66 lines
2.7 KiB
Markdown
66 lines
2.7 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- TIGER-Lab/VisCode-Multi-679K
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base_model:
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- Qwen/Qwen2.5-Coder-3B-Instruct
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library_name: transformers
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language:
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- en
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tags:
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- code
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---
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# VisCoder2-3B
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[🏠 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)
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**VisCoder2-3B** is a lightweight multi-language visualization coding model trained for **executable code generation, rendering, and iterative self-debugging**.
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---
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## 🧠 Model Description
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**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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---
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## 📊 Main Results on VisPlotBench
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We evaluate VisCoder2-3B 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.
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> **VisCoder2-3B** shows consistent performance across multiple languages and achieves notable improvements under the multi-round self-debug setting.
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---
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## 📁 Training Details
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- **Base model**: Qwen2.5-Coder-3B-Instruct
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- **Framework**: [ms-swift](https://github.com/modelscope/swift)
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- **Tuning method**: Full-parameter supervised fine-tuning (SFT)
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- **Dataset**: [VisCode-Multi-679K](https://huggingface.co/datasets/TIGER-Lab/VisCode-Multi-679K)
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---
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## 📖 Citation
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If you use VisCoder2-3B or related datasets in your research, please cite:
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```bibtex
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@article{ni2025viscoder2,
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title={VisCoder2: Building Multi-Language Visualization Coding Agents},
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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},
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journal={arXiv preprint arXiv:2510.23642},
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year={2025}
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}
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@article{ni2025viscoder,
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title={VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation},
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author={Ni, Yuansheng and Nie, Ping and Zou, Kai and Yue, Xiang and Chen, Wenhu},
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journal={arXiv preprint arXiv:2506.03930},
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year={2025}
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}
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```
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For evaluation scripts and more information, see our [GitHub repository](https://github.com/TIGER-AI-Lab/VisCoder2).
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