VisCoder-7B

🏠 Project Page | 📖 Paper | 💻 GitHub | 🤗 VisCode-200K | 🤗 VisCoder-3B

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, which tests executable visualization code generation across three major libraries. Our benchmark covers both standard generation and multi-round self-debugging.

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

@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.