Update README.md

README.md

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 - **Outperforms RLVR and Full SFT with 20× Less Compute:** One-Shot CFT outperforms both one-shot Reinforcement Learning with Verifiable Rewards (RLVR) and full-dataset supervised fine-tuning, while requiring only 5 GPU hours on a 7B model—offering a much more efficient and stable training alternative.
 - **Robust Across Seeds and Model Scales:** One-Shot CFT remains effective across different seed problem choices and model sizes—from 1.5B to 14B parameters—demonstrating strong generalization and scalability.
 
-**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.**
+**This specific model is the One-Shot CFT variant trained based on [Qwen2.5-1.5B-Math](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) with [DSR-CFT-p0](https://huggingface.co/datasets/TIGER-Lab/One-Shot-CFT-Data) dataset.**
 
 
 ## Main Results