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---
base_model:
- Qwen/Qwen2.5-Coder-7B-Instruct
datasets:
- TIGER-Lab/VisCode-Multi-679K
language:
- en
license: apache-2.0
tags:
- code
pipeline_tag: image-text-to-text
library_name: transformers
---
# VisCoder2-7B
[🏠 Project Page](https://tiger-ai-lab.github.io/VisCoder2) | [📖 Paper](https://arxiv.org/abs/2510.23642) | [💻 GitHub](https://github.com/TIGER-AI-Lab/VisCoder2) | [🤗 VisCode2](https://hf.co/collections/TIGER-Lab/viscoder2)
**VisCoder2-7B** is a lightweight multi-language visualization coding model trained for **executable code generation, rendering, and iterative self-debugging**.
---
## 🧠 Model Description
**VisCoder2-7B** 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.
---
## 📊 Main Results on VisPlotBench
We evaluate VisCoder2-7B on [**VisPlotBench**](https://huggingface.co/datasets/TIGER-Lab/VisPlotBench), which includes 888 executable visualization tasks spanning 8 languages, supporting both standard generation and multi-turn self-debugging.
> **VisCoder2-7B** shows consistent performance across multiple languages and achieves notable improvements under the multi-round self-debug setting.
---
## 📁 Training Details
- **Base model**: Qwen2.5-Coder-7B-Instruct
- **Framework**: [ms-swift](https://github.com/modelscope/swift)
- **Tuning method**: Full-parameter supervised fine-tuning (SFT)
- **Dataset**: [VisCode-Multi-679K](https://huggingface.co/datasets/TIGER-Lab/VisCode-Multi-679K)
---
## 📖 Citation
If you use VisCoder2-7B or related datasets in your research, please cite:
```bibtex
@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](https://github.com/TIGER-AI-Lab/VisCoder2). |