| --- |
| license: mit |
| language: |
| - en |
| metrics: |
| - accuracy |
| pipeline_tag: text-generation |
| --- |
| # MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning |
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| Paper: [https://arxiv.org/pdf/2310.03731.pdf](https://arxiv.org/pdf/2310.03731.pdf) |
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| Repo: [https://github.com/mathllm/MathCoder](https://github.com/mathllm/MathCoder) |
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| ## Introduction |
| We introduce MathCoder, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. |
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| | | Base Model: Llama-2 | Base Model: Code Llama | |
| |-------|-------------------------------------------------------------------|-----------------------------------------------------------------------| |
| | 7B | [MathCoder-L-7B](https://huggingface.co/MathLLM/MathCoder-L-7B) | [MathCoder-CL-7B](https://huggingface.co/MathLLM/MathCoder-CL-7B) | |
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| ## Training Data |
| The models are trained on the MathCodeInstruct Dataset. |
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| ## Training Procedure |
| The models are fine-tuned with the MathCodeInstruct dataset using the original Llama-2 and CodeLlama models as base models. Check out our paper and repo for more details. |
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| ## Evaluation |
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| <br> |
| <div align="center"> |
| <img src="result.png" width="100%" title="Result Figure"> |
| </div> |
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| ## Usage |
| You can use the models through Huggingface's Transformers library. Use the pipeline function to create a text-generation pipeline with the model of your choice, then feed in a math problem to get the solution. |
| Check our Github repo for datails. |
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| ## Citation |
| Please cite the paper if you use our data, model or code. |
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| ``` |
| @misc{wang2023mathcoder, |
| title={MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning}, |
| author={Ke Wang and Houxing Ren and Aojun Zhou and Zimu Lu and Sichun Luo and Weikang Shi and Renrui Zhang and Linqi Song and Mingjie Zhan and Hongsheng Li}, |
| year={2023}, |
| eprint={2310.03731}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |