Update README.md

README.md

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 - Qwen/Qwen2.5-Coder-3B-Instruct
 tags:
 - code
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+---
+
+# VisCoder-3B
+
+[🏠 Project Page](https://tiger-ai-lab.github.io/VisCoder) | [📖 Paper](https://arxiv.org/abs/2506.03930) | [💻 GitHub](https://github.com/TIGER-AI-Lab/VisCoder) | [🤗 Dataset: VisCode-200K](https://huggingface.co/datasets/TIGER-Lab/VisCode-200K)
+
+**VisCoder-3B** is a lightweight language model fine-tuned for **Python visualization code generation and iterative correction**. It is trained on **VisCode-200K**, a large-scale instruction-tuning dataset that integrates natural language instructions, validated Python code, and execution-guided revision supervision.
+
+## 🧠 Model Description
+
+**VisCoder-3B** is trained on **VisCode-200K**, a large-scale instruction-tuning dataset tailored for executable Python visualization tasks. It addresses a core challenge in data analysis: generating Python code that not only executes successfully but also produces **semantically meaningful plots** by aligning **natural language instructions**, **data structures**, and **visual outputs**.
+
+We propose a **self-debug evaluation protocol** that simulates real-world developer workflows. In this setting, models are allowed to revise previously failed generations over multiple rounds with guidance from **execution feedback**.
+
+## 📊 Main Results on PandasPlotBench
+
+We evaluate VisCoder-3B on [**PandasPlotBench**](https://github.com/TIGER-AI-Lab/VisCoder/tree/main/eval), which tests executable visualization code generation across **Matplotlib**, **Seaborn**, and **Plotly**. Evaluation includes both standard generation and **multi-turn self-debugging**.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64de37ee5e192985054be575/ZTicATvYEIVRe4OCj16GV.png)
+
+> VisCoder-3B outperforms existing open-source baselines on multiple libraries and shows consistent recovery improvements under the self-debug protocol.
+
+## 📁 Training Details
+
+- **Base model**: Qwen2.5-Coder-3B-Instruct  
+- **Framework**: [ms-swift](https://github.com/modelscope/swift)  
+- **Tuning method**: Full-parameter supervised fine-tuning (SFT)  
+- **Dataset**: [VisCode-200K](https://huggingface.co/datasets/TIGER-Lab/VisCode-200K), which includes:  
+  - 150K+ validated Python visualization samples with corresponding images  
+  - 45K+ multi-turn correction dialogues guided by execution results  
+
+## 📖 Citation
+
+If you use VisCoder-3B or VisCode-200K in your research, please cite:
+
+```bibtex
+@misc{ni2025viscoderfinetuningllmsexecutable,
+      title={VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation}, 
+      author={Yuansheng Ni and Ping Nie and Kai Zou and Xiang Yue and Wenhu Chen},
+      year={2025},
+      eprint={2506.03930},
+      archivePrefix={arXiv},
+      primaryClass={cs.SE},
+      url={https://arxiv.org/abs/2506.03930}
+}
+
+For evaluation scripts and more information, see our [GitHub repository](https://github.com/TIGER-AI-Lab/VisCoder).