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fxmeng/PiSSA-Qwen2-72B-4bit-r128-5iter

fxmeng

transformers

text-generation

--- 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]

Rxplore/llava_model

Rxplore

Entry not found

fxmeng/PiSSA-Qwen2-72B-Instruct-4bit-r64-5iter

fxmeng

transformers

text-generation

--- 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]

sunilghanchi/llama-3-8b-Instruct-bnb-4bit-linearloop

sunilghanchi

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** sunilghanchi - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-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)

sophiayk/MDEBERTA_2e-06_16_0.1_0.01_10ep

sophiayk

transformers

text-classification

--- 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]

SuMoss/dreamtobenlpsam_dpo_ckpt_5

SuMoss

peft

--- library_name: peft base_model: microsoft/phi-2 --- # 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 0.11.2.dev0

Tippawan/seallm-oz

Tippawan

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: SeaLLMs/SeaLLM-7B-v2.5 --- # Uploaded model - **Developed by:** Tippawan - **License:** apache-2.0 - **Finetuned from model :** SeaLLMs/SeaLLM-7B-v2.5 This gemma 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)

LarryAIDraw/clorinde_pony

LarryAIDraw

--- license: creativeml-openrail-m --- https://civitai.com/models/196862/genshinxl-clorinde-genshin-impact

Tippawan/seallm-oz-float16-for-VLLM

Tippawan

transformers

--- 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. 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]

dwb2023/llama38binstruct_summarize_v4

dwb2023

peft

--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: NousResearch/Meta-Llama-3-8B-Instruct datasets: - generator model-index: - name: llama38binstruct_summarize_v4 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. --> # llama38binstruct_summarize_v4 This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.4313 ## 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: 1 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5211 | 1.25 | 25 | 1.4041 | | 0.4473 | 2.5 | 50 | 1.3731 | | 0.2109 | 3.75 | 75 | 1.3571 | | 0.102 | 5.0 | 100 | 1.4313 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

YeBhoneLin10/chatbot

YeBhoneLin10

Entry not found

QuantaScriptor/Nexus_AI

QuantaScriptor

--- license: other license_name: commercial-license-agi-gpt-5-model license_link: LICENSE language: - en tags: - Federated Learning - Homomorphic Encryption - Secure Data Processing --- # 🎉 **Nexus AI** 🎉 Welcome to the **Nexus AI** repository! This project implements the Nexus AI model with modular neural networks, federated learning, and homomorphic encryption for secure data processing. --- ## 🌟 **Overview** 🌟 This repository contains: - **Modular Neural Networks**: Dynamic architecture adjustments for enhanced performance. - **Federated Learning**: Secure, scalable model deployment using federated learning frameworks. - **Homomorphic Encryption**: Secure data processing with state-of-the-art encryption techniques. --- ## 📂 **File Structure** 📂 ```plaintext project_root/ ├── config.py ├── main.py ├── model.py ├── federated.py ├── data.py ├── train.py ├── config.json ├── tokenizer_config.json ├── vocab.json ├── merges.txt ├── requirements.txt ├── setup.py └── README.md ``` --- ## 🛠️ **Setup and Installation** 🛠️ 1. **Clone the repository**: ```bash git clone https://github.com/Quantascriptor/Nexus_AI.git cd Nexus_AI ``` 2. **Install dependencies**: ```bash pip install -r requirements.txt ``` 3. **Run the main script**: ```bash python main.py ``` --- ## 📜 **Licensing** 📜 - **Non-Commercial Use**: Licensed under the Apache License 2.0. - **Commercial Use**: Refer to the commercial license details provided separately. For commercial licensing, please contact us: - 📧 Email: [sales@quantascript.com](mailto:sales@quantascript.com) - 📞 Phone: 650-440-7704 --- ## 📈 **Training the Model** 📈 To fine-tune the Nexus AI model, use the `train.py` script: ```bash python train.py --dataset path_to_your_dataset.txt ``` --- ## 🔧 **Configuration** 🔧 Modify `config.py` for hyperparameter settings: ```python # config.py LEARNING_RATE = 0.01 BATCH_SIZE = 10 EPOCHS = 10 ENCRYPTION_KEY = 1.234 ``` --- ## 🚀 **Federated Learning** 🚀 Implement federated learning using `federated.py`: ```python # Initialize global model global_model = ModularNetwork([nn.Linear(128, 256), nn.ReLU(), nn.Linear(256, 10)]) clients = prepare_federated_data() server = FederatedLearningServer(global_model) federated_training(server, clients) ``` --- ## 🔒 **Security Features** 🔒 Homomorphic encryption for secure data processing: ```python # Encrypt data encrypted_data = homomorphic_encrypt(data, ENCRYPTION_KEY) ``` --- ## 📧 **Contact Us** 📧 For more information, please reach out to us: - **Email**: [sales@quantascript.com](mailto:sales@quantascript.com) - **Phone**: 650-440-7704 --- **Happy Coding!** 🚀

manbeast3b/ZZZZZZZZZZZtest32

manbeast3b

transformers

text-generation

Entry not found

czhhh/path_to_saved_model

czhhh

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: runwayml/stable-diffusion-v1-5 inference: true instance_prompt: a photo of sks dog --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - czhhh/path_to_saved_model This is a dreambooth model derived from runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

crazyming/test

crazyming

Entry not found

AlekseyElygin/mistral-7b-bnb-8bit-LORA

AlekseyElygin

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-bnb-4bit --- # Uploaded model - **Developed by:** AlekseyElygin - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-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)

Jkverma1/llm-server

Jkverma1

Entry not found

bezzam/tapecam-mirflickr-trainable-inv-unet8M

bezzam

--- license: mit ---

Bibekananda/bk_lora_model1

Bibekananda

transformers

--- 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:** Bibekananda - **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)

sanwuge/roberta-factchecking

sanwuge

Entry not found

medallo/a2

medallo

--- license: mit ---

HealTether-Healthcare/llama3-8b-lora-finetuned-v1.1

HealTether-Healthcare

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** HealTether-Healthcare - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-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)

nginbm/korean

nginbm

Entry not found

swoos/llama-2-7b-unsloth-KoCoT-2000

swoos

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - **Developed by:** swoos - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Kraken777/mental-health-chat-bot

Kraken777

Entry not found

chohtet/qwen2_7b_lora_test

chohtet

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl base_model: unsloth/Qwen2-7B --- # Uploaded model - **Developed by:** chohtet - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2-7B This qwen2 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)

chohtet/qwen2_7b_4bit_test

chohtet

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - qwen2 - trl - sft base_model: unsloth/Qwen2-7B --- # Uploaded model - **Developed by:** chohtet - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen2-7B This qwen2 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)

DavidLacour/distillgpt2untrained

DavidLacour

transformers

text-generation

--- 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]

DavidLacour/distillgpt2DPOtrainedm2scitas

DavidLacour

transformers

text-generation

--- 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]

HyperdustProtocol/HyperAoto-llama2-7b-965

HyperdustProtocol

transformers

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-2-7b-bnb-4bit --- # Uploaded model - **Developed by:** HyperdustProtocol - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-2-7b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

AlopaxalgoWjd5480/c4ai-command-r-v01

AlopaxalgoWjd5480

transformers

text-generation

Entry not found

DBangshu/GPT2_e7_6_3

DBangshu

transformers

text-generation

--- 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]

ilseokoh/dummy

ilseokoh

transformers

fill-mask

--- license: mit tags: - generated_from_keras_callback base_model: camembert-base model-index: - name: dummy 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. --> # dummy 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 4.41.2 - TensorFlow 2.15.0 - Datasets 2.20.0 - Tokenizers 0.19.1

beyondkyleLee/lama3

beyondkyleLee

--- license: llama3 ---

zhanjun/7b

zhanjun

Entry not found

QuyenHY/Lallva

QuyenHY

Entry not found

Dongchao/new_data

Dongchao

Entry not found

AlienKevin/ids_ccr

AlienKevin

Entry not found

0xfaskety/Qwen-Qwen1.5-7B-1718352134

0xfaskety

Entry not found

QuyenHY/Quyen

QuyenHY

Entry not found

QuyenHY/Q

QuyenHY

Entry not found

tqphu12421/demo_ml

tqphu12421

Entry not found

kbrdek37/llama38binstruct_summarize

kbrdek37

peft

--- base_model: NousResearch/Meta-Llama-3-8B-Instruct datasets: - generator library_name: peft license: other tags: - trl - sft - generated_from_trainer model-index: - name: llama38binstruct_summarize 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. --> # llama38binstruct_summarize This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 1.7040 ## 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: 1 - 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: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.5571 | 1.25 | 25 | 1.2650 | | 0.518 | 2.5 | 50 | 1.4966 | | 0.2207 | 3.75 | 75 | 1.7554 | | 0.1096 | 5.0 | 100 | 1.7040 | ### Framework versions - PEFT 0.11.1 - Transformers 4.41.2 - Pytorch 2.2.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

ShapeKapseln33/Bioxtrim333

ShapeKapseln33

[neu] Bioxtrim Erfahrungen & Vorteile BioXtrim Gummies zur Gewichtsabnahme unterscheiden sich von herkömmlichen Nahrungsergänzungsmitteln zur Gewichtsabnahme und bieten eine praktische und köstliche Möglichkeit zur Unterstützung einer gesunden Gewichtskontrolle. Diese Gummies sind mit einer Mischung aus natürlichen Zutaten formuliert, die sorgfältig ausgewählt wurden, um die Fettverbrennung zu fördern, den Appetit zu unterdrücken und den Stoffwechsel anzukurbeln. Im Gegensatz zu starken Stimulanzien oder restriktiven Diäten bieten BioXtrim Gummies einen sanften und dennoch effektiven Ansatz zum Erreichen und Halten eines gesunden Gewichts. **[Klicken Sie hier, um jetzt auf der offiziellen Website von Bioxtrim zu kaufen](https://slim-gummies-deutschland.de/bioxtrim-de)** ##Vorteile von Bioxtrim Die Vorteile von Bioxtrim Gummies gegenüber anderen Schlankheitsprodukten sind vielschichtig. Sie werden nicht nur für ihre Wirksamkeit beim Abnehmen geschätzt, sondern auch für die Einfachheit ihrer Anwendung. Anwender schätzen folgende Vorteile: Einfache Integration in den Alltag: Bioxtrim Gummies lassen sich ohne großen Aufwand einfach in den Alltag integrieren. Positive Bewertungen: Die vielen positiven Erfahrungsberichte geben anderen potentiellen Anwendern die Zuversicht, ähnliche Ergebnisse erzielen zu können. Auch wenn Bioxtrim als sinnvolles Nahrungsergänzungsmittel zum Abnehmen angepriesen wird, ist es dennoch wichtig, dass Verbraucher neben der Einnahme von Abnehmgummies auf eine ausgewogene Ernährung und ausreichend Bewegung achten. Nur so ist eine nachhaltige und gesunde Gewichtsabnahme gewährleistet. ##Kaufinformationen Beim Kauf von BioXtrim Gummies ist es wichtig, die Preise und verfügbaren Angebote im Auge zu behalten. Kunden sollten sich auf der offiziellen Website über aktuelle Preise und mögliche Einsparungen durch Coupons oder Rabattcodes informieren. ##Preise Die Preise für BioXtrim Gummies können je nach Anbieter und Verpackung variieren. Es wird empfohlen, die Preise auf der offiziellen Website zu überprüfen, da diese aktuelle Preise und Informationen direkt vom Hersteller bereitstellt. Darüber hinaus können durch die Bestellung größerer Mengen Kosten gespart werden, da größere Pakete häufig einen reduzierten Preis pro Einheit bieten. ##Verfügbare Gutscheine und Rabattcodes Die offizielle BioXtrim-Website bietet häufig Gutscheine und Rabattcodes an, die an der Kasse eingelöst werden können. Diese Angebote sind in der Regel zeitlich begrenzt und können zu erheblichen Einsparungen führen. Interessenten sollten die Website regelmäßig besuchen oder sich für den Newsletter anmelden, um Informationen zu aktuellen Rabattaktionen zu erhalten. Eine Tabelle oder Liste der aktuellen Rabatte ist nicht verfügbar, da diese ständig variieren können. **[Klicken Sie hier, um jetzt auf der offiziellen Website von Bioxtrim zu kaufen](https://slim-gummies-deutschland.de/bioxtrim-de)** ##Richtlinien zu Ernährung und Bewegung Sowohl Ernährung als auch Bewegung spielen eine wichtige Rolle bei der Gewichtskontrolle mit Bioxtrim Gummies. Benutzer sollten verstehen, wie Bioxtrim mit einer ausgewogenen Ernährung und einem geeigneten Trainingsprogramm effektiv zusammenwirkt. ##Zusammenhang zwischen Ernährung und Bioxtrim Bioxtrim Gummies werden häufig als Nahrungsergänzungsmittel angepriesen, um Menschen dabei zu helfen, eine kalorienreduzierte Diät einzuhalten und das Abnehmen zu erleichtern. Es ist jedoch wichtig zu betonen, dass eine ausgewogene Ernährung mit viel Gemüse, Vollkorn und magerem Eiweiß weiterhin wichtig ist. Anwender sollten auf eine ausreichende Aufnahme von Makro- und Mikronährstoffen achten, um den Stoffwechsel anzukurbeln und Mangelerscheinungen vorzubeugen. Kalorienmanagement: Ein realistisches Kaloriendefizit einhalten. Makronährstoffverteilung: Proteine, Fette und Kohlenhydrate ausgewogen konsumieren. Flüssigkeitszufuhr: Ausreichend Wasser trinken, um Stoffwechselprozesse zu unterstützen. ##Kombination mit Bewegung Sport ist ein wesentlicher Bestandteil jedes Gewichtsmanagementprogramms. Er steigert die Kalorienaufnahme und baut Muskelmasse auf, was den Grundumsatz erhöht. Für ein ausgewogenes Trainingsprogramm sollten Anwender sowohl Kraft- als auch Ausdauertraining einbeziehen. Bioxtrim allein kann keine Wunder bewirken; kontinuierliche körperliche Aktivität ist erforderlich, um nachhaltige Gewichtsverlustergebnisse zu erzielen. Ausdauertraining: Mindestens 150 Minuten moderate Aktivität pro Woche, wie z. B. zügiges Gehen oder Schwimmen. Krafttraining: Muskelaufbauübungen für alle großen Muskelgruppen mindestens zweimal pro Woche. ##Verbraucherschutz und Herstellerinformationen In puncto Verbraucherschutz spielen unabhängige Testberichte und Herstellertransparenz eine zentrale Rolle für Verbraucher, die sich für BioXtrim Gummies interessieren. ##Bericht der Stiftung Warentest Stiftung Warentest hat bisher keine konkreten Ergebnisse zu BioXtrim Gummies veröffentlicht. Dies unterstreicht die Notwendigkeit, dass sich Verbraucher auf andere Quellen und Berichte verlassen müssen, um eine fundierte Entscheidung zu treffen. ##Transparenz des Herstellers Das Impressum des Herstellers und transparente Informationen sind entscheidend für das Vertrauen der Verbraucher. Bei BioXtrim sollten Sie darauf achten, ob klare Informationen zum Hersteller wie Firmenname, Adresse und Kontaktdaten vorhanden sind. Dies ist ein Indikator für Seriosität und Bereitschaft, Verbraucheranfragen zu beantworten. **[Klicken Sie hier, um jetzt auf der offiziellen Website von Bioxtrim zu kaufen](https://slim-gummies-deutschland.de/bioxtrim-de)**

blackhole33/llama-3-8b-Instruct-bnb-4bit-V3

blackhole33

transformers

--- language: - uz license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - **Developed by:** blackhole33 - **License:** apache-2.0 - **Finetuned from model :** llama-3-8b-Instruct-bnb-4bit

Coolwowsocoolwow/Hoopla

Coolwowsocoolwow

--- license: openrail ---

yangyida/llama_3_ecc_transcript_small_2

yangyida

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl pipeline_tag: text-generation base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - **Developed by:** yangyida - **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)

imdatta0/qwen2_Magiccoder_evol_10k_qlora_ortho

imdatta0

peft

--- license: apache-2.0 library_name: peft tags: - unsloth - generated_from_trainer base_model: Qwen/Qwen2-7B model-index: - name: qwen2_Magiccoder_evol_10k_qlora_ortho 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. --> # qwen2_Magiccoder_evol_10k_qlora_ortho This model is a fine-tuned version of [Qwen/Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9025 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.02 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 0.8992 | 0.0261 | 4 | 0.9547 | | 0.9045 | 0.0522 | 8 | 0.9234 | | 0.9145 | 0.0783 | 12 | 0.9166 | | 0.8688 | 0.1044 | 16 | 0.9117 | | 0.9222 | 0.1305 | 20 | 0.9097 | | 0.8108 | 0.1566 | 24 | 0.9090 | | 0.8194 | 0.1827 | 28 | 0.9083 | | 0.9616 | 0.2088 | 32 | 0.9086 | | 0.8624 | 0.2349 | 36 | 0.9083 | | 0.8898 | 0.2610 | 40 | 0.9088 | | 0.9476 | 0.2871 | 44 | 0.9085 | | 0.9156 | 0.3132 | 48 | 0.9091 | | 0.8388 | 0.3393 | 52 | 0.9091 | | 0.8429 | 0.3654 | 56 | 0.9087 | | 0.8651 | 0.3915 | 60 | 0.9081 | | 0.9228 | 0.4176 | 64 | 0.9082 | | 0.9167 | 0.4437 | 68 | 0.9076 | | 0.8769 | 0.4698 | 72 | 0.9068 | | 0.9009 | 0.4959 | 76 | 0.9069 | | 0.8611 | 0.5220 | 80 | 0.9074 | | 0.9496 | 0.5481 | 84 | 0.9070 | | 0.8562 | 0.5742 | 88 | 0.9067 | | 0.943 | 0.6003 | 92 | 0.9060 | | 0.8718 | 0.6264 | 96 | 0.9053 | | 0.9642 | 0.6525 | 100 | 0.9046 | | 0.8425 | 0.6786 | 104 | 0.9042 | | 0.886 | 0.7047 | 108 | 0.9040 | | 0.8576 | 0.7308 | 112 | 0.9043 | | 0.823 | 0.7569 | 116 | 0.9036 | | 0.8158 | 0.7830 | 120 | 0.9032 | | 0.8854 | 0.8091 | 124 | 0.9031 | | 0.8502 | 0.8352 | 128 | 0.9030 | | 0.9493 | 0.8613 | 132 | 0.9026 | | 0.8934 | 0.8874 | 136 | 0.9026 | | 0.9158 | 0.9135 | 140 | 0.9026 | | 0.8686 | 0.9396 | 144 | 0.9026 | | 0.9321 | 0.9657 | 148 | 0.9027 | | 0.8882 | 0.9918 | 152 | 0.9025 | ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

ar9av/phi-finetuned-value-charts-fixed

ar9av

transformers

text-generation

--- 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]

FanFierik/Zerocalcare

FanFierik

Entry not found

imdatta0/llama_2_13b_Magiccoder_evol_10k_qlora_ortho

imdatta0

peft

--- license: apache-2.0 library_name: peft tags: - unsloth - generated_from_trainer base_model: unsloth/llama-2-13b-bnb-4bit model-index: - name: llama_2_13b_Magiccoder_evol_10k_qlora_ortho 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. --> # llama_2_13b_Magiccoder_evol_10k_qlora_ortho This model is a fine-tuned version of [unsloth/llama-2-13b-bnb-4bit](https://huggingface.co/unsloth/llama-2-13b-bnb-4bit) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.0950 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 0.02 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.2067 | 0.0262 | 4 | 1.1823 | | 1.1675 | 0.0523 | 8 | 1.1498 | | 1.1004 | 0.0785 | 12 | 1.1349 | | 1.0531 | 0.1047 | 16 | 1.1288 | | 1.0946 | 0.1308 | 20 | 1.1246 | | 1.0602 | 0.1570 | 24 | 1.1215 | | 1.0636 | 0.1832 | 28 | 1.1175 | | 1.1078 | 0.2093 | 32 | 1.1151 | | 1.04 | 0.2355 | 36 | 1.1125 | | 1.115 | 0.2617 | 40 | 1.1123 | | 1.0994 | 0.2878 | 44 | 1.1102 | | 1.1379 | 0.3140 | 48 | 1.1098 | | 1.1145 | 0.3401 | 52 | 1.1064 | | 1.0849 | 0.3663 | 56 | 1.1088 | | 1.1317 | 0.3925 | 60 | 1.1087 | | 1.134 | 0.4186 | 64 | 1.1056 | | 1.0856 | 0.4448 | 68 | 1.1038 | | 1.0972 | 0.4710 | 72 | 1.1004 | | 1.044 | 0.4971 | 76 | 1.1005 | | 1.1311 | 0.5233 | 80 | 1.1004 | | 1.1474 | 0.5495 | 84 | 1.1002 | | 1.0886 | 0.5756 | 88 | 1.0999 | | 1.0372 | 0.6018 | 92 | 1.0973 | | 1.0376 | 0.6280 | 96 | 1.0968 | | 1.1006 | 0.6541 | 100 | 1.0965 | | 1.09 | 0.6803 | 104 | 1.0964 | | 1.0786 | 0.7065 | 108 | 1.0969 | | 1.111 | 0.7326 | 112 | 1.0970 | | 1.053 | 0.7588 | 116 | 1.0961 | | 1.0764 | 0.7850 | 120 | 1.0948 | | 1.0971 | 0.8111 | 124 | 1.0944 | | 1.0572 | 0.8373 | 128 | 1.0948 | | 0.999 | 0.8635 | 132 | 1.0949 | | 1.1098 | 0.8896 | 136 | 1.0951 | | 1.0215 | 0.9158 | 140 | 1.0951 | | 1.0759 | 0.9419 | 144 | 1.0951 | | 1.096 | 0.9681 | 148 | 1.0950 | | 1.08 | 0.9943 | 152 | 1.0950 | ### Framework versions - PEFT 0.7.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

giu-alb/q-FrozenLake-v1-4x4-noSlippery

giu-alb

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="giu-alb/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

QuyenHY/Lallva3

QuyenHY

Entry not found

Ajay098/resent50

Ajay098

--- license: mit ---

giu-alb/q-Taxi-v3

giu-alb

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.42 +/- 2.77 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="giu-alb/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

DBangshu/GPT2_e7_7_3

DBangshu

transformers

text-generation

--- 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]

binhnx8/TTS_v1

binhnx8

transformers

text2text-generation

Entry not found

ridhoyp/vanka-ai

ridhoyp

transformers

--- 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:** ridhoyp - **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)

trambui/Mistral-7B-Instruct-v0.1-sharded

trambui

--- license: cc ---

alibaba-yuanjing-aigclab/ViViD

alibaba-yuanjing-aigclab

# ViViD ViViD: Video Virtual Try-on using Diffusion Models [](https://arxiv.org/abs/2405.11794) [](https://alibaba-yuanjing-aigclab.github.io/ViViD) [](https://huggingface.co/alibaba-yuanjing-aigclab/ViViD) ## Installation ``` git clone https://github.com/alibaba-yuanjing-aigclab/ViViD cd ViViD ``` ### Environment ``` conda create -n vivid python=3.10 conda activate vivid pip install -r requirements.txt ``` ### Weights You can place the weights anywhere you like, for example, ```./ckpts```. If you put them somewhere else, you just need to update the path in ```./configs/prompts/*.yaml```. #### Stable Diffusion Image Variations ``` cd ckpts git lfs install git clone https://huggingface.co/lambdalabs/sd-image-variations-diffusers ``` #### SD-VAE-ft-mse ``` git lfs install git clone https://huggingface.co/stabilityai/sd-vae-ft-mse ``` #### Motion Module Download [mm_sd_v15_v2](https://huggingface.co/guoyww/animatediff/blob/main/mm_sd_v15_v2.ckpt) #### ViViD ``` git lfs install git clone git clone https://huggingface.co/alibaba-yuanjing-aigclab/ViViD ``` ## Inference We provide two demos in ```./configs/prompts/```, run the following commands to have a try😼. ``` python vivid.py --config ./configs/prompts/upper1.yaml python vivid.py --config ./configs/prompts/lower1.yaml ``` ## Data As illustrated in ```./data```, the following data should be provided. ```text ./data/ |-- agnostic | |-- video1.mp4 | |-- video2.mp4 | ... |-- agnostic_mask | |-- video1.mp4 | |-- video2.mp4 | ... |-- cloth | |-- cloth1.jpg | |-- cloth2.jpg | ... |-- cloth_mask | |-- cloth1.jpg | |-- cloth2.jpg | ... |-- densepose | |-- video1.mp4 | |-- video2.mp4 | ... |-- videos | |-- video1.mp4 | |-- video2.mp4 | ... ``` ### Agnostic and agnostic_mask video This part is a bit complex, you can obtain them through any of the following three ways: 1. Follow [OOTDiffusion](https://github.com/levihsu/OOTDiffusion) to extract them frame-by-frame.(recommended) 2. Use [SAM](https://github.com/facebookresearch/segment-anything) + Gaussian Blur.(see ```./tools/sam_agnostic.py``` for an example) 3. Mask editor tools. Note that the shape and size of the agnostic area may affect the try-on results. ### Densepose video See [vid2densepose](https://github.com/Flode-Labs/vid2densepose).(Thanks) ### Cloth mask Any detection tool is ok for obtaining the mask, like [SAM](https://github.com/facebookresearch/segment-anything). ## BibTeX ```text @misc{fang2024vivid, title={ViViD: Video Virtual Try-on using Diffusion Models}, author={Zixun Fang and Wei Zhai and Aimin Su and Hongliang Song and Kai Zhu and Mao Wang and Yu Chen and Zhiheng Liu and Yang Cao and Zheng-Jun Zha}, year={2024}, eprint={2405.11794}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Contact Us **Zixun Fang**: [zxfang1130@gmail.com](mailto:zxfang1130@gmail.com) **Yu Chen**: [chenyu.cheny@alibaba-inc.com](mailto:chenyu.cheny@alibaba-inc.com)

GGmorello/gpt2-imdb-llama-rlaif

GGmorello

transformers

text-generation

--- 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]

againeureka/multiplechoice_robertabase02_hallym_tmp

againeureka

Entry not found

wepzen/mnlp-rag-datasets

wepzen

Entry not found

stiucsib/gemma_it_quantfr_mixed

stiucsib

transformers

text-generation

--- library_name: transformers tags: - llama-factory --- # 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]

alireza20008/a1

alireza20008

--- license: openrail ---

mohibovais79/model

mohibovais79

transformers

--- 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:** mohibovais79 - **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)

TimeMobius/Mobius-RWKV-r6-12B

TimeMobius

--- license: apache-2.0 --- This is a experimental model, yet it is the Most powerful RNN model in the world. # Mobius RWKV r6 chat 12B 16k Mobius is a RWKV v6 arch chat model, benifit from [Matrix-Valued States and Dynamic Recurrence](https://arxiv.org/abs/2404.05892) ## Introduction Mobius is a RWKV v6 arch model, a state based RNN+CNN+Transformer Mixed language model pretrained on a certain amount of data. In comparison with the previous released Mobius, the improvements include: * Only 24G Vram to run this model locally with fp16; * Significant performance improvement in chinese; * Stable support of 16K context length. * function call support ; ## Usage Chat format: User: xxxx\n\nAssistant: xxx\n\n Recommend Temp and topp: 1 0.3 function call format example: ``` System: You are a helpful assistant with access to the following functions. Use them if required -{ "name": "get_exchange_rate", "description": "Get the exchange rate between two currencies", "parameters": { "type": "object", "properties": { "base_currency": { "type": "string", "description": "The currency to convert from" }, "target_currency": { "type": "string", "description": "The currency to convert to" } }, "required": [ "base_currency", "target_currency" ] } } User: Hi, I need to know the exchange rate from USD to EUR Assistant: xxxx Obersavtion: xxxx Assistant: xxxx ``` ## More details Mobius 12B 16k based on RWKV v6 arch, which is leading state based RNN+CNN+Transformer Mixed large language model which focus opensouce community * 10~100 trainning/inference cost reduce; * state based,selected memory, which mean good at grok; * community support. ## requirements 21.9G vram to run fp16, 13.7G for int8, 7.2 for nf4 with Ai00 server. * [RWKV Runner](https://github.com/josStorer/RWKV-Runner) * [Ai00 server](https://github.com/cgisky1980/ai00_rwkv_server) ## Benchmark ceval 63.53 cmmlu 76.07

mohibovais79/lora_model

mohibovais79

transformers

--- 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:** mohibovais79 - **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)

vuongminhkhoi4/mv_vton

vuongminhkhoi4

Entry not found

milouvollebregt/test

milouvollebregt

# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B") model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B")

gordonsong1225/bigbird-document-classifier

gordonsong1225

--- license: apache-2.0 --- Classifiers trained on MuSiQue, HotpotQA, and 2wikimultihopqa datasets respectively, are used to classify whether two passages can contribute to answering the same question.

RobertML/a-tribe-called-bittensor

RobertML

Entry not found

ahmedsamirio/mistral-sql-create-context-lora

ahmedsamirio

peft

--- license: apache-2.0 library_name: peft tags: - axolotl - generated_from_trainer base_model: mistralai/Mistral-7B-v0.3 model-index: - name: mistral-sql-create-context-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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.1` ```yaml base_model: mistralai/Mistral-7B-v0.3 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer load_in_8bit: false load_in_4bit: false strict: false datasets: - path: b-mc2/sql-create-context type: # JSONL file contains question, context, answer fields per line. # This gets mapped to instruction, input, output axolotl tags. field_instruction: question field_input: context field_output: answer # Format is used by axolotl to generate the prompt. format: |- [INST] Using the schema context below, generate a SQL query that answers the question. {input} {instruction} [/INST] tokens: # add new control tokens from the dataset to the model - "[INST]" - " [/INST]" - "[SQL]" - " [/SQL]" dataset_prepared_path: val_set_size: 0.05 output_dir: ./outputs/mistral-sql-create-context-lora hub_model_id: ahmedsamirio/mistral-sql-create-context-lora # This is set to 4096 in the modal config, why? # Since I'm using sample packing, decreasing the sequence length will create smaller batches # which can fit better into memory sequence_len: 8192 # These is set to false in the modal example, why? (Modal also uses FSDP which might be a reason) sample_packing: true eval_sample_packing: true pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: lora_modules_to_save: # required when adding new tokens to LLaMA/Mistral - embed_tokens - lm_head lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: mistral-sql-create-context wandb_entity: ahmedsamirio wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 2 micro_batch_size: 4 num_epochs: 1 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true # What is this? loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: # This wasn't set in modal config eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "<s>" eos_token: "</s>" unk_token: "<unk>" ``` </details>

MartaSamoilenko/phoneme_to_text_T5_3b

MartaSamoilenko

Entry not found

anilbhatt1/peft_phi2_v0_l4500

anilbhatt1

transformers

--- 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]

fanny-pack-dork/Llama-3-8b-chat-finetune

fanny-pack-dork

transformers

text-generation

Entry not found

ivan23-24-29/Alvin

ivan23-24-29

Entry not found

BiancaG/Mylabelai

BiancaG

--- license: artistic-2.0 ---

JeremyRivera/my_awesome_model

JeremyRivera

Entry not found

IntraFind/multilingual-e5-small

IntraFind

transformers

--- license: mit language: - multilingual --- This is a copy of [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) It contains the ONNX model in zip format & the config.json required by OpenSearch for adding it as a custom local model.

Yellownegi/01

Yellownegi

Entry not found

Peter2222/codec

Peter2222

--- license: mit ---

Adrien35/git-base-pokemon

Adrien35

transformers

text-generation

Entry not found

Dandan0K/Intervention-xls-FR-Ref

Dandan0K

transformers

automatic-speech-recognition

--- language: - fr license: apache-2.0 tags: - automatic-speech-recognition - fr - hf-asr-leaderboard - mozilla-foundation/common_voice_8_0 - robust-speech-event datasets: - mozilla-foundation/common_voice_8_0 model-index: - name: XLS-R Wav2Vec2 French by Jonatas Grosman results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 8 type: mozilla-foundation/common_voice_8_0 args: fr metrics: - name: Test WER type: wer value: 16.85 - name: Test CER type: cer value: 4.66 - name: Test WER (+LM) type: wer value: 16.32 - name: Test CER (+LM) type: cer value: 4.21 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: fr metrics: - name: Dev WER type: wer value: 22.34 - name: Dev CER type: cer value: 9.88 - name: Dev WER (+LM) type: wer value: 17.16 - name: Dev CER (+LM) type: cer value: 9.38 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: fr metrics: - name: Test WER type: wer value: 19.15 --- # Fine-tuned XLS-R 1B model for speech recognition in French Fine-tuned [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on French using the train and validation splits of [Common Voice 8.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0), [MediaSpeech](https://www.openslr.org/108/), [Multilingual TEDx](http://www.openslr.org/100), [Multilingual LibriSpeech](https://www.openslr.org/94/), and [Voxpopuli](https://github.com/facebookresearch/voxpopuli). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool, and thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) ## Usage Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library: ```python from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-xls-r-1b-french") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) ``` Writing your own inference script: ```python import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID = "fr" MODEL_ID = "jonatasgrosman/wav2vec2-xls-r-1b-french" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) # Preprocessing the datasets. # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) ``` ## Evaluation Commands 1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-french --dataset mozilla-foundation/common_voice_8_0 --config fr --split test ``` 2. To evaluate on `speech-recognition-community-v2/dev_data` ```bash python eval.py --model_id jonatasgrosman/wav2vec2-xls-r-1b-french --dataset speech-recognition-community-v2/dev_data --config fr --split validation --chunk_length_s 5.0 --stride_length_s 1.0 ``` ## Citation If you want to cite this model you can use this: ```bibtex @misc{grosman2021xlsr-1b-french, title={Fine-tuned {XLS-R} 1{B} model for speech recognition in {F}rench}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-french}}, year={2022} } ```

seungminh/ndxl-13k-blip

seungminh

Entry not found

DBangshu/GPT2_e7_8_3

DBangshu

transformers

text-generation

--- 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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Soumyajit2709/dataset

Soumyajit2709

Entry not found

vaibhavprajapati22/Image_Denoising_CBDNet

vaibhavprajapati22

transformers

--- language: en tags: - image-processing - image-denoising - deep-learning license: apache-2.0 model_name: Image Denoising Model --- # Convolutional Blind Denoising This Repo contains the weights and config of CBDNet Model. # Datasets - LOL Dataset - Smartphone Image Denoising Dataset # References https://arxiv.org/pdf/1807.04686v2.pdf

kg7/HindiGPT

kg7

--- license: mit ---

kvsreyas17/roberta-base-mitmovie-entity-recognition

kvsreyas17

--- language: - en metrics: - accuracy ---

HanaTNT/ALX_AI_W1

HanaTNT

Entry not found

DavidLacour/zephyr32bitsmilestone2

DavidLacour

Entry not found

onyxmytrojin/latencymodel

onyxmytrojin

--- license: llama3 ---

DavidLacour/zephyr4bitsmilestone2

DavidLacour

Entry not found

DavidLacour/zephyrmilstone216bits

DavidLacour

Entry not found

DavidLacour/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

DavidLacour

transformers

text-generation

--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/zephyr-sft --- # Uploaded model - **Developed by:** DavidLacour - **License:** apache-2.0 - **Finetuned from model :** unsloth/zephyr-sft 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)

Boostaro155/LeptiCell155

Boostaro155

# Lepti Cell US Reviews & Benefits – LeptiCell Ingredients, Doses & Intake Price, Buy Lepti Cell US Reviews & Benefits LeptiCell is a potent weight loss supplement that supports fast and effective weight loss. Its formula is based on recent research that suggests the true root cause of weight gain and weight loss resistance in individuals. ## **[Click Here To Buy Now From Official Website Of LeptiCell](https://slim-gummies-deutschland.de/lepticell-us)** ## Manufacturing Standards Of LeptiCell Supplement Now, let us look at the manufacturing standards followed by Phytage Labs while formulating LeptiCell. This nutrition brand has made each batch of the formula using 23 potent ingredients in lab facilities that are FDA-approved and GMP-compliant. After this, the supplement has been subjected to clinical testing in third-party labs. The formula is found to be free from gluten, sugar, GMOs, wheat, salt, starch, soy derivatives, corn, yeast, color, and lactose from all these tests. This is how the LeptiCell manufacturer guarantees safety and quality. ## Dosage And Instructions To Use LeptiCell Including LeptiCell in your daily routine seems quite simple as the formula comes in capsule form. Each bottle contains 60 capsules, that is, a 30-day serving. The suggested serving is to take two capsules per day at any time, with or without food. Make sure not to exceed the recommended dosage as you might have to deal with side effects like breathing issues, dizziness, and headache. It is suggested to take the LeptiCell pills regularly for around 2 to 4 months without fail to get visible results. Following a healthy and balanced diet and exercising can help boost the results and also improve overall health. ## LeptiCell Customer Reviews And Complaints Surveying the customer's responses to a health supplement is the best way to verify if it is safe and effective. Genuine responses to the LeptiCell formula are available on several reliable sources like healthcare forums and review websites. Most reviews are positive with people commenting on the ease of use and the significant weight loss they experienced. Well, a few complaints are also available pointing to the delay in results. This delay is because the formula is natural and requires time to adapt to each person’s requirements. So, LeptiCell has received an overwhelmingly positive response from customers, garnering an impressive average rating of 4.7 out of 5. ## Who Should And Should Not Use LeptiCell? The LeptiCell weight loss supplement has been created for all people above the age of 18 years. The dosage of ingredients used is as per the requirements of the adult body. So, children should not take the pills to lose weight. Also, people who have allergies, those with a known medical condition or those taking medications, pregnant or nursing women, and people awaiting surgeries should use LeptiCell only after consulting a health professional. ## Extra Tips To Boost Weight Loss Results Some additional tips can be included in your daily routine to boost your weight loss results. These are mentioned below: It is important to balance your plate by including a variety of foods such as protein, fat, and vegetables Try cardio workouts like running, cycling, swimming, jogging, etc. ## **[Click Here To Buy Now From Official Website Of LeptiCell](https://slim-gummies-deutschland.de/lepticell-us)**

Sampe12/tuned-palmyra-med

Sampe12

Entry not found

yuehanui/_teset

yuehanui

Entry not found

LypWhyton/LypMarkIII

LypWhyton

--- license: afl-3.0 ---