Update README.md

README.md

@@ -31,7 +31,6 @@ We introduce AceCoder, the first work to propose a fully automated pipeline for
 
 ## Performance on RM Bench
 
-```markdown
 | Model                                | Code | Chat  | Math  | Safety | Easy  | Normal | Hard | Avg  |
 | ------------------------------------ | ---- | ----- | ----- | ------ | ----- | ------ | ---- | ---- |
 | Skywork/Skywork-Reward-Llama-3.1-8B  | 54.5 | 69.5  | 60.6  | 95.7   | 89    | 74.7   | 46.6 | 70.1 |
@@ -50,7 +49,7 @@ We introduce AceCoder, the first work to propose a fully automated pipeline for
 | AceCoder-RM-32B                      | 72.1 | 73.7  | 70.5  | 88     | 84.5  | 78.3   | 65.5 | 76.1 |
 | Delta (AceCoder 7B - Others)         | 7.5  | \-4.6 | \-6.1 | \-6.1  | \-9.1 | \-0.3  | 6.1  | 2.1  |
 | Delta (AceCoder 32B - Others)        | 12.7 | 2.4   | \-0.9 | \-8    | \-4.5 | 3.6    | 9.4  | 6    |
-```
+
 
 ## Performance on Best-of-N sampling
 
@@ -62,11 +61,11 @@ We introduce AceCoder, the first work to propose a fully automated pipeline for
 
 ```python
 """pip install git+https://github.com/TIGER-AI-Lab/AceCoder"""
-from acecoder import AceCoderRM
+from acecoder import AceCodeRM
 from transformers import AutoTokenizer
 
 model_path = "TIGER-Lab/AceCodeRM-7B"
-model = AceCoderRM.from_pretrained(model_path, device_map="auto")
+model = AceCodeRM.from_pretrained(model_path, device_map="auto")
 tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 
 question = """\