Improve model card: add text generation pipeline tag and Github README information

This PR improves the model card by:

- Adding the `pipeline_tag` to ensure the model can be found at https://huggingface.co/models?pipeline_tag=text-generation
- Adding relevant information from the GitHub README

README.md

@@ -1,31 +1,31 @@
 ---
-license: mit
 library_name: transformers
+license: mit
+pipeline_tag: text-generation
 ---
 
 <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)).
+*   **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
+## Introduction
+
+We propose **CURE**, a novel reinforcement learning framework that co-evolves LLM coder and unit tester to improve the overall coding ability of large language models. Trained on just 4.5 K samples, our [ReasonFlux-Coder models](https://huggingface.co/collections/Gen-Verse/reasonflux-coder-6833109ed9300c62deb32c6b) outperform similarly sized Qwen Coder, DeepSeek Coder and Seed Coder. **Also, this is the first open-source Coding-RL project to make everything publicly available—including models, evaluation benchmarks, training and testing datasets, and training codes!**
+
+## Citation
 
 ```
 @article{wang2025cure,