MathLLMs/MathCodeInstruct-Plus

---
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
configs:
- config_name: MathCodeInstruct_PureGPT
data_files:
- split: train
path: train_all_cleaned.jsonl
task_categories:
- question-answering
- text-generation
tags:
- math
- code
- reasoning
- problem solving
size_categories:
- 10K<n<100K
---
# 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 |
|-------------------------------------------------------------------|-----------------------------------------------------------------------|
| [MathCoder-L-7B](https://huggingface.co/MathLLM/MathCoder-L-7B) | [MathCoder-CL-7B](https://huggingface.co/MathLLM/MathCoder-CL-7B) |
| [MathCoder-L-13B](https://huggingface.co/MathLLM/MathCoder-L-13B) | [MathCoder-CL-34B](https://huggingface.co/MathLLM/MathCoder-CL-34B) |

## Training Data
The models are trained on the [MathCodeInstruct](https://huggingface.co/datasets/MathLLM/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.

## 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. 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}
}
```

原始信息汇总

数据集概述

基本信息

• 许可证: Apache-2.0
• 语言: 英语
• 评估指标: 准确率
• 任务类别:
  • 问答
  • 文本生成
• 标签:
  • 数学
  • 代码
  • 推理
  • 问题解决
• 数据集大小: 10K<n<100K

配置

• 配置名称: MathCodeInstruct_PureGPT
• 数据文件:
  • 分割: 训练
  • 路径: train_all_cleaned.jsonl

介绍

MathCoder 是一系列专门针对一般数学问题解决的开源大型语言模型（LLMs）。

训练数据

模型在 MathCodeInstruct 数据集上进行训练。

训练过程

模型使用 MathCodeInstruct 数据集在原始的 Llama-2 和 CodeLlama 模型基础上进行微调。

使用方法

可以通过 Huggingface 的 Transformers 库使用这些模型。使用 pipeline 函数创建一个文本生成管道，然后输入一个数学问题以获取解决方案。

引用

如果使用我们的数据、模型或代码，请引用相关论文，并同时引用原始数据集论文。