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README.md

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+# ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation
+
+<a href="https://huggingface.co/datasets/xxxllz/Chart2Code-160k" target="_blank">🤗 Dataset(HuggingFace)</a>(TBD) | <a href="https://modelscope.cn/datasets/Noct25/Chart2Code-160k" target="_blank">🤖 Dataset(ModelScope)</a> | <a href="https://huggingface.co/xxxllz/ChartCoder" target="_blank">🤗 Model</a> | <a href="https://arxiv.org/abs/2501.06598" target="_blank">📑 Paper </a>
+
+This repository contains the code to train and infer ChartCoder. 
+
+
+## Installation
+1. Clone this repo
+```
+git clone https://github.com/thunlp/ChartCoder.git
+```
+2.  Create environment
+```
+cd MMedAgent
+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}, 
+}
+```