base_model, datasets, language, license, tags, library_name, pipeline_tag
| base_model | datasets | language | license | tags | library_name | pipeline_tag | ||||
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apache-2.0 |
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transformers | text-generation |
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.