Update README.md

README.md

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 # ​**PCL-Reasoner-V1**​
 
 ## Model Overview  
-We release ​PCL-Reasoner-V1​, a model trained based on ​Qwen2.5-32B-Base​ and undergoes high-performance supervised fine-tuning based on the ​MindSpore framework​ and ​Ascend hardware. After fine-tuning, the model demonstrates significant improvements in mathematical reasoning capabilities. PCL-Reasoner-V1 achieves 85.7% and 84.2% respectively on AIME 24 and AIME 25, which position PCL-Reasoner-V1 among the top-tier models in the 32B parameter class.
+We release ​PCL-Reasoner-V1​, a model trained based on ​Qwen2.5-32B-Base​ and undergoes high-performance supervised fine-tuning based on the ​MindSpore framework​ and ​Ascend hardware. After fine-tuning, the model demonstrates significant improvements in mathematical reasoning capabilities. PCL-Reasoner-V1 achieves 85.7% and 84.2% respectively on AIME 24 and AIME 25, which position PCL-Reasoner-V1 among the top-tier models in the 32B parameter class on AIME24/25.
 
 We have fully open-sourced the model weights, dataset and training code. Follow the tutorial below to deploy and explore post-training!
 
@@ -12,8 +12,8 @@ https://github.com/PCL-Reasoner/V1
 
 https://openi.pcl.ac.cn/PCL-Reasoner/V1
 
-#### Evaluation Results on AIME24/25  
-The table below compares mainstream models on the AIME24 and AIME25 benchmarks. For accuracy, we used the ​**Avg@32 metric**​ (averaging 32 sampling attempts per query):  
+#### Evaluation
+We used the ​**Avg@32 metric**​ (averaging 32 sampling attempts per query) for evaluation
 
 
 <table>