Add pipeline tag, library name and license

This PR improves the model card metadata by:

- Adding the `text-generation` pipeline tag so people can find your model at https://huggingface.co/models?pipeline_tag=text-generation
- Adding the `transformers` library name.
- Adding the CC-BY-4.0 license.

README.md

@@ -1,11 +1,15 @@
 ---
-language:
-- en
 base_model:
 - Qwen/Qwen2.5-Math-7B
+language:
+- en
 tags:
 - One-Shot-CFT
+pipeline_tag: text-generation
+library_name: transformers
+license: cc-by-4.0
 ---
+
 # One-Shot-CFT: Unleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem
 
 <p align="center">
@@ -16,15 +20,12 @@ tags:
   <a href="https://tiger-ai-lab.github.io/One-Shot-CFT/" target="_blank">🌐 Project Page</a>
 </p>
 
-
-
 ## 🧠 Overview
 
 One-Shot Critique Fine-Tuning (CFT) is a simple, robust, and compute-efficient training paradigm for unleashing the reasoning capabilities of pretrained LLMs in both mathematical and logical domains. By leveraging critiques on just one problem, One-Shot CFT enables models like Qwen and LLaMA to match or even outperform reinforcement learning, while using 20× less compute.
 
 Instead of learning from reference answers (as in supervised fine-tuning) or reward signals (as in reinforcement learning), One-Shot CFT enables models to learn from critiques of diverse solutions to a single problem, enhancing their exposure to varied reasoning patterns and mitigating overfitting. This exposes the LLMs to multiple perspectives and error types, thereby more effectively unleashing their reasoning potential. 
 
-
 ## ✨ Key Highlights
 
 - **Unleashes Reasoning with One Example:** One-Shot CFT uses critiques of diverse model-generated solutions to a single problem to significantly boost performance across math and logic tasks. For example, with just 5 GPU hours of training on Qwen2.5-Math-7B, One-Shot CFT achieves an average improvement of +15% on six math benchmarks and +16% on three logic reasoning benchmarks.
@@ -33,7 +34,6 @@ Instead of learning from reference answers (as in supervised fine-tuning) or rew
 
 **This specific model is the One-Shot CFT variant trained based on [Qwen2.5-7B-Math](https://huggingface.co/Qwen/Qwen2.5-Math-7B) with [DSR-CFT-p0](https://huggingface.co/datasets/TIGER-Lab/One-Shot-CFT-Data) dataset.**
 
-
 ## Main Results
 
 <p align="center">
@@ -42,12 +42,11 @@ Instead of learning from reference answers (as in supervised fine-tuning) or rew
 
 <p align="center"><em>
 One-shot CFT consistently improves mathematical and logical reasoning.  
-<strong>Left:</strong> Average accuracy on six mathematical reasoning benchmarks for Qwen and LLaMA models, comparing base, SFT, RLVR, and CFT with only one training example.  
-<strong>Right:</strong> In-domain accuracy on three logic reasoning benchmarks (BBEH subtasks) for Qwen2.5-Math-7B.  
+**Left:** Average accuracy on six mathematical reasoning benchmarks for Qwen and LLaMA models, comparing base, SFT, RLVR, and CFT with only one training example.  
+**Right:** In-domain accuracy on three logic reasoning benchmarks (BBEH subtasks) for Qwen2.5-Math-7B.  
 Across both domains, CFT with a single problem significantly outperforms standard SFT and matches or exceeds reinforcement learning with much lower compute.
 </em></p>
 
-
 ## Citation
 
 If you find our work helpful, please cite it as: