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dibyendubiswas1998/llm-test	dibyendubiswas1998	2024-05-30T06:52:04Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ", "region:us" ]	null	2024-05-30T06:50:44Z	--- library_name: peft base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
casual/whisper_tiny_til2	casual	2024-05-30T06:46:12Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:casual/whisper_tiny_24til", "base_model:finetune:casual/whisper_tiny_24til", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-30T02:56:16Z	--- base_model: casual/whisper_tiny_24til tags: - generated_from_trainer model-index: - name: whisper_tiny_til2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper_tiny_til2 This model is a fine-tuned version of [casual/whisper_tiny_24til](https://huggingface.co/casual/whisper_tiny_24til) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 0.0000 - eval_wer: 0.0 - eval_runtime: 780.534 - eval_samples_per_second: 4.484 - eval_steps_per_second: 0.561 - epoch: 6.2785 - step: 2750 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 4000 ### Framework versions - Transformers 4.40.2 - Pytorch 2.0.1+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
Niggendar/kingOfToons_v10	Niggendar	2024-05-30T06:44:57Z	94	0	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-30T06:37:13Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
NSC07/flan-t5-base-NvidiaQATrainedModel	NSC07	2024-05-30T06:41:41Z	108	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-30T06:40:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
chahattandon/wav2vec2-base-timit-demo-google-colab	chahattandon	2024-05-30T06:39:45Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:39:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
HanlinLiao-Harry/Taxi-v3_Q-learning	HanlinLiao-Harry	2024-05-30T06:34:16Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T06:34:14Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3_Q-learning results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.46 +/- 2.68 name: mean_reward verified: false --- # Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="HanlinLiao-Harry/Taxi-v3_Q-learning", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
HanlinLiao-Harry/q-FrozenLake-v1-4x4-noSlippery	HanlinLiao-Harry	2024-05-30T06:33:20Z	0	0	null	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T06:33:18Z	--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="HanlinLiao-Harry/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
IntellectusAI/zephyr_beta	IntellectusAI	2024-05-30T06:31:20Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/zephyr-7B-alpha-GPTQ", "base_model:adapter:TheBloke/zephyr-7B-alpha-GPTQ", "license:mit", "region:us" ]	null	2024-05-14T06:24:11Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: TheBloke/zephyr-7B-alpha-GPTQ model-index: - name: zephyr_beta results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/intellectus/huggingface/runs/xqhl9gft) # zephyr_beta This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
sebalnakji/gemma-ko-2b-it-02	sebalnakji	2024-05-30T06:30:51Z	146	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T06:26:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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NSC07/bloom-1b7-decodeSummary	NSC07	2024-05-30T06:29:02Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:28:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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just1nseo/openchat-onlinecost-UF20k-400step	just1nseo	2024-05-30T06:26:50Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openchat/openchat-3.5-0106", "base_model:adapter:openchat/openchat-3.5-0106", "region:us" ]	null	2024-05-30T05:53:12Z	--- library_name: peft base_model: openchat/openchat-3.5-0106 --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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donutglazed/dsp-room-background-lora	donutglazed	2024-05-30T06:23:35Z	1	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-2", "base_model:adapter:stabilityai/stable-diffusion-2", "license:mit", "region:us" ]	text-to-image	2024-05-30T06:09:35Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: interior of dsp room output: url: images/validation_21300_928494fdfa2f18606e3f.png base_model: stabilityai/stable-diffusion-2 instance_prompt: null license: mit --- # DSP Room Background LoRA <Gallery /> ## Model description a model fine-tuned to recreate a room called dsp room ## Download model Weights for this model are available in Safetensors format. [Download](/donutglazed/dsp-room-background-lora/tree/main) them in the Files & versions tab.
Niggendar/wowXLPD_wowPDV1	Niggendar	2024-05-30T06:18:11Z	89	2	diffusers	[ "diffusers", "safetensors", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-30T06:11:40Z	--- library_name: diffusers --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🧨 diffusers model that has been pushed on the Hub. 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RaphaelMourad/Mistral-Prot-v1-134M	RaphaelMourad	2024-05-30T06:13:50Z	204	1	transformers	[ "transformers", "safetensors", "mixtral", "text-generation", "pretrained", "mistral", "protein", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T18:54:46Z	--- license: apache-2.0 tags: - pretrained - mistral - protein --- # Model Card for Mistral-Prot-v1-134M (Mistral for protein) The Mistral-Prot-v1-134M Large Language Model (LLM) is a pretrained generative protein molecule model with 133.8M parameters. It is derived from Mixtral-8x7B-v0.1 model, which was simplified for protein: the number of layers and the hidden size were reduced. The model was pretrained using 10M protein strings from the uniprot 50 database. ## Model Architecture Like Mixtral-8x7B-v0.1, it is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer - Mixture of Experts ## Load the model from huggingface: ``` import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-Prot-v1-134M", trust_remote_code=True) model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Prot-v1-134M", trust_remote_code=True) ``` ## Calculate the embedding of a protein sequence ``` insulin = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN" inputs = tokenizer(insulin, return_tensors = 'pt')["input_ids"] hidden_states = model(inputs)[0] # [1, sequence_length, 256] # embedding with max pooling embedding_max = torch.max(hidden_states[0], dim=0)[0] print(embedding_max.shape) # expect to be 256 ``` ## Troubleshooting Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. ## Notice Mistral-Prot-v1-134M is a pretrained base model for protein. ## Contact Raphaël Mourad. [email protected]
Sadat07/phi-squad-1_5	Sadat07	2024-05-30T06:09:35Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:09:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf	RichardErkhov	2024-05-30T06:04:06Z	15	0	null	[ "gguf", "arxiv:2311.17487", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T03:19:50Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Taiwan-LLM-7B-v2.0-base - GGUF - Model creator: https://huggingface.co/yentinglin/ - Original model: https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.0-base/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Taiwan-LLM-7B-v2.0-base.Q2_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [Taiwan-LLM-7B-v2.0-base.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [Taiwan-LLM-7B-v2.0-base.Q3_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [Taiwan-LLM-7B-v2.0-base.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [Taiwan-LLM-7B-v2.0-base.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [Taiwan-LLM-7B-v2.0-base.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [Taiwan-LLM-7B-v2.0-base.Q4_1.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [Taiwan-LLM-7B-v2.0-base.Q5_1.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [Taiwan-LLM-7B-v2.0-base.Q6_K.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [Taiwan-LLM-7B-v2.0-base.Q8_0.gguf](https://huggingface.co/RichardErkhov/yentinglin_-_Taiwan-LLM-7B-v2.0-base-gguf/blob/main/Taiwan-LLM-7B-v2.0-base.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: --- license: apache-2.0 language: - zh widget: - text: "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT:" library_name: transformers pipeline_tag: text-generation extra_gated_heading: Acknowledge license to accept the repository. extra_gated_prompt: Please contact the author for access. extra_gated_button_content: Acknowledge license 同意以上內容 extra_gated_fields: Name: text Mail: text Organization: text Country: text Any utilization of the Taiwan LLM repository mandates the explicit acknowledgment and attribution to the original author: checkbox 使用Taiwan LLM必須明確地承認和歸功於優必達株式會社 Ubitus 以及原始作者: checkbox --- <img src="https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/CmusIT5OlSXvFrbTJ7l-C.png" alt="Taiwan LLM Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> # 🌟 Checkout [Taiwan-LLM Demo Chat-UI](http://www.twllm.com) 🌟 # Model Card for Taiwan LLM 7B v2.0 base Taiwan LLM is an advanced language model tailored for Traditional Chinese, focusing on the linguistic and cultural contexts of Taiwan. Developed from a large base model, it's enriched with diverse Taiwanese textual sources and refined through Supervised Fine-Tuning. This model excels in language understanding and generation, aligning closely with Taiwan's cultural nuances. It demonstrates improved performance on various benchmarks like TC-Eval, showcasing its contextual comprehension and cultural relevance. For detailed insights into Taiwan LLM's development and features, refer to our [technical report](https://github.com/MiuLab/Taiwan-LLaMa/blob/main/twllm_paper.pdf). ## Model description - Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets. - Language(s) (NLP): Primarily Traditional Chinese (zh-tw) - Finetuned from model: [meta-llama/Llama-2-7b-hf](https://huggingface.co/yentinglin/meta-llama/Llama-2-7b-hf) ### Model Sources <!-- Provide the basic links for the model. --> - Repository: https://github.com/MiuLab/Taiwan-LLaMa - Demo: https://twllm.com/ ## Performance ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/HTwIzw6RDha2-PhuWqSuI.png) ## Intended uses You should fine-tuned this model for instruction-following / chat application. ### Training hyperparameters ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/MdvHwdUvH-c926qyRAw7K.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/kKpkvxDzOEyiAoTqmzRYO.png) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/5df9c78eda6d0311fd3d541f/FsnlJ_fkRxf7fn5RKZnjE.png) The following hyperparameters were used during training: - learning_rate: 5e-05 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5.0 ## Citation If you find Taiwan LLM is useful in your work, please cite it with: ``` @misc{lin2023taiwan, title={Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model}, author={Yen-Ting Lin and Yun-Nung Chen}, year={2023}, eprint={2311.17487}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` # Acknowledgement Taiwan LLM v2 is conducted in collaboration with [Ubitus K.K.](http://ubitus.net). Ubitus provides valuable compute resources for the project.
coconana/Qwen-Qwen1.5-0.5B-1717048629	coconana	2024-05-30T06:03:44Z	146	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T05:57:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
DokHee/openBio-8b-VBioLLM-gguf	DokHee	2024-05-30T06:00:59Z	0	0	transformers	[ "transformers", "text-generation-inference", "unsloth", "llama", "gguf", "en", "base_model:aaditya/Llama3-OpenBioLLM-8B", "base_model:finetune:aaditya/Llama3-OpenBioLLM-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T06:00:57Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: aaditya/OpenBioLLM-Llama3-8B --- # Uploaded model - Developed by: DokHee - License: apache-2.0 - Finetuned from model : aaditya/OpenBioLLM-Llama3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Eomts/Mistral7b_test	Eomts	2024-05-30T05:54:34Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-29T08:41:25Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: results results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # results This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. ## Model description This model is for personal study.</br> I based on Ali Mobarekati's script, "Fine-Tuning Mistral 7b in Google Colab with QLoRA (complete guide)"</br> Here's url : https://medium.com/@codersama/fine-tuning-mistral-7b-in-google-colab-with-qlora-complete-guide-60e12d437cca</br> ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.36.2 - Pytorch 2.3.0+cu121 - Datasets 2.16.0 - Tokenizers 0.15.2
taskydata/pile-t5-base-ins-no_robots	taskydata	2024-05-30T05:54:06Z	106	0	transformers	[ "transformers", "tensorboard", "safetensors", "umt5", "text2text-generation", "generated_from_trainer", "en", "base_model:EleutherAI/pile-t5-base", "base_model:finetune:EleutherAI/pile-t5-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-30T04:27:33Z	--- base_model: EleutherAI/pile-t5-base tags: - generated_from_trainer model-index: - name: pile-t5-base-inst results: [] language: - en metrics: - rouge library_name: transformers --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pile-t5-base-inst This model is a fine-tuned version of [EleutherAI/pile-t5-base](https://huggingface.co/EleutherAI/pile-t5-base) on [taskydata/Pile-T5-Instruction](https://huggingface.co/datasets/taskydata/Pile-T5-Instruction) dataset. It achieves the following results on the evaluation set: - Loss: 2.5082 - Rouge2 Precision: 0.2496 - Rouge2 Recall: 0.1633 - Rouge2 Fmeasure: 0.1786 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 5.8554 \| 0.58 \| 1500 \| 3.2533 \| 0.1 \| 0.0744 \| 0.0721 \| \| 4.2403 \| 1.16 \| 3000 \| 2.7020 \| 0.1704 \| 0.1174 \| 0.1248 \| \| 3.8091 \| 1.74 \| 4500 \| 2.5844 \| 0.2476 \| 0.1617 \| 0.1767 \| \| 3.6589 \| 2.32 \| 6000 \| 2.5289 \| 0.2467 \| 0.1621 \| 0.1769 \| \| 3.5802 \| 2.9 \| 7500 \| 2.5082 \| 0.2496 \| 0.1633 \| 0.1786 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
MelitaCruces/Llama3-gsm8k-100	MelitaCruces	2024-05-30T05:53:31Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:53:23Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: MelitaCruces - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
a-n-a-n-y-a-123/finetuned_ner_model	a-n-a-n-y-a-123	2024-05-30T05:53:26Z	120	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "base_model:google-bert/bert-base-uncased", "base_model:finetune:google-bert/bert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-30T05:52:50Z	--- license: apache-2.0 base_model: bert-base-uncased tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_ner_model results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9407259407259407 - name: Recall type: recall value: 0.9513386091934669 - name: F1 type: f1 value: 0.9460025115110924 - name: Accuracy type: accuracy value: 0.9893461620863604 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned_ner_model This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0474 - Precision: 0.9407 - Recall: 0.9513 - F1: 0.9460 - Accuracy: 0.9893 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1704 \| 1.0 \| 878 \| 0.0490 \| 0.9249 \| 0.9348 \| 0.9298 \| 0.9865 \| \| 0.0369 \| 2.0 \| 1756 \| 0.0492 \| 0.9307 \| 0.9471 \| 0.9388 \| 0.9874 \| \| 0.0185 \| 3.0 \| 2634 \| 0.0447 \| 0.9430 \| 0.9520 \| 0.9475 \| 0.9893 \| \| 0.0118 \| 4.0 \| 3512 \| 0.0474 \| 0.9407 \| 0.9513 \| 0.9460 \| 0.9893 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mradermacher/MistralHermesPipe-7B-slerp-GGUF	mradermacher	2024-05-30T05:51:31Z	14	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "en", "base_model:YorkieOH10/MistralHermesPipe-7B-slerp", "base_model:quantized:YorkieOH10/MistralHermesPipe-7B-slerp", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:03:12Z	--- base_model: YorkieOH10/MistralHermesPipe-7B-slerp language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/YorkieOH10/MistralHermesPipe-7B-slerp <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MistralHermesPipe-7B-slerp-GGUF/resolve/main/MistralHermesPipe-7B-slerp.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
squeeze-ai-lab/TinyAgent-7B	squeeze-ai-lab	2024-05-30T05:50:08Z	25	3	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "function calling", "on-device language model", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-05-27T18:48:38Z	--- library_name: transformers model-index: - name: TinyAgent-7B results: [] tags: - function calling - on-device language model inference: false space: false spaces: false language: - en --- # TinyAgent: Function Calling at the Edge <p align="center"> <a href="https://github.com/SqueezeAILab/TinyAgent/raw/main/TinyAgent.zip">Get the desktop app</a>‎ ‎ \| <a href="https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/">Read the blog post</a> </p> ![Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/a1YuQosFiJQJ_7Ejribrd.png) TinyAgent aims to enable complex reasoning and function calling capabilities in Small Language Models (SLMs) that can be deployed securely and privately at the edge. Traditional Large Language Models (LLMs) like GPT-4 and Gemini-1.5, while powerful, are often too large and resource-intensive for edge deployment, posing challenges in terms of privacy, connectivity, and latency. TinyAgent addresses these challenges by training specialized SLMs with high-quality, curated data, and focusing on function calling with [LLMCompiler](https://github.com/SqueezeAILab/LLMCompiler). As a driving application, TinyAgent can interact with various MacOS applications, assisting users with day-to-day tasks such as composing emails, managing contacts, scheduling calendar events, and organizing Zoom meetings. Model Developers: Squeeze AI Lab at University of California, Berkeley. Variations: TinyAgent models come in 2 sizes: TinyAgent-1.1B and TinyAgent-7B License: MIT ## Demo <a href="https://youtu.be/0GvaGL9IDpQ" target="_blank" rel="noopener noreferrer"> <img src="https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/BpN-zPzfqa8wcRuJiYOYC.png" alt="TinyAgent Demo" width="700"> </a> ## How to Use Please see our [Github](https://github.com/SqueezeAILab/TinyAgent) for details on how to use TinyAgent models. TinyAgent models can be used programmatically or through our user interface. ## Training Details Dataset: We curated a [dataset](https://huggingface.co/datasets/squeeze-ai-lab/TinyAgent-dataset) of 40,000 real-life use cases. We use GPT-3.5-Turbo to generate real-world instructions. These are then used to obtain synthetic execution plans using GPT-4-Turbo. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our dataset. Fine-tuning Procedure: TinyAgent models are fine-tuned from base models. Below is a table of each TinyAgent model with its base counterpart \| Model \| Success Rate \| \| ----------------------------------------------------------------------------------------------------------------------------------------------------------- \| ------------ \| \| GPT-3.5-turbo \| 65.04% \| \| GPT-4-turbo \| 79.08% \| \| [TinyLLama-1.1B-32K-Instruct](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct) \| 12.71% \| \| [WizardLM-2-7b](https://huggingface.co/MaziyarPanahi/WizardLM-2-7B-GGUF) \| 41.25% \| \| TinyAgent-1.1B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B-GGUF)] \| 80.06% \| \| TinyAgent-7B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B-GGUF)] \| 84.95% \| Using the synthetic data generation process described above, we use parameter-efficient fine-tuning with LoRA to fine-tune the base models for 3 epochs. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our fine-tuning procedure. ### 🛠️ ToolRAG When faced with challenging tasks, SLM agents require appropriate tools and in-context examples to guide them. If the model sees irrelevant examples, it can hallucinate. Likewise, if the model sees the descriptions of the tools that it doesn’t need, it usually gets confused, and these tools take up unnecessary prompt space. To tackle this, TinyAgent uses ToolRAG to retrieve the best tools and examples suited for a given query. This process has minimal latency and increases the accuracy of TinyAgent substantially. Please take a look at our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) and our [ToolRAG model](https://huggingface.co/squeeze-ai-lab/TinyAgent-ToolRAG) for more details. ## Links Blog Post: https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/ Github: https://github.com/SqueezeAILab/TinyAgent
Emptier8126/PPO-LunarLander-v2	Emptier8126	2024-05-30T05:50:04Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-02-18T14:02:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 278.90 +/- 22.20 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
squeeze-ai-lab/TinyAgent-ToolRAG	squeeze-ai-lab	2024-05-30T05:47:12Z	118	15	transformers	[ "transformers", "safetensors", "deberta-v2", "text-classification", "function calling", "on-device language model", "en", "autotrain_compatible", "region:us" ]	text-classification	2024-05-27T07:26:44Z	--- library_name: transformers model-index: - name: TinyAgent-ToolRAG results: [] tags: - function calling - on-device language model inference: false space: false spaces: false language: - en --- # TinyAgent: Function Calling at the Edge <p align="center"> <a href="https://github.com/SqueezeAILab/TinyAgent/raw/main/TinyAgent.zip">Get the desktop app</a>‎ ‎ \| <a href="https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/">Read the blog post</a> </p> ![Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/a1YuQosFiJQJ_7Ejribrd.png) TinyAgent aims to enable complex reasoning and function calling capabilities in Small Language Models (SLMs) that can be deployed securely and privately at the edge. Traditional Large Language Models (LLMs) like GPT-4 and Gemini-1.5, while powerful, are often too large and resource-intensive for edge deployment, posing challenges in terms of privacy, connectivity, and latency. TinyAgent addresses these challenges by training specialized SLMs with high-quality, curated data, and focusing on function calling with [LLMCompiler](https://github.com/SqueezeAILab/LLMCompiler). As a driving application, TinyAgent can interact with various MacOS applications, assisting users with day-to-day tasks such as composing emails, managing contacts, scheduling calendar events, and organizing Zoom meetings. When faced with challenging tasks, SLM agents require appropriate tools and in-context examples to guide them. If the model sees irrelevant examples, it can hallucinate. Likewise, if the model sees the descriptions of the tools that it doesn’t need, it usually gets confused, and these tools take up unnecessary prompt space. To tackle this, TinyAgent uses ToolRAG to retrieve the best tools and examples suited for a given query. This process has minimal latency and increases the accuracy of TinyAgent substantially. Please take a look at our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details. Model Developers: Squeeze AI Lab at University of California, Berkeley. Variations: TinyAgent models come in 2 sizes: TinyAgent-1.1B and TinyAgent-7B License: MIT ## Demo <a href="https://youtu.be/0GvaGL9IDpQ" target="_blank" rel="noopener noreferrer"> <img src="https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/BpN-zPzfqa8wcRuJiYOYC.png" alt="TinyAgent Demo" width="700"> </a> ## How to Use Please see our [Github](https://github.com/SqueezeAILab/TinyAgent) for details on how to use TinyAgent models. TinyAgent models can be used programmatically or through our user interface. ## Training Details Dataset: We curated a [dataset](https://huggingface.co/datasets/squeeze-ai-lab/TinyAgent-dataset) of 40,000 real-life use cases. We use GPT-3.5-Turbo to generate real-world instructions. These are then used to obtain synthetic execution plans using GPT-4-Turbo. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our dataset. Fine-tuning Procedure: TinyAgent models are fine-tuned from base models. Below is a table of each TinyAgent model with its base counterpart \| Model \| Success Rate \| \| ----------------------------------------------------------------------------------------------------------------------------------------------------------- \| ------------ \| \| GPT-3.5-turbo \| 65.04% \| \| GPT-4-turbo \| 79.08% \| \| [TinyLLama-1.1B-32K-Instruct](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct) \| 12.71% \| \| [WizardLM-2-7b](https://huggingface.co/MaziyarPanahi/WizardLM-2-7B-GGUF) \| 41.25% \| \| TinyAgent-1.1B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B-GGUF)] \| 80.06% \| \| TinyAgent-7B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B-GGUF)] \| 84.95% \| Using the synthetic data generation process described above, we use parameter-efficient fine-tuning with LoRA to fine-tune the base models for 3 epochs. Please check out our [blog post](https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/) for more details on our fine-tuning procedure. ## Links Blog Post: https://bair.berkeley.edu/blog/2024/05/29/tiny-agent/ Github: https://github.com/SqueezeAILab/TinyAgent
vuongnhathien/convnext-nano-new-1e-4	vuongnhathien	2024-05-30T05:43:45Z	199	0	transformers	[ "transformers", "tensorboard", "safetensors", "convnextv2", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:facebook/convnextv2-nano-22k-384", "base_model:finetune:facebook/convnextv2-nano-22k-384", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T03:22:00Z	--- license: apache-2.0 base_model: facebook/convnextv2-nano-22k-384 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: convnext-nano-new-1e-4 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9333333333333333 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # convnext-nano-new-1e-4 This model is a fine-tuned version of [facebook/convnextv2-nano-22k-384](https://huggingface.co/facebook/convnextv2-nano-22k-384) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2467 - Accuracy: 0.9333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.8703 \| 1.0 \| 550 \| 0.5359 \| 0.8473 \| \| 0.6685 \| 2.0 \| 1100 \| 0.4169 \| 0.8855 \| \| 0.5564 \| 3.0 \| 1650 \| 0.3499 \| 0.9042 \| \| 0.4515 \| 4.0 \| 2200 \| 0.3165 \| 0.9141 \| \| 0.442 \| 5.0 \| 2750 \| 0.3228 \| 0.9082 \| \| 0.3799 \| 6.0 \| 3300 \| 0.3089 \| 0.9157 \| \| 0.3311 \| 7.0 \| 3850 \| 0.2746 \| 0.9252 \| \| 0.2726 \| 8.0 \| 4400 \| 0.2689 \| 0.9249 \| \| 0.2711 \| 9.0 \| 4950 \| 0.2651 \| 0.9276 \| \| 0.2758 \| 10.0 \| 5500 \| 0.2618 \| 0.9308 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
subhavarshith/donut_exp1e-5	subhavarshith	2024-05-30T05:41:31Z	9	0	transformers	[ "transformers", "safetensors", "vision-encoder-decoder", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-29T10:43:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hanzohazashi1/lora_model	hanzohazashi1	2024-05-30T05:31:34Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:31:29Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: meta-llama/Meta-Llama-3-8B-Instruct --- # Uploaded model - Developed by: hanzohazashi1 - License: apache-2.0 - Finetuned from model : meta-llama/Meta-Llama-3-8B-Instruct This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF	mradermacher	2024-05-30T05:29:19Z	16	0	transformers	[ "transformers", "gguf", "trl", "dpo", "ko", "base_model:haes95/POLAR-10.7B-HES-DPO-v0.1", "base_model:quantized:haes95/POLAR-10.7B-HES-DPO-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:50:30Z	--- base_model: haes95/POLAR-10.7B-HES-DPO-v0.1 language: - ko library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - trl - dpo --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/haes95/POLAR-10.7B-HES-DPO-v0.1 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q2_K.gguf) \| Q2_K \| 4.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.IQ3_XS.gguf) \| IQ3_XS \| 4.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q3_K_S.gguf) \| Q3_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.IQ3_S.gguf) \| IQ3_S \| 4.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.IQ3_M.gguf) \| IQ3_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q3_K_M.gguf) \| Q3_K_M \| 5.3 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q3_K_L.gguf) \| Q3_K_L \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.IQ4_XS.gguf) \| IQ4_XS \| 5.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q4_K_S.gguf) \| Q4_K_S \| 6.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q4_K_M.gguf) \| Q4_K_M \| 6.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q5_K_S.gguf) \| Q5_K_S \| 7.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q5_K_M.gguf) \| Q5_K_M \| 7.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q6_K.gguf) \| Q6_K \| 8.9 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/POLAR-10.7B-HES-DPO-v0.1-GGUF/resolve/main/POLAR-10.7B-HES-DPO-v0.1.Q8_0.gguf) \| Q8_0 \| 11.5 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf	RichardErkhov	2024-05-30T05:17:34Z	8	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-30T01:45:37Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Samantha-Nebula-7B - GGUF - Model creator: https://huggingface.co/Weyaxi/ - Original model: https://huggingface.co/Weyaxi/Samantha-Nebula-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Samantha-Nebula-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [Samantha-Nebula-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [Samantha-Nebula-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [Samantha-Nebula-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [Samantha-Nebula-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [Samantha-Nebula-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [Samantha-Nebula-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [Samantha-Nebula-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [Samantha-Nebula-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [Samantha-Nebula-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [Samantha-Nebula-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [Samantha-Nebula-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [Samantha-Nebula-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [Samantha-Nebula-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [Samantha-Nebula-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [Samantha-Nebula-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [Samantha-Nebula-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [Samantha-Nebula-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [Samantha-Nebula-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [Samantha-Nebula-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [Samantha-Nebula-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [Samantha-Nebula-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_Samantha-Nebula-7B-gguf/blob/main/Samantha-Nebula-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- datasets: - garage-bAInd/Open-Platypus language: - en license: apache-2.0 --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/cKySe1S5IW_KnbZpKmozQ.png) <a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> # Samantha-Nebula-7B Samantha-Nebula-7B is a merge of [ehartford/samantha-mistral-7b](https://huggingface.co/ehartford/samantha-mistral-7b) and [PulsarAI/Nebula-7B](https://huggingface.co/PulsarAI/Nebula-7B-Lora) # Evaluation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)) \| Metric \| Value \| \|-----------------------\|-------\| \| Avg. \| \| \| ARC (25-shot) \| \| \| HellaSwag (10-shot) \| \| \| MMLU (5-shot) \| \| \| TruthfulQA (0-shot) \| \| # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Weyaxi__Samantha-Nebula-7B) \| Metric \| Value \| \|-----------------------\|---------------------------\| \| Avg. \| 52.87 \| \| ARC (25-shot) \| 57.0 \| \| HellaSwag (10-shot) \| 82.25 \| \| MMLU (5-shot) \| 54.21 \| \| TruthfulQA (0-shot) \| 49.58 \| \| Winogrande (5-shot) \| 73.09 \| \| GSM8K (5-shot) \| 11.37 \| \| DROP (3-shot) \| 42.57 \|
jsfs11/WestOrcaMonarch-DPO-7B	jsfs11	2024-05-30T05:15:02Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "axolotl", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T04:57:01Z	--- license: apache-2.0 tags: - axolotl --- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) This model is a fine-tuned version of [jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B](https://huggingface.co/jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B) on the OpenHermes2.5-dpo-binarized-alpha dataset. ### The following hyperparameters were used during training: - base_model: jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: true strict: false rl: dpo chat_template: chatml datasets: - path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha split: train type: chatml.intel dataset_prepared_path: val_set_size: 0.01 output_dir: ./out adapter: qlora lora_model_dir: sequence_len: 1800 sample_packing: false pad_to_sequence_len: false lora_r: 32 lora_alpha: 32 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules: wandb_project: axolotl wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 8 micro_batch_size: 1 num_epochs: 1 optimizer: paged_adamw_32bit lr_scheduler: cosine learning_rate: 5e-7 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: true gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 100 evals_per_epoch: 1 eval_table_size: eval_table_max_new_tokens: 128 save_steps: 1080 max_steps: 1080 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ### Training results "train/loss": 0.4733, "train/grad_norm": 15.831088066101074, "train/learning_rate": 0, "train/rewards/chosen": -0.6122800707817078, "train/rewards/rejected": -1.650345802307129, "train/rewards/accuracies": 0.875, "train/rewards/margins": 1.0380656719207764, "train/logps/rejected": -379.778564453125, "train/logps/chosen": -250.2126007080078, "train/logits/rejected": -2.0232465267181396, "train/logits/chosen": -2.1629369258880615, "train/epoch": 2.08594881699662, "train/global_step": 1080, "_timestamp": 1717044966.608197, "_runtime": 12949.461512088776, "_step": 1080, "train_runtime": 12950.5619, "train_samples_per_second": 1.334, "train_steps_per_second": 0.083, "total_flos": 0, "train_loss": 0.560937881635295, ###
carpit680/ppo-Huggy	carpit680	2024-05-30T05:14:52Z	6	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-30T05:10:41Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: carpit680/ppo-Huggy 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
fhnw/Llama-3-pineapple-2x8B-Q4_K_M-GGUF	fhnw	2024-05-30T05:14:13Z	1	0	null	[ "gguf", "moe", "frankenmoe", "merge", "mergekit", "fhnw/Llama-3-8B-pineapple-pizza-orpo", "fhnw/Llama-3-8B-pineapple-recipe-sft", "llama-cpp", "gguf-my-repo", "base_model:fhnw/Llama-3-8B-pineapple-pizza-orpo", "base_model:merge:fhnw/Llama-3-8B-pineapple-pizza-orpo", "base_model:fhnw/Llama-3-8B-pineapple-recipe-sft", "base_model:merge:fhnw/Llama-3-8B-pineapple-recipe-sft", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T05:01:35Z	--- tags: - moe - frankenmoe - merge - mergekit - fhnw/Llama-3-8B-pineapple-pizza-orpo - fhnw/Llama-3-8B-pineapple-recipe-sft - llama-cpp - gguf-my-repo base_model: - fhnw/Llama-3-8B-pineapple-pizza-orpo - fhnw/Llama-3-8B-pineapple-recipe-sft --- # fhnw/Llama-3-pineapple-2x8B-Q4_K_M-GGUF This model was converted to GGUF format from [`fhnw/Llama-3-pineapple-2x8B`](https://huggingface.co/fhnw/Llama-3-pineapple-2x8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/fhnw/Llama-3-pineapple-2x8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo fhnw/Llama-3-pineapple-2x8B-Q4_K_M-GGUF --model llama-3-pineapple-2x8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo fhnw/Llama-3-pineapple-2x8B-Q4_K_M-GGUF --model llama-3-pineapple-2x8b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m llama-3-pineapple-2x8b-q4_k_m.gguf -n 128 ```
haturusinghe/f1_0_629_date_30_05-0512_xlm-roberta-base_mrp_2e-05_16_pre_finetuned_8875_ckpt	haturusinghe	2024-05-30T05:13:49Z	36	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:12:16Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Raneechu/textbookbig14_ft	Raneechu	2024-05-30T05:11:51Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-30T05:11:45Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig14_ft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # textbookbig14_ft This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
haturusinghe/f1_0_614_date_30_05-0507_xlm-roberta-base_mrp_2e-05_16_pre_finetuned_8617_ckpt	haturusinghe	2024-05-30T05:08:29Z	36	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:07:11Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
M00dler/whisper-small-malay	M00dler	2024-05-30T05:07:33Z	90	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "my", "dataset:malaysia-ai/malay-conversational-speech-corpus", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-27T06:28:17Z	--- language: - my license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - malaysia-ai/malay-conversational-speech-corpus metrics: - wer model-index: - name: Whisper small Malay (4 batch size) - Gab results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: malay-conversational-speech-corpus type: malaysia-ai/malay-conversational-speech-corpus args: 'config: malay, split: test' metrics: - name: Wer type: wer value: 27.394540942928042 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper small Malay (4 batch size) - Gab This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the malay-conversational-speech-corpus dataset. It achieves the following results on the evaluation set: - Loss: 0.7126 - Wer: 27.3945 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.0217 \| 6.1728 \| 1000 \| 0.5993 \| 28.8586 \| \| 0.0013 \| 12.3457 \| 2000 \| 0.6816 \| 28.0397 \| \| 0.0003 \| 18.5185 \| 3000 \| 0.7018 \| 27.8660 \| \| 0.0002 \| 24.6914 \| 4000 \| 0.7126 \| 27.3945 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Raneechu/textbookbig14	Raneechu	2024-05-30T05:07:09Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-30T05:07:01Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig14 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # textbookbig14 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.3723 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.4451 \| 0.0171 \| 1 \| 2.3723 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
haturusinghe/f1_0_633_date_30_05-0503_xlm-roberta-base_mrp_2e-05_16_pre_finetuned_8753_ckpt	haturusinghe	2024-05-30T05:05:16Z	36	0	transformers	[ "transformers", "safetensors", "xlm-roberta", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T05:03:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
samim2024/llama2test1	samim2024	2024-05-30T05:01:28Z	2	0	peft	[ "peft", "pytorch", "llama", "region:us" ]	null	2024-05-30T04:49:05Z	--- library_name: peft --- ## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0
gray311/vlm_unlearned_ft_retain_llava_v1.6_vicuna_7b	gray311	2024-05-30T04:56:41Z	6	0	transformers	[ "transformers", "safetensors", "llava", "image-text-to-text", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-30T04:46:24Z	--- license: apache-2.0 ---
wizardofchance/formAI-trial-2	wizardofchance	2024-05-30T04:53:52Z	121	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-30T04:38:44Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy model-index: - name: formAI-trial-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # formAI-trial-2 This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2667 - Accuracy: 0.9055 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.4366 \| 1.0 \| 500 \| 0.2896 \| 0.9015 \| \| 0.2664 \| 2.0 \| 1000 \| 0.2667 \| 0.9055 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mradermacher/Miss-Claude-7b-GGUF	mradermacher	2024-05-30T04:52:41Z	21	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T04:26:58Z	--- base_model: CoprolaliacPress/Miss-Claude-7b language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/CoprolaliacPress/Miss-Claude-7b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Miss-Claude-7b-GGUF/resolve/main/Miss-Claude-7b.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
JoshEe00/whisper-small-bn-finetuned	JoshEe00	2024-05-30T04:50:22Z	92	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:JoshEe00/bengaliai-speech-dataset", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-28T17:03:35Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - JoshEe00/bengaliai-speech-dataset model-index: - name: whisper-small-bn-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-small-bn-finetuned This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the JoshEe00/bengaliai-speech-dataset dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 50 - training_steps: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Ja-ck/mistral-7b-tag-extractor-v1	Ja-ck	2024-05-30T04:48:13Z	10	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-26T04:25:05Z	--- license: apache-2.0 --- # Tag Extractor v1 주어진 본문에서 주요 단어를 추출하는 모델입니다. 사용 예시 ```python from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig model = AutoModelForCausalLM.from_pretrained("Ja-ck/mistral-7b-tag-extractor-v1") tokenizer = AutoTokenizer.from_pretrained("Ja-ck/mistral-7b-tag-extractor-v1") gen_config = GenerationConfig( pad_token_id=tokenizer.pad_token_id, temperature=0.2, top_p=1, top_k=40, num_beams=1, repetition_penalty=1.11, do_sample=True, eos_token_id=tokenizer.eos_token_id, ) messages = [ {"role": "user", "content": """“이 책을 읽기 전으로 되돌아갈 수 없다!” 공감을 불러일으키는 걸작인가, 피하고 싶은 문제작인가? 누적 판매 50만 부 돌파, 화제의 베스트셀러! 2023년 영화 〈정욕〉 일본 개봉 제34회 시바타 렌자부로상 수상 2022년 서점 대상 4위 오디오 북 대상 2023 무제한 청취 부문 대상 〈다빈치〉 플래티넘 도서 OF THE YEAR 2021 〈다빈치〉 BOOK OF THE YEAR 2023 문고 1위 일본 서평 사이트 북로그 2021년 연간 등록 1위, ‘#최고의책’ 최다 등록 일본 최대 서점 기노쿠니야 선정 2022년 베스트셀러 2위 2021년 출간 이후, 일본 최고 문제작이자 화제작으로 떠오른 장편소설, ‘《정욕正欲》’이 리드비에서 소개된다. 최연소 남성 나오키상 수상 작가 아사이 료의 데뷔 10주년 기념작이기도 한 이 작품은 성적 욕망을 뜻하는 ‘정욕(情慾)’, 마음속의 욕구를 다룬 ‘정욕(情欲)’이 아닌 ‘바른 욕망’이란 뜻의 ‘正欲’이란 한자를 제목으로 삼고 있다. 《정욕》은 ‘다양성 존중의 시대’를 살고 있다고 자부하는 우리가 받아들일 수 있는 범위는 과연 어디까지인지, 과감하고도 도발적인 질문을 던진다. ‘다양성’에 대한 일반인의 상식을 뒤엎는 파격적인 전개로 격렬한 찬반 논쟁을 불러일으킨 《정욕》은 제34회 시바타 렌자부로상, 2022년 서점 대상 4위 등 비평적 찬사는 물론, 2021년부터 현재까지 각종 도서 랭킹 상위에 오르며 일본 문학계 최고의 화제작으로 자리 잡았다. 《정욕》은 2023년 이나가키 고로, 아라가키 유이 주연 영화로 제작됐으며, 영화 또한 소설 못지않은 화제를 불러일으키며 제36회 도쿄 국제 영화제에서 최우수 감독상, 관객상을 수상했다. 영화 〈정욕〉은 2024년, 국내 개봉을 앞두고 있다."""} ] inputs = tokenizer( [ tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True), ], return_tensors='pt').to("cuda") outputs = model.generate(**inputs, generation_config=gen_config, max_new_tokens=512,use_cache=True) result = tokenizer.batch_decode(outputs,skip_special_tokens=True) result ``` 결과 ```python ['### 질문: 아래 텍스트에서 주제어를 최대 20개 추출해주세요. 출력 형식은 {{"topics": [ "주제어1","주제어2", "주제어3", ...] }}과 동일한 형식으로 합니다. 짧은 단어 보다는 두개 이상의 단어들로 주제어를 추출합니다. 명사나 명사구가 좋습니다. 중복되는 주제어는 제외합니다. 텍스트: “이 책을 읽기 전으로 되돌아갈 수 없다!”\n공감을 불러일으키는 걸작인가, 피하고 싶은 문제작인가?\n\n누적 판매 50만 부 돌파, 화제의 베스트셀러!\n2023년 영화 〈정욕〉 일본 개봉\n제34회 시바타 렌자부로상 수상\n2022년 서점 대상 4위\n오디오 북 대상 2023 무제한 청취 부문 대상\n〈다빈치〉 플래티넘 도서 OF THE YEAR 2021\n〈다빈치〉 BOOK OF THE YEAR 2023 문고 1위\n일본 서평 사이트 북로그 2021년 연간 등록 1위, ‘#최고의책’ 최다 등록\n일본 최대 서점 기노쿠니야 선정 2022년 베스트셀러 2위\n\n2021년 출간 이후, 일본 최고 문제작이자 화제작으로 떠오른 장편소설, ‘《정욕正欲》’이 리드비에서 소개된다. 최연소 남성 나오키상 수상 작가 아사이 료의 데뷔 10주년 기념작이기도 한 이 작품은 성적 욕망을 뜻하는 ‘정욕(情慾)’, 마음속의 욕구를 다룬 ‘정욕(情欲)’이 아닌 ‘바른 욕망’이란 뜻의 ‘正欲’이란 한자를 제목으로 삼고 있다.\n\n《정욕》은 ‘다양성 존중의 시대’를 살고 있다고 자부하는 우리가 받아들일 수 있는 범위는 과연 어디까지인지, 과감하고도 도발적인 질문을 던진다. ‘다양성’에 대한 일반인의 상식을 뒤엎는 파격적인 전개로 격렬한 찬반 논쟁을 불러일으킨 《정욕》은 제34회 시바타 렌자부로상, 2022년 서점 대상 4위 등 비평적 찬사는 물론, 2021년부터 현재까지 각종 도서 랭킹 상위에 오르며 일본 문학계 최고의 화제작으로 자리 잡았다.\n\n《정욕》은 2023년 이나가키 고로, 아라가키 유이 주연 영화로 제작됐으며, 영화 또한 소설 못지않은 화제를 불러일으키며 제36회 도쿄 국제 영화제에서 최우수 감독상, 관객상을 수상했다. 영화 〈정욕〉은 2024년, 국내 개봉을 앞두고 있다. ### 답변: {"topics": ["문제작", "화제의 베스트셀러", "성적 욕망", "다양성 존중", "영화 애호가"]}'] ``` # License --- license: apache-2.0 language: - ko ---
HyperdustProtocol/HyperAutoGGUF-q4	HyperdustProtocol	2024-05-30T04:45:43Z	6	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-2-7b-bnb-4bit", "base_model:quantized:unsloth/llama-2-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:32:01Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - Developed by: HyperdustProtocol - License: apache-2.0 - Finetuned from model : unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
mradermacher/Llama-3-Neurona-8b-GGUF	mradermacher	2024-05-30T04:45:03Z	114	0	transformers	[ "transformers", "gguf", "synthetic", "es", "en", "dataset:pinzhenchen/alpaca-cleaned-es", "dataset:Danielbrdz/Barcenas-Economia", "dataset:HiTZ/casimedicos-exp", "dataset:somosnlp/coser_resumenes", "dataset:csebuetnlp/CrossSum", "dataset:Iker/Document-Translation-en-es", "dataset:somosnlp/es-inclusive-language-it", "dataset:FreedomIntelligence/evol-instruct-spanish", "dataset:glaiveai/glaive-code-assistant-v3", "dataset:glaiveai/glaive-function-calling-v2", "dataset:Iker/InstructTranslation-EN-ES", "dataset:somosnlp/lenguaje-claro-dataset", "dataset:somosnlp/LingComp_QA", "dataset:bltlab/lr-sum", "dataset:Iker/NoticIA", "dataset:xaviviro/oasst2_es_gpt", "dataset:teknium/OpenHermes-2.5", "dataset:Iker/OpenHermes-2.5-Spanish", "dataset:Helsinki-NLP/opus-100", "dataset:projecte-aina/RAG_Multilingual", "dataset:sem_eval_2018_task_1", "dataset:davidstap/ted_talks", "dataset:HiTZ/This-is-not-a-dataset", "dataset:wikipedia", "base_model:Iker/Llama-3-Neurona-8b", "base_model:quantized:Iker/Llama-3-Neurona-8b", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T10:33:31Z	--- base_model: Iker/Llama-3-Neurona-8b datasets: - pinzhenchen/alpaca-cleaned-es - Danielbrdz/Barcenas-Economia - HiTZ/casimedicos-exp - somosnlp/coser_resumenes - csebuetnlp/CrossSum - Iker/Document-Translation-en-es - somosnlp/es-inclusive-language-it - FreedomIntelligence/evol-instruct-spanish - glaiveai/glaive-code-assistant-v3 - glaiveai/glaive-function-calling-v2 - Iker/InstructTranslation-EN-ES - somosnlp/lenguaje-claro-dataset - somosnlp/LingComp_QA - bltlab/lr-sum - Iker/NoticIA - xaviviro/oasst2_es_gpt - teknium/OpenHermes-2.5 - Iker/OpenHermes-2.5-Spanish - Helsinki-NLP/opus-100 - projecte-aina/RAG_Multilingual - sem_eval_2018_task_1 - davidstap/ted_talks - HiTZ/This-is-not-a-dataset - wikipedia language: - es - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - synthetic --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/Iker/Llama-3-Neurona-8b <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-Neurona-8b-GGUF/resolve/main/Llama-3-Neurona-8b.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mradermacher/Flammen-Mahou-mistral-7B-GGUF	mradermacher	2024-05-30T04:41:53Z	22	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "en", "base_model:nbeerbower/Flammen-Mahou-mistral-7B", "base_model:quantized:nbeerbower/Flammen-Mahou-mistral-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T05:54:21Z	--- base_model: nbeerbower/Flammen-Mahou-mistral-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nbeerbower/Flammen-Mahou-mistral-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Flammen-Mahou-mistral-7B-GGUF/resolve/main/Flammen-Mahou-mistral-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
asiansoul/U-GO-GIRL-Remix-Llama-3-KoEn-8B	asiansoul	2024-05-30T04:41:07Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "base_model:NousResearch/Hermes-2-Theta-Llama-3-8B", "base_model:merge:NousResearch/Hermes-2-Theta-Llama-3-8B", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:merge:NousResearch/Meta-Llama-3-8B", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:merge:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:allganize/Llama-3-Alpha-Ko-8B-Instruct", "base_model:merge:allganize/Llama-3-Alpha-Ko-8B-Instruct", "base_model:asiansoul/U-GO-GIRL-Llama-3-KoEn-8B", "base_model:merge:asiansoul/U-GO-GIRL-Llama-3-KoEn-8B", "base_model:nayohan/llama3-instrucTrans-enko-8b", "base_model:merge:nayohan/llama3-instrucTrans-enko-8b", "base_model:rombodawg/Llama-3-8B-Instruct-Coder", "base_model:merge:rombodawg/Llama-3-8B-Instruct-Coder", "base_model:saltlux/Ko-Llama3-Luxia-8B", "base_model:merge:saltlux/Ko-Llama3-Luxia-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:51:53Z	--- base_model: - saltlux/Ko-Llama3-Luxia-8B - allganize/Llama-3-Alpha-Ko-8B-Instruct - nayohan/llama3-instrucTrans-enko-8b - NousResearch/Meta-Llama-3-8B - asiansoul/U-GO-GIRL-Llama-3-KoEn-8B - rombodawg/Llama-3-8B-Instruct-Coder - NousResearch/Hermes-2-Theta-Llama-3-8B - NousResearch/Meta-Llama-3-8B-Instruct library_name: transformers tags: - mergekit - merge --- # U-GO-GIRL-Remix-Llama-3-KoEn-8B <a href="https://ibb.co/jDSymM3"><img src="https://i.ibb.co/Hqjt6zG/vibe.png" alt="vibe" border="0"></a><br /> There are millions of people in the world who like me, but there are probably tens of millions of people who hate me. I will focus on those who like me. Because they made me who I am today. Because eventually you guys will come back here to watch me play~~~ "Back to the basics" [Allen Iverson](https://en.wikipedia.org/wiki/Allen_Iverson) [Toonation Donation](https://toon.at/donate/asiansoul) ETH/USDT(ERC20) Donation : 0x8BB117dD4Cc0E19E5536ab211070c0dE039a85c0 ### Models Merged The following models were included in the merge: * [asiansoul/U-GO-GIRL-Llama-3-KoEn-8B](https://huggingface.co/asiansoul/U-GO-GIRL-Llama-3-KoEn-8B) * [saltlux/Ko-Llama3-Luxia-8B](https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B) * [allganize/Llama-3-Alpha-Ko-8B-Instruct](https://huggingface.co/allganize/Llama-3-Alpha-Ko-8B-Instruct) * [nayohan/llama3-instrucTrans-enko-8b](https://huggingface.co/nayohan/llama3-instrucTrans-enko-8b) * [rombodawg/Llama-3-8B-Instruct-Coder](https://huggingface.co/rombodawg/Llama-3-8B-Instruct-Coder) * [NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) ## Citation Language Mix Model ```text @misc{U-GO_GIRL, author = {JayLee aka "asiansoul"}, title = {U-GO_GIRL Mix Model}, year = {2024}, }, } ```
ahmedesmail16/Train-Test-Augmentation-V4-beit-base	ahmedesmail16	2024-05-30T04:38:45Z	202	0	transformers	[ "transformers", "tensorboard", "safetensors", "beit", "image-classification", "generated_from_trainer", "base_model:microsoft/beit-base-patch16-224-pt22k-ft22k", "base_model:finetune:microsoft/beit-base-patch16-224-pt22k-ft22k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T02:14:20Z	--- license: apache-2.0 base_model: microsoft/beit-base-patch16-224-pt22k-ft22k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Train-Test-Augmentation-V4-beit-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Train-Test-Augmentation-V4-beit-base This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4701 - Accuracy: 0.8557 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6584 \| 1.0 \| 55 \| 0.6744 \| 0.7946 \| \| 0.2762 \| 2.0 \| 110 \| 0.5429 \| 0.8234 \| \| 0.1144 \| 3.0 \| 165 \| 0.5259 \| 0.8336 \| \| 0.0487 \| 4.0 \| 220 \| 0.5111 \| 0.8404 \| \| 0.0218 \| 5.0 \| 275 \| 0.4701 \| 0.8557 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.19.1 - Tokenizers 0.15.2
ZaneHorrible/ViTL-32-224-1e4-batch_16_epoch_4_classes_24	ZaneHorrible	2024-05-30T04:35:02Z	198	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-large-patch32-224-in21k", "base_model:finetune:google/vit-large-patch32-224-in21k", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-30T02:46:47Z	--- license: apache-2.0 base_model: google/vit-large-patch32-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: ViTL-32-224-1e4-batch_16_epoch_4_classes_24 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9410919540229885 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ViTL-32-224-1e4-batch_16_epoch_4_classes_24 This model is a fine-tuned version of [google/vit-large-patch32-224-in21k](https://huggingface.co/google/vit-large-patch32-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3192 - Accuracy: 0.9411 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3387 \| 0.03 \| 100 \| 1.3149 \| 0.7328 \| \| 0.7705 \| 0.07 \| 200 \| 0.7867 \| 0.8003 \| \| 0.5818 \| 0.1 \| 300 \| 0.6799 \| 0.8204 \| \| 0.537 \| 0.14 \| 400 \| 0.4596 \| 0.8836 \| \| 0.4053 \| 0.17 \| 500 \| 0.5233 \| 0.8592 \| \| 0.3401 \| 0.21 \| 600 \| 0.6987 \| 0.8032 \| \| 0.5161 \| 0.24 \| 700 \| 0.5360 \| 0.8405 \| \| 0.3592 \| 0.28 \| 800 \| 0.4567 \| 0.8664 \| \| 0.284 \| 0.31 \| 900 \| 0.3531 \| 0.8966 \| \| 0.2266 \| 0.35 \| 1000 \| 0.4766 \| 0.8678 \| \| 0.2876 \| 0.38 \| 1100 \| 0.6849 \| 0.8233 \| \| 0.3459 \| 0.42 \| 1200 \| 0.4300 \| 0.8851 \| \| 0.2598 \| 0.45 \| 1300 \| 0.3651 \| 0.9052 \| \| 0.5085 \| 0.49 \| 1400 \| 0.4353 \| 0.8736 \| \| 0.4432 \| 0.52 \| 1500 \| 0.4327 \| 0.8678 \| \| 0.2403 \| 0.56 \| 1600 \| 0.4481 \| 0.8736 \| \| 0.4616 \| 0.59 \| 1700 \| 0.5625 \| 0.8549 \| \| 0.244 \| 0.63 \| 1800 \| 0.4537 \| 0.8664 \| \| 0.4304 \| 0.66 \| 1900 \| 0.4377 \| 0.8879 \| \| 0.1581 \| 0.7 \| 2000 \| 0.4487 \| 0.8851 \| \| 0.1273 \| 0.73 \| 2100 \| 0.5803 \| 0.8649 \| \| 0.1073 \| 0.77 \| 2200 \| 0.4146 \| 0.8865 \| \| 0.2694 \| 0.8 \| 2300 \| 0.3707 \| 0.9080 \| \| 0.1699 \| 0.84 \| 2400 \| 0.3477 \| 0.9152 \| \| 0.2632 \| 0.87 \| 2500 \| 0.4382 \| 0.8951 \| \| 0.1191 \| 0.91 \| 2600 \| 0.3614 \| 0.9095 \| \| 0.1634 \| 0.94 \| 2700 \| 0.3786 \| 0.9167 \| \| 0.1704 \| 0.98 \| 2800 \| 0.4049 \| 0.8865 \| \| 0.0117 \| 1.01 \| 2900 \| 0.3248 \| 0.9080 \| \| 0.0522 \| 1.04 \| 3000 \| 0.3518 \| 0.9066 \| \| 0.179 \| 1.08 \| 3100 \| 0.4117 \| 0.9080 \| \| 0.0079 \| 1.11 \| 3200 \| 0.4204 \| 0.9023 \| \| 0.1191 \| 1.15 \| 3300 \| 0.4253 \| 0.9066 \| \| 0.0444 \| 1.18 \| 3400 \| 0.4485 \| 0.9080 \| \| 0.2814 \| 1.22 \| 3500 \| 0.4029 \| 0.9167 \| \| 0.1599 \| 1.25 \| 3600 \| 0.4882 \| 0.8937 \| \| 0.0156 \| 1.29 \| 3700 \| 0.4070 \| 0.9152 \| \| 0.2496 \| 1.32 \| 3800 \| 0.3230 \| 0.9282 \| \| 0.0407 \| 1.36 \| 3900 \| 0.3894 \| 0.9167 \| \| 0.1122 \| 1.39 \| 4000 \| 0.4924 \| 0.8980 \| \| 0.0803 \| 1.43 \| 4100 \| 0.4620 \| 0.8937 \| \| 0.1398 \| 1.46 \| 4200 \| 0.3461 \| 0.9109 \| \| 0.1072 \| 1.5 \| 4300 \| 0.4346 \| 0.9080 \| \| 0.0855 \| 1.53 \| 4400 \| 0.3444 \| 0.9267 \| \| 0.0065 \| 1.57 \| 4500 \| 0.4178 \| 0.9023 \| \| 0.0143 \| 1.6 \| 4600 \| 0.3257 \| 0.9224 \| \| 0.041 \| 1.64 \| 4700 \| 0.3396 \| 0.9195 \| \| 0.0042 \| 1.67 \| 4800 \| 0.3481 \| 0.9253 \| \| 0.0117 \| 1.71 \| 4900 \| 0.4299 \| 0.9037 \| \| 0.132 \| 1.74 \| 5000 \| 0.3819 \| 0.9195 \| \| 0.0223 \| 1.78 \| 5100 \| 0.4280 \| 0.9152 \| \| 0.0009 \| 1.81 \| 5200 \| 0.4115 \| 0.9239 \| \| 0.0578 \| 1.85 \| 5300 \| 0.3844 \| 0.9267 \| \| 0.0014 \| 1.88 \| 5400 \| 0.4024 \| 0.9296 \| \| 0.002 \| 1.92 \| 5500 \| 0.4511 \| 0.9095 \| \| 0.0186 \| 1.95 \| 5600 \| 0.3562 \| 0.9353 \| \| 0.1249 \| 1.99 \| 5700 \| 0.3672 \| 0.9253 \| \| 0.0615 \| 2.02 \| 5800 \| 0.3567 \| 0.9310 \| \| 0.0031 \| 2.06 \| 5900 \| 0.3148 \| 0.9325 \| \| 0.0212 \| 2.09 \| 6000 \| 0.3752 \| 0.9267 \| \| 0.0008 \| 2.12 \| 6100 \| 0.3394 \| 0.9339 \| \| 0.0007 \| 2.16 \| 6200 \| 0.3566 \| 0.9339 \| \| 0.0771 \| 2.19 \| 6300 \| 0.3514 \| 0.9310 \| \| 0.0007 \| 2.23 \| 6400 \| 0.4172 \| 0.9253 \| \| 0.0018 \| 2.26 \| 6500 \| 0.4019 \| 0.9267 \| \| 0.0058 \| 2.3 \| 6600 \| 0.3383 \| 0.9368 \| \| 0.0032 \| 2.33 \| 6700 \| 0.3362 \| 0.9339 \| \| 0.0006 \| 2.37 \| 6800 \| 0.3186 \| 0.9382 \| \| 0.0005 \| 2.4 \| 6900 \| 0.3366 \| 0.9382 \| \| 0.0006 \| 2.44 \| 7000 \| 0.3802 \| 0.9296 \| \| 0.0919 \| 2.47 \| 7100 \| 0.4116 \| 0.9296 \| \| 0.0005 \| 2.51 \| 7200 \| 0.3063 \| 0.9425 \| \| 0.0004 \| 2.54 \| 7300 \| 0.3466 \| 0.9339 \| \| 0.0005 \| 2.58 \| 7400 \| 0.3435 \| 0.9368 \| \| 0.0004 \| 2.61 \| 7500 \| 0.3080 \| 0.9411 \| \| 0.0016 \| 2.65 \| 7600 \| 0.3310 \| 0.9425 \| \| 0.0004 \| 2.68 \| 7700 \| 0.3398 \| 0.9368 \| \| 0.0004 \| 2.72 \| 7800 \| 0.3446 \| 0.9353 \| \| 0.0004 \| 2.75 \| 7900 \| 0.3294 \| 0.9382 \| \| 0.1075 \| 2.79 \| 8000 \| 0.3090 \| 0.9425 \| \| 0.0004 \| 2.82 \| 8100 \| 0.3218 \| 0.9382 \| \| 0.0004 \| 2.86 \| 8200 \| 0.3160 \| 0.9425 \| \| 0.0004 \| 2.89 \| 8300 \| 0.3270 \| 0.9397 \| \| 0.0004 \| 2.93 \| 8400 \| 0.3273 \| 0.9397 \| \| 0.0003 \| 2.96 \| 8500 \| 0.3184 \| 0.9440 \| \| 0.0004 \| 3.0 \| 8600 \| 0.3192 \| 0.9411 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
ebowwa/human-biases-people-v0.5-gguf	ebowwa	2024-05-30T04:30:48Z	4	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T04:28:36Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit --- # Uploaded model - Developed by: ebowwa - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Angelectronic/iai-T5	Angelectronic	2024-05-30T04:30:44Z	0	0	transformers	[ "transformers", "safetensors", "text2text-generation", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T04:11:00Z	--- library_name: transformers pipeline_tag: text2text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details - 44.877 bleu en-vi on PhoMT test, 41.1204 bleu vi-en - 38.5858 bleu en-vi on IWSLT15, 38.1521 vi-en ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mradermacher/AlchemistCoder-L-7B-GGUF	mradermacher	2024-05-30T04:29:18Z	21	0	transformers	[ "transformers", "gguf", "code generation", "en", "base_model:internlm/AlchemistCoder-L-7B", "base_model:quantized:internlm/AlchemistCoder-L-7B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:05:08Z	--- base_model: internlm/AlchemistCoder-L-7B language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - code generation --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/internlm/AlchemistCoder-L-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AlchemistCoder-L-7B-GGUF/resolve/main/AlchemistCoder-L-7B.f16.gguf) \| f16 \| 13.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
ahmedgongi/Llama_dev3model_finale16	ahmedgongi	2024-05-30T04:29:07Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:29:04Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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ahmedgongi/Llama_dev3tokenizer_finale16	ahmedgongi	2024-05-30T04:28:59Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T04:28:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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Bagus/wav2vec2_swbd_emodb	Bagus	2024-05-30T04:26:28Z	2	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "base_model:facebook/wav2vec2-large-robust-ft-swbd-300h", "base_model:finetune:facebook/wav2vec2-large-robust-ft-swbd-300h", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T02:59:58Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-robust-ft-swbd-300h tags: - generated_from_trainer model-index: - name: wav2vec2_swbd_emodb results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned This model is a fine-tuned version of [facebook/wav2vec2-large-robust-ft-swbd-300h](https://huggingface.co/facebook/wav2vec2-large-robust-ft-swbd-300h) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0281 - Uar: 0.7318 - Acc: 0.7721 For the test set: - UAR: 0.74 - ACC: 0.794 ## Model description This model is to predict four emotion categories given and audio file. Labels are anger', 'happiness', 'sadness', 'neutral'. This wav2vec2-based model is known cannot detect 'happiness'. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Uar \| Acc \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| No log \| 0.15 \| 1 \| 1.3899 \| 0.25 \| 0.1985 \| \| No log \| 0.31 \| 2 \| 1.3850 \| 0.25 \| 0.1985 \| \| No log \| 0.46 \| 3 \| 1.3815 \| 0.25 \| 0.1985 \| \| No log \| 0.62 \| 4 \| 1.3772 \| 0.25 \| 0.1985 \| \| No log \| 0.77 \| 5 \| 1.3714 \| 0.25 \| 0.4044 \| \| No log \| 0.92 \| 6 \| 1.3656 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.08 \| 7 \| 1.3610 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.23 \| 8 \| 1.3583 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.38 \| 9 \| 1.3549 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.54 \| 10 \| 1.3518 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.69 \| 11 \| 1.3491 \| 0.25 \| 0.4044 \| \| 1.4878 \| 1.85 \| 12 \| 1.3458 \| 0.25 \| 0.4044 \| \| 1.4878 \| 2.0 \| 13 \| 1.3425 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.15 \| 14 \| 1.3401 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.31 \| 15 \| 1.3380 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.46 \| 16 \| 1.3354 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.62 \| 17 \| 1.3326 \| 0.25 \| 0.4044 \| \| 1.2316 \| 2.77 \| 18 \| 1.3292 \| 0.2778 \| 0.4265 \| \| 1.2316 \| 2.92 \| 19 \| 1.3250 \| 0.2963 \| 0.4412 \| \| 1.3835 \| 3.08 \| 20 \| 1.3212 \| 0.3519 \| 0.4853 \| \| 1.3835 \| 3.23 \| 21 \| 1.3158 \| 0.4029 \| 0.5221 \| \| 1.3835 \| 3.38 \| 22 \| 1.3096 \| 0.5047 \| 0.6029 \| \| 1.3835 \| 3.54 \| 23 \| 1.3019 \| 0.5695 \| 0.6544 \| \| 1.3835 \| 3.69 \| 24 \| 1.2944 \| 0.6485 \| 0.7059 \| \| 1.3835 \| 3.85 \| 25 \| 1.2856 \| 0.6534 \| 0.6985 \| \| 1.3835 \| 4.0 \| 26 \| 1.2773 \| 0.6768 \| 0.7059 \| \| 1.1038 \| 4.15 \| 27 \| 1.2688 \| 0.6540 \| 0.6691 \| \| 1.1038 \| 4.31 \| 28 \| 1.2554 \| 0.6404 \| 0.6471 \| \| 1.1038 \| 4.46 \| 29 \| 1.2404 \| 0.6359 \| 0.6397 \| \| 1.1038 \| 4.62 \| 30 \| 1.2222 \| 0.6586 \| 0.6765 \| \| 1.1038 \| 4.77 \| 31 \| 1.2057 \| 0.6631 \| 0.6838 \| \| 1.1038 \| 4.92 \| 32 \| 1.1874 \| 0.6769 \| 0.6985 \| \| 1.075 \| 5.08 \| 33 \| 1.1624 \| 0.6953 \| 0.7206 \| \| 1.075 \| 5.23 \| 34 \| 1.1427 \| 0.7182 \| 0.75 \| \| 1.075 \| 5.38 \| 35 \| 1.1270 \| 0.7182 \| 0.75 \| \| 1.075 \| 5.54 \| 36 \| 1.1085 \| 0.7227 \| 0.7574 \| \| 1.075 \| 5.69 \| 37 \| 1.0982 \| 0.7227 \| 0.7574 \| \| 1.075 \| 5.85 \| 38 \| 1.0943 \| 0.7227 \| 0.7574 \| \| 1.075 \| 6.0 \| 39 \| 1.0930 \| 0.7136 \| 0.7426 \| \| 0.7211 \| 6.15 \| 40 \| 1.0903 \| 0.7091 \| 0.7353 \| \| 0.7211 \| 6.31 \| 41 \| 1.0858 \| 0.7091 \| 0.7353 \| \| 0.7211 \| 6.46 \| 42 \| 1.0816 \| 0.7045 \| 0.7279 \| \| 0.7211 \| 6.62 \| 43 \| 1.0734 \| 0.7091 \| 0.7353 \| \| 0.7211 \| 6.77 \| 44 \| 1.0617 \| 0.7136 \| 0.7426 \| \| 0.7211 \| 6.92 \| 45 \| 1.0536 \| 0.7136 \| 0.7426 \| \| 0.6595 \| 7.08 \| 46 \| 1.0450 \| 0.7318 \| 0.7721 \| \| 0.6595 \| 7.23 \| 47 \| 1.0370 \| 0.7364 \| 0.7794 \| \| 0.6595 \| 7.38 \| 48 \| 1.0323 \| 0.7364 \| 0.7794 \| \| 0.6595 \| 7.54 \| 49 \| 1.0301 \| 0.7364 \| 0.7794 \| \| 0.6595 \| 7.69 \| 50 \| 1.0307 \| 0.7364 \| 0.7794 \| \| 0.6595 \| 7.85 \| 51 \| 1.0302 \| 0.7318 \| 0.7721 \| \| 0.6595 \| 8.0 \| 52 \| 1.0307 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.15 \| 53 \| 1.0317 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.31 \| 54 \| 1.0324 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.46 \| 55 \| 1.0324 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.62 \| 56 \| 1.0326 \| 0.7273 \| 0.7647 \| \| 0.5067 \| 8.77 \| 57 \| 1.0315 \| 0.7318 \| 0.7721 \| \| 0.5067 \| 8.92 \| 58 \| 1.0297 \| 0.7318 \| 0.7721 \| \| 0.5617 \| 9.08 \| 59 \| 1.0287 \| 0.7318 \| 0.7721 \| \| 0.5617 \| 9.23 \| 60 \| 1.0281 \| 0.7318 \| 0.7721 \| ### Framework versions - Transformers 4.32.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.13.3
mradermacher/AtomPro-Coder-7B-GGUF	mradermacher	2024-05-30T04:20:42Z	39	0	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "GritLM/GritLM-7B", "Nexusflow/Starling-LM-7B-beta", "en", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T03:23:49Z	--- base_model: powermove72/AtomPro-Coder-7B language: - en library_name: transformers quantized_by: mradermacher tags: - merge - mergekit - lazymergekit - GritLM/GritLM-7B - Nexusflow/Starling-LM-7B-beta --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/powermove72/AtomPro-Coder-7B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/AtomPro-Coder-7B-GGUF/resolve/main/AtomPro-Coder-7B.f16.gguf) \| f16 \| 14.6 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
soufiane001/NeuralPipe-7B-slerp	soufiane001	2024-05-30T04:16:10Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:OpenPipe/mistral-ft-optimized-1218", "base_model:merge:OpenPipe/mistral-ft-optimized-1218", "base_model:mlabonne/NeuralHermes-2.5-Mistral-7B", "base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T04:12:10Z	--- tags: - merge - mergekit - lazymergekit - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B base_model: - OpenPipe/mistral-ft-optimized-1218 - mlabonne/NeuralHermes-2.5-Mistral-7B --- # NeuralPipe-7B-slerp NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218) * [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: OpenPipe/mistral-ft-optimized-1218 layer_range: [0, 32] - model: mlabonne/NeuralHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: OpenPipe/mistral-ft-optimized-1218 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "soufiane001/NeuralPipe-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
sunoaiysha/gpt2-company	sunoaiysha	2024-05-30T04:15:58Z	148	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T04:15:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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chitb/Lavy-instruct	chitb	2024-05-30T04:14:26Z	10	0	peft	[ "peft", "safetensors", "llava_mistral", "arxiv:1910.09700", "base_model:Viet-Mistral/Vistral-7B-Chat", "base_model:adapter:Viet-Mistral/Vistral-7B-Chat", "region:us" ]	null	2024-05-30T04:10:48Z	--- library_name: peft base_model: Viet-Mistral/Vistral-7B-Chat --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.9.0
acl-srw-2024/llama3-8b-unsloth-sft-awq-4bit-v2	acl-srw-2024	2024-05-30T04:12:35Z	74	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-30T04:09:58Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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mateomarin/dqn-SpaceInvadersNoFrameskip-v4	mateomarin	2024-05-30T04:03:21Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T04:02:56Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 329.00 +/- 157.97 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mateomarin -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga mateomarin -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga mateomarin ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 500000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.01), ('learning_starts', 100000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
njprogrammer/e5-large-mounjaro	njprogrammer	2024-05-30T03:51:19Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:51:13Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sridhar1ga/wav2vec-dys-large	sridhar1ga	2024-05-30T03:45:07Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:15:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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ElevenHu/ruozhiba-llama3-chines	ElevenHu	2024-05-30T03:43:29Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T03:28:37Z	--- license: apache-2.0 ---
Nogu-t/llama-3-8b-ver3_3	Nogu-t	2024-05-30T03:42:44Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T18:19:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Paulie-Aditya/sign-language-detection	Paulie-Aditya	2024-05-30T03:31:36Z	0	0	null	[ "medical", "image-classification", "region:us" ]	image-classification	2024-03-26T07:43:13Z	--- pipeline_tag: image-classification tags: - medical --- # Novel Approach 1 ## Stacked Classifier: RF + SVM + XGB metrics: - Accuracy: 0.9911734164070612 - Balanced Accuracy: 0.9903422714760236 - MCC: 0.990784932183338 - ROC AUC Score: 0.999934898058849 - F1 Score: 0.9911734164070612 - Jaccard Score: 0.9825012866700978 - Log Loss: 0.033553756349283356 - Precision: 0.9911734164070612 - Recall: 0.9911734164070612 # Novel Approach 2 ## Stacked Classifier: RF + SVM + KNN + XGB metrics: - Accuracy: 0.9922118380062306 - Balanced Accuracy: 0.9913200369813552 - MCC: 0.9918690348004674 - ROC AUC Score: 0.9999193482927975 - F1 Score: 0.9922118380062306 - Jaccard Score: 0.9845440494590417 - Log Loss: 0.03136301122428542 - Precision: 0.9922118380062306 - Recall: 0.9922118380062306
thewordsmiths/Meta-Llama-3-8B_sft_merged_100000	thewordsmiths	2024-05-30T03:30:29Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T03:22:36Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: meta-llama/Meta-Llama-3-8B --- # Uploaded model - Developed by: thewordsmiths - License: apache-2.0 - Finetuned from model : meta-llama/Meta-Llama-3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ReySajju742/Sajjad_NLP	ReySajju742	2024-05-30T03:28:29Z	0	0	transformers	[ "transformers", "nlp", "nltk", "en", "ur", "dataset:wikipedia", "doi:10.57967/hf/2339", "license:cc", "endpoints_compatible", "region:us" ]	null	2024-05-29T10:35:41Z	--- license: cc datasets: - wikipedia language: - en - ur tags: - nlp - nltk library_name: transformers ---
MdGolamMostofa/Mr.X	MdGolamMostofa	2024-05-30T03:26:51Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T03:22:16Z	--- license: apache-2.0 ---
QuantFactory/NeuralLlama-3-8B-Instruct-abliterated-GGUF	QuantFactory	2024-05-30T03:24:41Z	150	0	null	[ "gguf", "abliterated", "text-generation", "dataset:mlabonne/orpo-dpo-mix-40k", "base_model:mlabonne/NeuralLlama-3-8B-Instruct-abliterated", "base_model:quantized:mlabonne/NeuralLlama-3-8B-Instruct-abliterated", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-29T23:58:17Z	--- license: other datasets: - mlabonne/orpo-dpo-mix-40k tags: - abliterated pipeline_tag: text-generation base_model: mlabonne/NeuralLlama-3-8B-Instruct-abliterated --- # Llama-3-8B-Instruct-abliterated-dpomix-GGUF This is quantized version of [mlabonne/NeuralLlama-3-8B-Instruct-abliterated](https://huggingface.co/mlabonne/NeuralLlama-3-8B-Instruct-abliterated) created using llama.cpp # Model Description This model is an experimental DPO fine-tune of an abliterated Llama 3 8B Instruct model on the full [mlabonne/orpo-dpo-mix-40k](https://huggingface.co/datasets/mlabonne/orpo-dpo-mix-40k) dataset. It improves Llama 3 8B Instruct's performance while being uncensored. ## 🔎 Applications This is an uncensored model. You can use it for any application that doesn't require alignment, like role-playing. Tested on LM Studio using the "Llama 3" preset. ## 🏆 Evaluation ### Open LLM Leaderboard This model improves the performance of the abliterated source model and recovers the MMLU that was lost in the abliteration process. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/sCO69BltMkGrq6u7yCIcP.png) ### Nous \| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \| \|---\|---:\|---:\|---:\|---:\|---:\| \| [mlabonne/Llama-3-8B-Instruct-abliterated-dpomix](https://huggingface.co/mlabonne/Llama-3-8B-Instruct-abliterated-dpomix) [📄](https://gist.github.com/mlabonne/d711548df70e2c04771cc68ab33fe2b9) \| 52.26 \| 41.6 \| 69.95 \| 54.22 \| 43.26 \| \| [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) [📄](https://gist.github.com/mlabonne/8329284d86035e6019edb11eb0933628) \| 51.34 \| 41.22 \| 69.86 \| 51.65 \| 42.64 \| \| [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3) [📄](https://gist.github.com/mlabonne/f46cce0262443365e4cce2b6fa7507fc) \| 51.21 \| 40.23 \| 69.5 \| 52.44 \| 42.69 \| \| [abacusai/Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B) [📄](https://gist.github.com/mlabonne/91369d9c372f80b6a42a978b454d3b5e) \| 49.65 \| 37.15 \| 69.12 \| 51.66 \| 40.67 \| \| [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B) [📄](https://gist.github.com/mlabonne/22896a1ae164859931cc8f4858c97f6f) \| 48.63 \| 34.17 \| 70.59 \| 52.39 \| 37.36 \| \| [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) [📄](https://gist.github.com/mlabonne/616b6245137a9cfc4ea80e4c6e55d847) \| 45.42 \| 31.1 \| 69.95 \| 43.91 \| 36.7 \|
Crysiss/llama-3-8B-welfare-sft-test	Crysiss	2024-05-30T03:22:46Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T03:22:04Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: Crysiss - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
trailios/h	trailios	2024-05-30T03:17:30Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T03:17:30Z	--- license: apache-2.0 ---
iRyanBell/ARC1	iRyanBell	2024-05-30T03:10:56Z	34	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "orpo", "conversational", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T02:18:14Z	--- library_name: transformers tags: - unsloth - trl - orpo license: llama3 --- # Model Card for ARC1 Self-instruction llama3-8b-instruct QLoRA fine-tune on generative abstraction & reasoning problem set. # Prompt Template Instruction ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ``` Chat ``` <\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|> {{ system_prompt }}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {{ user_message_1 }}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> {{ model_answer_1 }}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|> {{ user_message_2 }}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|> ```
RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf	RichardErkhov	2024-05-30T03:10:29Z	5	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T00:18:56Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) OpenOrca-Nebula-7B - GGUF - Model creator: https://huggingface.co/Weyaxi/ - Original model: https://huggingface.co/Weyaxi/OpenOrca-Nebula-7B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [OpenOrca-Nebula-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [OpenOrca-Nebula-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [OpenOrca-Nebula-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [OpenOrca-Nebula-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [OpenOrca-Nebula-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [OpenOrca-Nebula-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [OpenOrca-Nebula-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [OpenOrca-Nebula-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [OpenOrca-Nebula-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [OpenOrca-Nebula-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [OpenOrca-Nebula-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [OpenOrca-Nebula-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [OpenOrca-Nebula-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [OpenOrca-Nebula-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [OpenOrca-Nebula-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [OpenOrca-Nebula-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [OpenOrca-Nebula-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [OpenOrca-Nebula-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [OpenOrca-Nebula-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [OpenOrca-Nebula-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [OpenOrca-Nebula-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [OpenOrca-Nebula-7B.Q8_0.gguf](https://huggingface.co/RichardErkhov/Weyaxi_-_OpenOrca-Nebula-7B-gguf/blob/main/OpenOrca-Nebula-7B.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: apache-2.0 datasets: - garage-bAInd/Open-Platypus language: - en --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/cKySe1S5IW_KnbZpKmozQ.png) <a href="https://www.buymeacoffee.com/PulsarAI" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> # OpenOrca-Nebula-7B OpenOrca-Nebula-7B is a merge of [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and [PulsarAI/Nebula-7B](https://huggingface.co/Weyaxi/PulsarAI/Nebula-7B) # Evaluation Results ([Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)) \| Metric \| Value \| \|-----------------------\|-------\| \| Avg. \| \| \| ARC (25-shot) \| \| \| HellaSwag (10-shot) \| \| \| MMLU (5-shot) \| \| \| TruthfulQA (0-shot) \| \|
indirajith-jithu/llama-3-8b-tenjin-Q4_0-GGUF	indirajith-jithu	2024-05-30T03:06:23Z	1	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T03:06:05Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo --- # indirajith-jithu/llama-3-8b-tenjin-Q4_0-GGUF This model was converted to GGUF format from [`indirajith-jithu/llama-3-8b-tenjin`](https://huggingface.co/indirajith-jithu/llama-3-8b-tenjin) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/indirajith-jithu/llama-3-8b-tenjin) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo indirajith-jithu/llama-3-8b-tenjin-Q4_0-GGUF --model llama-3-8b-tenjin-q4_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo indirajith-jithu/llama-3-8b-tenjin-Q4_0-GGUF --model llama-3-8b-tenjin-q4_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m llama-3-8b-tenjin-q4_0.gguf -n 128 ```
Nared45/llama-2-7b-distractor	Nared45	2024-05-30T03:05:22Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-14T10:21:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
samink/sia_model_epoch10	samink	2024-05-30T03:04:57Z	161	0	transformers	[ "transformers", "safetensors", "roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	feature-extraction	2024-05-30T03:04:23Z	Created with the following command: python3 run_classifier.py \ --train \ --train_data=data/rounds_1_2_prior_utt.csv \ --num_train_epochs=10 \ --text_cols=prior_text,teacher_text \ --label_col=attribution \ --predict_index_col=exchange_idx \ --balance_labels Run details: https://wandb.ai/edunlp-cm/attribution/runs/a464fkcg
tsavage68/UTI_M2_225steps_1e7rate_SFT	tsavage68	2024-05-30T03:04:12Z	4	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "sft", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T15:21:32Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - trl - sft - generated_from_trainer model-index: - name: UTI_M2_225steps_1e7rate_SFT results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # UTI_M2_225steps_1e7rate_SFT This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0452 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-07 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 225 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 2.3115 \| 0.3333 \| 25 \| 2.3023 \| \| 2.3052 \| 0.6667 \| 50 \| 2.2848 \| \| 2.2536 \| 1.0 \| 75 \| 2.2429 \| \| 2.1452 \| 1.3333 \| 100 \| 2.1703 \| \| 2.0782 \| 1.6667 \| 125 \| 2.1043 \| \| 1.9786 \| 2.0 \| 150 \| 2.0632 \| \| 1.981 \| 2.3333 \| 175 \| 2.0483 \| \| 2.0521 \| 2.6667 \| 200 \| 2.0459 \| \| 2.0483 \| 3.0 \| 225 \| 2.0452 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
wuttong/drivelm_ft_visualglm	wuttong	2024-05-30T03:02:26Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-27T13:22:31Z	--- license: apache-2.0 ---
osouza/llama-3-8B-programming-questions-v2	osouza	2024-05-30T02:59:55Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T02:53:46Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
juanzinser/Pixelcopter-PLE-v0	juanzinser	2024-05-30T02:55:22Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-29T23:33:14Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 36.60 +/- 28.49 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
ismichel/humor_model_v2	ismichel	2024-05-30T02:45:46Z	167	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-05-30T02:28:44Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: humor_model_v2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # humor_model_v2 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2683 - Accuracy: 0.9639 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.9524 \| 5 \| 0.6652 \| 0.9157 \| \| 0.6722 \| 1.9048 \| 10 \| 0.5931 \| 0.9217 \| \| 0.6722 \| 2.8571 \| 15 \| 0.5272 \| 0.9337 \| \| 0.5461 \| 4.0 \| 21 \| 0.4712 \| 0.8554 \| \| 0.5461 \| 4.9524 \| 26 \| 0.3943 \| 0.8916 \| \| 0.3891 \| 5.9048 \| 31 \| 0.3369 \| 0.9337 \| \| 0.3891 \| 6.8571 \| 36 \| 0.3099 \| 0.9398 \| \| 0.2976 \| 8.0 \| 42 \| 0.2811 \| 0.9578 \| \| 0.2976 \| 8.9524 \| 47 \| 0.2713 \| 0.9578 \| \| 0.2393 \| 9.5238 \| 50 \| 0.2683 \| 0.9639 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
mbiarreta/ButterflyClasifModel	mbiarreta	2024-05-30T02:43:27Z	194	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-27T14:18:35Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: ButterflyModel results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # ButterflyModel This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0560 - Accuracy: 0.9856 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\| \| 0.4686 \| 3.4483 \| 100 \| 0.0743 \| 0.9808 \| \| 0.0445 \| 6.8966 \| 200 \| 0.0560 \| 0.9856 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
lsmille/lora_evo_ta_all_layers_14	lsmille	2024-05-30T02:42:20Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:togethercomputer/evo-1-8k-base", "base_model:adapter:togethercomputer/evo-1-8k-base", "license:apache-2.0", "region:us" ]	null	2024-05-30T02:12:17Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: togethercomputer/evo-1-8k-base model-index: - name: lora_evo_ta_all_layers_14 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lora_evo_ta_all_layers_14 This model is a fine-tuned version of [togethercomputer/evo-1-8k-base](https://huggingface.co/togethercomputer/evo-1-8k-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4933 ## Model description Trained on 5K dataset lora_alpha = 256 lora_dropout = 0.05 lora_r = 128 epochs = 3 learning rate = 3e-4 warmup_steps=100 gradient_accumulation_steps = 1 train_batch = 2 eval_batch = 2 ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_steps: 100 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.6071 \| 1.0 \| 8108 \| 2.5267 \| \| 2.5037 \| 2.0 \| 16216 \| 2.5007 \| \| 2.4762 \| 3.0 \| 24324 \| 2.4933 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
LiteLLMs/Tess-2.0-Mixtral-8x22B-GGUF	LiteLLMs	2024-05-30T02:36:52Z	18	0	null	[ "gguf", "GGUF", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-07T22:23:03Z	--- license: apache-2.0 tags: - GGUF quantized_by: andrijdavid --- # Tess-2.0-Mixtral-8x22B-GGUF - Original model: [Tess-2.0-Mixtral-8x22B](https://huggingface.co/migtissera/Tess-2.0-Mixtral-8x22B) <!-- description start --> ## Description This repo contains GGUF format model files for [Tess-2.0-Mixtral-8x22B](https://huggingface.co/migtissera/Tess-2.0-Mixtral-8x22B). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/Tess-2.0-Mixtral-8x22B-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/Tess-2.0-Mixtral-8x22B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/Tess-2.0-Mixtral-8x22B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Tess-2.0-Mixtral-8x22B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Tess-2.0-Mixtral-8x22B ![Tesoro](https://huggingface.co/migtissera/Tess-2.0-Mixtral-8x22B/resolve/main/Tess-2.png) # Tess-2.0-Mixtral-8x22B Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-2.0-Mixtral-8x22B was trained on the mistral-community/Mixtral-8x22B-v0.1 base. # Prompt Format ``` SYSTEM: <ANY SYSTEM CONTEXT> USER: ASSISTANT: ``` # Training Methodology Tess-2.0-Mixtral-8x22B was trained on the Tess-2.0 dataset. Tess-2.0 dataset and the training methodology follows LIMA (Less-Is-More) principles, and contains ~25K high-quality code and general training samples. The dataset is highly uncensored, hence the model will almost always follow instructions. The model was only fine-tuned for 1-epoch to try and preserve its entropy as much as possible. # Sample code to run inference ```python import torch, json from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "migtissera/Tess-2.0-Mixtral-8x22B" output_file_path = "./conversations.jsonl" model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto", load_in_8bit=False, trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) def generate_text(instruction): tokens = tokenizer.encode(instruction) tokens = torch.LongTensor(tokens).unsqueeze(0) tokens = tokens.to("cuda") instance = { "input_ids": tokens, "top_p": 1.0, "temperature": 0.5, "generate_len": 1024, "top_k": 50, } length = len(tokens[0]) with torch.no_grad(): rest = model.generate( input_ids=tokens, max_length=length + instance["generate_len"], use_cache=True, do_sample=True, top_p=instance["top_p"], temperature=instance["temperature"], top_k=instance["top_k"], num_return_sequences=1, ) output = rest[0][length:] string = tokenizer.decode(output, skip_special_tokens=True) answer = string.split("USER:")[0].strip() return f"{answer}" conversation = f"SYSTEM: Answer the question thoughtfully and intelligently. Always answer without hesitation." while True: user_input = input("You: ") llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: " answer = generate_text(llm_prompt) print(answer) conversation = f"{llm_prompt}{answer}" json_data = {"prompt": user_input, "answer": answer} ## Save your conversation with open(output_file_path, "a") as output_file: output_file.write(json.dumps(json_data) + "\n") ``` # Join My General AI Discord (NeuroLattice): https://discord.gg/Hz6GrwGFKD # Limitations & Biases: While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. Exercise caution and cross-check information when necessary. This is an uncensored model. <!-- original-model-card end -->
Spatiallysaying/detr-finetuned-runwaymarkings-Horizontal-v1	Spatiallysaying	2024-05-30T02:33:18Z	188	0	transformers	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	object-detection	2024-04-28T02:14:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Regigivire/RVC_Dumpster	Regigivire	2024-05-30T02:30:19Z	0	0	null	[ "license:openrail", "region:us" ]	null	2024-05-05T10:40:47Z	--- license: openrail --- welcome to my rvc repo. you dont necessarily need to credit me if you use these, but it would be nice.
SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps-256M	SanghyukChun	2024-05-30T02:29:55Z	54	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-26T02:32:03Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- ### Official implementation of PCME++ pre-trained model on CC3M, CC12M and RedCaps. Zero-shot ImageNet-1k top-1 accuracy: 41.812% (with longer training iterations than the previous version) - Paper: https://openreview.net/forum?id=ft1mr3WlGM - GitHub: https://github.com/naver-ai/pcmepp - Check the official version with ImageNet-1k top-1 accuracy 34.642% (mean-only ZS classification) at [SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps](https://huggingface.co/SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps) ```python import requests from PIL import Image import torch from transformers import CLIPProcessor # Check hf_models code here: https://github.com/naver-ai/pcmepp/tree/main/hf_models from hf_models import HfPCMEPPModel, tokenize processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch16") # IN-top1: 34.64% # model = HfPCMEPPModel.from_pretrained("SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps") # IN-top1: 41.81% model = HfPCMEPPModel.from_pretrained("SanghyukChun/PCMEPP-ViT-B-16-CC3M-12M-RedCaps-256M") url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt", padding=True) texts = ["a photo of a cat", "a photo of a dog"] texts = tokenize(texts) outputs = model(images=inputs["pixel_values"], texts=texts) print("Logits:", outputs["image_features"] @ outputs["text_features"].T) print("Image uncertainty: ", torch.exp(outputs["image_stds"]).mean(dim=-1)) print("Text uncertainty: ", torch.exp(outputs["text_stds"]).mean(dim=-1)) ``` ``` @inproceedings{ chun2024pcmepp, title={Improved Probabilistic Image-Text Representations}, author={Sanghyuk Chun}, booktitle={The Twelfth International Conference on Learning Representations}, year={2024}, url={https://openreview.net/forum?id=ft1mr3WlGM} } ```
abuer/modelabuer	abuer	2024-05-30T02:25:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-29T03:00:15Z	--- license: apache-2.0 ---
sebastiansarasti/Reinforce-CartPole-v1	sebastiansarasti	2024-05-30T02:25:32Z	0	0	null	[ "CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T02:10:15Z	--- tags: - CartPole-v1 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-CartPole-v1 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: CartPole-v1 type: CartPole-v1 metrics: - type: mean_reward value: 500.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
hdve/Qwen-Qwen1.5-7B-1717035718	hdve	2024-05-30T02:25:22Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T02:22:02Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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alecwangcq/Meta-Llama-3-8B-Instruct-sft	alecwangcq	2024-05-30T02:19:35Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T02:14:43Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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shane062/whisper-base-finetuned	shane062	2024-05-30T02:17:58Z	122	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-29T23:45:12Z	--- license: apache-2.0 base_model: openai/whisper-base tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: whisper-base-finetuned results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: test args: default metrics: - name: Wer type: wer value: 67.56756756756756 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-base-finetuned This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9952 - Wer Ortho: 67.5676 - Wer: 67.5676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 10 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.0652 \| 16.6667 \| 50 \| 0.9612 \| 67.5676 \| 67.5676 \| \| 0.0004 \| 33.3333 \| 100 \| 0.9952 \| 67.5676 \| 67.5676 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cpu - Datasets 2.19.1 - Tokenizers 0.19.1
BahaaEldin0/bert-base-uncased-reward-model	BahaaEldin0	2024-05-30T02:15:44Z	108	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-30T01:27:47Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Meta-Llama-3-8B-capitals-random-standardized-random-names	EleutherAI	2024-05-30T02:11:58Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T23:57:35Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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EleutherAI/Meta-Llama-3-8B-hemisphere-random-standardized-random-names	EleutherAI	2024-05-30T02:10:17Z	10	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T23:58:20Z	--- library_name: transformers tags: - trl - sft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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terry69/llama5p	terry69	2024-05-30T02:00:56Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "alignment-handbook", "trl", "sft", "generated_from_trainer", "conversational", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:meta-llama/Meta-Llama-3-8B", "base_model:finetune:meta-llama/Meta-Llama-3-8B", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T22:59:16Z	--- license: llama3 base_model: meta-llama/Meta-Llama-3-8B tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - generated_from_trainer datasets: - HuggingFaceH4/ultrachat_200k model-index: - name: llama5p results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama5p This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: 1.1369 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.8472 \| 1.0 \| 406 \| 1.1369 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

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