初始化项目，由ModelHub XC社区提供模型

Model: TIGER-Lab/VisCoder-7B
Source: Original Platform

README.md

@@ -0,0 +1,59 @@
+---
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+datasets:
+- TIGER-Lab/VisCode-200K
+language:
+- en
+license: apache-2.0
+tags:
+- code
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# VisCoder-7B
+
+[🏠 Project Page](https://tiger-ai-lab.github.io/VisCoder) | [📖 Paper](https://arxiv.org/abs/2506.03930) | [💻 GitHub](https://github.com/TIGER-AI-Lab/VisCoder) | [🤗 VisCode-200K](https://huggingface.co/datasets/TIGER-Lab/VisCode-200K) | [🤗 VisCoder-3B](https://huggingface.co/TIGER-Lab/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**](https://github.com/TIGER-AI-Lab/VisCoder/tree/main/eval), which tests executable visualization code generation across three major libraries. Our benchmark covers both standard generation and **multi-round self-debugging**.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64de37ee5e192985054be575/ZTicATvYEIVRe4OCj16GV.png)
+
+> 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](https://github.com/modelscope/swift)
+- **Tuning method**: Full-parameter supervised fine-tuning (SFT)
+- **Dataset**: [VisCode-200K](https://huggingface.co/datasets/TIGER-Lab/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:
+
+```bibtex
+@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/VisCoder).