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--- |
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license: mit |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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--- |
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# CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images |
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<div align="center"> |
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<a href="https://math-vr.github.io"><img src="https://img.shields.io/badge/Project-Homepage-green" alt="Home"></a> |
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<a href="https://huggingface.co/papers/2510.11718"><img src="https://img.shields.io/badge/Paper-red" alt="Paper"></a> |
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<a href="https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT"><img src="https://img.shields.io/badge/GitHub-Code-keygen.svg?logo=github&style=flat-square" alt="GitHub"></a> |
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</div> |
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This repository contains the **CodePlot-CoT** model, a core component of the paper [CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images](https://huggingface.co/papers/2510.11718). |
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CodePlot-CoT is an innovative code-driven Chain-of-Thought (CoT) paradigm designed to enable Vision Language Models (VLMs) to "think with images" when solving mathematical problems. Instead of generating pixel-based images directly, the model outputs executable plotting code to represent its "visual thoughts". This code is then executed to render a precise figure, which is reinput to the model as a visual input for subsequent reasoning steps. |
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The model is built upon the Qwen2.5-VL architecture and is compatible with the `transformers` library. |
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<div align="center"> |
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<img src="https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT/raw/main/figures/teaser.png" width="100%"/> |
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</div> |
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## Sample Usage |
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### Installation |
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To get started with CodePlot-CoT, clone the repository and install the required packages: |
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```bash |
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conda create -n codeplot python==3.10 |
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conda activate codeplot |
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git clone git@github.com:HKU-MMLab/Math-VR-CodePlot-CoT.git |
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cd CodePlot-CoT |
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pip install -r requirements.txt |
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pip install flash_attn==2.7.4.post1 |
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``` |
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For benchmark evaluation only (additional dependencies): |
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```bash |
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pip install openai==4.1.1 |
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pip install datasets==2.0.0 |
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``` |
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### Model Weights |
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Ensure your directory structure for the models looks like this: |
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``` |
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CodePlot-CoT |
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├── ckpts |
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│ ├── CodePlot-CoT |
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│ ├── MatPlotCode |
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├── ... |
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``` |
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### Inference |
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You can perform inference using the provided scripts: |
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```python |
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# Convert image to python code with MatPlotCode |
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python image_to_code.py |
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# Solve math problems with CodePlot-CoT |
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python math_infer.py |
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``` |
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For more details on evaluation and benchmarks, please refer to the [project homepage](https://math-vr.github.io) and the [GitHub repository](https://github.com/HKU-MMLab/Math-VR-CodePlot-CoT). |
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## Citation |
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If you find this work helpful, please consider citing our paper: |
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```bibtex |
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@article{duan2025code, |
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title={CodePlot-CoT: Mathematical Visual Reasoning by Thinking with Code-Driven Images}, |
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author={Duan, Chengqi and Fang, Rongyao and Wang, Yuqing and Wang, Kun and Huang, Linjiang and Zeng, Xingyu and Li, Hongsheng and Liu, Xihui}, |
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journal={arXiv preprint arXiv:2510.11718}, |
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year={2025} |
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} |
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``` |
