Mathcoder CL 7B

🚀 MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning

MathCoder is a series of open - source large language models designed for general math problem - solving, integrating code seamlessly to enhance mathematical reasoning.

🚀 Quick Start

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 details.

✨ Features

• MathCoder is a series of open - source large language models (LLMs) specifically tailored for general math problem - solving.
• It offers different variants based on different base models (Llama - 2 and Code Llama) to meet various needs.

📦 Installation

No specific installation steps are provided in the original README. So, this section is skipped.

💻 Usage Examples

No code examples are provided in the original README. So, this section is skipped.

📚 Documentation

Introduction

We introduce MathCoder, a series of open - source large language models (LLMs) specifically tailored for general math problem - solving.

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

🔧 Technical Details

No detailed technical implementation information (more than 50 words) is provided in the original README. So, this section is skipped.

📄 License

The project is licensed under the Apache - 2.0 license.

📄 Citation

Please cite the paper if you use our data, model or code. Please also kindly cite the original dataset papers.

@inproceedings{
wang2024mathcoder,
title={MathCoder: Seamless Code Integration in {LLM}s 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},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=z8TW0ttBPp}
}

@inproceedings{
zhou2024solving,
title={Solving Challenging Math Word Problems Using {GPT}-4 Code Interpreter with Code-based Self-Verification},
author={Aojun Zhou and Ke Wang and Zimu Lu and Weikang Shi and Sichun Luo and Zipeng Qin and Shaoqing Lu and Anya Jia and Linqi Song and Mingjie Zhan and Hongsheng Li},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=c8McWs4Av0}
}