VisCoder2-3B/README.md

license, datasets, base_model, library_name, language, tags

license	datasets	base_model	library_name	language	tags
apache-2.0	TIGER-Lab/VisCode-Multi-679K	Qwen/Qwen2.5-Coder-3B-Instruct	transformers	en	code

VisCoder2-3B

🏠 Project Page | 📖 Paper | 💻 GitHub | 🤗 VisCode2

VisCoder2-3B is a lightweight multi-language visualization coding model trained for executable code generation, rendering, and iterative self-debugging.

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🧠 Model Description

VisCoder2-3B is trained on the VisCode-Multi-679K dataset, a large-scale instruction-tuning dataset for executable visualization tasks across 12 programming language. It addresses a core challenge in multi-language visualization: generating code that not only executes successfully but also produces semantically consistent visual outputs by aligning natural-language instructions and rendering results.

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📊 Main Results on VisPlotBench

We evaluate VisCoder2-3B on VisPlotBench, which includes 888 executable visualization tasks spanning 8 languages, supporting both standard generation and multi-turn self-debugging.

VisCoder2-3B shows consistent performance across multiple languages and achieves notable improvements under the multi-round self-debug setting.

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📁 Training Details

• Base model: Qwen2.5-Coder-3B-Instruct
• Framework: ms-swift
• Tuning method: Full-parameter supervised fine-tuning (SFT)
• Dataset: VisCode-Multi-679K

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📖 Citation

If you use VisCoder2-3B or related datasets in your research, please cite:

@article{ni2025viscoder2,
  title={VisCoder2: Building Multi-Language Visualization Coding Agents},
  author={Ni, Yuansheng and Cai, Songcheng and Chen, Xiangchao and Liang, Jiarong and Lyu, Zhiheng and Deng, Jiaqi and Zou, Kai and Nie, Ping and Yuan, Fei and Yue, Xiang and others},
  journal={arXiv preprint arXiv:2510.23642},
  year={2025}
}

@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.