# ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation (ACL25 Main)

<a href="https://huggingface.co/datasets/xxxllz/Chart2Code-160k" target="_blank">🤗 Dataset(HuggingFace)</a> | <a href="https://modelscope.cn/datasets/Noct25/Chart2Code-160k" target="_blank">🤖 Dataset(ModelScope)</a> | <a href="https://arxiv.org/abs/2501.06598" target="_blank">📑 Paper </a>

This repository contains the code to train and infer ChartCoder. **Our Github repository [ChartCoder](https://github.com/thunlp/ChartCoder) updates with more details and news.**


## Installation
1. Clone this repo
```
git clone https://github.com/thunlp/ChartCoder.git
```
2.  Create environment
```
conda create -n chartcoder python=3.10 -y
conda activate chartcoder
pip install --upgrade pip  # enable PEP 660 support
pip install -e .
```
3. Additional packages required for training
```
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
```

## Train
The whole training process consists of two stages. To train the ChartCoder, ```siglip-so400m-patch14-384``` and ```deepseek-coder-6.7b-instruct``` should be downloaded first.

For **Pre-training**, run
```
bash scripts/train/pretrain_siglip.sh
```
For **SFT**, run 
```
bash scripts/train/finetune_siglip_a4.sh
```
Please change the model path to your local path. See the corresponding ```.sh ``` file for details. 
We also provide other training scripts, such as using CLIP ```_clip``` and multiple machines ```_m```. See ``` scripts/train ``` for further information.


## Citation
If you find this work useful, consider giving this repository a star ⭐️ and citing 📝 our paper as follows:
```
@misc{zhao2025chartcoderadvancingmultimodallarge,
      title={ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation}, 
      author={Xuanle Zhao and Xianzhen Luo and Qi Shi and Chi Chen and Shuo Wang and Wanxiang Che and Zhiyuan Liu and Maosong Sun},
      year={2025},
      eprint={2501.06598},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2501.06598}, 
}
```