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--- |
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library_name: transformers |
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license: other |
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datasets: |
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- Locutusque/hercules-v4.0 |
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language: |
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- en |
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inference: |
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parameters: |
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do_sample: true |
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temperature: 1 |
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top_p: 0.7 |
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top_k: 4 |
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max_new_tokens: 250 |
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repetition_penalty: 1.1 |
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--- |
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# Hercules-Mini-1.8B |
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<!-- Provide a quick summary of what the model is/does. --> |
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We fine-tuned Qwen1.5-1.8B on Locutusque's Hercules-v4. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model has capabilities in math, coding, function calling, roleplay, and more. We fine-tuned it using 700,000 examples of Hercules-v4. |
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- **Developed by:** M4-ai |
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- **Language(s) (NLP):** English and maybe Chinese |
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- **License:** tongyi-qianwen license |
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- **Finetuned from model:** [Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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General purpose assistant, question answering, chain-of-thought, etc.. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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The eos token was not setup properly, so to prevent infinite generation you'll need to implement a stopping criteria when the model generates the <|im_end|> token. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## Evaluation |
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Coming soon |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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https://huggingface.co/datasets/Locutusque/hercules-v4.0 |
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#### Training Hyperparameters |
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- **Training regime:** bf16 non-mixed precision |
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## Technical Specifications |
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#### Hardware |
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We used 8 Kaggle TPUs, and we trained at a global batch size of 256 and sequence length of 1536 |
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## Contributions |
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Thanks to @Tonic, @aloobun, @fhai50032, and @Locutusque for their contributions to this model. |
