VisCoder-3B

🏠 Project Page | 📖 Paper | 💻 GitHub | 🤗 VisCode-200K | 🤗 VisCoder-7B

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, which tests executable visualization code generation across Matplotlib, Seaborn, and Plotly. Evaluation includes both standard generation and multi-turn self-debugging

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
• Tuning method: Full-parameter supervised fine-tuning (SFT)
• Dataset: 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:

@article{ni2025viscoder,
  title={VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation},
  author={Ni, Yuansheng and Nie, Ping and Zou, Kai and Yue, Xiang and Chen, Wenhu},
  journal={arXiv preprint arXiv:2506.03930},
  year={2025}
}

For evaluation scripts and more information, see our GitHub repository.