license: apache-2.0
datasets:
- TIGER-Lab/VisCode-200K
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- code
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:
@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.