Datasets:

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ftopal

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huggingface-models

like 0

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 192\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 24\n* eval\\_batch\\_size: 24\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 192\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 1\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

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

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "peft", "tensorboard", "safetensors", "trl", "sft", "unsloth", "translation", "generated_from_trainer", "base_model:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 4269\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\\_steps: 1\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.16.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#peft #tensorboard #safetensors #trl #sft #unsloth #translation #generated_from_trainer #base_model-unsloth/mistral-7b-bnb-4bit #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 4269\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\\_steps: 1\n* num\\_epochs: 5\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* PEFT 0.10.0\n* Transformers 4.39.3\n* Pytorch 2.2.2+cu121\n* Datasets 2.16.0\n* Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "conversational", "en", "dataset:flytech/python-codes-25k", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:llama3", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]

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[ "en" ]

[ "# Finetune-Llama3-genertate-python-code\n\n--\n\n- Developed by: Wittawat Kitipatthavorn\n- License: llama 3 URL\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\n--\n\n Unsloth\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 #llama #text-generation #text-generation-inference #unsloth #trl #sft #conversational #en #dataset-flytech/python-codes-25k #base_model-unsloth/llama-3-8b-bnb-4bit #license-llama3 #autotrain_compatible #endpoints_compatible #4-bit #region-us \n", "# Finetune-Llama3-genertate-python-code\n\n--\n\n- Developed by: Wittawat Kitipatthavorn\n- License: llama 3 URL\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\n--\n\n Unsloth\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>" ]

[ "region:us" ]

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[ "# PostureCraft: Controllable Human Pose Generation with Diffusion Models\n\nMultiple posture control models (based on ControlNet) are presented in this repo. We show that our models have better performance and also better results over existing models in human posture control.", "## Segmented Depth Model\navaiable at 'URL'", "## Depth Annotated Pose Model\navaiable at 'URL'\n\nPlease refer to the report for the detailed specification of the models." ]

[ "TAGS\n#region-us \n", "# PostureCraft: Controllable Human Pose Generation with Diffusion Models\n\nMultiple posture control models (based on ControlNet) are presented in this repo. We show that our models have better performance and also better results over existing models in human posture control.", "## Segmented Depth Model\navaiable at 'URL'", "## Depth Annotated Pose Model\navaiable at 'URL'\n\nPlease refer to the report for the detailed specification of the models." ]

[ "transformers", "tensorboard", "safetensors", "blenderbot-small", "text-generation", "generated_from_trainer", "dataset:eli5_category", "base_model:facebook/blenderbot_small-90M", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 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 #blenderbot-small #text-generation #generated_from_trainer #dataset-eli5_category #base_model-facebook/blenderbot_small-90M #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: 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", "deberta-v2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

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[ "# 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 #deberta-v2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "conversational", "en", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "6-bit", "region:us" ]

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[ "en" ]

[ "### ChaoticSoliloquy-4x8B", "## Models used\n\n- ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B\n- jeiku/Chaos_RP_l3_8B\n- openlynn/Llama-3-Soliloquy-8B\n- Sao10K/L3-Solana-8B-v1", "## Vision\n\nllama3_mmproj\n!image/png", "## Prompt format: Llama 3" ]

[ "TAGS\n#transformers #safetensors #mixtral #text-generation #moe #conversational #en #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us \n", "### ChaoticSoliloquy-4x8B", "## Models used\n\n- ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B\n- jeiku/Chaos_RP_l3_8B\n- openlynn/Llama-3-Soliloquy-8B\n- Sao10K/L3-Solana-8B-v1", "## Vision\n\nllama3_mmproj\n!image/png", "## Prompt format: Llama 3" ]

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

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[ "en" ]

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

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

[ "transformers", "safetensors", "mixtral", "text-generation", "moe", "conversational", "en", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "5-bit", "region:us" ]

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[ "en" ]

[ "### ChaoticSoliloquy-4x8B", "## Models used\n\n- ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B\n- jeiku/Chaos_RP_l3_8B\n- openlynn/Llama-3-Soliloquy-8B\n- Sao10K/L3-Solana-8B-v1", "## Vision\n\nllama3_mmproj\n!image/png", "## Prompt format: Llama 3" ]

[ "TAGS\n#transformers #safetensors #mixtral #text-generation #moe #conversational #en #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us \n", "### ChaoticSoliloquy-4x8B", "## Models used\n\n- ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B\n- jeiku/Chaos_RP_l3_8B\n- openlynn/Llama-3-Soliloquy-8B\n- Sao10K/L3-Solana-8B-v1", "## Vision\n\nllama3_mmproj\n!image/png", "## Prompt format: Llama 3" ]

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:google-bert/bert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]

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[ "# model_dl_22\n\nThis model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:", "## 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- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n- training_precision: mixed_float16", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- TensorFlow 2.15.0\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-google-bert/bert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n", "# model_dl_22\n\nThis model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset.\nIt achieves the following results on the evaluation set:", "## 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- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n- training_precision: mixed_float16", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- TensorFlow 2.15.0\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "llama", "text-generation", "llama-3", "bagel", "conversational", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #llama-3 #bagel #conversational #dataset-ai2_arc #dataset-allenai/ultrafeedback_binarized_cleaned #dataset-argilla/distilabel-intel-orca-dpo-pairs #dataset-jondurbin/airoboros-3.2 #dataset-codeparrot/apps #dataset-facebook/belebele #dataset-bluemoon-fandom-1-1-rp-cleaned #dataset-boolq #dataset-camel-ai/biology #dataset-camel-ai/chemistry #dataset-camel-ai/math #dataset-camel-ai/physics #dataset-jondurbin/contextual-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-jondurbin/py-dpo-v0.1 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-LDJnr/Capybara #dataset-jondurbin/cinematika-v0.1 #dataset-WizardLM/WizardLM_evol_instruct_70k #dataset-glaiveai/glaive-function-calling-v2 #dataset-grimulkan/LimaRP-augmented #dataset-lmsys/lmsys-chat-1m #dataset-ParisNeo/lollms_aware_dataset #dataset-TIGER-Lab/MathInstruct #dataset-Muennighoff/natural-instructions #dataset-openbookqa #dataset-kingbri/PIPPA-shareGPT #dataset-piqa #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-ropes #dataset-cakiki/rosetta-code #dataset-Open-Orca/SlimOrca #dataset-b-mc2/sql-create-context #dataset-squad_v2 #dataset-mattpscott/airoboros-summarization #dataset-migtissera/Synthia-v1.3 #dataset-unalignment/toxic-dpo-v0.2 #dataset-WhiteRabbitNeo/WRN-Chapter-1 #dataset-WhiteRabbitNeo/WRN-Chapter-2 #dataset-winogrande #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

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

[ "1910.09700" ]

[]

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

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

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:state-spaces/mamba-1.4b-hf", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-state-spaces/mamba-1.4b-hf #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

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

[]

[ "en" ]

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

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

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

[]

[]

[ "# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "tf", "bert", "question-answering", "generated_from_keras_callback", "base_model:huggingface-course/bert-finetuned-squad", "endpoints_compatible", "region:us" ]

[]

[]

[ "# model_dl_1y\n\nThis model is a fine-tuned version of huggingface-course/bert-finetuned-squad on an unknown dataset.\nIt achieves the following results on the evaluation set:", "## 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- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n- training_precision: mixed_float16", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- TensorFlow 2.15.0\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tf #bert #question-answering #generated_from_keras_callback #base_model-huggingface-course/bert-finetuned-squad #endpoints_compatible #region-us \n", "# model_dl_1y\n\nThis model is a fine-tuned version of huggingface-course/bert-finetuned-squad on an unknown dataset.\nIt achieves the following results on the evaluation set:", "## 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- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 1e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}\n- training_precision: mixed_float16", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- TensorFlow 2.15.0\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:minds14", "base_model:jonatasgrosman/wav2vec2-large-xlsr-53-french", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[]

[ "# asr_model_french\n\nThis model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-french on the minds14 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9463\n- eval_wer: 0.9535\n- eval_runtime: 0.8229\n- eval_samples_per_second: 7.292\n- eval_steps_per_second: 1.215\n- epoch: 333.3333\n- step: 500", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 250\n- training_steps: 1000\n- mixed_precision_training: Native AMP", "### 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 #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-minds14 #base_model-jonatasgrosman/wav2vec2-large-xlsr-53-french #license-apache-2.0 #endpoints_compatible #region-us \n", "# asr_model_french\n\nThis model is a fine-tuned version of jonatasgrosman/wav2vec2-large-xlsr-53-french on the minds14 dataset.\nIt achieves the following results on the evaluation set:\n- eval_loss: 0.9463\n- eval_wer: 0.9535\n- eval_runtime: 0.8229\n- eval_samples_per_second: 7.292\n- eval_steps_per_second: 1.215\n- epoch: 333.3333\n- step: 500", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0001\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 2\n- total_train_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 250\n- training_steps: 1000\n- mixed_precision_training: Native AMP", "### 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", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "safetensors", "llama", "text-generation", "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", "gpt2", "text-generation", "generated_from_trainer", "base_model:gpt2", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 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- gradient_accumulation_steps: 8\n- total_train_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_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-gpt2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# codeparrot-ds\n\nThis model is a fine-tuned version of gpt2 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- gradient_accumulation_steps: 8\n- total_train_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_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

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

[ "1910.09700" ]

[]

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

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

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

[]

[ "en" ]

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

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

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:projecte-aina/roberta-base-ca-v2-cased-te", "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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* lr\\_scheduler\\_warmup\\_steps: 4\n* num\\_epochs: 10\n* label\\_smoothing\\_factor: 0.1", "### 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 #safetensors #roberta #text-classification #generated_from_trainer #base_model-projecte-aina/roberta-base-ca-v2-cased-te #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: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* lr\\_scheduler\\_warmup\\_steps: 4\n* num\\_epochs: 10\n* label\\_smoothing\\_factor: 0.1", "### 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", "llama", "text-generation", "llama-3", "bagel", "conversational", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #llama-3 #bagel #conversational #dataset-ai2_arc #dataset-allenai/ultrafeedback_binarized_cleaned #dataset-argilla/distilabel-intel-orca-dpo-pairs #dataset-jondurbin/airoboros-3.2 #dataset-codeparrot/apps #dataset-facebook/belebele #dataset-bluemoon-fandom-1-1-rp-cleaned #dataset-boolq #dataset-camel-ai/biology #dataset-camel-ai/chemistry #dataset-camel-ai/math #dataset-camel-ai/physics #dataset-jondurbin/contextual-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-jondurbin/py-dpo-v0.1 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-LDJnr/Capybara #dataset-jondurbin/cinematika-v0.1 #dataset-WizardLM/WizardLM_evol_instruct_70k #dataset-glaiveai/glaive-function-calling-v2 #dataset-grimulkan/LimaRP-augmented #dataset-lmsys/lmsys-chat-1m #dataset-ParisNeo/lollms_aware_dataset #dataset-TIGER-Lab/MathInstruct #dataset-Muennighoff/natural-instructions #dataset-openbookqa #dataset-kingbri/PIPPA-shareGPT #dataset-piqa #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-ropes #dataset-cakiki/rosetta-code #dataset-Open-Orca/SlimOrca #dataset-b-mc2/sql-create-context #dataset-squad_v2 #dataset-mattpscott/airoboros-summarization #dataset-migtissera/Synthia-v1.3 #dataset-unalignment/toxic-dpo-v0.2 #dataset-WhiteRabbitNeo/WRN-Chapter-1 #dataset-WhiteRabbitNeo/WRN-Chapter-2 #dataset-winogrande #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "peft", "safetensors", "ko", "dataset:JosephLee/society_textbook_elementary_kor", "arxiv:1910.09700", "base_model:google/gemma-1.1-2b-it", "region:us" ]

[ "1910.09700" ]

[ "ko" ]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.9.0" ]

[ "TAGS\n#peft #safetensors #ko #dataset-JosephLee/society_textbook_elementary_kor #arxiv-1910.09700 #base_model-google/gemma-1.1-2b-it #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.9.0" ]

[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]

[]

[]

[ "# llama-7b-qlora-ultrachat\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 16\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: constant\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 100", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.2\n- Datasets 2.15.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-meta-llama/Llama-2-7b-hf #license-llama2 #region-us \n", "# llama-7b-qlora-ultrachat\n\nThis model is a fine-tuned version of meta-llama/Llama-2-7b-hf on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 16\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: constant\n- lr_scheduler_warmup_ratio: 0.03\n- training_steps: 100", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.2\n- Datasets 2.15.0\n- Tokenizers 0.15.2" ]

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

[ "1910.09700" ]

[]

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

[ "TAGS\n#transformers #safetensors #deberta-v2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "safetensors", "m3d_clip", "feature-extraction", "3D medical CLIP", "Image-text retrieval", "image-feature-extraction", "custom_code", "arxiv:2404.00578", "license:apache-2.0", "region:us" ]

[ "2404.00578" ]

[]

[ "# Quickstart\n\n\n\nIf you feel helpful from our work, please consider citing the following work:" ]

[ "TAGS\n#transformers #safetensors #m3d_clip #feature-extraction #3D medical CLIP #Image-text retrieval #image-feature-extraction #custom_code #arxiv-2404.00578 #license-apache-2.0 #region-us \n", "# Quickstart\n\n\n\nIf you feel helpful from our work, please consider citing the following work:" ]

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# codeparrot-ds\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# codeparrot-ds\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]

[ "transformers", "safetensors", "gemma", "text-generation", "pytorch", "instruct", "finetune", "translation", "conversational", "ko", "base_model:beomi/gemma-ko-2b", "license:gemma", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[ "ko" ]

[ "# Gemma 2B Translation v0.125\n\n- Eval Loss: '0.80386'\n- Train Loss: '0.75039'\n- lr: '6e-05'\n- optimizer: adamw\n- lr_scheduler_type: cosine", "## Prompt Template", "## Model Description\n\n- Developed by: 'lemon-mint'\n- Model type: Gemma\n- Language(s) (NLP): English\n- License: gemma-terms-of-use\n- Finetuned from model: beomi/gemma-ko-2b" ]

[ "TAGS\n#transformers #safetensors #gemma #text-generation #pytorch #instruct #finetune #translation #conversational #ko #base_model-beomi/gemma-ko-2b #license-gemma #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Gemma 2B Translation v0.125\n\n- Eval Loss: '0.80386'\n- Train Loss: '0.75039'\n- lr: '6e-05'\n- optimizer: adamw\n- lr_scheduler_type: cosine", "## Prompt Template", "## Model Description\n\n- Developed by: 'lemon-mint'\n- Model type: Gemma\n- Language(s) (NLP): English\n- License: gemma-terms-of-use\n- Finetuned from model: beomi/gemma-ko-2b" ]

[ "transformers", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# codeparrot-ds-distilgpt2\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# codeparrot-ds-distilgpt2\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.2.2+cu121\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

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

[]

[ "en" ]

[ "# KobbleTinyV2-1.1B\n</div>\n\nThis is a finetune of URL trained on a small 50mb subset of the Kobble Dataset. \nTraining was done in under 2 hours on a single Nvidia RTX 2060 Mobile GPU with qLora (LR 1.5e-4, rank 8, alpha 16, batch size 2, gradient acc. 4, 2048 ctx). \n\nYou can obtain the GGUF quantization of this model here: URL\n\nUpdate: KobbleTiny has been upgraded to V2! The old V1 is still available at this link.\n\n<video width=\"320\" controls autoplay src=\"URL\n\nTry it live now: URL", "## Dataset and Objectives\n\nThe Kobble Dataset is a semi-private aggregated dataset made from multiple online sources and web scrapes. \nIt contains content chosen and formatted specifically to work with KoboldAI software and Kobold Lite.", "#### Dataset Categories:\n- Instruct: Single turn instruct examples presented in the Alpaca format, with an emphasis on uncensored and unrestricted responses.\n- Chat: Two participant roleplay conversation logs in a multi-turn raw chat format that KoboldAI uses.\n- Story: Unstructured fiction excerpts, including literature containing various erotic and provocative content.", "## Prompt template: Alpaca\n\n\n\n\n\nNote: *No assurances will be provided about the origins, safety, or copyright status of this model, or of any content within the Kobble dataset.* \n*If you belong to a country or organization that has strict AI laws or restrictions against unlabelled or unrestricted content, you are advised not to use this model.*" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# KobbleTinyV2-1.1B\n</div>\n\nThis is a finetune of URL trained on a small 50mb subset of the Kobble Dataset. \nTraining was done in under 2 hours on a single Nvidia RTX 2060 Mobile GPU with qLora (LR 1.5e-4, rank 8, alpha 16, batch size 2, gradient acc. 4, 2048 ctx). \n\nYou can obtain the GGUF quantization of this model here: URL\n\nUpdate: KobbleTiny has been upgraded to V2! The old V1 is still available at this link.\n\n<video width=\"320\" controls autoplay src=\"URL\n\nTry it live now: URL", "## Dataset and Objectives\n\nThe Kobble Dataset is a semi-private aggregated dataset made from multiple online sources and web scrapes. \nIt contains content chosen and formatted specifically to work with KoboldAI software and Kobold Lite.", "#### Dataset Categories:\n- Instruct: Single turn instruct examples presented in the Alpaca format, with an emphasis on uncensored and unrestricted responses.\n- Chat: Two participant roleplay conversation logs in a multi-turn raw chat format that KoboldAI uses.\n- Story: Unstructured fiction excerpts, including literature containing various erotic and provocative content.", "## Prompt template: Alpaca\n\n\n\n\n\nNote: *No assurances will be provided about the origins, safety, or copyright status of this model, or of any content within the Kobble dataset.* \n*If you belong to a country or organization that has strict AI laws or restrictions against unlabelled or unrestricted content, you are advised not to use this model.*" ]

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:motherfucker0/zhun01", "base_model:motherfucker0/zhun02", "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/zhun01\n* motherfucker0/zhun02", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-motherfucker0/zhun01 #base_model-motherfucker0/zhun02 #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/zhun01\n* motherfucker0/zhun02", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

[ "TAGS\n#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

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

[]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* 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: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* 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" ]

[ "transformers", "safetensors", "llama", "text-generation", "llama-3", "bagel", "conversational", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #llama-3 #bagel #conversational #dataset-ai2_arc #dataset-allenai/ultrafeedback_binarized_cleaned #dataset-argilla/distilabel-intel-orca-dpo-pairs #dataset-jondurbin/airoboros-3.2 #dataset-codeparrot/apps #dataset-facebook/belebele #dataset-bluemoon-fandom-1-1-rp-cleaned #dataset-boolq #dataset-camel-ai/biology #dataset-camel-ai/chemistry #dataset-camel-ai/math #dataset-camel-ai/physics #dataset-jondurbin/contextual-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-jondurbin/py-dpo-v0.1 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-LDJnr/Capybara #dataset-jondurbin/cinematika-v0.1 #dataset-WizardLM/WizardLM_evol_instruct_70k #dataset-glaiveai/glaive-function-calling-v2 #dataset-grimulkan/LimaRP-augmented #dataset-lmsys/lmsys-chat-1m #dataset-ParisNeo/lollms_aware_dataset #dataset-TIGER-Lab/MathInstruct #dataset-Muennighoff/natural-instructions #dataset-openbookqa #dataset-kingbri/PIPPA-shareGPT #dataset-piqa #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-ropes #dataset-cakiki/rosetta-code #dataset-Open-Orca/SlimOrca #dataset-b-mc2/sql-create-context #dataset-squad_v2 #dataset-mattpscott/airoboros-summarization #dataset-migtissera/Synthia-v1.3 #dataset-unalignment/toxic-dpo-v0.2 #dataset-WhiteRabbitNeo/WRN-Chapter-1 #dataset-WhiteRabbitNeo/WRN-Chapter-2 #dataset-winogrande #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:c-s-ale/RiddleLegalEasy", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #mergekit #merge #en #base_model-c-s-ale/RiddleLegalEasy #endpoints_compatible #region-us \n" ]

[ "transformers", "safetensors", "llama", "text-generation", "ko", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[ "1910.09700" ]

[ "ko" ]

[ "# Model Details\n\n!logo", "## Model Description\n\n\nPOLAR is a Korean LLM developed by Plateer's AI-lab. It was inspired by Upstage's SOLAR. We will continue to evolve this model and hope to contribute to the Korean LLM ecosystem.\n\n- Developed by: AI-Lab of Plateer(Woomun Jung, Eunsoo Ha, MinYoung Joo, Seongjun Son)\n- Model type: Language model\n- Language(s) (NLP): ko\n- License: apache-2.0\n- Parent Model: upstage/SOLAR-10.7B-v1.0", "# Uses", "## Direct Use", "## Downstream Use [Optional]", "## Out-of-Scope Use", "# Bias, Risks, and Limitations\n\n\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations", "# Training Details", "## Training Data\n\n\n\nMore information on training data needed", "## Training Procedure", "### Preprocessing\n\nMore information needed", "### Speeds, Sizes, Times\n\n\n\nMore information needed", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n\n\n\nMore information needed", "### Factors\n\n\n\nMore information needed", "### Metrics\n\n\n\nMore information needed", "## Results \n\nMore information needed", "# Model Examination\n\nMore information needed", "# Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: More information needed\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n\nMore information needed", "## Compute Infrastructure\n\nMore information needed", "### Hardware\n\nMore information needed", "### Software\n\nMore information needed\n\nBibTeX:\n\nMore information needed\n\nAPA:\n\nMore information needed", "# Glossary [optional]\n\n\n\nMore information needed", "# More Information [optional]\n\nIf you would like more information about our company, please visit the link below. \nURL", "# Model Card Authors [optional]\n\n\n\nWoomun Jung, MinYoung Joo, Eunsu Ha, Seungjun Son", "# Model Card Contact\n\nMore information needed", "# How to Get Started with the Model\n\nUse the code below to get started with the model.\n\n<details>\n<summary> Click to expand </summary>\n\nMore information needed\n\n</details>" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #ko #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Details\n\n!logo", "## Model Description\n\n\nPOLAR is a Korean LLM developed by Plateer's AI-lab. It was inspired by Upstage's SOLAR. We will continue to evolve this model and hope to contribute to the Korean LLM ecosystem.\n\n- Developed by: AI-Lab of Plateer(Woomun Jung, Eunsoo Ha, MinYoung Joo, Seongjun Son)\n- Model type: Language model\n- Language(s) (NLP): ko\n- License: apache-2.0\n- Parent Model: upstage/SOLAR-10.7B-v1.0", "# Uses", "## Direct Use", "## Downstream Use [Optional]", "## Out-of-Scope Use", "# Bias, Risks, and Limitations\n\n\n\nSignificant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.", "## Recommendations", "# Training Details", "## Training Data\n\n\n\nMore information on training data needed", "## Training Procedure", "### Preprocessing\n\nMore information needed", "### Speeds, Sizes, Times\n\n\n\nMore information needed", "# Evaluation", "## Testing Data, Factors & Metrics", "### Testing Data\n\n\n\nMore information needed", "### Factors\n\n\n\nMore information needed", "### Metrics\n\n\n\nMore information needed", "## Results \n\nMore information needed", "# Model Examination\n\nMore information needed", "# Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: More information needed\n- Hours used: More information needed\n- Cloud Provider: More information needed\n- Compute Region: More information needed\n- Carbon Emitted: More information needed", "# Technical Specifications [optional]", "## Model Architecture and Objective\n\nMore information needed", "## Compute Infrastructure\n\nMore information needed", "### Hardware\n\nMore information needed", "### Software\n\nMore information needed\n\nBibTeX:\n\nMore information needed\n\nAPA:\n\nMore information needed", "# Glossary [optional]\n\n\n\nMore information needed", "# More Information [optional]\n\nIf you would like more information about our company, please visit the link below. \nURL", "# Model Card Authors [optional]\n\n\n\nWoomun Jung, MinYoung Joo, Eunsu Ha, Seungjun Son", "# Model Card Contact\n\nMore information needed", "# How to Get Started with the Model\n\nUse the code below to get started with the model.\n\n<details>\n<summary> Click to expand </summary>\n\nMore information needed\n\n</details>" ]

[ "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" ]

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_SOAPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_SOAPL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "region:us" ]

[]

[]

[]

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

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

[ "1910.09700" ]

[]

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

[ "TAGS\n#transformers #safetensors #deberta-v2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[]

[ "# lab7_prec\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### 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 #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# lab7_prec\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### 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", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google/flan-t5-base", "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: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 45", "### 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-google/flan-t5-base #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: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 45", "### 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", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# CS505_COQE_viT5_train_Instruction0_SOAPL_v2\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_SOAPL_v2\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "pytorch", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# lab7_model\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.40.1\n- Pytorch 2.0.0+cu118\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# lab7_model\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 4\n- eval_batch_size: 1\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.40.1\n- Pytorch 2.0.0+cu118\n- Datasets 2.18.0\n- Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:motherfucker0/zhun01", "base_model:motherfucker0/zhun02", "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/zhun01\n* motherfucker0/zhun02", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #base_model-motherfucker0/zhun01 #base_model-motherfucker0/zhun02 #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/zhun01\n* motherfucker0/zhun02", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# codeparrot-ds\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### 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 #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# codeparrot-ds\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### 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", "pytorch", "fr", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "fr", "en" ]

[]

[ "TAGS\n#transformers #pytorch #fr #en #license-apache-2.0 #endpoints_compatible #region-us \n" ]

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

[]

[ "en" ]

[ "### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.", "#### Transformers pipeline", "#### Transformers AutoModelForCausalLM", "### Use with 'llama3'\n\n\nPlease, follow the instructions in the repository\n\n\nTo download Original checkpoints, see the example command below leveraging 'huggingface-cli':\n\n\nFor Hugging Face support, we recommend using transformers or TGI, but a similar command works.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.\n\n\nCarbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\n\nCO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.\n\n\nTraining Data\n-------------\n\n\nOverview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.\n\n\nData Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.\n\n\nBenchmarks\n----------\n\n\nIn this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.", "### Base pretrained models", "### Instruction tuned models", "### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.", "#### Llama 3-Instruct\n\n\nAs outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.\n\n\nSafety\n\n\nFor our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.\n\n\nRefusals\n\n\nIn addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.\n\n\nWe built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.", "#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL", "#### Critical risks\n\n\nCBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)\n\n\nWe have conducted a two fold assessment of the safety of the model in this area:\n\n\n* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.\n* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).", "### Cyber Security\n\n\nWe have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.", "### Child Safety\n\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.", "### Community\n\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.\n\n\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL\n\n\ninstructions\n\n\n@article{llama3modelcard,\n\n\ntitle={Llama 3 Model Card},\n\n\nauthor={AI@Meta},\n\n\nyear={2024},\n\n\nurl = {URL\n\n\n}\n\n\nContributors\n------------\n\n\nAaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #facebook #meta #pytorch #llama-3 #conversational #en #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.", "#### Transformers pipeline", "#### Transformers AutoModelForCausalLM", "### Use with 'llama3'\n\n\nPlease, follow the instructions in the repository\n\n\nTo download Original checkpoints, see the example command below leveraging 'huggingface-cli':\n\n\nFor Hugging Face support, we recommend using transformers or TGI, but a similar command works.\n\n\nHardware and Software\n---------------------\n\n\nTraining Factors We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.\n\n\nCarbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.\n\n\n\nCO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.\n\n\nTraining Data\n-------------\n\n\nOverview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.\n\n\nData Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.\n\n\nBenchmarks\n----------\n\n\nIn this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see here.", "### Base pretrained models", "### Instruction tuned models", "### Responsibility & Safety\n\n\nWe believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.\n\n\nFoundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.\n\n\nRather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.\n\n\nAs part of the Llama 3 release, we updated our Responsible Use Guide to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including Meta Llama Guard 2 and Code Shield safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a reference implementation to get you started.", "#### Llama 3-Instruct\n\n\nAs outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.\n\n\nSafety\n\n\nFor our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.\n\n\nRefusals\n\n\nIn addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.\n\n\nWe built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.", "#### Responsible release\n\n\nIn addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.\n\n\nMisuse\n\n\nIf you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at URL", "#### Critical risks\n\n\nCBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)\n\n\nWe have conducted a two fold assessment of the safety of the model in this area:\n\n\n* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.\n* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).", "### Cyber Security\n\n\nWe have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of equivalent coding capability.", "### Child Safety\n\n\nChild Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.", "### Community\n\n\nGenerative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our Github repository.\n\n\nFinally, we put in place a set of resources including an output reporting mechanism and bug bounty program to continuously improve the Llama technology with the help of the community.\n\n\nEthical Considerations and Limitations\n--------------------------------------\n\n\nThe core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.\n\n\nBut Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating Purple Llama solutions into your workflows and specifically Llama Guard which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.\n\n\nPlease see the Responsible Use Guide available at URL\n\n\ninstructions\n\n\n@article{llama3modelcard,\n\n\ntitle={Llama 3 Model Card},\n\n\nauthor={AI@Meta},\n\n\nyear={2024},\n\n\nurl = {URL\n\n\n}\n\n\nContributors\n------------\n\n\nAaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos" ]

[ "transformers", "safetensors", "llama", "text-generation", "llama-3", "bagel", "conversational", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "5-bit", "region:us" ]

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[]

[ "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #llama-3 #bagel #conversational #dataset-ai2_arc #dataset-allenai/ultrafeedback_binarized_cleaned #dataset-argilla/distilabel-intel-orca-dpo-pairs #dataset-jondurbin/airoboros-3.2 #dataset-codeparrot/apps #dataset-facebook/belebele #dataset-bluemoon-fandom-1-1-rp-cleaned #dataset-boolq #dataset-camel-ai/biology #dataset-camel-ai/chemistry #dataset-camel-ai/math #dataset-camel-ai/physics #dataset-jondurbin/contextual-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-jondurbin/py-dpo-v0.1 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-LDJnr/Capybara #dataset-jondurbin/cinematika-v0.1 #dataset-WizardLM/WizardLM_evol_instruct_70k #dataset-glaiveai/glaive-function-calling-v2 #dataset-grimulkan/LimaRP-augmented #dataset-lmsys/lmsys-chat-1m #dataset-ParisNeo/lollms_aware_dataset #dataset-TIGER-Lab/MathInstruct #dataset-Muennighoff/natural-instructions #dataset-openbookqa #dataset-kingbri/PIPPA-shareGPT #dataset-piqa #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-ropes #dataset-cakiki/rosetta-code #dataset-Open-Orca/SlimOrca #dataset-b-mc2/sql-create-context #dataset-squad_v2 #dataset-mattpscott/airoboros-summarization #dataset-migtissera/Synthia-v1.3 #dataset-unalignment/toxic-dpo-v0.2 #dataset-WhiteRabbitNeo/WRN-Chapter-1 #dataset-WhiteRabbitNeo/WRN-Chapter-2 #dataset-winogrande #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #5-bit #region-us \n", "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "transformers", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:projecte-aina/roberta-base-ca-v2-cased-te", "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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 3\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* lr\\_scheduler\\_warmup\\_steps: 4\n* num\\_epochs: 10\n* label\\_smoothing\\_factor: 0.2", "### 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 #safetensors #roberta #text-classification #generated_from_trainer #base_model-projecte-aina/roberta-base-ca-v2-cased-te #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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 3\n* total\\_train\\_batch\\_size: 48\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* lr\\_scheduler\\_warmup\\_steps: 4\n* num\\_epochs: 10\n* label\\_smoothing\\_factor: 0.2", "### 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" ]

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

[ "1910.09700" ]

[]

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

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

[ "transformers", "safetensors", "bert", "feature-extraction", "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 #bert #feature-extraction #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", "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", "imatrix", "jeiku", "Rosa_Writing_3B", "text-generation", "en", "license:other", "region:us" ]

[]

[ "en" ]

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

[ "TAGS\n#transformers #gguf #imatrix #jeiku #Rosa_Writing_3B #text-generation #en #license-other #region-us \n", "# From original readme\n\n..." ]

[ "transformers", "safetensors", "llama", "feature-extraction", "arxiv:1910.09700", "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 #feature-extraction #arxiv-1910.09700 #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", "mergekit", "merge", "en", "base_model:jeiku/Average_Normie_v2_l3_8B", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#transformers #gguf #mergekit #merge #en #base_model-jeiku/Average_Normie_v2_l3_8B #endpoints_compatible #region-us \n" ]

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

[ "1910.09700" ]

[]

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

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

[ "mlx", "safetensors", "mistral", "finetuned", "text-generation", "conversational", "license:apache-2.0", "region:us" ]

[]

[]

[ "# Wordpress-Mistral-7B-Fine-Tune\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']().\nRefer to the original model card for more details on the model.", "## Use with mlx" ]

[ "TAGS\n#mlx #safetensors #mistral #finetuned #text-generation #conversational #license-apache-2.0 #region-us \n", "# Wordpress-Mistral-7B-Fine-Tune\nThis model was converted to MLX format from ['mistralai/Mistral-7B-Instruct-v0.2']().\nRefer to the original model card for more details on the model.", "## Use with mlx" ]

[ "transformers", "safetensors", "llama", "text-generation", "llama-3", "bagel", "conversational", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #llama-3 #bagel #conversational #dataset-ai2_arc #dataset-allenai/ultrafeedback_binarized_cleaned #dataset-argilla/distilabel-intel-orca-dpo-pairs #dataset-jondurbin/airoboros-3.2 #dataset-codeparrot/apps #dataset-facebook/belebele #dataset-bluemoon-fandom-1-1-rp-cleaned #dataset-boolq #dataset-camel-ai/biology #dataset-camel-ai/chemistry #dataset-camel-ai/math #dataset-camel-ai/physics #dataset-jondurbin/contextual-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-jondurbin/py-dpo-v0.1 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-LDJnr/Capybara #dataset-jondurbin/cinematika-v0.1 #dataset-WizardLM/WizardLM_evol_instruct_70k #dataset-glaiveai/glaive-function-calling-v2 #dataset-grimulkan/LimaRP-augmented #dataset-lmsys/lmsys-chat-1m #dataset-ParisNeo/lollms_aware_dataset #dataset-TIGER-Lab/MathInstruct #dataset-Muennighoff/natural-instructions #dataset-openbookqa #dataset-kingbri/PIPPA-shareGPT #dataset-piqa #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-ropes #dataset-cakiki/rosetta-code #dataset-Open-Orca/SlimOrca #dataset-b-mc2/sql-create-context #dataset-squad_v2 #dataset-mattpscott/airoboros-summarization #dataset-migtissera/Synthia-v1.3 #dataset-unalignment/toxic-dpo-v0.2 #dataset-WhiteRabbitNeo/WRN-Chapter-1 #dataset-WhiteRabbitNeo/WRN-Chapter-2 #dataset-winogrande #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

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

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[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]

[ "transformers", "safetensors", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/swin-tiny-patch4-window7-224", "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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "TAGS\n#transformers #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0\n* Pytorch 2.1.0\n* Datasets 2.14.6\n* Tokenizers 0.14.1" ]

[ "gguf", "text-generation", "hu", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:HuggingFaceH4/ultrafeedback_binarized", "dataset:HuggingFaceH4/cai-conversation-harmless", "arxiv:2404.05829", "license:llama2", "region:us" ]

[ "2404.05829" ]

[ "hu", "en" ]

[ "# SambaLingo-Hungarian-Chat\n\n<img src=\"SambaLingo_Logo.png\" width=\"340\" style=\"margin-left:'auto' margin-right:'auto' display:'block'\"/>\n\n\nSambaLingo-Hungarian-Chat is a human aligned chat model trained in Hungarian and English. It is trained using direct preference optimization on top the base model SambaLingo-Hungarian-Base. The base model adapts Llama-2-7b to Hungarian by training on 59 billion tokens from the Hungarian split of the Cultura-X dataset. Try This Model at SambaLingo-chat-space.", "## Model Description\n\n\n- Developed by: SambaNova Systems\n- Model type: Language Model\n- Language(s): Hungarian, English\n- Finetuned from model: Llama-2-7b\n- Try This Model: SambaLingo-chat-space\n- Paper: SambaLingo: Teaching Large Language Models New Languages\n- Blog Post: sambalingo-open-source-language-experts", "## Getting Started", "### Loading Model With Hugging Face\nPlease make sure to set use_fast=False when loading the tokenizer.", "### Interacting With Model Pipeline\nPlease make sure to set use_fast=False when loading the tokenizer.", "### Suggested Inference Parameters\n- Temperature: 0.8\n- Repetition penalty: 1.0\n- Top-p: 0.9", "### Prompting Guidelines\nTo prompt this model, please use the following chat template:", "### Example Prompts and Generations", "## Training Details\nThe alignment phase follows the recipe for Zephyr-7B, and comprises two stages: supervised fine-tuning (SFT) and Direct Performance Optimization (DPO).\n\nThe SFT phase was done on the ultrachat_200k dataset mixed with the Google translated version of the ultrachat_200k dataset. It was trained for one epoch with global batch size 512 and max sequence length 2048 tokens. We used a linear decay learning rate of 2e-5 and 10% warmup.\n\nThe DPO phase was done on the ultrafeedback dataset and cai-conversation-harmless dataset, mixed with 10% of the data Google translated. It was trained with global batch size 32 and for three epochs. We used a linear decay learning rate of 5e-7, 10% warmup and β=0.1 as the regularization factor for DPO.", "## Tokenizer Details\nWe extended the vocabulary of the base llama model from 32,000 tokens to 57,000 tokens by adding up to 25,000 non-overlapping tokens from the new language.", "## Evaluation\nFor evaluation results see our paper: SambaLingo: Teaching Large Language Models New Languages", "## Uses", "### Direct Use\n\n\nUse of this model is governed by the Meta’s Llama 2 Community License Agreement. Please review and accept the license before downloading the model weights.", "### Out-of-Scope Use\n\n\nSambaLingo should NOT be used for:\n\n- Mission-critical applications\n- Applications that involve the safety of others\n- Making highly important decisions", "## Bias, Risks, and Limitations\n\n\n\nLike all LLMs, SambaLingo has certain limitations:\n- Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.\n- Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output.\n- Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses.\n- Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.\n- Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content.", "## Acknowledgments\nWe extend our heartfelt gratitude to the open-source AI community; this endeavor would not have been possible without open source. SambaNova embraces the open-source community and aspires to actively contribute to this initiative.\n\nWe would like to give a special thanks to the following groups:\n- Meta for open sourcing LLama 2 and open sourcing FLORES-200 dataset\n- Nguyen et al for open sourcing CulturaX dataset\n- CohereAI for releasing AYA-101 and open sourcing a multilingual instruction tuning dataset\n- EleutherAI for their open source evaluation framework\n- Hugging Face-H4 team for open source the zephyr training recipe and alignment handbook repo", "## Cite SambaLingo" ]

[ "TAGS\n#gguf #text-generation #hu #en #dataset-HuggingFaceH4/ultrachat_200k #dataset-HuggingFaceH4/ultrafeedback_binarized #dataset-HuggingFaceH4/cai-conversation-harmless #arxiv-2404.05829 #license-llama2 #region-us \n", "# SambaLingo-Hungarian-Chat\n\n<img src=\"SambaLingo_Logo.png\" width=\"340\" style=\"margin-left:'auto' margin-right:'auto' display:'block'\"/>\n\n\nSambaLingo-Hungarian-Chat is a human aligned chat model trained in Hungarian and English. It is trained using direct preference optimization on top the base model SambaLingo-Hungarian-Base. The base model adapts Llama-2-7b to Hungarian by training on 59 billion tokens from the Hungarian split of the Cultura-X dataset. Try This Model at SambaLingo-chat-space.", "## Model Description\n\n\n- Developed by: SambaNova Systems\n- Model type: Language Model\n- Language(s): Hungarian, English\n- Finetuned from model: Llama-2-7b\n- Try This Model: SambaLingo-chat-space\n- Paper: SambaLingo: Teaching Large Language Models New Languages\n- Blog Post: sambalingo-open-source-language-experts", "## Getting Started", "### Loading Model With Hugging Face\nPlease make sure to set use_fast=False when loading the tokenizer.", "### Interacting With Model Pipeline\nPlease make sure to set use_fast=False when loading the tokenizer.", "### Suggested Inference Parameters\n- Temperature: 0.8\n- Repetition penalty: 1.0\n- Top-p: 0.9", "### Prompting Guidelines\nTo prompt this model, please use the following chat template:", "### Example Prompts and Generations", "## Training Details\nThe alignment phase follows the recipe for Zephyr-7B, and comprises two stages: supervised fine-tuning (SFT) and Direct Performance Optimization (DPO).\n\nThe SFT phase was done on the ultrachat_200k dataset mixed with the Google translated version of the ultrachat_200k dataset. It was trained for one epoch with global batch size 512 and max sequence length 2048 tokens. We used a linear decay learning rate of 2e-5 and 10% warmup.\n\nThe DPO phase was done on the ultrafeedback dataset and cai-conversation-harmless dataset, mixed with 10% of the data Google translated. It was trained with global batch size 32 and for three epochs. We used a linear decay learning rate of 5e-7, 10% warmup and β=0.1 as the regularization factor for DPO.", "## Tokenizer Details\nWe extended the vocabulary of the base llama model from 32,000 tokens to 57,000 tokens by adding up to 25,000 non-overlapping tokens from the new language.", "## Evaluation\nFor evaluation results see our paper: SambaLingo: Teaching Large Language Models New Languages", "## Uses", "### Direct Use\n\n\nUse of this model is governed by the Meta’s Llama 2 Community License Agreement. Please review and accept the license before downloading the model weights.", "### Out-of-Scope Use\n\n\nSambaLingo should NOT be used for:\n\n- Mission-critical applications\n- Applications that involve the safety of others\n- Making highly important decisions", "## Bias, Risks, and Limitations\n\n\n\nLike all LLMs, SambaLingo has certain limitations:\n- Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.\n- Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output.\n- Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses.\n- Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.\n- Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content.", "## Acknowledgments\nWe extend our heartfelt gratitude to the open-source AI community; this endeavor would not have been possible without open source. SambaNova embraces the open-source community and aspires to actively contribute to this initiative.\n\nWe would like to give a special thanks to the following groups:\n- Meta for open sourcing LLama 2 and open sourcing FLORES-200 dataset\n- Nguyen et al for open sourcing CulturaX dataset\n- CohereAI for releasing AYA-101 and open sourcing a multilingual instruction tuning dataset\n- EleutherAI for their open source evaluation framework\n- Hugging Face-H4 team for open source the zephyr training recipe and alignment handbook repo", "## Cite SambaLingo" ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 1.13.0\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\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\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 1.13.0\n* Datasets 2.18.0\n* Tokenizers 0.15.2" ]

[ "text-to-image", "en", "dataset:TrgTuan10/dpt", "region:us" ]

[]

[ "en" ]

[]

[ "TAGS\n#text-to-image #en #dataset-TrgTuan10/dpt #region-us \n" ]

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:westlake-repl/SaProt_35M_AF2", "region:us" ]

[ "1910.09700" ]

[]

[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-westlake-repl/SaProt_35M_AF2 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.10.0" ]

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

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 2000\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 #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-small #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: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 2000\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" ]

[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "llama-cpp", "gguf-my-repo", "audio-to-audio", "en", "base_model:LeroyDyer/Mixtral_AI_Cyber_4.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]

[]

[ "en" ]

[ "# LeroyDyer/Mixtral_AI_CyberVoice-Q4_K_S-GGUF\nThis model was converted to GGUF format from 'LeroyDyer/Mixtral_AI_CyberVoice' 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\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

[ "TAGS\n#transformers #gguf #text-generation-inference #unsloth #mistral #trl #llama-cpp #gguf-my-repo #audio-to-audio #en #base_model-LeroyDyer/Mixtral_AI_Cyber_4.0 #license-apache-2.0 #endpoints_compatible #region-us \n", "# LeroyDyer/Mixtral_AI_CyberVoice-Q4_K_S-GGUF\nThis model was converted to GGUF format from 'LeroyDyer/Mixtral_AI_CyberVoice' 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\n\nNote: You can also use this checkpoint directly through the usage steps listed in the URL repo as well." ]

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

[ "1910.09700" ]

[]

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

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

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

[ "1910.09700" ]

[]

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

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

[ "peft", "safetensors", "llama", "axolotl", "generated_from_trainer", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "4-bit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.40.0\n* Pytorch 2.4.0.dev20240326+rocm6.0\n* Datasets 2.15.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#peft #safetensors #llama #axolotl #generated_from_trainer #base_model-meta-llama/Meta-Llama-3-8B #license-other #4-bit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 128\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* PEFT 0.9.0\n* Transformers 4.40.0\n* Pytorch 2.4.0.dev20240326+rocm6.0\n* Datasets 2.15.0\n* Tokenizers 0.19.1" ]

[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:cardiffnlp/twitter-roberta-base-sentiment", "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", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.1\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-cardiffnlp/twitter-roberta-base-sentiment #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", "### 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" ]

[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

[ "TAGS\n#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

[ "TAGS\n#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:updated", "dataset:original", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# 0.001_ablation_4iters_bs256_nodpo_useresponse_iter_1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-updated #dataset-original #base_model-HuggingFaceH4/mistral-7b-sft-beta #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# 0.001_ablation_4iters_bs256_nodpo_useresponse_iter_1\n\nThis model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on the updated and the original datasets.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 8\n- gradient_accumulation_steps: 4\n- total_train_batch_size: 256\n- total_eval_batch_size: 64\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1", "### Training results", "### Framework versions\n\n- Transformers 4.36.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2" ]

[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

[]

[]

[ "# ugm-slerp\n\nugm-slerp is a merge of the following models using mergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* mlabonne/NeuralHermes-2.5-Mistral-7B", "## Configuration" ]

[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #OpenPipe/mistral-ft-optimized-1218 #mlabonne/NeuralHermes-2.5-Mistral-7B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ugm-slerp\n\nugm-slerp is a merge of the following models using mergekit:\n* OpenPipe/mistral-ft-optimized-1218\n* mlabonne/NeuralHermes-2.5-Mistral-7B", "## Configuration" ]

[ "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" ]

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

[]

[ "en" ]

[ "# KobbleTinyV2-1.1B\n</div>\n\nThis is the GGUF quantization of URL\n\nYou can use KoboldCpp to run this model. With only 1B parameters, this model is ideal for running on mobile or low-end devices.\n\nUpdate: KobbleTiny has been upgraded to V2! The old V1 GGUF is still available at this link.\n\n<video width=\"320\" controls autoplay src=\"URL\n\nTry it live now: URL", "## Dataset and Objectives\n\nThe Kobble Dataset is a semi-private aggregated dataset made from multiple online sources and web scrapes. \nIt contains content chosen and formatted specifically to work with KoboldAI software and Kobold Lite.", "#### Dataset Categories:\n- Instruct: Single turn instruct examples presented in the Alpaca format, with an emphasis on uncensored and unrestricted responses.\n- Chat: Two participant roleplay conversation logs in a multi-turn raw chat format that KoboldAI uses.\n- Story: Unstructured fiction excerpts, including literature containing various erotic and provocative content.", "## Prompt template: Alpaca\n\n\n\n\n\nNote: *No assurances will be provided about the origins, safety, or copyright status of this model, or of any content within the Kobble dataset.* \n*If you belong to a country or organization that has strict AI laws or restrictions against unlabelled or unrestricted content, you are advised not to use this model.*" ]

[ "TAGS\n#gguf #en #license-apache-2.0 #region-us \n", "# KobbleTinyV2-1.1B\n</div>\n\nThis is the GGUF quantization of URL\n\nYou can use KoboldCpp to run this model. With only 1B parameters, this model is ideal for running on mobile or low-end devices.\n\nUpdate: KobbleTiny has been upgraded to V2! The old V1 GGUF is still available at this link.\n\n<video width=\"320\" controls autoplay src=\"URL\n\nTry it live now: URL", "## Dataset and Objectives\n\nThe Kobble Dataset is a semi-private aggregated dataset made from multiple online sources and web scrapes. \nIt contains content chosen and formatted specifically to work with KoboldAI software and Kobold Lite.", "#### Dataset Categories:\n- Instruct: Single turn instruct examples presented in the Alpaca format, with an emphasis on uncensored and unrestricted responses.\n- Chat: Two participant roleplay conversation logs in a multi-turn raw chat format that KoboldAI uses.\n- Story: Unstructured fiction excerpts, including literature containing various erotic and provocative content.", "## Prompt template: Alpaca\n\n\n\n\n\nNote: *No assurances will be provided about the origins, safety, or copyright status of this model, or of any content within the Kobble dataset.* \n*If you belong to a country or organization that has strict AI laws or restrictions against unlabelled or unrestricted content, you are advised not to use this model.*" ]

[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

[ "TAGS\n#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 60\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

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

[ "1910.09700" ]

[]

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

[ "TAGS\n#transformers #safetensors #deberta-v2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]

[ "safetensors", "generated_from_trainer", "base_model:microsoft/phi-2", "license:mit", "region:us" ]

[]

[]

[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 80\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

[ "TAGS\n#safetensors #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* lr\\_scheduler\\_warmup\\_steps: 80\n* num\\_epochs: 3\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.36.0.dev0\n* Pytorch 2.1.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.14.1" ]

[ "transformers", "pytorch", "bert", "license:cc-by-sa-4.0", "endpoints_compatible", "region:us" ]

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[ "### Prerequisites\n\n\n\n\n---\n\n\nPlease see the original SimCSE for installation details. The model will als be hosted on Zenodo (DOI: 10.5281/zenodo.11046580).", "### Usage\n\n\nRun the following code to get the sentence embeddings:\n\n\nThe retrieved embeddings can be utilized as input for a machine learning classifier to perform classification.\n\n\nPerformance on evaluation tasks\n-------------------------------\n\n\nFind out more about the datasets and access in the paper (TBA)", "### Task 1: Detection of colorectal cancer cases (after oversampling)\n\n\n5-fold Cross Validation accuracy: LightGBM, Test accuracy: 91\n5-fold Cross Validation accuracy: Random Forest, Test accuracy: 94\n5-fold Cross Validation accuracy: XGBoost, Test accuracy: 93\n5-fold Cross Validation accuracy: CNN, Test accuracy: 42\n\n\n5-fold Cross Validation F1: LightGBM, Test F1: 91\n5-fold Cross Validation F1: Random Forest, Test F1: 94\n5-fold Cross Validation F1: XGBoost, Test F1: 93\n5-fold Cross Validation F1: CNN, Test F1: 41", "### Task 2: Prediction of the Gleason grade group (after oversampling)\n\n\n5-fold Cross Validation accuracy: LightGBM, Test accuracy: 97\n5-fold Cross Validation accuracy: Random Forest, Test accuracy: 98\n5-fold Cross Validation accuracy: XGBoost, Test accuracy: 97\n5-fold Cross Validation accuracy: CNN, Test accuracy: 35\n\n\n5-fold Cross Validation F1: LightGBM, Test F1: 97\n5-fold Cross Validation F1: Random Forest, Test F1: 98\n5-fold Cross Validation F1: XGBoost, Test F1: 97\n5-fold Cross Validation F1: CNN, Test F1: 33", "### Task 3: Detection of human TATA sequences (after oversampling)\n\n\n5-fold Cross Validation accuracy: LightGBM, Test accuracy: 98\n5-fold Cross Validation accuracy: Random Forest, Test accuracy: 99\n5-fold Cross Validation accuracy: XGBoost, Test accuracy: 99\n5-fold Cross Validation accuracy: CNN, Test accuracy: 38\n\n\n5-fold Cross Validation F1: LightGBM, Test F1: 98\n5-fold Cross Validation F1: Random Forest, Test F1: 99\n5-fold Cross Validation F1: XGBoost, Test F1: 99\n5-fold Cross Validation F1: CNN, Test F1: 58\n\n\nAuthors\n-------\n\n\n\n\n---\n\n\n* Written by : Mpho Mokoatle, Vukosi Marivate, Darlington Mapiye, Riana Bornman, Vanessa M. Hayes\n* Contact details : u19394277@URL\n\n\n\n\n---\n\n\nBibtex Reference TBA", "### References\n\n\n[1]\nGao, Tianyu, Xingcheng Yao, and Danqi Chen. \"Simcse: Simple contrastive learning of sentence embeddings.\" arXiv preprint arXiv:2104.08821 (2021)." ]

[ "TAGS\n#transformers #pytorch #bert #license-cc-by-sa-4.0 #endpoints_compatible #region-us \n", "### Prerequisites\n\n\n\n\n---\n\n\nPlease see the original SimCSE for installation details. The model will als be hosted on Zenodo (DOI: 10.5281/zenodo.11046580).", "### Usage\n\n\nRun the following code to get the sentence embeddings:\n\n\nThe retrieved embeddings can be utilized as input for a machine learning classifier to perform classification.\n\n\nPerformance on evaluation tasks\n-------------------------------\n\n\nFind out more about the datasets and access in the paper (TBA)", "### Task 1: Detection of colorectal cancer cases (after oversampling)\n\n\n5-fold Cross Validation accuracy: LightGBM, Test accuracy: 91\n5-fold Cross Validation accuracy: Random Forest, Test accuracy: 94\n5-fold Cross Validation accuracy: XGBoost, Test accuracy: 93\n5-fold Cross Validation accuracy: CNN, Test accuracy: 42\n\n\n5-fold Cross Validation F1: LightGBM, Test F1: 91\n5-fold Cross Validation F1: Random Forest, Test F1: 94\n5-fold Cross Validation F1: XGBoost, Test F1: 93\n5-fold Cross Validation F1: CNN, Test F1: 41", "### Task 2: Prediction of the Gleason grade group (after oversampling)\n\n\n5-fold Cross Validation accuracy: LightGBM, Test accuracy: 97\n5-fold Cross Validation accuracy: Random Forest, Test accuracy: 98\n5-fold Cross Validation accuracy: XGBoost, Test accuracy: 97\n5-fold Cross Validation accuracy: CNN, Test accuracy: 35\n\n\n5-fold Cross Validation F1: LightGBM, Test F1: 97\n5-fold Cross Validation F1: Random Forest, Test F1: 98\n5-fold Cross Validation F1: XGBoost, Test F1: 97\n5-fold Cross Validation F1: CNN, Test F1: 33", "### Task 3: Detection of human TATA sequences (after oversampling)\n\n\n5-fold Cross Validation accuracy: LightGBM, Test accuracy: 98\n5-fold Cross Validation accuracy: Random Forest, Test accuracy: 99\n5-fold Cross Validation accuracy: XGBoost, Test accuracy: 99\n5-fold Cross Validation accuracy: CNN, Test accuracy: 38\n\n\n5-fold Cross Validation F1: LightGBM, Test F1: 98\n5-fold Cross Validation F1: Random Forest, Test F1: 99\n5-fold Cross Validation F1: XGBoost, Test F1: 99\n5-fold Cross Validation F1: CNN, Test F1: 58\n\n\nAuthors\n-------\n\n\n\n\n---\n\n\n* Written by : Mpho Mokoatle, Vukosi Marivate, Darlington Mapiye, Riana Bornman, Vanessa M. Hayes\n* Contact details : u19394277@URL\n\n\n\n\n---\n\n\nBibtex Reference TBA", "### References\n\n\n[1]\nGao, Tianyu, Xingcheng Yao, and Danqi Chen. \"Simcse: Simple contrastive learning of sentence embeddings.\" arXiv preprint arXiv:2104.08821 (2021)." ]

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# CS505_COQE_viT5_train_Instruction0_SAPOL_v2\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_SAPOL_v2\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:projecte-aina/roberta-base-ca-v2-cased-te", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 10\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 20\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* num\\_epochs: 10\n* label\\_smoothing\\_factor: 0.1", "### 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-classification #generated_from_trainer #base_model-projecte-aina/roberta-base-ca-v2-cased-te #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: 10\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 20\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.06\n* num\\_epochs: 10\n* label\\_smoothing\\_factor: 0.1", "### 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", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:VietAI/vit5-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# CS505_COQE_viT5_train_Instruction0_SAPOL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-VietAI/vit5-large #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# CS505_COQE_viT5_train_Instruction0_SAPOL_v1\n\nThis model is a fine-tuned version of VietAI/vit5-large on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 64\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 20\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2" ]

[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# codeparrot-ds\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### 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 #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# codeparrot-ds\n\nThis model is a fine-tuned version of distilgpt2 on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 256\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 1000\n- num_epochs: 1\n- mixed_precision_training: Native AMP", "### Training results", "### 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", "llama", "text-generation", "llama-3", "bagel", "conversational", "dataset:ai2_arc", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:argilla/distilabel-intel-orca-dpo-pairs", "dataset:jondurbin/airoboros-3.2", "dataset:codeparrot/apps", "dataset:facebook/belebele", "dataset:bluemoon-fandom-1-1-rp-cleaned", "dataset:boolq", "dataset:camel-ai/biology", "dataset:camel-ai/chemistry", "dataset:camel-ai/math", "dataset:camel-ai/physics", "dataset:jondurbin/contextual-dpo-v0.1", "dataset:jondurbin/gutenberg-dpo-v0.1", "dataset:jondurbin/py-dpo-v0.1", "dataset:jondurbin/truthy-dpo-v0.1", "dataset:LDJnr/Capybara", "dataset:jondurbin/cinematika-v0.1", "dataset:WizardLM/WizardLM_evol_instruct_70k", "dataset:glaiveai/glaive-function-calling-v2", "dataset:grimulkan/LimaRP-augmented", "dataset:lmsys/lmsys-chat-1m", "dataset:ParisNeo/lollms_aware_dataset", "dataset:TIGER-Lab/MathInstruct", "dataset:Muennighoff/natural-instructions", "dataset:openbookqa", "dataset:kingbri/PIPPA-shareGPT", "dataset:piqa", "dataset:Vezora/Tested-22k-Python-Alpaca", "dataset:ropes", "dataset:cakiki/rosetta-code", "dataset:Open-Orca/SlimOrca", "dataset:b-mc2/sql-create-context", "dataset:squad_v2", "dataset:mattpscott/airoboros-summarization", "dataset:migtissera/Synthia-v1.3", "dataset:unalignment/toxic-dpo-v0.2", "dataset:WhiteRabbitNeo/WRN-Chapter-1", "dataset:WhiteRabbitNeo/WRN-Chapter-2", "dataset:winogrande", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "6-bit", "region:us" ]

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[ "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "TAGS\n#transformers #safetensors #llama #text-generation #llama-3 #bagel #conversational #dataset-ai2_arc #dataset-allenai/ultrafeedback_binarized_cleaned #dataset-argilla/distilabel-intel-orca-dpo-pairs #dataset-jondurbin/airoboros-3.2 #dataset-codeparrot/apps #dataset-facebook/belebele #dataset-bluemoon-fandom-1-1-rp-cleaned #dataset-boolq #dataset-camel-ai/biology #dataset-camel-ai/chemistry #dataset-camel-ai/math #dataset-camel-ai/physics #dataset-jondurbin/contextual-dpo-v0.1 #dataset-jondurbin/gutenberg-dpo-v0.1 #dataset-jondurbin/py-dpo-v0.1 #dataset-jondurbin/truthy-dpo-v0.1 #dataset-LDJnr/Capybara #dataset-jondurbin/cinematika-v0.1 #dataset-WizardLM/WizardLM_evol_instruct_70k #dataset-glaiveai/glaive-function-calling-v2 #dataset-grimulkan/LimaRP-augmented #dataset-lmsys/lmsys-chat-1m #dataset-ParisNeo/lollms_aware_dataset #dataset-TIGER-Lab/MathInstruct #dataset-Muennighoff/natural-instructions #dataset-openbookqa #dataset-kingbri/PIPPA-shareGPT #dataset-piqa #dataset-Vezora/Tested-22k-Python-Alpaca #dataset-ropes #dataset-cakiki/rosetta-code #dataset-Open-Orca/SlimOrca #dataset-b-mc2/sql-create-context #dataset-squad_v2 #dataset-mattpscott/airoboros-summarization #dataset-migtissera/Synthia-v1.3 #dataset-unalignment/toxic-dpo-v0.2 #dataset-WhiteRabbitNeo/WRN-Chapter-1 #dataset-WhiteRabbitNeo/WRN-Chapter-2 #dataset-winogrande #base_model-meta-llama/Meta-Llama-3-8B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #6-bit #region-us \n", "### Data sources\n\n\nThere are many data sources used in the bagel models. See URL for more information.\n\n\n***Only train splits are used, and a decontamination by cosine similarity is performed at the end as a sanity check against common benchmarks. If you don't know the difference between train and test, please learn.***\n\n\n\nSFT data sources\n* ai2\\_arc\n\t+ Abstraction and reasoning dataset, useful in measuring \"intelligence\" to a certain extent.\n* airoboros\n\t+ Variety of categories of synthetic instructions generated by gpt-4.\n* apps\n\t+ Python coding dataset with 10k problems.\n* belebele\n\t+ Multi-lingual reading comprehension dataset.\n* bluemoon\n\t+ Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT.\n* boolq\n\t+ Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)\n* camel-ai biology\n\t+ GPT-4 generated biology instructions.\n* camel-ai chemistry\n\t+ GPT-4 generated chemistryinstructions.\n* camel-ai math\n\t+ GPT-4 generated math instructions.\n* camel-ai physics\n\t+ GPT-4 generated physics instructions.\n* capybara\n\t+ Multi-turn dataset used to create the capybara models.\n* cinematika (instruction and plain text)\n\t+ RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.\n* emobank\n\t+ Emotion annotations using the Valence-Arousal-Domninance scheme.\n* evol-instruct\n\t+ WizardLM's evol instruct 70k dataset.\n* glaive-function-calling-v2\n\t+ GlaiveAI function calling dataset.\n* gutenberg (plain text)\n\t+ Books/plain text, again to make the model less boring, only a handful of examples supported by chapterize\n* limarp-augmented\n\t+ Augmented and further modified version of LimaRP\n* lmsys\\_chat\\_1m (only gpt-4 items, also used for DPO)\n\t+ Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.\n* lollms\n\t+ LoLLMs question answering dataset by ParisNeo, with helpful question answer pairs for using LoLLMs.\n* mathinstruct\n\t+ Composite dataset with a variety of math-related tasks and problem/question formats.\n* natural\\_instructions\n\t+ Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)\n* openbookqa\n\t+ Question answering dataset.\n* pippa\n\t+ Deduped version of PIPPA in ShareGPT format.\n* piqa\n\t+ Phyiscal interaction question answering.\n* python\\_alpaca\n\t+ Python instruction response pairs, validated as functional.\n* ropes\n\t+ Reasoning Over PAragraph Effects in Situations - enhances ability to apply knowledge from a passage of text to a new situation.\n* rosetta\\_code\n\t+ Code problems and solutions in a variety of programming languages taken from URL.\n* slimorca\n\t+ Collection of ~500k gpt-4 verified chats from OpenOrca.\n* sql-create-context\n\t+ SQL-targeted dataset, combining WikiSQL and Spider.\n* squad\\_v2\n\t+ Contextual question answering (RAG).\n* airoboros-summarization\n\t+ Combination of various summarization datasets, formatted into the airoboros context-obedient format.\n* synthia\n\t+ GPT-4 generated data using advanced prompting from Migel Tissera.\n* whiterabbitneo chapter 1 and chapter 2\n\t+ Offensive cybersecurity dataset by WhiteRabbitNeo/Migel Tissera\n* winogrande\n\t+ Fill in the blank style prompts.\n\n\n\n\nDPO data sources\n* airoboros 3.2 vs airoboros m2.0\n\t+ The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the \"rejected\" value and the rerolled response as \"chosen\"\n* contextual-dpo\n\t+ Contextual prompt/response dataset using the airoboros context-obedient question answering format.\n* helpsteer\n\t+ Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest \"correctness\" value were used for DPO here, with the highest scoring output as \"chosen\" and random lower scoring value as \"rejected\"\n* distilabel\\_orca\\_dpo\\_pairs\n\t+ Another interesting dataset, originally by Intel, enhanced by argilla with distilabel which provides various DPO pairs generated from prompts included in the SlimOrca dataset.\n* gutenberg-dpo\n\t+ DPO pairs meant to increase the models novel writing abilities, using public domain books from URL\n* py-dpo\n\t+ Python DPO dataset (based on the SFT python\\_alpaca dataset above)\n* toxic-dpo\n\t+ ***highly toxic and potentially illegal content!*** De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.\n* truthy\n\t+ DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.\n* ultrafeedback\n\t+ One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.\n\n\n\nPrompt formatting\n-----------------\n\n\nThis model uses the llama-3-instruct prompt template, and is provided in the tokenizer config. You can use the 'apply\\_chat\\_template' method to accurate format prompts, e.g.:\n\n\nPrompting strategies\n--------------------\n\n\n\n\n**Context obedient question answering**\n \n\n This is a special prompt format made specifically for answering questions from provided context, e.g. RAG.\n \nBy obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.\n\n\nThe format for a closed-context prompt is as follows:\n\n\nIt's also helpful to add \"Don't make up answers if you don't know.\" to your instruction block to make sure if the context is completely unrelated it doesn't make something up.\n\n\n*The **only** prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*\n\n\nI know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.\n\n\n* 'BEGININPUT' - denotes a new input block\n* 'BEGINCONTEXT' - denotes the block of context (metadata key/value pairs) to associate with the current input block\n* 'ENDCONTEXT' - denotes the end of the metadata block for the current input\n* [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.\n* 'ENDINPUT' - denotes the end of the current input block\n* [repeat as many input blocks in this format as you want]\n* 'BEGININSTRUCTION' - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.\n* [instruction(s)]\n* 'ENDINSTRUCTION' - denotes the end of instruction set\n\n\nIt sometimes works without 'ENDINSTRUCTION', but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.\n\n\n**Use a very low temperature!**\n\n\nHere's a trivial, but important example to prove the point:\n\n\nAnd the response:\n\n\nYou can also add an instruction similar to the following, to have a more deterministic response when the context doesn't provide an answer to the question:\n\n\n\n\n\n**Summarization**\n \n\n Same prompt format as context obedient question answering, but meant for summarization tasks.\n \nSummarization is primarily fine-tuned with this dataset, which uses the same format as above, e.g.:\n\n\n\n\n\n**Function calling**\n \n\n Two primary formats for prompting for function calling use-cases.\n \n There are two function-calling related formats used in fine-tuning this model.\n1. Providing an input and list of possible functions within the instruction (from airoboros dataset), e.g.:\n\n\nPrompt:\n\n\nResponse:\n\n\n2. GlaiveAI function calling, which uses special tags and adds function specs in the system prompt:\n\n\nPrompt:\n\n\nResponse:\n\n\nThen, you re-prompt the model with the function response.\n\n\nWhich has a response of:\n\n\n\n\n\n**Chain of thought**\n \n\n Useful for having the model propose multiple possible responses, reasoning through each, and selecting a final, most probable answer.\n \nYou can ask for several possible responses to a given problem, with a ranking and final answer selection.\n\n\nExample prompt:\n\n\nExample response:\n\n\n\n\n\n**reWOO style function planning/execution**\n \n\n Useful for a longer, complex chain of function calls without having to continue re-prompting manually.\n \nThe model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions!\n\n\nExample prompt:\n\n\nResponse:\n\n\nFor this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening:\n\n\n\n\n\n**Creating roleplay character cards**\n \n\n Useful in creating YAML formatted character cards for roleplay/creative writing tasks.\n \nIncluded in the cinematika dataset, you can create YAML formatted character cards easily, e.g.:\n\n\n\n\n\n**Conversational memory creation**\n \n\n Summarization style prompt to create memories from previous chat turns, useful when context becomes long.\n \nAlso part of cinematika dataset, you can use a summarization style prompt to create memories from previous chat turns, which can then be used in a RAG system to populate your prompts when context becomes too long.\n\n\n\n\n\n**Novel writing, chapter by chapter**\n \n\n Based on the public domain books in project Gutenberg, this style of prompting creates very long, novel style writing.\n \nWriting the first chapter:\n\n\nWriting subsequent chapters:\n\n\nIn other words, write the first chapter, then use a summarization prompt for it, then include the summary in the next chapter's prompt.\n\n\n\n\n\n**Boolean questions**\n \n\n For content filtering and other use-cases which only require a true/false response.\n \nThe prompts in the fine-tuning dataset are formatted as follows:\n\n\nThe model will then, theoretically, respond with only a single word.\n\n\n\n\n\n**SQL queries**\n \n\n Generating SQL queries given a table definition.\n \nFor example:\n\n\nResponse:\n\n\n\n\n\n**Emotion detection**\n \n\n You can produce Valence-Arousal-Dominance scores for a given input text, which can in turn be mapped to human emotions (e.g. with k-means clustering on V and A)\n \nExample prompt:\n\n\nResponse:\n\n\n\n\n\n**Multi-character chat director**\n \n\n Select which NPC should speak next.\n \nThe scope of the entire multi-NPC chat mechanism is a bit too large to include here, but essentially you want separate prompts for each character, as well as a \"director\" prompt which selects which NPC should speak next.\n\n\nSystem prompt:\n\n\nFirst round instruction, i.e. selecting who should speak first:\n\n\nResponse for the first round:\n\n\nNow, you'd prompt the model for a response from Aria.\n\n\nAfterwards, you'd add Aria's response to the \"director\" prompt to see who speaks next, e.g.:\n\n\n\nRenting instances to run the model\n----------------------------------", "### Massed Compute Virtual Machine\n\n\nMassed Compute has created a Virtual Machine (VM) pre-loaded with TGI and Text Generation WebUI.\n\n\n1. For this model, create an account in Massed Compute. When renting a Virtual Machine use the code 'JonDurbin' for 50% your rental.\n2. After you created your account update your billing and navigate to the deploy page.\n3. Select the following\n\t* GPU Type: A6000\n\t* GPU Quantity: 1\n\t* Category: Creator\n\t* Image: Jon Durbin\n\t* Coupon Code: JonDurbin\n4. Deploy the VM!\n5. Navigate to 'Running Instances' to retrieve instructions to login to the VM\n6. Once inside the VM, open the terminal and run 'volume=$PWD/data'\n7. Run 'model=jondurbin/bagel-8b-v1.0'\n8. 'sudo docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data URL --model-id $model'\n9. The model will take some time to load...\n10. Once loaded the model will be available on port 8080\n\n\nSample command within the VM\n\n\nYou can also access the model from outside the VM\n\n\nFor assistance with the VM join the Massed Compute Discord Server", "### URL\n\n\nLatitude has h100 instances available (as of today, 2024-02-08) for $3/hr! A single h100 works great for this model, though you probably want to decrease the context length from 200k to 8k or 16k.\n\n\nSupport me\n----------\n\n\n* URL\n* ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11\n* BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf" ]

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:fleurs", "base_model:facebook/wav2vec2-xls-r-300m", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

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[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\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\\_steps: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.17.1\n* Tokenizers 0.19.1" ]

[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-fleurs #base_model-facebook/wav2vec2-xls-r-300m #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 4\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\\_steps: 0.1\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.17.1\n* Tokenizers 0.19.1" ]

[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:unsloth/gemma-1.1-2b-it", "base_model:beomi/gemma-ko-2b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

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[ "# 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* unsloth/gemma-1.1-2b-it\n* beomi/gemma-ko-2b", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]

[ "TAGS\n#transformers #safetensors #gemma #text-generation #mergekit #merge #conversational #base_model-unsloth/gemma-1.1-2b-it #base_model-beomi/gemma-ko-2b #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* unsloth/gemma-1.1-2b-it\n* beomi/gemma-ko-2b", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]