18 and R-18G expressions (but zoning is required (please think about your little friends))\n\n\n×できないこと/What you cannot do\n\n音声モデルの二次配布　Secondary distribution of voice models\n\n人を批判・攻撃すること　Criticizing or attacking others\n\n特定の政治的立場・宗教・思想への賛同または反対を呼びかけること　Calling for support or opposition to a particular political position, religion, or ideology\n\n刺激の強い表現をゾーニングなしで公開すること　Publishing R-18 voice without zoning\n\nなりすましなど、提供者に不利益をもたらすこと　detrimental to the provider", "# 商用利用可能なVRMも作りました。/ VRM(Vroid) model for commercial use\n\n\nAITuberや動画解説などに用いてください。/Please use this for AITuber and video creations\n\n\nVRM(Vroid)Model", "# できればやって欲しいこと/If you like\nX(Twitter)や説明文でこのモデルを使ったことを書いてもらえると作者が喜びます。(必須ではありません)\nIf you write that you are using this model, I will be glad!", "# モデルの使い方/how to use　（コードはgoogle colab用です。 For google colab）\n\n２通りの使用方法があります。必要に応じて選択してください。There are 2 ways to use model.\n\n１．style-bert-vits2のアプリを使ってボイスを生成する/to use style-bert-vits2 app\n\n①Style-Bert-VITS2 インストール先の Style-Bert-VITS2/model_assets/rikka_botan/ フォルダに URL, safetensors, style_vectors.npy の 3ファイルを置きます。\nPut 3 files on Style-Bert-VITS2/model_assets/rikka_botan/ folder\n\n以下のプログラムで自動的に保存できます。By using this program, we can save files.\n\n\n②以下のプログラムを実行します execute this program\n\n\n③public URLにアクセスします。access public url\n\n２．以下のコードを利用します。use this code", "# 謝辞/Acknowledgments\nstyle-bert-vits2-jp-extraを開発してくださったlitaginさんに感謝いたします。\nまた、本モデルは多くの研究者さんの努力の上にできています。先人たちの努力に深く感謝します。 \nWe would like to thank Mr./Ms. litagin for developing style-bert-vits2-jp-extra.\n Additionally, this model was created based on the efforts of many developers. We are deeply grateful for the efforts of our predecessors." ]

[ "TAGS\n#transformers #style-bert-vits2 #style-bert-vits2-jp-extra #tts #childish #childish voice #japanese #text2audio #text-to-audio #text to audio #audio #text-to-speech #ja #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n", "# このモデルの長所は幼げなおっとりしたボイス生成を商用・非商用問わず無料で自由に使える点です。", "# The advantage of this model is that you can freely use the childish and unapologetic voice generation for free, both commercial and non-commercial.\n\nこのモデルはRikkaBotanのクールバージョンです。\n説明文の読み上げに適しています。\nもしもっと感情的に話してほしい場合は、sweetバージョン\n英語で話してほしい場合は英語バージョン\nささやき声で話してほしい場合はASMRバージョン\nを試してみてください。\n\nThis model is cool version.\nIt is suitable for reading explanatory text.\nIf you want them to speak more emotionally, try the sweet version\nIf you want them to speak in English, try the English version\nIf you want them to speak whisper voice, try the ASMR version.", "# モデルのサンプル音声/sample voice\n\nこのモデルのサンプル音声①です\n\n<audio controls src=\"URL\n\nこのモデルのサンプル音声②です。\n\n<audio controls src=\"URL", "# モデルの説明/model description\n\nこのモデルはTTS（text-to-speech）モデルである、\nstyle_bert_vits2_jp_extraを独自の音声データで学習させたモデルです。\nstyle_bert_vits2_jp_extraは日本語に特化した音声生成モデルであり、\nこれまでのモデルと比較して高精度かつ自然な音声生成が可能となっています。\n学習データはモデルを作成した研究者本人の音声のみであるため、\nライセンスはstyle_bert_vits2_jp_extraと同様に\n商用・非商用問わず、自由に無料でご使用いただけます。\n\nThis model is a TTS (text-to-speech) model.\nThis is a model that has trained style_bert_vits2_jp_extra with my own voice data.\nstyle_bert_vits2_jp_extra is a speech generation model specialized for Japanese.\nCompared to previous models, it is possible to generate highly accurate and natural speech.\nSince the training data is only the voice of the researcher who created the model,\nThe license is the same as style_bert_vits2_jp_extra\nYou can use it freely and free of charge, regardless of whether it is commercial or non-commercial.", "# モデルを使うときのお約束/limitation\n\n〇できること/What you can do\n\n成果物の加工 Processing of deliverables\n\n成果物の商用利用 Commercial use of deliverables\n\n成果物の学習素材としての利用 Use of deliverables as learning materials\n\nR-18、R-18G表現への利用（ただしゾーニングは必須です（小さなお友達のことをちゃんと考えてあげてね））\n\nUse for R-18 and R-18G expressions (but zoning is required (please think about your little friends))\n\n\n×できないこと/What you cannot do\n\n音声モデルの二次配布　Secondary distribution of voice models\n\n人を批判・攻撃すること　Criticizing or attacking others\n\n特定の政治的立場・宗教・思想への賛同または反対を呼びかけること　Calling for support or opposition to a particular political position, religion, or ideology\n\n刺激の強い表現をゾーニングなしで公開すること　Publishing R-18 voice without zoning\n\nなりすましなど、提供者に不利益をもたらすこと　detrimental to the provider", "# 商用利用可能なVRMも作りました。/ VRM(Vroid) model for commercial use\n\n\nAITuberや動画解説などに用いてください。/Please use this for AITuber and video creations\n\n\nVRM(Vroid)Model", "# できればやって欲しいこと/If you like\nX(Twitter)や説明文でこのモデルを使ったことを書いてもらえると作者が喜びます。(必須ではありません)\nIf you write that you are using this model, I will be glad!", "# モデルの使い方/how to use　（コードはgoogle colab用です。 For google colab）\n\n２通りの使用方法があります。必要に応じて選択してください。There are 2 ways to use model.\n\n１．style-bert-vits2のアプリを使ってボイスを生成する/to use style-bert-vits2 app\n\n①Style-Bert-VITS2 インストール先の Style-Bert-VITS2/model_assets/rikka_botan/ フォルダに URL, safetensors, style_vectors.npy の 3ファイルを置きます。\nPut 3 files on Style-Bert-VITS2/model_assets/rikka_botan/ folder\n\n以下のプログラムで自動的に保存できます。By using this program, we can save files.\n\n\n②以下のプログラムを実行します execute this program\n\n\n③public URLにアクセスします。access public url\n\n２．以下のコードを利用します。use this code", "# 謝辞/Acknowledgments\nstyle-bert-vits2-jp-extraを開発してくださったlitaginさんに感謝いたします。\nまた、本モデルは多くの研究者さんの努力の上にできています。先人たちの努力に深く感謝します。 \nWe would like to thank Mr./Ms. litagin for developing style-bert-vits2-jp-extra.\n Additionally, this model was created based on the efforts of many developers. We are deeply grateful for the efforts of our predecessors." ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:FacebookAI/xlm-roberta-base", "license:mit", "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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #text-generation #generated_from_trainer #dataset-eli5_category #base_model-FacebookAI/xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "electra", "text-classification", "classification", "generated_from_trainer", "base_model:mrm8488/electricidad-base-discriminator", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### 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 #safetensors #electra #text-classification #classification #generated_from_trainer #base_model-mrm8488/electricidad-base-discriminator #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### 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", "vision-encoder-decoder", "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 #vision-encoder-decoder #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", "gguf", "en", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:microsoft/orca-math-word-problems-200k", "dataset:abacusai/SystemChat-1.1", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:cognitivecomputations/dolphin-2.9-llama3-70b", "license:llama3", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #en #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-cognitivecomputations/dolphin-2.9-llama3-70b #license-llama3 #endpoints_compatible #region-us \n" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZhang/0.001_ablation_4iters_bs128_nodpo_iter_2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_ablation_4iters_bs128_nodpo_iter_3\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_ablation_4iters_bs128_nodpo_iter_2 on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-updated #dataset-original #base_model-ShenaoZhang/0.001_ablation_4iters_bs128_nodpo_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_ablation_4iters_bs128_nodpo_iter_3\n\nThis model is a fine-tuned version of ShenaoZhang/0.001_ablation_4iters_bs128_nodpo_iter_2 on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 128\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]

[]

[]

[ "# BTD ARTSTYLE XL\n\n<Gallery />", "## Trigger words\n\nYou should use 'Manhwa' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]

[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n", "# BTD ARTSTYLE XL\n\n<Gallery />", "## Trigger words\n\nYou should use 'Manhwa' to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]

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

[]

[ "tr" ]

[ "# NikolayKozloff/Orbita-v0.1-Q5_K_M-GGUF\nThis model was converted to GGUF format from 'Orbina/Orbita-v0.1' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "TAGS\n#gguf #llama-cpp #gguf-my-repo #tr #license-apache-2.0 #model-index #region-us \n", "# NikolayKozloff/Orbita-v0.1-Q5_K_M-GGUF\nThis model was converted to GGUF format from 'Orbina/Orbita-v0.1' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.", "## Use with URL\n\nInstall URL through brew.\n\n\nInvoke the URL server or the CLI.\n\nCLI:\n\n\n\nServer:\n\n\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "transformers", "safetensors", "llama", "text-generation", "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", "gguf", "imatrix", "bigscience", "bloom-3b", "text-generation", "en", "license:other", "region:us" ]

[]

[ "en" ]

[ "# From original readme\n\n..." ]

[ "TAGS\n#transformers #gguf #imatrix #bigscience #bloom-3b #text-generation #en #license-other #region-us \n", "# From original readme\n\n..." ]

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test4.7-8B-3", "conversational", "base_model:Kaoeiri/Keiana-L3-Test4.7-8B-3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Keiana-L3-Test4.8-8B-4", "# Keep in mind that it's not yet tested, and I unsure if would work as planned.\n\n\nKeiana-L3-Test4.8-8B-4 is a merge of the following models using LazyMergekit:\n* Kaoeiri/Keiana-L3-Test4.7-8B-3", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Kaoeiri/Keiana-L3-Test4.7-8B-3 #conversational #base_model-Kaoeiri/Keiana-L3-Test4.7-8B-3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Keiana-L3-Test4.8-8B-4", "# Keep in mind that it's not yet tested, and I unsure if would work as planned.\n\n\nKeiana-L3-Test4.8-8B-4 is a merge of the following models using LazyMergekit:\n* Kaoeiri/Keiana-L3-Test4.7-8B-3", "## Configuration", "## Usage" ]

[ "sentence-transformers", "pytorch", "tf", "rust", "safetensors", "bert", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:s2orc", "dataset:flax-sentence-embeddings/stackexchange_xml", "dataset:ms_marco", "dataset:gooaq", "dataset:yahoo_answers_topics", "dataset:code_search_net", "dataset:search_qa", "dataset:eli5", "dataset:snli", "dataset:multi_nli", "dataset:wikihow", "dataset:natural_questions", "dataset:trivia_qa", "dataset:embedding-data/sentence-compression", "dataset:embedding-data/flickr30k-captions", "dataset:embedding-data/altlex", "dataset:embedding-data/simple-wiki", "dataset:embedding-data/QQP", "dataset:embedding-data/SPECTER", "dataset:embedding-data/PAQ_pairs", "dataset:embedding-data/WikiAnswers", "arxiv:1904.06472", "arxiv:2102.07033", "arxiv:2104.08727", "arxiv:1704.05179", "arxiv:1810.09305", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[ "1904.06472", "2102.07033", "2104.08727", "1704.05179", "1810.09305" ]

[ "en" ]

[ "### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.", "### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.\nWe then apply the cross entropy loss by comparing with true pairs.", "#### Hyper parameters\n\n\nWe trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with\na 2e-5 learning rate. The full training script is accessible in this current repository: 'train\\_script.py'.", "#### Training data\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.\nWe sampled each dataset given a weighted probability which configuration is detailed in the 'data\\_config.json' file." ]

[ "TAGS\n#sentence-transformers #pytorch #tf #rust #safetensors #bert #feature-extraction #sentence-similarity #transformers #en #dataset-s2orc #dataset-flax-sentence-embeddings/stackexchange_xml #dataset-ms_marco #dataset-gooaq #dataset-yahoo_answers_topics #dataset-code_search_net #dataset-search_qa #dataset-eli5 #dataset-snli #dataset-multi_nli #dataset-wikihow #dataset-natural_questions #dataset-trivia_qa #dataset-embedding-data/sentence-compression #dataset-embedding-data/flickr30k-captions #dataset-embedding-data/altlex #dataset-embedding-data/simple-wiki #dataset-embedding-data/QQP #dataset-embedding-data/SPECTER #dataset-embedding-data/PAQ_pairs #dataset-embedding-data/WikiAnswers #arxiv-1904.06472 #arxiv-2102.07033 #arxiv-2104.08727 #arxiv-1704.05179 #arxiv-1810.09305 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Pre-training\n\n\nWe use the pretrained 'nreimers/MiniLM-L6-H384-uncased' model. Please refer to the model card for more detailed information about the pre-training procedure.", "### Fine-tuning\n\n\nWe fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.\nWe then apply the cross entropy loss by comparing with true pairs.", "#### Hyper parameters\n\n\nWe trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).\nWe use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with\na 2e-5 learning rate. The full training script is accessible in this current repository: 'train\\_script.py'.", "#### Training data\n\n\nWe use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.\nWe sampled each dataset given a weighted probability which configuration is detailed in the 'data\\_config.json' file." ]

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "en", "base_model:ChaoticNeutrals/IQ_Test_l3_8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "5-bit", "region:us" ]

[]

[ "en" ]

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

[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #base_model-ChaoticNeutrals/IQ_Test_l3_8B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us \n", "# Chaos RP\n\n!image/png\n\nA chaotic force beckons for you, will you heed her call?\n\nBuilt upon an intelligent foundation and tuned for roleplaying, this model will fulfill your wildest fantasies with the bare minimum of effort.\n\nEnjoy!" ]

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/deit-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: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.0a0+81ea7a4\n* Datasets 2.18.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-facebook/deit-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: 5e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.0a0+81ea7a4\n* Datasets 2.18.0\n* Tokenizers 0.19.1" ]

[ "transformers", "gguf", "en", "base_model:ResplendentAI/SOVL_Llama3_8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #en #base_model-ResplendentAI/SOVL_Llama3_8B #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "license:mit", "region:us" ]

[]

[]

[]

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

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

[]

[]

[ "# opt-350m_ft_test5\n\nThis model was trained from scratch on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #safetensors #opt #text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# opt-350m_ft_test5\n\nThis model was trained from scratch on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3.0", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.19.1" ]

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

[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "diffusers-training", "base_model:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

[]

[]

[ "# Textual inversion text2image fine-tuning - BKM1804/textual_inversion_cat\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]

[ "TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #textual_inversion #diffusers-training #base_model-runwayml/stable-diffusion-v1-5 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# Textual inversion text2image fine-tuning - BKM1804/textual_inversion_cat\nThese are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.", "## Intended uses & limitations", "#### How to use", "#### Limitations and bias\n\n[TODO: provide examples of latent issues and potential remediations]", "## Training details\n\n[TODO: describe the data used to train the model]" ]

[ "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", "tensorboard", "safetensors", "roberta", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:FacebookAI/roberta-base", "license:mit", "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: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-generation #generated_from_trainer #dataset-eli5_category #base_model-FacebookAI/roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "safetensors", "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", "tensorboard", "safetensors", "mistral", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# DPO-PairRM-5-SMI-lr-1e6-iteration-5-t-7e-beta-15e3-1-iteration-6e1-confidence-D1-D2_smi\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6444\n- Rewards/chosen: -2.4793\n- Rewards/rejected: -2.9560\n- Rewards/accuracies: 0.6667\n- Rewards/margins: 0.4767\n- Rewards/mix Margin: 0.1749\n- Logps/rejected: -481.8095\n- Logps/chosen: -453.2426\n- Logits/rejected: -1.7012\n- Logits/chosen: -1.7287", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-06\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2\n- Datasets 2.17.1\n- Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #trl #dpo #generated_from_trainer #conversational #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# DPO-PairRM-5-SMI-lr-1e6-iteration-5-t-7e-beta-15e3-1-iteration-6e1-confidence-D1-D2_smi\n\nThis model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6444\n- Rewards/chosen: -2.4793\n- Rewards/rejected: -2.9560\n- Rewards/accuracies: 0.6667\n- Rewards/margins: 0.4767\n- Rewards/mix Margin: 0.1749\n- Logps/rejected: -481.8095\n- Logps/chosen: -453.2426\n- Logits/rejected: -1.7012\n- Logits/chosen: -1.7287", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-06\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2\n- Datasets 2.17.1\n- Tokenizers 0.15.1" ]

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:Falconsai/text_summarization", "license:apache-2.0", "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: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-Falconsai/text_summarization #license-apache-2.0 #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: 2e-05\n* train\\_batch\\_size: 1\n* eval\\_batch\\_size: 1\n* seed: 42\n* distributed\\_type: multi-GPU\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:motherfucker0/zhun02", "base_model:motherfucker0/zhun01", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* motherfucker0/zhun02\n* motherfucker0/zhun01", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-motherfucker0/zhun02 #base_model-motherfucker0/zhun01 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* motherfucker0/zhun02\n* motherfucker0/zhun01", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "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", "tensorboard", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# ppo_zephyr4\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta 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: 3e-06\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #mistral #text-generation #generated_from_trainer #conversational #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ppo_zephyr4\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta 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: 3e-06\n- train_batch_size: 1\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 32\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 1", "### Framework versions\n\n- Transformers 4.40.0\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]

[ "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", "gguf", "en", "base_model:cloudyu/Meta-Llama-3-70B-Instruct-DPO", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #en #base_model-cloudyu/Meta-Llama-3-70B-Instruct-DPO #license-apache-2.0 #endpoints_compatible #region-us \n" ]

[ "gguf", "license:apache-2.0", "region:us" ]

[]

[]

[ "# #llama-3 #experimental #work-in-progress\n\nGGUF-IQ-Imatrix quants for @jeiku's ResplendentAI/SOVL_Llama3_8B. <br> Give them some love!\n\n> [!IMPORTANT] \n> Updated!\n> These quants have been redone with the fixes from URL in mind. <br>\n> Use KoboldCpp version 1.64 or higher.\n\n> [!NOTE]\n> Well...! <br>\n> Turns out it was not just a hallucination and this model actually is pretty cool so give it a chance! <br>\n> For 8GB VRAM GPUs, I recommend the Q4_K_M-imat quant for up to 12288 context sizes.\n\n> [!WARNING]\n> Use the provided presets. <br>\n> Compatible SillyTavern presets here (simple) or here (Virt's roleplay).\n> Use the latest version of KoboldCpp.\n\n!image/png" ]

[ "TAGS\n#gguf #license-apache-2.0 #region-us \n", "# #llama-3 #experimental #work-in-progress\n\nGGUF-IQ-Imatrix quants for @jeiku's ResplendentAI/SOVL_Llama3_8B. <br> Give them some love!\n\n> [!IMPORTANT] \n> Updated!\n> These quants have been redone with the fixes from URL in mind. <br>\n> Use KoboldCpp version 1.64 or higher.\n\n> [!NOTE]\n> Well...! <br>\n> Turns out it was not just a hallucination and this model actually is pretty cool so give it a chance! <br>\n> For 8GB VRAM GPUs, I recommend the Q4_K_M-imat quant for up to 12288 context sizes.\n\n> [!WARNING]\n> Use the provided presets. <br>\n> Compatible SillyTavern presets here (simple) or here (Virt's roleplay).\n> Use the latest version of KoboldCpp.\n\n!image/png" ]

[ "transformers", "safetensors", "llama", "text-generation", "merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test4.8-8B-4", "vicgalle/Roleplay-Llama-3-8B", "conversational", "base_model:Kaoeiri/Keiana-L3-Test4.8-8B-4", "base_model:vicgalle/Roleplay-Llama-3-8B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# Keiana-L3-Test4.9-8B-5", "# Keep in mind that it's not yet tested, and I unsure if would work as planned.\n\n\nKeiana-L3-Test4.9-8B-5 is a merge of the following models using LazyMergekit:\n* Kaoeiri/Keiana-L3-Test4.8-8B-4\n* vicgalle/Roleplay-Llama-3-8B", "## Configuration", "## Usage" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #Kaoeiri/Keiana-L3-Test4.8-8B-4 #vicgalle/Roleplay-Llama-3-8B #conversational #base_model-Kaoeiri/Keiana-L3-Test4.8-8B-4 #base_model-vicgalle/Roleplay-Llama-3-8B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Keiana-L3-Test4.9-8B-5", "# Keep in mind that it's not yet tested, and I unsure if would work as planned.\n\n\nKeiana-L3-Test4.9-8B-5 is a merge of the following models using LazyMergekit:\n* Kaoeiri/Keiana-L3-Test4.8-8B-4\n* vicgalle/Roleplay-Llama-3-8B", "## Configuration", "## Usage" ]

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

[]

[]

[ "# robust_llm_pythia-160m_mz-130_IMDB_n-its-10-seed-0\n\nThis model is a fine-tuned version of EleutherAI/pythia-160m 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: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 0\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- Transformers 4.39.3\n- Pytorch 2.2.1\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-160m #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# robust_llm_pythia-160m_mz-130_IMDB_n-its-10-seed-0\n\nThis model is a fine-tuned version of EleutherAI/pythia-160m 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: 1e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 0\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- Transformers 4.39.3\n- Pytorch 2.2.1\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]

[ "TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16", "### Framework versions\n\n\n- PEFT 0.7.0.dev0" ]