Update README.md

README.md

@@ -30,10 +30,12 @@ Key Highlights:
   - Best of N: By leveraging a combination of response sampling and Best-of-N strategies, we choose the response of top score judged by reward model, yielding better results with spending more inference time. For example, Qwen2.5-Math-1.5B-Instruct obtains 83.9 on MATH in RM@8 setting and even surpasses the performance of Qwen2.5-Math-7B-Instruct 83.6 with greedy decoding.
   - Comparasion with majority voting (Maj@N): RM@N scores are substantially better than Maj@N scores aross almost all benchmarks and models.
 
+![](http://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2.5/qwen2.5-math-pipeline.jpeg)
+
 
 ## Model Details
 
-For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen2-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2-Math).
+For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen2.5-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2.5-Math).
 
 
 ## Requirements
@@ -46,7 +48,7 @@ For more details, please refer to our [blog post](https://qwenlm.github.io/blog/
 > </b>
 > </div>
 
-For requirements on GPU memory and the respective throughput, see similar results of Qwen2.5 [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+For requirements on GPU memory and the respective throughput, see similar results of Qwen2 [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
 
 ## Quick Start