M4-ai
/

Hercules-Mini-1.8B

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 library_name: transformers
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+license: other
+datasets:
+- Locutusque/hercules-v4.0
+language:
+- en
 ---
+# Hercules-Mini-1.8B
 <!-- Provide a quick summary of what the model is/does. -->
+We fine-tuned Qwen1.5-1.8B on Locutusque's Hercules-v4.
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
+This model has capabilities in math, coding, function calling, roleplay, and more. We fine-tuned it using 700,000 examples of Hercules-v4.
+- **Developed by:** M4-ai
+- **Language(s) (NLP):** English and maybe Chinese
+- **License:** tongyi-qianwen license
+- **Finetuned from model:** [Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B)
 ## 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. -->
+General purpose assistant, question answering, chain-of-thought, etc..
 ## Bias, Risks, and Limitations
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+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.
 ### Recommendations
 Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## Evaluation
+Coming soon
 ## 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. -->
+https://huggingface.co/datasets/Locutusque/hercules-v4.0
 #### Training Hyperparameters
+- **Training regime:** bf16 non-mixed precision
+## Technical Specifications
 #### Hardware
+We used 8 Kaggle TPUs, and we trained at a global batch size of 256 and sequence length of 1536
+## Contributions
+Thanks to @Tonic, @aloobun, @fhai50032, and @Locutusque for their contributions to this model.