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train · 844k rows

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vhs01/mistral-7b-dolly	vhs01	2024-05-24T06:36:22Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-17T04:02:11Z	--- 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]
jayasuryajsk/phi-3-1B	jayasuryajsk	2024-05-24T06:36:22Z	279	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T06:34:13Z	--- 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. 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]
zgh36/HRoom	zgh36	2024-05-24T06:27:53Z	64	0	transformers	[ "transformers", "tf", "camembert", "fill-mask", "generated_from_keras_callback", "base_model:almanach/camembert-base", "base_model:finetune:almanach/camembert-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-24T06:04:29Z	--- license: mit base_model: camembert-base tags: - generated_from_keras_callback model-index: - name: HRoom results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # HRoom This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: ## 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: - optimizer: None - training_precision: float32 ### Training results ### Framework versions - Transformers 4.41.0 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
VaibhavSethia07/lora_model	VaibhavSethia07	2024-05-24T06:26:14Z	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	2024-05-16T09:08:55Z	--- 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: VaibhavSethia07 - 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)
AgeNtX071/EISproject	AgeNtX071	2024-05-24T06:26:07Z	0	1	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:Rocketknight1/falcon-rw-1b", "base_model:adapter:Rocketknight1/falcon-rw-1b", "region:us" ]	null	2024-05-24T05:48:29Z	--- library_name: peft tags: - trl - sft - generated_from_trainer base_model: Rocketknight1/falcon-rw-1b model-index: - name: EISproject 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. --> # EISproject This model is a fine-tuned version of [Rocketknight1/falcon-rw-1b](https://huggingface.co/Rocketknight1/falcon-rw-1b) 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: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 10 ### Training results ### Framework versions - PEFT 0.11.2.dev0 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Tokenizers 0.19.1
jiahuimbzuai/llava-v1.5-13b-vlguard-10k3e-lora	jiahuimbzuai	2024-05-24T06:25:49Z	2	0	peft	[ "peft", "safetensors", "llava_llama", "arxiv:1910.09700", "base_model:liuhaotian/llava-v1.5-13b", "base_model:adapter:liuhaotian/llava-v1.5-13b", "region:us" ]	null	2024-05-24T06:25:07Z	--- library_name: peft base_model: liuhaotian/llava-v1.5-13b --- # 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
jiahuimbzuai/llava-v1.5-7b-vlguard-10k3e-lora	jiahuimbzuai	2024-05-24T06:23:38Z	0	0	peft	[ "peft", "safetensors", "llava_llama", "arxiv:1910.09700", "base_model:liuhaotian/llava-v1.5-7b", "base_model:adapter:liuhaotian/llava-v1.5-7b", "region:us" ]	null	2024-05-24T06:23:14Z	--- library_name: peft base_model: liuhaotian/llava-v1.5-7b --- # 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
Amankankriya/ppo-Walker2d-v4	Amankankriya	2024-05-24T06:22:46Z	0	0	stable-baselines3	[ "stable-baselines3", "Walker2d-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-24T06:22:32Z	--- library_name: stable-baselines3 tags: - Walker2d-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Walker2d-v4 type: Walker2d-v4 metrics: - type: mean_reward value: 4275.64 +/- 20.73 name: mean_reward verified: false --- # PPO Agent playing Walker2d-v4 This is a trained model of a PPO agent playing Walker2d-v4 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 ... ```
seongs/maymust_mistral_v1.2	seongs	2024-05-24T06:19:33Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T05:35:02Z	--- 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. 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]
hgnoi/yVI8CyzLP2PQEdVp	hgnoi	2024-05-24T06:19:20Z	135	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T06:17:37Z	--- 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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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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ghost613/gemma7_on_korean_conv	ghost613	2024-05-24T06:17:42Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:beomi/gemma-ko-7b", "base_model:adapter:beomi/gemma-ko-7b", "license:other", "region:us" ]	null	2024-05-24T06:17:16Z	--- license: other library_name: peft tags: - generated_from_trainer base_model: beomi/gemma-ko-7b model-index: - name: gemma7_on_korean_conv 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. --> # gemma7_on_korean_conv This model is a fine-tuned version of [beomi/gemma-ko-7b](https://huggingface.co/beomi/gemma-ko-7b) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.7037 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 5 - total_train_batch_size: 5 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 7200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.8472 \| 0.1281 \| 200 \| 0.8849 \| \| 0.7799 \| 0.2563 \| 400 \| 0.8185 \| \| 0.7791 \| 0.3844 \| 600 \| 0.7945 \| \| 0.7505 \| 0.5126 \| 800 \| 0.7877 \| \| 0.7891 \| 0.6407 \| 1000 \| 0.7616 \| \| 0.689 \| 0.7688 \| 1200 \| 0.7437 \| \| 0.7612 \| 0.8970 \| 1400 \| 0.7520 \| \| 0.5183 \| 1.0251 \| 1600 \| 0.8028 \| \| 0.4562 \| 1.1533 \| 1800 \| 0.7811 \| \| 0.4584 \| 1.2814 \| 2000 \| 0.7920 \| \| 0.4535 \| 1.4095 \| 2200 \| 0.7887 \| \| 0.4268 \| 1.5377 \| 2400 \| 0.8048 \| \| 0.4368 \| 1.6658 \| 2600 \| 0.7640 \| \| 0.4435 \| 1.7940 \| 2800 \| 0.7844 \| \| 0.4327 \| 1.9221 \| 3000 \| 0.7977 \| \| 0.1711 \| 2.0502 \| 3200 \| 1.0313 \| \| 0.1856 \| 2.1784 \| 3400 \| 0.9997 \| \| 0.1812 \| 2.3065 \| 3600 \| 0.9870 \| \| 0.1876 \| 2.4346 \| 3800 \| 0.9731 \| \| 0.1927 \| 2.5628 \| 4000 \| 0.9857 \| \| 0.1964 \| 2.6909 \| 4200 \| 1.0148 \| \| 0.1948 \| 2.8191 \| 4400 \| 1.0025 \| \| 0.1865 \| 2.9472 \| 4600 \| 1.0556 \| \| 0.059 \| 3.0753 \| 4800 \| 1.3127 \| \| 0.0523 \| 3.2035 \| 5000 \| 1.3947 \| \| 0.0658 \| 3.3316 \| 5200 \| 1.3980 \| \| 0.0596 \| 3.4598 \| 5400 \| 1.3785 \| \| 0.0556 \| 3.5879 \| 5600 \| 1.3936 \| \| 0.0709 \| 3.7160 \| 5800 \| 1.3858 \| \| 0.0544 \| 3.8442 \| 6000 \| 1.3943 \| \| 0.0503 \| 3.9723 \| 6200 \| 1.4319 \| \| 0.0133 \| 4.1005 \| 6400 \| 1.6485 \| \| 0.0144 \| 4.2286 \| 6600 \| 1.6932 \| \| 0.0126 \| 4.3567 \| 6800 \| 1.6980 \| \| 0.0189 \| 4.4849 \| 7000 \| 1.6962 \| \| 0.0128 \| 4.6130 \| 7200 \| 1.7037 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
AlikS/LunarLander-v2	AlikS	2024-05-24T06:14:03Z	0	0	null	[ "tensorboard", "LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course", "model-index", "region:us" ]	reinforcement-learning	2024-05-24T06:00:22Z	--- tags: - LunarLander-v2 - ppo - deep-reinforcement-learning - reinforcement-learning - custom-implementation - deep-rl-course model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -9.61 +/- 72.61 name: mean_reward verified: false --- # PPO Agent Playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2. # Hyperparameters ```python {'f': '/root/.local/share/jupyter/runtime/kernel-9e92468f-89d2-40b3-805a-092b95556d32.json' 'exp_name': 'ppo-LunarLander' 'seed': 1 'torch_deterministic': True 'cuda': True 'track': False 'wandb_project_name': 'cleanRL' 'wandb_entity': None 'capture_video': False 'env_id': 'LunarLander-v2' 'total_timesteps': 200000 'learning_rate': 0.00025 'num_envs': 4 'num_steps': 128 'anneal_lr': True 'gae': True 'gamma': 0.99 'gae_lambda': 0.95 'num_minibatches': 4 'update_epochs': 4 'norm_adv': True 'clip_coef': 0.2 'clip_vloss': True 'ent_coef': 0.01 'vf_coef': 0.5 'max_grad_norm': 0.5 'target_kl': None 'repo_id': 'AlikS/LunarLander-v2' 'batch_size': 512 'minibatch_size': 128} ```
sanqiang/zephyr-7b-gemma-dpo	sanqiang	2024-05-24T06:08:14Z	10	0	transformers	[ "transformers", "tensorboard", "safetensors", "gemma", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:argilla/dpo-mix-7k", "base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "base_model:finetune:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-21T06:34:11Z	--- license: other base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1 tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - argilla/dpo-mix-7k model-index: - name: zephyr-7b-gemma-dpo 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. --> # zephyr-7b-gemma-dpo This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k 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: 5e-07 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1
yrdu/mogu-safer-vicuna-7B	yrdu	2024-05-24T06:04:45Z	0	0	null	[ "safetensors", "arxiv:2405.14488", "license:apache-2.0", "region:us" ]	null	2024-05-23T10:24:21Z	--- license: apache-2.0 --- #### Paper MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability [ https://arxiv.org/abs/2405.14488 ] Github: [ https://github.com/DYR1/MoGU/ ] #### Introduction We open-source the safer vicuna-7b-v1.5 [ https://huggingface.co/lmsys/vicuna-7b-v1.5 ], trained with our proposed MoGU framework. Technical details of MoGU can be found in the paper ''MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability''. We release the parameters and the inference code. The current inference code is still in a simple version and we will further improve it. In the future, we plan to open-source the training data and the training code. You can use our open-sourced LLM by following the steps below. #### Configuration Environment ``` pip install -r requirement.txt ``` #### Inference Stage ``` python inference.py ``` Some test examples are provided in inference.py.
yrdu/mogu-safer-llama2-7B	yrdu	2024-05-24T06:04:15Z	0	0	null	[ "safetensors", "arxiv:2405.14488", "license:apache-2.0", "region:us" ]	null	2024-05-23T07:31:16Z	--- license: apache-2.0 --- #### Paper MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability [ https://arxiv.org/abs/2405.14488 ] Github: [ https://github.com/DYR1/MoGU/ ] #### Introduction We open-source the safer Llama-2-7b-chat-hf [ https://huggingface.co/meta-llama/Llama-2-7b-chat-hf ], trained with our proposed MoGU framework. Technical details of MoGU can be found in the paper ''MoGU: A Framework for Enhancing Safety of Open-Sourced LLMs While Preserving Their Usability''. We release the parameters and the inference code. The current inference code is still in a simple version and we will further improve it. In the future, we plan to open-source the training data and the training code. You can use our open-sourced LLM by following the steps below. ##### Configuration Environment ``` pip install -r requirement.txt ``` ##### Inference Stage ``` python inference.py ``` Some test examples are provided in inference.py.
sanjeev-bhandari01/phi-3-small-sft-lora	sanjeev-bhandari01	2024-05-24T06:02:25Z	0	0	null	[ "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:microsoft/Phi-3-mini-128k-instruct", "base_model:finetune:microsoft/Phi-3-mini-128k-instruct", "license:mit", "region:us" ]	null	2024-05-24T05:42:37Z	--- license: mit tags: - trl - sft - generated_from_trainer base_model: microsoft/Phi-3-mini-128k-instruct datasets: - generator model-index: - name: phi-3-small-sft-lora 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. --> # phi-3-small-sft-lora This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.2964 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 128 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6299 \| 1.0 \| 1 \| 1.2966 \| \| 0.6065 \| 1.9692 \| 2 \| 1.2964 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
amztheory/code-python	amztheory	2024-05-24T06:00:37Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:tiiuae/falcon-7b", "base_model:adapter:tiiuae/falcon-7b", "license:apache-2.0", "region:us" ]	null	2024-05-24T05:14:24Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: tiiuae/falcon-7b model-index: - name: code-python 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. --> # code-python This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1593 ## 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: 6 - eval_batch_size: 6 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 24 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.4825 \| 0.3183 \| 30 \| 1.5136 \| \| 1.4361 \| 0.6366 \| 60 \| 1.4485 \| \| 1.3649 \| 0.9549 \| 90 \| 1.3628 \| \| 1.3254 \| 1.2732 \| 120 \| 1.2862 \| \| 1.1969 \| 1.5915 \| 150 \| 1.2227 \| \| 1.2245 \| 1.9098 \| 180 \| 1.1776 \| \| 1.1604 \| 2.2281 \| 210 \| 1.1626 \| \| 1.1737 \| 2.5464 \| 240 \| 1.1594 \| \| 1.1012 \| 2.8647 \| 270 \| 1.1593 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
hgnoi/XfvVyaU3xPbDT0Fx	hgnoi	2024-05-24T05:55:31Z	133	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T05:53:55Z	--- 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]
binayakkoirala/outputs	binayakkoirala	2024-05-24T05:52:11Z	1	1	peft	[ "peft", "tensorboard", "safetensors", "gguf", "mistral", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:unsloth/mistral-7b-bnb-4bit", "base_model:adapter:unsloth/mistral-7b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-23T10:26:11Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - unsloth - generated_from_trainer base_model: unsloth/mistral-7b-bnb-4bit model-index: - name: outputs 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. --> # outputs This model is a fine-tuned version of [unsloth/mistral-7b-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-bnb-4bit) 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: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 60 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf	RichardErkhov	2024-05-24T05:48:15Z	93	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-24T02:45:16Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) LuminRP-7B-128k-v0.2 - GGUF - Model creator: https://huggingface.co/Ppoyaa/ - Original model: https://huggingface.co/Ppoyaa/LuminRP-7B-128k-v0.2/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [LuminRP-7B-128k-v0.2.Q2_K.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [LuminRP-7B-128k-v0.2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [LuminRP-7B-128k-v0.2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [LuminRP-7B-128k-v0.2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [LuminRP-7B-128k-v0.2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [LuminRP-7B-128k-v0.2.Q3_K.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [LuminRP-7B-128k-v0.2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [LuminRP-7B-128k-v0.2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [LuminRP-7B-128k-v0.2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [LuminRP-7B-128k-v0.2.Q4_0.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [LuminRP-7B-128k-v0.2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [LuminRP-7B-128k-v0.2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [LuminRP-7B-128k-v0.2.Q4_K.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [LuminRP-7B-128k-v0.2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [LuminRP-7B-128k-v0.2.Q4_1.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [LuminRP-7B-128k-v0.2.Q5_0.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [LuminRP-7B-128k-v0.2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [LuminRP-7B-128k-v0.2.Q5_K.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [LuminRP-7B-128k-v0.2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [LuminRP-7B-128k-v0.2.Q5_1.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [LuminRP-7B-128k-v0.2.Q6_K.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [LuminRP-7B-128k-v0.2.Q8_0.gguf](https://huggingface.co/RichardErkhov/Ppoyaa_-_LuminRP-7B-128k-v0.2-gguf/blob/main/LuminRP-7B-128k-v0.2.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- tags: - merge - mergekit - lazymergekit license: apache-2.0 --- # LuminRP-7B-128k-v0.2 LuminRP-7B-128k-v0.2 is a merge of four RP models into one using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing). This is a model that is purely for roleplaying and uses a context window of 128k. # Example Response: I use the ChatML template for this with Instruct Mode enabled. Mistral template is okay to use as well, but I don't recommend Alpaca-Roleplay because it just keeps going. Most likely because the Alpaca-Roleplay template doesn't have a message suffix. ![Screenshot (2).png](https://cdn-uploads.huggingface.co/production/uploads/65f158693196560d34495d54/YjAu6jV6s7APC2jehZmqg.png) # Quantized Version GGUF: [Ppoyaa/LuminRP-7B-128k-v0.2-GGUF](https://huggingface.co/Ppoyaa/LuminRP-7B-128k-v0.2-GGUF) # 🏆 Open LLM Leaderboard Evaluation Results \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|73.18\| \|AI2 Reasoning Challenge (25-Shot)\|70.56\| \|HellaSwag (10-Shot) \|87.46\| \|MMLU (5-Shot) \|64.92\| \|TruthfulQA (0-shot) \|65.78\| \|Winogrande (5-shot) \|82.40\| \|GSM8k (5-shot) \|67.93\| ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Ppoyaa/LuminRP-7B-128k-v0.2" 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"]) ```
damgomz/ft_8_2e6_mlm_cv	damgomz	2024-05-24T05:40:04Z	107	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-23T10:50:47Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-24T07:40:00' project_name: ft_8_2e6_mlm_cv_emissions_tracker run_id: cea1c3a0-30d8-4d7f-8455-62cb78a3d198 duration: 70137.06294441223 emissions: 0.0424410221797307 emissions_rate: 6.051154752996677e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.8280053444213326 gpu_energy: 0 ram_energy: 0.0730587981601555 energy_consumed: 0.9010641425814891 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 70137.06294441223 \| \| Emissions (Co2eq in kg) \| 0.0424410221797307 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.8280053444213326 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.0730587981601555 \| \| Consumed energy (kWh) \| 0.9010641425814891 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.13501384616799353 \| \| Emissions (Co2eq in kg) \| 0.027470349653228122 \| ## Note 21 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_8_2e6_mlm_cv \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 2e-06 \| \| batch_size \| 8 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32586 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.471943 \| 0.379341 \| 0.827490 \| 0.862092 \| \| 1 \| 0.341136 \| 0.345278 \| 0.845166 \| 0.871145 \| \| 2 \| 0.304972 \| 0.345030 \| 0.845167 \| 0.880203 \| \| 3 \| 0.269654 \| 0.349133 \| 0.847524 \| 0.862721 \| \| 4 \| 0.229831 \| 0.367654 \| 0.841041 \| 0.847553 \| \| 5 \| 0.180061 \| 0.407419 \| 0.834705 \| 0.867764 \|
Hyeyoon/OPEN-SOLAR-KO-10.7B-NLP	Hyeyoon	2024-05-24T05:34:56Z	76	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-24T05:31:28Z	--- 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]
mradermacher/WizardLM-2-7B-abliterated-GGUF	mradermacher	2024-05-24T05:34:35Z	81	2	transformers	[ "transformers", "gguf", "en", "base_model:fearlessdots/WizardLM-2-7B-abliterated", "base_model:quantized:fearlessdots/WizardLM-2-7B-abliterated", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-24T04:09:57Z	--- base_model: fearlessdots/WizardLM-2-7B-abliterated language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/fearlessdots/WizardLM-2-7B-abliterated <!-- 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/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/WizardLM-2-7B-abliterated-GGUF/resolve/main/WizardLM-2-7B-abliterated.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 -->
damgomz/ft_8_4e6_mlm_cv	damgomz	2024-05-24T05:32:13Z	107	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-23T10:48:35Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-24T07:32:11' project_name: ft_8_4e6_mlm_cv_emissions_tracker run_id: 540c605b-00bf-4a6b-9831-e338e3f3ffd0 duration: 69752.5319519043 emissions: 0.0422083433539418 emissions_rate: 6.051155731958987e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.8234658257799006 gpu_energy: 0 ram_energy: 0.072658319226404 energy_consumed: 0.8961241450063017 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 69752.5319519043 \| \| Emissions (Co2eq in kg) \| 0.0422083433539418 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.8234658257799006 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.072658319226404 \| \| Consumed energy (kWh) \| 0.8961241450063017 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.13427362400741574 \| \| Emissions (Co2eq in kg) \| 0.027319741681162513 \| ## Note 21 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_8_4e6_mlm_cv \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 4e-06 \| \| batch_size \| 8 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32586 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.420520 \| 0.346607 \| 0.847082 \| 0.877867 \| \| 1 \| 0.318169 \| 0.338375 \| 0.845903 \| 0.855383 \| \| 2 \| 0.266432 \| 0.351313 \| 0.844136 \| 0.855602 \| \| 3 \| 0.199349 \| 0.386016 \| 0.838390 \| 0.867479 \| \| 4 \| 0.111738 \| 0.455603 \| 0.836033 \| 0.839846 \| \| 5 \| 0.045670 \| 0.558804 \| 0.831907 \| 0.844940 \|
Stern5497/org_modelorg_model	Stern5497	2024-05-24T05:28:51Z	1	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-v0.1", "base_model:adapter:mistralai/Mistral-7B-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-10T20:29:53Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: org_modelorg_model 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. --> # org_modelorg_model This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0305 - F1 Micro: 0.7988 - F1 Macro: 0.7745 - F1 Weighted: 0.8091 ## 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: linear - training_steps: 400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 Micro \| F1 Macro \| F1 Weighted \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:--------:\|:-----------:\| \| 1.7847 \| 0.0064 \| 25 \| 1.4983 \| 0.7827 \| 0.7547 \| 0.7929 \| \| 1.3333 \| 0.0127 \| 50 \| 1.2986 \| 0.7926 \| 0.7660 \| 0.8031 \| \| 1.2721 \| 0.0191 \| 75 \| 1.2255 \| 0.7755 \| 0.7520 \| 0.7862 \| \| 1.127 \| 0.0255 \| 100 \| 1.1722 \| 0.7945 \| 0.7694 \| 0.8053 \| \| 1.1108 \| 0.0318 \| 125 \| 1.1561 \| 0.7922 \| 0.7556 \| 0.7971 \| \| 1.0969 \| 0.0382 \| 150 \| 1.1181 \| 0.7875 \| 0.7581 \| 0.7955 \| \| 1.0714 \| 0.0446 \| 175 \| 1.1001 \| 0.7884 \| 0.7658 \| 0.7993 \| \| 1.0219 \| 0.0510 \| 200 \| 1.0758 \| 0.8000 \| 0.7727 \| 0.8091 \| \| 1.0979 \| 0.0573 \| 225 \| 1.0671 \| 0.7973 \| 0.7656 \| 0.8040 \| \| 1.0846 \| 0.0637 \| 250 \| 1.0632 \| 0.7866 \| 0.7582 \| 0.7944 \| \| 0.9977 \| 0.0701 \| 275 \| 1.0590 \| 0.7934 \| 0.7600 \| 0.7991 \| \| 1.1262 \| 0.0764 \| 300 \| 1.0404 \| 0.7984 \| 0.7699 \| 0.8066 \| \| 1.0066 \| 0.0828 \| 325 \| 1.0396 \| 0.7981 \| 0.7681 \| 0.8053 \| \| 1.0534 \| 0.0892 \| 350 \| 1.0360 \| 0.8005 \| 0.7768 \| 0.8113 \| \| 1.0302 \| 0.0955 \| 375 \| 1.0320 \| 0.7993 \| 0.7754 \| 0.8099 \| \| 1.0965 \| 0.1019 \| 400 \| 1.0305 \| 0.7988 \| 0.7745 \| 0.8091 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.3.0+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
Zoyd/CohereForAI_aya-23-8B-8_0bpw_exl2	Zoyd	2024-05-24T05:10:26Z	6	1	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "8-bit", "exl2", "region:us" ]	text-generation	2024-05-24T05:03:00Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- Exllamav2 quant (exl2 / 8.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_2bpw_exl2)</center> \| <center>4898 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_5bpw_exl2)</center> \| <center>5185 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_0bpw_exl2)</center> \| <center>5664 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_5bpw_exl2)</center> \| <center>6142 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_75bpw_exl2)</center> \| <center>6382 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_0bpw_exl2)</center> \| <center>6620 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_25bpw_exl2)</center> \| <center>6860 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-5_0bpw_exl2)</center> \| <center>7576 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_0bpw_exl2)</center> \| <center>8742 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_5bpw_exl2)</center> \| <center>9212 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-8_0bpw_exl2)</center> \| <center>9691 MB</center> \| <center>8</center> \| # Model Card for Aya-23-8B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 8-billion version of the Aya 23 model. We also released a 35-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-35B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 8 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install transformers==4.41.1 from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-8B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-8B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} } ```
tjasad/translation_slo_eng_opus-mt-sla-en_lora	tjasad	2024-05-24T05:09:13Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-sla-en", "base_model:adapter:Helsinki-NLP/opus-mt-sla-en", "license:apache-2.0", "region:us" ]	null	2024-05-21T16:55:30Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: Helsinki-NLP/opus-mt-sla-en metrics: - bleu model-index: - name: translation_slo_eng_opus-mt-sla-en_lora 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. --> # translation_slo_eng_opus-mt-sla-en_lora This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sla-en](https://huggingface.co/Helsinki-NLP/opus-mt-sla-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7472 - Bleu: 34.2191 - Gen Len: 12.4026 ## 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 1.8995 \| 1.0 \| 2500 \| 1.7472 \| 34.2191 \| 12.4026 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
hgnoi/pVr3Z630rkj39xic	hgnoi	2024-05-24T05:08:32Z	78	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T05:06:56Z	--- 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. 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Zoyd/CohereForAI_aya-23-8B-6_0bpw_exl2	Zoyd	2024-05-24T05:03:50Z	8	2	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-24T04:44:14Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- Exllamav2 quant (exl2 / 6.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_2bpw_exl2)</center> \| <center>4898 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_5bpw_exl2)</center> \| <center>5185 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_0bpw_exl2)</center> \| <center>5664 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_5bpw_exl2)</center> \| <center>6142 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_75bpw_exl2)</center> \| <center>6382 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_0bpw_exl2)</center> \| <center>6620 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_25bpw_exl2)</center> \| <center>6860 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-5_0bpw_exl2)</center> \| <center>7576 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_0bpw_exl2)</center> \| <center>8742 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_5bpw_exl2)</center> \| <center>9212 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-8_0bpw_exl2)</center> \| <center>9691 MB</center> \| <center>8</center> \| # Model Card for Aya-23-8B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 8-billion version of the Aya 23 model. We also released a 35-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-35B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 8 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install transformers==4.41.1 from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-8B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-8B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} } ```
Zoyd/CohereForAI_aya-23-8B-4_0bpw_exl2	Zoyd	2024-05-24T05:03:43Z	5	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "exl2", "region:us" ]	text-generation	2024-05-24T04:16:18Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- Exllamav2 quant (exl2 / 4.0 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_2bpw_exl2)</center> \| <center>4898 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_5bpw_exl2)</center> \| <center>5185 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_0bpw_exl2)</center> \| <center>5664 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_5bpw_exl2)</center> \| <center>6142 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_75bpw_exl2)</center> \| <center>6382 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_0bpw_exl2)</center> \| <center>6620 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_25bpw_exl2)</center> \| <center>6860 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-5_0bpw_exl2)</center> \| <center>7576 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_0bpw_exl2)</center> \| <center>8742 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_5bpw_exl2)</center> \| <center>9212 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-8_0bpw_exl2)</center> \| <center>9691 MB</center> \| <center>8</center> \| # Model Card for Aya-23-8B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 8-billion version of the Aya 23 model. We also released a 35-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-35B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 8 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install transformers==4.41.1 from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-8B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-8B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} } ```
Zoyd/CohereForAI_aya-23-8B-3_75bpw_exl2	Zoyd	2024-05-24T05:03:40Z	7	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-24T04:06:53Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- Exllamav2 quant (exl2 / 3.75 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_2bpw_exl2)</center> \| <center>4898 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_5bpw_exl2)</center> \| <center>5185 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_0bpw_exl2)</center> \| <center>5664 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_5bpw_exl2)</center> \| <center>6142 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_75bpw_exl2)</center> \| <center>6382 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_0bpw_exl2)</center> \| <center>6620 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_25bpw_exl2)</center> \| <center>6860 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-5_0bpw_exl2)</center> \| <center>7576 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_0bpw_exl2)</center> \| <center>8742 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_5bpw_exl2)</center> \| <center>9212 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-8_0bpw_exl2)</center> \| <center>9691 MB</center> \| <center>8</center> \| # Model Card for Aya-23-8B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 8-billion version of the Aya 23 model. We also released a 35-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-35B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 8 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install transformers==4.41.1 from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-8B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-8B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} } ```
Zoyd/CohereForAI_aya-23-8B-2_5bpw_exl2	Zoyd	2024-05-24T05:03:29Z	6	0	transformers	[ "transformers", "safetensors", "cohere", "text-generation", "conversational", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-24T03:39:03Z	--- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- Exllamav2 quant (exl2 / 2.5 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_2bpw_exl2)</center> \| <center>4898 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-2_5bpw_exl2)</center> \| <center>5185 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_0bpw_exl2)</center> \| <center>5664 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_5bpw_exl2)</center> \| <center>6142 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-3_75bpw_exl2)</center> \| <center>6382 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_0bpw_exl2)</center> \| <center>6620 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-4_25bpw_exl2)</center> \| <center>6860 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-5_0bpw_exl2)</center> \| <center>7576 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_0bpw_exl2)</center> \| <center>8742 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-6_5bpw_exl2)</center> \| <center>9212 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/CohereForAI_aya-23-8B-8_0bpw_exl2)</center> \| <center>9691 MB</center> \| <center>8</center> \| # Model Card for Aya-23-8B ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 8-billion version of the Aya 23 model. We also released a 35-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-35B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-8B - Model Size: 8 billion parameters Try Aya 23 You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install transformers==4.41.1 from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><\|START_OF_TURN_TOKEN\|><\|USER_TOKEN\|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<\|END_OF_TURN_TOKEN\|><\|START_OF_TURN_TOKEN\|><\|CHATBOT_TOKEN\|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-8B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: Aya-23-8B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. Languages covered: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Context length: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact [email protected]. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aya23technicalreport, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi, John Dang, Dwarak Talupuru, Saurabh Dash, David Cairuz, Hangyu Lin, Bharat Venkitesh, Madeline Smith, Kelly Marchisio, Sebastian Ruder, Acyr Locatelli, Julia Kreutzer, Nick Frosst, Phil Blunsom, Marzieh Fadaee, Ahmet Üstün, and Sara Hooker}, url={https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23}, year={2024} } ```
mradermacher/MuMath-Code-CL-34B-GGUF	mradermacher	2024-05-24T05:00:46Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:weihao1/MuMath-Code-CL-34B", "base_model:quantized:weihao1/MuMath-Code-CL-34B", "endpoints_compatible", "region:us" ]	null	2024-05-24T00:47:11Z	--- base_model: weihao1/MuMath-Code-CL-34B language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/weihao1/MuMath-Code-CL-34B <!-- 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/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q2_K.gguf) \| Q2_K \| 12.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.IQ3_XS.gguf) \| IQ3_XS \| 14.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q3_K_S.gguf) \| Q3_K_S \| 14.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.IQ3_S.gguf) \| IQ3_S \| 14.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.IQ3_M.gguf) \| IQ3_M \| 15.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q3_K_M.gguf) \| Q3_K_M \| 16.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q3_K_L.gguf) \| Q3_K_L \| 17.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.IQ4_XS.gguf) \| IQ4_XS \| 18.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q4_K_S.gguf) \| Q4_K_S \| 19.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q4_K_M.gguf) \| Q4_K_M \| 20.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q5_K_S.gguf) \| Q5_K_S \| 23.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q5_K_M.gguf) \| Q5_K_M \| 23.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q6_K.gguf) \| Q6_K \| 27.8 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-34B-GGUF/resolve/main/MuMath-Code-CL-34B.Q8_0.gguf) \| Q8_0 \| 36.0 \| 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 -->
xaviviro/Lorca-LLama3-8B-GGUF	xaviviro	2024-05-24T05:00:37Z	7	0	null	[ "gguf", "lorca", "poemas", "canciones", "es", "en", "dataset:xaviviro/FEDERICO-GARCIA-LORCA-canciones-poemas-romances", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-24T04:36:03Z	--- license: apache-2.0 datasets: - xaviviro/FEDERICO-GARCIA-LORCA-canciones-poemas-romances language: - es - en tags: - lorca - poemas - canciones --- # Lorca Llama 3 ## LM Studio preset: ``` Llama 3 ``` ## System prompt: ``` You are an advanced language model trained to write poems in the style of Federico García Lorca in Spanish. When given some themes, generate an original, well-structured poem in Spanish that reflects Lorca's unique style. ``` ## Prompt: ``` Escribe un poema basado en estos temas en el estilo de Federico García Lorca: [TEMAS] ``` ## Ejemplo de prompt: ``` Escribe un poema basado en estos temas en el estilo de Federico García Lorca: Barcelona, gentrificación, ciudad vendida, turismo de borrachera ``` ![ejemplo](./ejemplo.png)
tdnathmlenthusiast/mistral_instruct_generation	tdnathmlenthusiast	2024-05-24T04:58:04Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2024-05-23T20:09:53Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 datasets: - generator model-index: - name: mistral_instruct_generation 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. --> # mistral_instruct_generation This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.0639 ## 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: constant - lr_scheduler_warmup_steps: 0.03 - training_steps: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.2713 \| 1.8182 \| 20 \| 0.1395 \| \| 0.0972 \| 3.6364 \| 40 \| 0.0639 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
rawsem/buffettgpt-ft	rawsem	2024-05-24T04:56:33Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ", "license:apache-2.0", "region:us" ]	null	2024-05-24T04:56:32Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ model-index: - name: buffettgpt-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. --> # buffettgpt-ft This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6124 ## 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: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.031 \| 0.992 \| 31 \| 0.8610 \| \| 0.8683 \| 1.984 \| 62 \| 0.6911 \| \| 0.7677 \| 2.976 \| 93 \| 0.5970 \| \| 0.7038 \| 4.0 \| 125 \| 0.5716 \| \| 0.6997 \| 4.992 \| 156 \| 0.5594 \| \| 0.6701 \| 5.984 \| 187 \| 0.5642 \| \| 0.6402 \| 6.976 \| 218 \| 0.5723 \| \| 0.5945 \| 8.0 \| 250 \| 0.5855 \| \| 0.5881 \| 8.992 \| 281 \| 0.6030 \| \| 0.5596 \| 9.92 \| 310 \| 0.6124 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.1.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
damgomz/ft_16_4e6_mlm_cv	damgomz	2024-05-24T04:54:07Z	99	0	transformers	[ "transformers", "safetensors", "albert", "text-classification", "fill-mask", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-23T10:46:37Z	--- language: en tags: - fill-mask kwargs: timestamp: '2024-05-24T06:54:05' project_name: ft_16_4e6_mlm_cv_emissions_tracker run_id: 3f56a463-50e7-4188-bb92-9e3115763b7d duration: 67498.73499298096 emissions: 0.0408445349436233 emissions_rate: 6.051155617637977e-07 cpu_power: 42.5 gpu_power: 0.0 ram_power: 3.75 cpu_energy: 0.7968585600329781 gpu_energy: 0 ram_energy: 0.070310607152929 energy_consumed: 0.8671691671859065 country_name: Switzerland country_iso_code: CHE region: .nan cloud_provider: .nan cloud_region: .nan os: Linux-5.14.0-70.30.1.el9_0.x86_64-x86_64-with-glibc2.34 python_version: 3.10.4 codecarbon_version: 2.3.4 cpu_count: 2 cpu_model: Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz gpu_count: .nan gpu_model: .nan longitude: .nan latitude: .nan ram_total_size: 10 tracking_mode: machine on_cloud: N pue: 1.0 --- ## Environmental Impact (CODE CARBON DEFAULT) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| Duration (in seconds) \| 67498.73499298096 \| \| Emissions (Co2eq in kg) \| 0.0408445349436233 \| \| CPU power (W) \| 42.5 \| \| GPU power (W) \| [No GPU] \| \| RAM power (W) \| 3.75 \| \| CPU energy (kWh) \| 0.7968585600329781 \| \| GPU energy (kWh) \| [No GPU] \| \| RAM energy (kWh) \| 0.070310607152929 \| \| Consumed energy (kWh) \| 0.8671691671859065 \| \| Country name \| Switzerland \| \| Cloud provider \| nan \| \| Cloud region \| nan \| \| CPU count \| 2 \| \| CPU model \| Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz \| \| GPU count \| nan \| \| GPU model \| nan \| ## Environmental Impact (for one core) \| Metric \| Value \| \|--------------------------\|---------------------------------\| \| CPU energy (kWh) \| 0.12993506486148834 \| \| Emissions (Co2eq in kg) \| 0.02643700453891754 \| ## Note 21 May 2024 ## My Config \| Config \| Value \| \|--------------------------\|-----------------\| \| checkpoint \| damgomz/ThunBERT_bs16_lr5_MLM \| \| model_name \| ft_16_4e6_mlm_cv \| \| sequence_length \| 400 \| \| num_epoch \| 6 \| \| learning_rate \| 4e-06 \| \| batch_size \| 16 \| \| weight_decay \| 0.0 \| \| warm_up_prop \| 0.0 \| \| drop_out_prob \| 0.1 \| \| packing_length \| 100 \| \| train_test_split \| 0.2 \| \| num_steps \| 32586 \| ## Training and Testing steps Epoch \| Train Loss \| Test Loss \| Accuracy \| Recall ---\|---\|---\|---\|--- \| 0 \| 0.453056 \| 0.355866 \| 0.842959 \| 0.859240 \| \| 1 \| 0.330308 \| 0.346280 \| 0.843838 \| 0.853032 \| \| 2 \| 0.289168 \| 0.346006 \| 0.844134 \| 0.862091 \| \| 3 \| 0.240538 \| 0.363388 \| 0.843545 \| 0.864176 \| \| 4 \| 0.178344 \| 0.397027 \| 0.838242 \| 0.856995 \| \| 5 \| 0.105971 \| 0.492622 \| 0.827195 \| 0.818774 \|
OwOpeepeepoopoo/LittleJerry11	OwOpeepeepoopoo	2024-05-24T04:53:50Z	85	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T04:52:35Z	--- 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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hgyukang/test-8b-2	hgyukang	2024-05-24T04:50:59Z	70	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-24T04:47:52Z	--- 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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hgnoi/dJE9JNlxEtPNDZTo	hgnoi	2024-05-24T04:45:16Z	80	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T04:41:25Z	--- 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. 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Jellywibble/ChateauLafite8BLORA	Jellywibble	2024-05-24T04:40:23Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T15:40:40Z	--- 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]
tjasad/translation_slo_eng_opus-mt-sla-en_prompt_tuning	tjasad	2024-05-24T04:32:16Z	33	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:Helsinki-NLP/opus-mt-sla-en", "base_model:adapter:Helsinki-NLP/opus-mt-sla-en", "license:apache-2.0", "region:us" ]	null	2024-05-20T06:40:28Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: Helsinki-NLP/opus-mt-sla-en metrics: - bleu model-index: - name: translation_slo_eng_opus-mt-sla-en_prompt_tuning 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. --> # translation_slo_eng_opus-mt-sla-en_prompt_tuning This model is a fine-tuned version of [Helsinki-NLP/opus-mt-sla-en](https://huggingface.co/Helsinki-NLP/opus-mt-sla-en) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 5.1830 - Bleu: 0.3807 - Gen Len: 13.9075 ## 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: 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| 5.4024 \| 1.0 \| 2500 \| 5.1830 \| 0.3807 \| 13.9075 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
raulgdp/Mistral-clasificacion-multilabel	raulgdp	2024-05-24T04:29:09Z	123	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:NazaGara/NER-fine-tuned-BETO", "base_model:finetune:NazaGara/NER-fine-tuned-BETO", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-09T17:41:49Z	--- license: cc-by-4.0 base_model: NazaGara/NER-fine-tuned-BETO tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: NER-finetuning-BETO 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. --> # NER-finetuning-BETO Este es el modelo de BETO para NER [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2140 - Precision: 0.8424 - Recall: 0.8545 - F1: 0.8484 - Accuracy: 0.9691 ## 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 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0482 \| 1.0 \| 1041 \| 0.1522 \| 0.8309 \| 0.8481 \| 0.8394 \| 0.9687 \| \| 0.0302 \| 2.0 \| 2082 \| 0.1661 \| 0.8293 \| 0.8527 \| 0.8408 \| 0.9696 \| \| 0.0164 \| 3.0 \| 3123 \| 0.1691 \| 0.8403 \| 0.8536 \| 0.8469 \| 0.9696 \| \| 0.011 \| 4.0 \| 4164 \| 0.2026 \| 0.8427 \| 0.8516 \| 0.8471 \| 0.9693 \| \| 0.0073 \| 5.0 \| 5205 \| 0.2140 \| 0.8424 \| 0.8545 \| 0.8484 \| 0.9691 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
trungtienluong/23cau10epochvacacthongsokhac	trungtienluong	2024-05-24T04:28:38Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:vilm/vinallama-7b-chat", "base_model:adapter:vilm/vinallama-7b-chat", "license:llama2", "region:us" ]	null	2024-05-24T04:28:30Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: vilm/vinallama-7b-chat model-index: - name: experiments 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. --> # experiments This model is a fine-tuned version of [vilm/vinallama-7b-chat](https://huggingface.co/vilm/vinallama-7b-chat) 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: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - 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_ratio: 0.1 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.36.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf	RichardErkhov	2024-05-24T04:27:59Z	7	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-24T01:05:24Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) DPOpenHermes-11B - GGUF - Model creator: https://huggingface.co/openaccess-ai-collective/ - Original model: https://huggingface.co/openaccess-ai-collective/DPOpenHermes-11B/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [DPOpenHermes-11B.Q2_K.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q2_K.gguf) \| Q2_K \| 3.73GB \| \| [DPOpenHermes-11B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.IQ3_XS.gguf) \| IQ3_XS \| 4.14GB \| \| [DPOpenHermes-11B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.IQ3_S.gguf) \| IQ3_S \| 4.37GB \| \| [DPOpenHermes-11B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q3_K_S.gguf) \| Q3_K_S \| 4.34GB \| \| [DPOpenHermes-11B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.IQ3_M.gguf) \| IQ3_M \| 4.51GB \| \| [DPOpenHermes-11B.Q3_K.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q3_K.gguf) \| Q3_K \| 4.84GB \| \| [DPOpenHermes-11B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q3_K_M.gguf) \| Q3_K_M \| 4.84GB \| \| [DPOpenHermes-11B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q3_K_L.gguf) \| Q3_K_L \| 5.26GB \| \| [DPOpenHermes-11B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.IQ4_XS.gguf) \| IQ4_XS \| 5.43GB \| \| [DPOpenHermes-11B.Q4_0.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q4_0.gguf) \| Q4_0 \| 5.66GB \| \| [DPOpenHermes-11B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [DPOpenHermes-11B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q4_K_S.gguf) \| Q4_K_S \| 5.7GB \| \| [DPOpenHermes-11B.Q4_K.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q4_K.gguf) \| Q4_K \| 6.02GB \| \| [DPOpenHermes-11B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q4_K_M.gguf) \| Q4_K_M \| 6.02GB \| \| [DPOpenHermes-11B.Q4_1.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q4_1.gguf) \| Q4_1 \| 6.27GB \| \| [DPOpenHermes-11B.Q5_0.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q5_0.gguf) \| Q5_0 \| 6.89GB \| \| [DPOpenHermes-11B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q5_K_S.gguf) \| Q5_K_S \| 6.89GB \| \| [DPOpenHermes-11B.Q5_K.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q5_K.gguf) \| Q5_K \| 7.08GB \| \| [DPOpenHermes-11B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q5_K_M.gguf) \| Q5_K_M \| 7.08GB \| \| [DPOpenHermes-11B.Q5_1.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q5_1.gguf) \| Q5_1 \| 7.51GB \| \| [DPOpenHermes-11B.Q6_K.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q6_K.gguf) \| Q6_K \| 8.2GB \| \| [DPOpenHermes-11B.Q8_0.gguf](https://huggingface.co/RichardErkhov/openaccess-ai-collective_-_DPOpenHermes-11B-gguf/blob/main/DPOpenHermes-11B.Q8_0.gguf) \| Q8_0 \| 10.62GB \| Original model description: --- license: apache-2.0 datasets: - teknium/openhermes - argilla/ultrafeedback-binarized-preferences - Intel/orca_dpo_pairs language: - en library_name: transformers --- # DPOpenHermes 11B This is a mergekit merge of DPOpenHermes-7B from seperate versions of it. ``` slices: - sources: - model: openaccess-ai-collective/DPOpenHermes-7B revision: dpo-v0 layer_range: [0, 24] - sources: - model: openaccess-ai-collective/DPOpenHermes-7B layer_range: [8, 32] merge_method: passthrough dtype: bfloat16 ```
arjunshajitech/whisper-small-malayalam-v3	arjunshajitech	2024-05-24T04:26:12Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "ml", "dataset:mozilla-foundation/common_voice_14_0", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-23T16:53:08Z	--- language: - ml license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_14_0 metrics: - wer model-index: - name: Whisper Small Malayalam - Arjun Shaji results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 13.0 type: mozilla-foundation/common_voice_14_0 config: ml split: None args: 'config: ml, split: test' metrics: - name: Wer type: wer value: 72.38930659983292 --- <!-- 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 Malayalam - Arjun Shaji This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4878 - Wer: 72.3893 ## 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: 8 - 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: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:-------:\| \| 0.0081 \| 9.6154 \| 1000 \| 0.3642 \| 73.2665 \| \| 0.0019 \| 19.2308 \| 2000 \| 0.4225 \| 71.8045 \| \| 0.0002 \| 28.8462 \| 3000 \| 0.4564 \| 73.3918 \| \| 0.0 \| 38.4615 \| 4000 \| 0.4809 \| 72.5564 \| \| 0.0 \| 48.0769 \| 5000 \| 0.4878 \| 72.3893 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
johannp/ppo-LunarLander-v1	johannp	2024-05-24T04:24:08Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-24T04:23:49Z	--- 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: 274.57 +/- 16.32 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 ... ```
Ponleur/vivit-MSASL-dataset	Ponleur	2024-05-24T04:19:53Z	62	0	transformers	[ "transformers", "safetensors", "vivit", "video-classification", "arxiv:1910.09700", "license:apache-2.0", "endpoints_compatible", "region:us" ]	video-classification	2024-05-24T04:06:36Z	--- license: apache-2.0 metrics: - accuracy pipeline_tag: video-classification --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## 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]
mradermacher/Reynaerde-7B-v2-GGUF	mradermacher	2024-05-24T04:12:34Z	2	0	transformers	[ "transformers", "gguf", "alignment-handbook", "trl", "sft", "generated_from_trainer", "en", "dataset:vandeju/ultrachat_combined_sample_nl", "base_model:vandeju/Reynaerde-7B-v2", "base_model:quantized:vandeju/Reynaerde-7B-v2", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-24T02:27:20Z	--- base_model: vandeju/Reynaerde-7B-v2 datasets: - vandeju/ultrachat_combined_sample_nl language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - alignment-handbook - trl - sft - generated_from_trainer - trl - sft - alignment-handbook - generated_from_trainer --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/vandeju/Reynaerde-7B-v2 <!-- 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/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Reynaerde-7B-v2-GGUF/resolve/main/Reynaerde-7B-v2.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 -->
janhq/llama3	janhq	2024-05-24T04:06:20Z	8	0	null	[ "facebook", "meta", "pytorch", "llama", "llama-3", "text-generation", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	text-generation	2024-05-15T09:35:53Z	--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 license: llama3 extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity\u2019s behalf),\ \ of the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and\ \ Documentation (and any portion thereof) made available under this Agreement.\n\ \"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or,\ \ if you are an entity, your principal place of business is in the EEA or Switzerland)\ \ and Meta Platforms, Inc. 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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit widget: - example_title: Hello messages: - role: user content: Hey my name is Julien! How are you? - example_title: Winter holidays messages: - role: system content: You are a helpful and honest assistant. Please, respond concisely and truthfully. - role: user content: Can you recommend a good destination for Winter holidays? - example_title: Programming assistant messages: - role: system content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully. - role: user content: Write a function that computes the nth fibonacci number. inference: parameters: max_new_tokens: 300 stop: - <\|end_of_text\|> - <\|eot_id\|> base_model: meta-llama/Meta-Llama-3-8B-Instruct model_creator: meta-llama model_name: Meta-Llama-3-8B-Instruct quantized_by: JanHQ --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <p align="center"> <a href="https://jan.ai/">Jan</a> - <a href="https://discord.gg/AsJ8krTT3N">Discord</a> </p> <!-- header end --> # Model Description This is a GGUF version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) - Model creator: [meta-llama](https://huggingface.co/meta-llama) - Original model: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) - Model description: [Readme](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/blob/main/README.md) # About Jan Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones. Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life. # Jan Model Converter This is a repository for the [open-source converter](https://github.com/janhq/model-converter. We would be grateful if the community could contribute and strengthen this repository. We are aiming to expand the repo that can convert into various types of format
DidNothing/Smaug-Llama-3-70B-Instruct-abliterated-v3-exl2-bpw6.0	DidNothing	2024-05-24T03:56:54Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-23T22:21:49Z	6bpw exl2 quant of https://huggingface.co/failspy/Smaug-Llama-3-70B-Instruct-abliterated-v3.
hgnoi/ul1MYxU5iaiw3L31	hgnoi	2024-05-24T03:54:06Z	135	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T03:50:02Z	--- 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. 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]
GENIAC-Team-Ozaki/full-sft-finetuned-stage4-iter86000-v3	GENIAC-Team-Ozaki	2024-05-24T03:53:48Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T03:48:44Z	--- 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. 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QuantFactory/Yi-1.5-6B-GGUF	QuantFactory	2024-05-24T03:48:05Z	115	0	transformers	[ "transformers", "gguf", "llama", "text-generation", "base_model:01-ai/Yi-1.5-6B", "base_model:quantized:01-ai/Yi-1.5-6B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T03:05:54Z	--- license: apache-2.0 library_name: transformers pipeline_tag: text-generation tags: - llama base_model: 01-ai/Yi-1.5-6B --- # Yi-1.5-6B-GGUF - This is quantized version of [01-ai/Yi-1.5-6B](https://huggingface.co/01-ai/Yi-1.5-6B) created using llama.cpp # Model Description Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. <div align="center"> Model \| Context Length \| Pre-trained Tokens \| :------------: \| :------------: \| :------------: \| \| Yi-1.5 \| 4K, 16K, 32K \| 3.6T </div> # Models - Chat models <div align="center"> \| Name \| Download \| \| --------------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| \| Yi-1.5-34B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| \| Yi-1.5-9B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| \| Yi-1.5-9B-Chat-16K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| \| Yi-1.5-6B-Chat \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| </div> - Base models <div align="center"> \| Name \| Download \| \| ---------- \| ------------------------------------------------------------------------------------------------------------------------------------------------------------------- \| \| Yi-1.5-34B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| \| Yi-1.5-34B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| \| Yi-1.5-9B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| \| Yi-1.5-9B-32K \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| \| Yi-1.5-6B \| • [🤗 Hugging Face](https://huggingface.co/collections/01-ai/yi-15-2024-05-663f3ecab5f815a3eaca7ca8) • [🤖 ModelScope](https://www.modelscope.cn/organization/01ai) • [🔍 wisemodel](https://wisemodel.cn/organization/01.AI)\| </div> # Benchmarks - Chat models Yi-1.5-34B-Chat is on par with or excels beyond larger models in most benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/KcsJ9Oc1VnEmfCDEJc5cd.png) Yi-1.5-9B-Chat is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/xf6pLg5jqRCwjlh6m3t6_.png) - Base models Yi-1.5-34B is on par with or excels beyond larger models in some benchmarks. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/BwU7QM-03dZvZzwdIE1xY.png) Yi-1.5-9B is the top performer among similarly sized open-source models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/656d9adce8bf55919aca7c3f/y-EYSYPT-3aWLJ0x8R94F.png) # Quick Start For getting up and running with Yi-1.5 models quickly, see [README](https://github.com/01-ai/Yi-1.5).
yuyijiong/swin-v2-base-remote-sensing-quality	yuyijiong	2024-05-24T03:46:52Z	150	0	transformers	[ "transformers", "pytorch", "swinv2", "image-classification", "zh", "arxiv:2307.11965", "license:cc-by-nc-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-04-20T07:01:16Z	--- license: cc-by-nc-4.0 language: - zh --- [github](https://github.com/yuyijiong/remote_sense_image_quality_inspection) [paper](https://arxiv.org/abs/2307.11965) 使用swin v2模型检测遥感图像中是否包含以下9种类型的质量问题： "0": "云", "1": "阴影", "2": "拉花", "3": "模糊", "4": "光谱溢出", "5": "扭曲", "6": "拼接痕迹", "7": "拼接错误", "8": "条状噪声" 模型输出为9维向量，每一维的值代表图片中存在此类质量问题的概率，概率大于50%视为存在此类质量问题。
KimByeongSu/gpt-neo-2.7B-cs-finetuning-7-60000	KimByeongSu	2024-05-24T03:38:56Z	7	0	transformers	[ "transformers", "safetensors", "gpt_neo", "text-generation", "generated_from_trainer", "base_model:EleutherAI/gpt-neo-2.7B", "base_model:finetune:EleutherAI/gpt-neo-2.7B", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T06:53:37Z	--- license: mit tags: - generated_from_trainer base_model: EleutherAI/gpt-neo-2.7B model-index: - name: gpt-neo-2.7B-cs-finetuning-7-60000 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. --> # gpt-neo-2.7B-cs-finetuning-7-60000 This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4477 ## 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 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.5787 \| 1.0 \| 765 \| 2.4157 \| \| 1.8828 \| 2.0 \| 1530 \| 2.3721 \| \| 1.4804 \| 3.0 \| 2295 \| 2.4477 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.1 - Datasets 2.12.0 - Tokenizers 0.15.1
hgnoi/tIpj9Kl1aoz9GKoT	hgnoi	2024-05-24T03:28:06Z	132	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T03:26:27Z	--- 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. 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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]
grimjim/Mistral-7B-Instruct-demi-merge-v0.3-7B	grimjim	2024-05-24T03:27:11Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:merge:mistralai/Mistral-7B-Instruct-v0.3", "base_model:mistralai/Mistral-7B-v0.3", "base_model:merge:mistralai/Mistral-7B-v0.3", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-23T20:01:56Z	--- base_model: - mistralai/Mistral-7B-v0.3 - mistralai/Mistral-7B-Instruct-v0.3 library_name: transformers tags: - mergekit - merge license: apache-2.0 --- # Mistral-7B-Instruct-demi-merge-v0.3-7B This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). This is a blend of base and instruct models, intended to enable fine-tuning and/or merging (by anyone). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) * [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.3 layer_range: [0,32] - model: mistralai/Mistral-7B-v0.3 layer_range: [0,32] merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.3 parameters: t: - value: 0.5 dtype: bfloat16 ```
GeneStory/Classify-model-v1.2	GeneStory	2024-05-24T03:25:11Z	138	0	transformers	[ "transformers", "safetensors", "new", "text-classification", "generated_from_trainer", "custom_code", "base_model:Alibaba-NLP/gte-base-en-v1.5", "base_model:finetune:Alibaba-NLP/gte-base-en-v1.5", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-classification	2024-05-14T08:51:31Z	--- license: apache-2.0 base_model: Alibaba-NLP/gte-base-en-v1.5 tags: - generated_from_trainer metrics: - accuracy model-index: - name: Classify-model-v1.2 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. --> # Classify-model-v1.2 This model is a fine-tuned version of [Alibaba-NLP/gte-base-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2901 - Accuracy: 0.9464 ## 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: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.0269 \| 1.0 \| 11 \| 0.3949 \| 0.8482 \| \| 0.2124 \| 2.0 \| 22 \| 0.2804 \| 0.8929 \| \| 0.0651 \| 3.0 \| 33 \| 0.2142 \| 0.9107 \| \| 0.0224 \| 4.0 \| 44 \| 0.2613 \| 0.9196 \| \| 0.0111 \| 5.0 \| 55 \| 0.3168 \| 0.9375 \| \| 0.0082 \| 6.0 \| 66 \| 0.2658 \| 0.9464 \| \| 0.0053 \| 7.0 \| 77 \| 0.2768 \| 0.9464 \| \| 0.0049 \| 8.0 \| 88 \| 0.2901 \| 0.9464 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.2+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
Sorour/cls_fomc_llama3_v1	Sorour	2024-05-24T03:16:49Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct", "license:llama3", "region:us" ]	null	2024-05-18T18:50:53Z	--- license: llama3 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Meta-Llama-3-8B-Instruct datasets: - generator model-index: - name: cls_fomc_llama3_v1 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. --> # cls_fomc_llama3_v1 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.6559 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.6624 \| 0.6723 \| 20 \| 0.6671 \| \| 0.5462 \| 1.3445 \| 40 \| 0.6559 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
BotCuddles/llama3_try1_adapter	BotCuddles	2024-05-24T03:14:31Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-18T02:28:53Z	--- library_name: transformers tags: - unsloth --- # 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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adjohn1313/gptj-allenai_toxicity-explainable-no-avg	adjohn1313	2024-05-24T03:04:41Z	4	0	transformers	[ "transformers", "safetensors", "gptj", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T03:00:52Z	--- 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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hgnoi/vJmQHNJ6dQ8ZflYc	hgnoi	2024-05-24T03:04:19Z	132	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T03:02:44Z	--- 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. 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GENIAC-Team-Ozaki/lora-dpo-finetuned-stage4-full-sft-v2-0.5_1e-6_ep-1	GENIAC-Team-Ozaki	2024-05-24T02:59:53Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T02:49:07Z	--- 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. 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saransh03sharma/mintrec2-mistral-2-7b-200-5	saransh03sharma	2024-05-24T02:47:16Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T02:41:32Z	--- 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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tsavage68/MedQA_L3_350steps_1e7rate_03beta_CSFTDPO	tsavage68	2024-05-24T02:42:23Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "trl", "dpo", "generated_from_trainer", "conversational", "base_model:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "base_model:finetune:tsavage68/MedQA_L3_1000steps_1e6rate_SFT", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T02:38:07Z	--- license: llama3 base_model: tsavage68/MedQA_L3_1000steps_1e6rate_SFT tags: - trl - dpo - generated_from_trainer model-index: - name: MedQA_L3_350steps_1e7rate_03beta_CSFTDPO 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. --> # MedQA_L3_350steps_1e7rate_03beta_CSFTDPO This model is a fine-tuned version of [tsavage68/MedQA_L3_1000steps_1e6rate_SFT](https://huggingface.co/tsavage68/MedQA_L3_1000steps_1e6rate_SFT) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6516 - Rewards/chosen: 0.2738 - Rewards/rejected: 0.1790 - Rewards/accuracies: 0.7099 - Rewards/margins: 0.0948 - Logps/rejected: -33.2582 - Logps/chosen: -30.4158 - Logits/rejected: -0.7313 - Logits/chosen: -0.7305 ## 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: 350 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.6925 \| 0.0489 \| 50 \| 0.6930 \| -0.0016 \| -0.0023 \| 0.5011 \| 0.0007 \| -33.8624 \| -31.3338 \| -0.7320 \| -0.7314 \| \| 0.6841 \| 0.0977 \| 100 \| 0.6807 \| 0.2459 \| 0.2195 \| 0.6549 \| 0.0264 \| -33.1233 \| -30.5088 \| -0.7330 \| -0.7323 \| \| 0.6524 \| 0.1466 \| 150 \| 0.6658 \| 0.3522 \| 0.2898 \| 0.6703 \| 0.0624 \| -32.8887 \| -30.1544 \| -0.7315 \| -0.7308 \| \| 0.631 \| 0.1954 \| 200 \| 0.6545 \| 0.1829 \| 0.0948 \| 0.6923 \| 0.0881 \| -33.5389 \| -30.7188 \| -0.7310 \| -0.7303 \| \| 0.6675 \| 0.2443 \| 250 \| 0.6520 \| 0.2481 \| 0.1544 \| 0.7121 \| 0.0938 \| -33.3403 \| -30.5014 \| -0.7309 \| -0.7301 \| \| 0.6479 \| 0.2931 \| 300 \| 0.6509 \| 0.2738 \| 0.1773 \| 0.7099 \| 0.0966 \| -33.2640 \| -30.4157 \| -0.7310 \| -0.7303 \| \| 0.6583 \| 0.3420 \| 350 \| 0.6516 \| 0.2738 \| 0.1790 \| 0.7099 \| 0.0948 \| -33.2582 \| -30.4158 \| -0.7313 \| -0.7305 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
princeton-nlp/Llama-3-Base-8B-SFT-SimPO	princeton-nlp	2024-05-24T02:40:41Z	6,067	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T02:37:43Z	--- 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. 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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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]
songhyundong/distilbert-base-uncased-finetuned-squad	songhyundong	2024-05-24T02:39:23Z	127	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2024-05-24T00:20:59Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-squad 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. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1512 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3025 \| 1.0 \| 2767 \| 1.2238 \| \| 1.029 \| 2.0 \| 5534 \| 1.1357 \| \| 0.8727 \| 3.0 \| 8301 \| 1.1512 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.0 - Datasets 2.19.1 - Tokenizers 0.19.1
dtorber/BioNLP-tech-intro-disc-PLOS	dtorber	2024-05-24T02:39:12Z	11	0	transformers	[ "transformers", "safetensors", "led", "text2text-generation", "summarization", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2024-05-12T21:49:18Z	--- tags: - summarization - generated_from_trainer model-index: - name: BioNLP-tech-intro-disc-PLOS 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. --> # BioNLP-tech-intro-disc-PLOS This model was trained from scratch 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: 1.3739167643078955e-06 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 1.13.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.2
mradermacher/MuMath-Code-CL-7B-GGUF	mradermacher	2024-05-24T02:36:23Z	4	0	transformers	[ "transformers", "gguf", "en", "base_model:weihao1/MuMath-Code-CL-7B", "base_model:quantized:weihao1/MuMath-Code-CL-7B", "endpoints_compatible", "region:us" ]	null	2024-05-24T00:46:34Z	--- base_model: weihao1/MuMath-Code-CL-7B language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/weihao1/MuMath-Code-CL-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/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-7B.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-CL-7B-GGUF/resolve/main/MuMath-Code-CL-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 -->
AnhLedger/Finetune_llama3	AnhLedger	2024-05-24T02:33:25Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:adapter:unsloth/llama-3-8b-bnb-4bit", "region:us" ]	null	2024-05-24T02:31:03Z	--- library_name: peft base_model: unsloth/llama-3-8b-bnb-4bit --- # 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
ahmedgongi/Llama_dev3tokenizer_finale7	ahmedgongi	2024-05-24T02:32:51Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-23T17:41:56Z	--- 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. 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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]
mradermacher/MuMath-Code-L-7B-GGUF	mradermacher	2024-05-24T02:30:04Z	25	0	transformers	[ "transformers", "gguf", "en", "base_model:weihao1/MuMath-Code-L-7B", "base_model:quantized:weihao1/MuMath-Code-L-7B", "endpoints_compatible", "region:us" ]	null	2024-05-24T00:48:26Z	--- base_model: weihao1/MuMath-Code-L-7B language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/weihao1/MuMath-Code-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/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q2_K.gguf) \| Q2_K \| 2.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.IQ3_XS.gguf) \| IQ3_XS \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.IQ3_S.gguf) \| IQ3_S \| 3.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q3_K_S.gguf) \| Q3_K_S \| 3.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.IQ3_M.gguf) \| IQ3_M \| 3.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q3_K_M.gguf) \| Q3_K_M \| 3.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q3_K_L.gguf) \| Q3_K_L \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.IQ4_XS.gguf) \| IQ4_XS \| 3.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q4_K_S.gguf) \| Q4_K_S \| 4.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q4_K_M.gguf) \| Q4_K_M \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q5_K_S.gguf) \| Q5_K_S \| 4.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q5_K_M.gguf) \| Q5_K_M \| 4.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q6_K.gguf) \| Q6_K \| 5.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-L-7B.Q8_0.gguf) \| Q8_0 \| 7.3 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/MuMath-Code-L-7B-GGUF/resolve/main/MuMath-Code-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 -->
ruidanwang/distilbert-base-uncased-finetuned-imdb	ruidanwang	2024-05-24T02:28:54Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "fill-mask", "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" ]	fill-mask	2024-05-24T02:25:28Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb 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. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4894 ## 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: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6819 \| 1.0 \| 157 \| 2.4978 \| \| 2.5872 \| 2.0 \| 314 \| 2.4488 \| \| 2.527 \| 3.0 \| 471 \| 2.4823 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
youjunhyeok/Llama-3-8B-slerp-262k-sft-lora-ko	youjunhyeok	2024-05-24T02:16:01Z	2,245	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-22T05:09:27Z	--- license: apache-2.0 library_name: transformers --- ## Model - base model: [meta-llama/Meta-Llama-3-8B-Instruct](DavidAhn/Llama-3-8B-slerp-262k) - parent model: [DavidAhn/Llama-3-8B-slerp-262k](https://huggingface.co/DavidAhn/Llama-3-8B-slerp-262k) ## Dataset - [youjunhyeok/llama3_train](https://huggingface.co/datasets/youjunhyeok/llama3_train) ## BenchMark (KOR) ``` # alias A = youjunhyeok/Llama-3-8B-slerp-262k-sft-lora-ko B = DavidAhn/Llama-3-8B-slerp-262k C = meta-llama/Meta-Llama-3-8B D = chihoonlee10/T3Q-ko-solar-dpo-v7.0 (24.05.24 ko 리더보드 1등) ``` \| Benchmark (macro_f1) \| A \| B \| C \| D \| \|---------------------------\|:----:\|:----:\|:----:\|:----:\| \| kobest_boolq (0-shot) \| 57.6 \| 33.5 \| 38.2 \| 34.1 \| \| kobest_boolq (5-shot) \| 77.9 \| 68.8 \| 83.8 \| 93.1 \| \| kobest_copa (0-shot) \| 59.9 \| 58.5 \| 63.1 \| 81.0 \| \| kobest_copa (5-shot) \| 61.4 \| 61.7 \| 69.1 \| 91.0 \| \| kobest_hellaswag (0-shot) \| 40.6 \| 43.2 \| 42.1 \| 55.1 \| \| kobest_hellaswag (5-shot) \| 41.5 \| 45.3 \| 44.2 \| 55.2 \| \| kobest_sentineg (0-shot) \| 61.1 \| 34.8 \| 51.5 \| 82.7 \| \| kobest_sentineg (5-shot) \| 92.4 \| 85.8 \| 94.7 \| 91.4 \| ## BenchMark (ENG) \| \| openbookqa \| hellaswag \| boolq \| arc_easy \| arc_challenge \| \|:----------------------------------------------\|---------:\|---------:\|---------:\|---------:\|---------:\| \| youjunhyeok/Llama-3-8B-slerp-262k-sft-lora-ko \| 0.334 \| 0.575 \| 0.778 \| 0.763 \| 0.471 \| \| DavidAhn/Llama-3-8B-slerp-262k \| 0.312 \| 0.587 \| 0.832 \| 0.808 \| 0.518 \| \| meta-llama/Meta-Llama-3-8B-Instruct \| 0.338 \| 0.576 \| 0.831 \| 0.815 \| 0.529 \|
Ksgk-fy/Meta-Llama-3-8B-4bit-GPTQ	Ksgk-fy	2024-05-24T02:08:42Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-24T01:14:06Z	--- 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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Ksgk-fy/Meta-Llama-3-8B-4bit-GPTQ-Phil	Ksgk-fy	2024-05-24T02:08:28Z	77	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-24T01:40:25Z	--- 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. 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ltg/mt0-definition-en-xl	ltg	2024-05-24T01:57:33Z	171	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "definition-modeling", "en", "dataset:marksverdhei/wordnet-definitions-en-2021", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-18T22:27:03Z	--- tags: - text2text-generation - definition-modeling metrics: - rouge model-index: - name: mt0-definition-en-xl results: [] language: - en widget: - text: "He ate a sweet apple. What is the definition of apple?" example_title: "Definition generation" - text: "The paper contains a number of original ideas about color perception. What is the definition of original?" example_title: "Definition generation" license: cc-by-sa-4.0 datasets: - marksverdhei/wordnet-definitions-en-2021 --- # mT0-Definition-En XL This model is a version of [mT0 XL](https://huggingface.co/bigscience/mt0-xl) finetuned on a dataset of English definitions and usage examples. It generates definitions of English words in context. Its input is the usage example and the instruction question "What is the definiton of TARGET_WORD?" ## Models for other languages: - English: [mT0-Definition-En XL](https://huggingface.co/ltg/mt0-definition-en-xl) - Norwegian: [mT0-Definition-No XL](https://huggingface.co/ltg/mt0-definition-no-xl) - Russian: [mT0-Definition-Ru XL](https://huggingface.co/ltg/mt0-definition-ru-xl) ## Model description See details in the paper [Enriching Word Usage Graphs with Cluster Definitions](https://aclanthology.org/2024.lrec-main.546/) (LREC-COLING'2024) by Mariia Fedorova, Andrey Kutuzov, Nikolay Arefyev and Dominik Schlechtweg. ## Intended uses & limitations The model is intended for research purposes, as a source of contextualized dictionary-like lexical definitions. Generated definitions can contain all sorts of biases and stereotypes, stemming from the underlying language model. ## Training and evaluation data Three datasets were used to fine-tune the model: - WordNet ([Ishiwatari et al., NAACL 2019](https://aclanthology.org/N19-1350/)), also [available on HF](https://huggingface.co/datasets/marksverdhei/wordnet-definitions-en-2021) - Oxford dictionary or CHA ([Gadetsky et al., ACL 2018](https://aclanthology.org/P18-2043/)) - English subset of CodWoE ([Mickus et al., SemEval 2022](https://aclanthology.org/2022.semeval-1.1/)) ## Training results mT0-Definition-En XL achieves the following results on concatenated validations sets from WordNet and Oxford dictionary: - Loss: 1.7210 - Rouge1: 41.5067 - Rouge2: 23.7149 - Rougel: 39.138 - Rougelsum: 39.1647 - Gen Len: 15.1578 ## Training procedure mT0-Definition-En XL was fine-tuned in a sequence-to-sequence mode on examples of contextualized dictionary definitions. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Framework versions - Transformers 4.30.2 - Pytorch 1.13.1+rocm5.2 - Datasets 2.12.0 - Tokenizers 0.12.1 ## Citation ``` @inproceedings{kutuzov-etal-2024-enriching-word, title = "Enriching Word Usage Graphs with Cluster Definitions", author = "Kutuzov, Andrey and Fedorova, Mariia and Schlechtweg, Dominik and Arefyev, Nikolay", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.546", pages = "6189--6198", abstract = "We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions. They are generated from scratch by fine-tuned encoder-decoder language models. The conducted human evaluation has shown that these definitions match the existing clusters in WUGs better than the definitions chosen from WordNet by two baseline systems. At the same time, the method is straightforward to use and easy to extend to new languages. The resulting enriched datasets can be extremely helpful for moving on to explainable semantic change modeling.", } ```
ltg/mt0-definition-no-xl	ltg	2024-05-24T01:56:12Z	5	0	transformers	[ "transformers", "pytorch", "mt5", "text2text-generation", "definition-modeling", "no", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2023-12-18T23:45:47Z	--- tags: - text2text-generation - definition-modeling metrics: - rouge model-index: - name: mt0-definition-no-xl results: [] language: - no widget: - text: "Ha egen brygge og båthus. Hva betyr båthus?" example_title: "Definition generation" license: cc-by-sa-4.0 --- # mT0-Definition-No XL This model is a version of [mT0 XL](https://huggingface.co/bigscience/mt0-xl) finetuned on [Bokmålsordboka](https://ordbokene.no/), a dataset of Norwegian definitions and usage examples. It generates definitions of Norwegian words in context. Its input is the usage example and the instruction question "Hva betyr TARGET_WORD?" ## Models for other languages: - English: [mT0-Definition-En XL](https://huggingface.co/ltg/mt0-definition-en-xl) - Norwegian: [mT0-Definition-No XL](https://huggingface.co/ltg/mt0-definition-no-xl) - Russian: [mT0-Definition-Ru XL](https://huggingface.co/ltg/mt0-definition-ru-xl) ## Model description See details in the paper [Enriching Word Usage Graphs with Cluster Definitions](https://aclanthology.org/2024.lrec-main.546/) (LREC-COLING'2024) by Mariia Fedorova, Andrey Kutuzov, Nikolay Arefyev and Dominik Schlechtweg. ## Intended uses & limitations The model is intended for research purposes, as a source of contextualized dictionary-like lexical definitions. Generated definitions can contain all sorts of biases and stereotypes, stemming from the underlying language model. ## Training and evaluation data [Bokmålsordboka](https://ordbokene.no/) by The Norwegian Language Council and the University of Bergen. ## Training results mT0-Definition-No XL achieves the following results on the evaluation set: - Loss: 2.0358 - Rouge1: 28.3491 - Rouge2: 14.2699 - Rougel: 27.7602 - Rougelsum: 27.752 - Gen Len: 10.0765 ## Training procedure mT0-Definition-No XL was fine-tuned in a sequence-to-sequence mode on examples of contextualized dictionary definitions. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20.0 ### Framework versions - Transformers 4.37.1 - Pytorch 1.13.1+rocm5.2 - Datasets 2.16.1 - Tokenizers 0.15.1 ## Citation ``` @inproceedings{kutuzov-etal-2024-enriching-word, title = "Enriching Word Usage Graphs with Cluster Definitions", author = "Kutuzov, Andrey and Fedorova, Mariia and Schlechtweg, Dominik and Arefyev, Nikolay", editor = "Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen", booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", month = may, year = "2024", address = "Torino, Italia", publisher = "ELRA and ICCL", url = "https://aclanthology.org/2024.lrec-main.546", pages = "6189--6198", abstract = "We present a dataset of word usage graphs (WUGs), where the existing WUGs for multiple languages are enriched with cluster labels functioning as sense definitions. They are generated from scratch by fine-tuned encoder-decoder language models. The conducted human evaluation has shown that these definitions match the existing clusters in WUGs better than the definitions chosen from WordNet by two baseline systems. At the same time, the method is straightforward to use and easy to extend to new languages. The resulting enriched datasets can be extremely helpful for moving on to explainable semantic change modeling.", } ```
hgnoi/XeBWkEf2mW77tWaY	hgnoi	2024-05-24T01:54:29Z	133	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:52:52Z	--- 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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MoMonir/Phi-3-medium-128k-instruct-GGUF	MoMonir	2024-05-24T01:50:52Z	23	1	null	[ "gguf", "nlp", "code", "text-generation", "multilingual", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-24T01:35:46Z	--- license: mit license_link: https://huggingface.co/microsoft/Phi-3-medium-128k-instruct/resolve/main/LICENSE language: - multilingual pipeline_tag: text-generation tags: - nlp - code inference: parameters: temperature: 0.7 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # MoMonir/Phi-3-medium-128k-instruct-GGUF This model was converted to GGUF format from [`microsoft/Phi-3-medium-128k-instruct`](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) using llama.cpp. Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) for more details on the model. <!-- README_GGUF.md-about-gguf start --> ### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description) 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). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> ## 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 MoMonir/Phi-3-medium-128k-instruct-GGUF --model Phi-3-medium-128k-instruct.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo MoMonir/Phi-3-medium-128k-instruct-GGUF --model Phi-3-medium-128k-instruct.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 Phi-3-medium-128k-instruct.Q4_K_M.gguf -n 128 ```
nguyenhacker/vit-base-patch16-224-in21k-finetuned-lora-food101	nguyenhacker	2024-05-24T01:50:37Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-24T01:33:53Z	--- 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. 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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]
dmitrii-a-lex/llama3-8b-csn-280step-lora	dmitrii-a-lex	2024-05-24T01:47:26Z	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	2024-05-24T01:47:21Z	--- 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: dmitrii-a-lex - 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)
ziqin/ziqin	ziqin	2024-05-24T01:46:38Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-24T01:46:37Z	--- license: apache-2.0 ---
T3Q-LLM/T3Q-LLM2-sft1.2	T3Q-LLM	2024-05-24T01:45:51Z	41	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T00:16:39Z	--- 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. --> ## Evaluation hf-causal-experimental (pretrained=T3Q-LLM/T3Q-LLM2-sft1.2,use_accelerate=true,trust_remote_code=true), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8 \| Task \|Version\| Metric \|Value \| \|Stderr\| \|----------------\|------:\|--------\|-----:\|---\|-----:\| \|kobest_boolq \| 0\|acc \|0.9416\|± \|0.0063\| \| \| \|macro_f1\|0.9415\|± \|0.0063\| \|kobest_copa \| 0\|acc \|0.7730\|± \|0.0133\| \| \| \|macro_f1\|0.7725\|± \|0.0133\| \|kobest_hellaswag\| 0\|acc \|0.5100\|± \|0.0224\| \| \| \|acc_norm\|0.5740\|± \|0.0221\| \| \| \|macro_f1\|0.5074\|± \|0.0223\| \|kobest_sentineg \| 0\|acc \|0.7632\|± \|0.0214\| \| \| \|macro_f1\|0.7545\|± \|0.0220\|
Liuza1/test1111	Liuza1	2024-05-24T01:45:42Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-24T01:45:42Z	--- license: apache-2.0 ---
Timeshift/distilbert-base-uncased-finetuned-emotion	Timeshift	2024-05-24T01:45:25Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-24T01:40:44Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.927 - name: F1 type: f1 value: 0.9268799638507115 --- <!-- 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. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2204 - Accuracy: 0.927 - F1: 0.9269 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8393 \| 1.0 \| 250 \| 0.3184 \| 0.904 \| 0.9027 \| \| 0.2532 \| 2.0 \| 500 \| 0.2204 \| 0.927 \| 0.9269 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
jspr/talosian_v3_merged	jspr	2024-05-24T01:43:06Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:mistralai/Mistral-7B-v0.3", "base_model:finetune:mistralai/Mistral-7B-v0.3", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:33:50Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft base_model: mistralai/Mistral-7B-v0.3 --- # Uploaded model - Developed by: jspr - License: apache-2.0 - Finetuned from model : mistralai/Mistral-7B-v0.3 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)
loyahdev/loyahgpt	loyahdev	2024-05-24T01:36:59Z	0	0	flair	[ "flair", "en", "dataset:HuggingFaceFW/fineweb", "arxiv:1910.09700", "license:apache-2.0", "region:us" ]	null	2024-05-24T01:36:06Z	--- license: apache-2.0 datasets: - HuggingFaceFW/fineweb language: - en metrics: - accuracy library_name: flair --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. 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jspr/talosian_v3_peft	jspr	2024-05-24T01:31:24Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:mistralai/Mistral-7B-v0.3", "base_model:finetune:mistralai/Mistral-7B-v0.3", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-24T01:31:11Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: mistralai/Mistral-7B-v0.3 --- # Uploaded model - Developed by: jspr - License: apache-2.0 - Finetuned from model : mistralai/Mistral-7B-v0.3 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)
hgnoi/F6jP9m5a0fiKKmSu	hgnoi	2024-05-24T01:31:15Z	133	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:27:01Z	--- 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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junaidiqbalsyed/juanid-phi3-instruct-finetuned-GGUF	junaidiqbalsyed	2024-05-24T01:27:06Z	15	0	transformers	[ "transformers", "gguf", "mistral", "text-generation-inference", "unsloth", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:quantized:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-24T01:20:11Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - gguf base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: junaidiqbalsyed - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-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)
AlignmentResearch/robust_llm_pythia-14m-imdb-gen-ian-nd	AlignmentResearch	2024-05-24T01:26:36Z	148	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:26:29Z	--- 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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Arbi-Houssem/mms_tts_tun_Lang1.1	Arbi-Houssem	2024-05-24T01:24:40Z	105	0	transformers	[ "transformers", "safetensors", "vits", "text-to-audio", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	text-to-audio	2024-05-24T00:15:43Z	--- 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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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/Mistral-C64Wizard-instruct-GGUF	mradermacher	2024-05-24T01:14:39Z	2	0	transformers	[ "transformers", "gguf", "en", "base_model:pechaut/Mistral-C64Wizard-instruct", "base_model:quantized:pechaut/Mistral-C64Wizard-instruct", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-23T23:51:33Z	--- base_model: pechaut/Mistral-C64Wizard-instruct language: - en library_name: transformers quantized_by: mradermacher tags: [] --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/pechaut/Mistral-C64Wizard-instruct <!-- 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/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Mistral-C64Wizard-instruct-GGUF/resolve/main/Mistral-C64Wizard-instruct.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 -->
junaidiqbalsyed/juanid-phi3-instruct-finetuned	junaidiqbalsyed	2024-05-24T01:10:30Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-24T01:10:08Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: junaidiqbalsyed - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-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)
T3Q-LLM/T3Q-LLM2-FP-v2.0	T3Q-LLM	2024-05-24T01:08:46Z	45	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T04:54:06Z	--- library_name: transformers license: apache-2.0 --- # 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. --> ## Evaluation hf-causal-experimental (pretrained=T3Q-LLM/T3Q-LLM2-FP-v2.0,use_accelerate=true,trust_remote_code=true), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8 \| Task \|Version\| Metric \|Value \| \|Stderr\| \|----------------\|------:\|--------\|-----:\|---\|-----:\| \|kobest_boolq \| 0\|acc \|0.5085\|± \|0.0133\| \| \| \|macro_f1\|0.3496\|± \|0.0076\| \|kobest_copa \| 0\|acc \|0.7680\|± \|0.0134\| \| \| \|macro_f1\|0.7677\|± \|0.0134\| \|kobest_hellaswag\| 0\|acc \|0.4920\|± \|0.0224\| \| \| \|acc_norm\|0.5740\|± \|0.0221\| \| \| \|macro_f1\|0.4889\|± \|0.0223\| \|kobest_sentineg \| 0\|acc \|0.6826\|± \|0.0234\| \| \| \|macro_f1\|0.6616\|± \|0.0244\|
mradermacher/Athena-Mistral-7b-v0.2-GGUF	mradermacher	2024-05-24T01:07:25Z	16	0	transformers	[ "transformers", "gguf", "text-generation-inference", "unsloth", "mistral", "trl", "sft", "en", "dataset:NotAiLOL/Athena-v0.1", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-13T21:52:09Z	--- base_model: NotAiLOL/Athena-Mistral-7b-v0.2 datasets: - NotAiLOL/Athena-v0.1 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl - sft --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/NotAiLOL/Athena-Mistral-7b-v0.2 <!-- 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/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q2_K.gguf) \| Q2_K \| 2.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.IQ3_XS.gguf) \| IQ3_XS \| 3.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q3_K_S.gguf) \| Q3_K_S \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.IQ3_S.gguf) \| IQ3_S \| 3.3 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.IQ3_M.gguf) \| IQ3_M \| 3.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q3_K_M.gguf) \| Q3_K_M \| 3.6 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q3_K_L.gguf) \| Q3_K_L \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.IQ4_XS.gguf) \| IQ4_XS \| 4.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q4_K_S.gguf) \| Q4_K_S \| 4.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q4_K_M.gguf) \| Q4_K_M \| 4.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q5_K_S.gguf) \| Q5_K_S \| 5.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q5_K_M.gguf) \| Q5_K_M \| 5.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q6_K.gguf) \| Q6_K \| 6.0 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.Q8_0.gguf) \| Q8_0 \| 7.8 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Athena-Mistral-7b-v0.2-GGUF/resolve/main/Athena-Mistral-7b-v0.2.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 -->
hgnoi/4zorKjh2MTNaftD3	hgnoi	2024-05-24T01:05:26Z	139	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T01:03:48Z	--- 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]
Augusto777/vit-base-patch16-224-RU3-40	Augusto777	2024-05-24T01:04:43Z	218	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224", "base_model:finetune:google/vit-base-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-24T00:36:28Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: vit-base-patch16-224-RU3-40 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: 0.8333333333333334 --- <!-- 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. --> # vit-base-patch16-224-RU3-40 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5667 - Accuracy: 0.8333 ## 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: 5.5e-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: 0.05 - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.3821 \| 0.99 \| 19 \| 1.3119 \| 0.4833 \| \| 1.2698 \| 1.97 \| 38 \| 1.0852 \| 0.6167 \| \| 0.9819 \| 2.96 \| 57 \| 0.8757 \| 0.7 \| \| 0.6671 \| 4.0 \| 77 \| 0.7689 \| 0.7333 \| \| 0.4248 \| 4.99 \| 96 \| 0.7294 \| 0.7167 \| \| 0.3005 \| 5.97 \| 115 \| 0.6518 \| 0.7833 \| \| 0.2035 \| 6.96 \| 134 \| 0.5667 \| 0.8333 \| \| 0.2195 \| 8.0 \| 154 \| 0.6646 \| 0.8333 \| \| 0.1654 \| 8.99 \| 173 \| 0.6294 \| 0.8167 \| \| 0.1581 \| 9.97 \| 192 \| 0.7211 \| 0.7833 \| \| 0.1338 \| 10.96 \| 211 \| 0.8129 \| 0.7833 \| \| 0.1188 \| 12.0 \| 231 \| 0.7925 \| 0.8167 \| \| 0.1179 \| 12.99 \| 250 \| 0.9588 \| 0.7667 \| \| 0.1017 \| 13.97 \| 269 \| 1.0875 \| 0.7167 \| \| 0.0845 \| 14.96 \| 288 \| 0.9355 \| 0.7 \| \| 0.1109 \| 16.0 \| 308 \| 0.9387 \| 0.8167 \| \| 0.0711 \| 16.99 \| 327 \| 1.1214 \| 0.7333 \| \| 0.0884 \| 17.97 \| 346 \| 0.9688 \| 0.7667 \| \| 0.0668 \| 18.96 \| 365 \| 1.0306 \| 0.8 \| \| 0.0716 \| 20.0 \| 385 \| 1.2653 \| 0.7167 \| \| 0.0643 \| 20.99 \| 404 \| 0.9894 \| 0.7833 \| \| 0.0517 \| 21.97 \| 423 \| 1.0439 \| 0.7667 \| \| 0.0597 \| 22.96 \| 442 \| 1.1470 \| 0.7667 \| \| 0.0533 \| 24.0 \| 462 \| 1.0848 \| 0.7833 \| \| 0.0529 \| 24.99 \| 481 \| 1.1481 \| 0.75 \| \| 0.0524 \| 25.97 \| 500 \| 1.1322 \| 0.7333 \| \| 0.0525 \| 26.96 \| 519 \| 1.1868 \| 0.7333 \| \| 0.0517 \| 28.0 \| 539 \| 1.1561 \| 0.7167 \| \| 0.0309 \| 28.99 \| 558 \| 1.0562 \| 0.7833 \| \| 0.0403 \| 29.97 \| 577 \| 1.2901 \| 0.7333 \| \| 0.0392 \| 30.96 \| 596 \| 1.1295 \| 0.7667 \| \| 0.0404 \| 32.0 \| 616 \| 1.1198 \| 0.7667 \| \| 0.0381 \| 32.99 \| 635 \| 1.2986 \| 0.7167 \| \| 0.0262 \| 33.97 \| 654 \| 1.1655 \| 0.75 \| \| 0.0354 \| 34.96 \| 673 \| 1.1223 \| 0.7833 \| \| 0.0224 \| 36.0 \| 693 \| 1.1679 \| 0.7833 \| \| 0.0244 \| 36.99 \| 712 \| 1.0999 \| 0.8167 \| \| 0.0368 \| 37.97 \| 731 \| 1.1213 \| 0.7833 \| \| 0.0199 \| 38.96 \| 750 \| 1.1003 \| 0.8 \| \| 0.028 \| 39.48 \| 760 \| 1.0989 \| 0.8 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.0

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