metadata

license: apache-2.0
language:
  - en
metrics:
  - accuracy
pipeline_tag: image-text-to-text
tags:
  - mathematics
  - reasoning
  - multi-modal-qa
  - math-qa
  - figure-qa
  - geometry-qa
  - math-word-problem
  - textbook-qa
  - vqa
  - geometry-diagram
  - synthetic-scene
  - chart
  - plot
  - scientific-figure
  - table
  - function-plot
  - abstract-scene
  - puzzle-test
  - document-image
  - science
library_name: transformers
base_model:
  - OpenGVLab/InternVL2-8B
datasets:
  - MathLLMs/MM-MathInstruct

MathCoder-VL: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning

Repo: https://github.com/mathllm/MathCoder

Paper: https://huggingface.co/papers/2505.10557

Introduction

We introduce MathCoder-VL, a series of open-source large multimodal models (LMMs) specifically tailored for general math problem-solving. We also introduce FigCodifier-8B, an image-to-code model.

Base Model	Ours
Mini-InternVL-Chat-2B-V1-5	MathCoder-VL-2B
InternVL2-8B	MathCoder-VL-8B
InternVL2-8B	FigCodifier-8B

Usage

For training and inference code, please refer to InternVL.

from datasets import load_dataset
from PIL import Image
from io import BytesIO

mm_mathinstruct = load_dataset("MathLLMs/MM-MathInstruct")
print(mm_mathinstruct)

# show the last image
img = Image.open(BytesIO(mm_mathinstruct['train'][-1]['image']))
img.show()

It should print:

DatasetDict({
    train: Dataset({
        features: ['id', 'image', 'question', 'solution', 'image_path'],
        num_rows: 2871988
    })
})

Motivation

Construction of FigCodifier

Construction of MathCoder-VL

Performance

Citation

Please cite the paper if you use our data, model or code.

@inproceedings{
wang2025mathcodervl,
title={MathCoder-{VL}: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning},
author={Ke Wang and Junting Pan and Linda Wei and Aojun Zhou and Weikang Shi and Zimu Lu and Han Xiao and Yunqiao Yang and Houxing Ren and Mingjie Zhan and Hongsheng Li},
booktitle={The 63rd Annual Meeting of the Association for Computational Linguistics},
year={2025},
url={https://openreview.net/forum?id=nuvtX1imAb}
}

@inproceedings{
lu2025mathcoder2,
title={MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code},
author={Zimu Lu and Aojun Zhou and Ke Wang and Houxing Ren and Weikang Shi and Junting Pan and Mingjie Zhan and Hongsheng Li},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=1Iuw1jcIrf}
}

@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}
}