Update README.md

README.md

@@ -8,4 +8,53 @@ base_model:
 - Qwen/Qwen2.5-Coder-7B-Instruct
 tags:
 - code
----
+---
+
+# VisCoder-7B
+
+[🏠 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-7B** is a large language model fine-tuned for **Python visualization code generation and multi-turn self-correction**. It is trained on **VisCode-200K**, a large-scale instruction-tuning dataset that integrates validated executable code, natural language instructions, and revision supervision from execution feedback.
+
+
+## 🧠 Model Description
+
+**VisCoder-7B** 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-7B on [**PandasPlotBench**](https://github.com/TIGER-AI-Lab/VisCoder/tree/main/eval), which tests executable visualization code generation across three major libraries. Our benchmark covers both standard generation and **multi-round self-debugging**.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64de37ee5e192985054be575/ZTicATvYEIVRe4OCj16GV.png)
+
+> VisCoder-7B achieves over **90% execution pass rate** on both **Matplotlib** and **Seaborn** under the self-debug setting, outperforming open-source baselines and approaching GPT-4o performance.
+
+
+## 📁 Training Details
+
+- **Base model**: Qwen2.5-Coder-7B-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 images
+  - 45K+ multi-turn correction dialogues with execution feedback
+
+## 📖 Citation
+
+If you use VisCoder-7B 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).