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license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
---
# MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
Paper: [https://arxiv.org/pdf/2310.03731.pdf](https://arxiv.org/pdf/2310.03731.pdf)
Repo: [https://github.com/mathllm/MathCoder](https://github.com/mathllm/MathCoder)
## Introduction
We introduce MathCoder, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving.
| | 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) |
## Training Data
The models are trained on the MathCodeInstruct Dataset.
## 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.
## Evaluation
<br>
<div align="center">
<img src="result.png" width="100%" title="Result Figure">
</div>
## 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.
## Citation
Please cite the paper if you use our data, model or code.
```
@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}
}
``` |