初始化项目，由ModelHub XC社区提供模型

Model: Gen-Verse/ReasonFlux-Coder-4B
Source: Original Platform

README.md

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+---
+license: mit
+library_name: transformers
+---
+
+<p align="center">
+  <img src="https://github.com/yinjjiew/Data/raw/main/cure/overviewplot.png" width="100%"/>
+</p>
+
+
+<p align="center">
+  <img src="https://github.com/yinjjiew/Data/raw/main/cure/results.png" width="100%"/>
+</p>
+
+
+
+
+
+# Introduction to our ReasonFlux-Coders
+
+We introduce **ReasonFlux-Coders**, trained with **CURE**, our algorithm for co-evolving an LLM's coding and unit test generation abilities.
+
+* **ReasonFlux-Coder-7B** and **ReasonFlux-Coder-14B** outperform similarly sized Qwen Coders, DeepSeek Coders, and Seed-Coders, and naturally integrate into common test-time scaling and agentic coding pipelines.
+* **ReasonFlux-Coder-4B** is our Long-CoT model, outperforming Qwen3-4B while achieving 64.8% efficiency in unit test generation. We have demonstrated its ability to serve as a reward model for training base models via reinforcement learning (see our [paper](https://arxiv.org/abs/2506.03136)).
+
+[Paper](https://arxiv.org/abs/2506.03136) | [Code](https://github.com/Gen-Verse/CURE)
+
+# Citation
+
+```
+@article{wang2025cure,
+  title={Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning},
+  author={Wang, Yinjie and Yang, Ling and Tian, Ye and Shen, Ke and Wang, Mengdi},
+  journal={arXiv preprint arXiv:2506.03136},
+  year={2025}
+}
+```