Update README.md

README.md

@@ -1,13 +1,66 @@
----
+```yaml
 library_name: transformers
 license: apache-2.0
 language:
 - en
 base_model:
 - Qwen/Qwen2.5-7B-Instruct
----
-
+```
 
 ## Model Details
 
-The reward model used in paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
+The generative reward model used in paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
+
+Inputting the question, label and the response to be evaluated, the model will judge if the response is right.
+
+Demo:
+
+> ```python
+> # Load model directly
+> from transformers import AutoTokenizer, AutoModelForCausalLM
+> 
+> tokenizer = AutoTokenizer.from_pretrained("virtuoussy/Qwen2.5-7B-Instruct-RLVR")
+> model = AutoModelForCausalLM.from_pretrained("virtuoussy/Qwen2.5-7B-Instruct-RLVR")
+> 
+> PROMPT= '''
+> Given a problem, determine whether the final answer in the provided (incomplete) solution process matches the reference answer.  
+> The reference answer may be one single option character (e.g., A, B, C, D), a numerical value, an expression, or a list of answers if multiple questions are involved.  
+> **The reference answer may be in Chinese or another language, but your evaluation should be language-agnostic.**  
+> 
+> Your task:  
+> - Compare the final output of the solution process with the reference answer.  
+> - If they **match exactly**, output **YES**.  
+> - If they **do not match**, output **NO**.  
+> - If the solution process is unclear, incomplete, or ambiguous, assume it is incorrect and output **NO**.  
+> 
+> Your output must be strictly **'YES'** or **'NO'**, with no additional words, punctuation, or explanation.  
+> 
+> ---
+> 
+> **Question:**  
+> {question}  
+> 
+> **Solution Process (Final Step Only):**  
+> {response}  
+> 
+> **Reference Answer:**  
+> {reference}  
+> 
+> **Output:**  
+> '''
+> 
+> 
+> question="The founder of China's first public kindergarten teacher training school - Jiangxi Experimental Kindergarten Teacher School is (　　)."
+> label="Chen Heqin"
+> answer="heqin chen"
+> 
+> prompt_question = PROMPT.format(question=question, reference=label, response=answer)
+> messages=[
+>            {"role": "system", "content": "You are a helpful assistant."},
+>            {"role": "user", "content": prompt_question},
+>          ]
+> input_ids=tokenizer.apply_chat_template(messages,return_tensors="pt")
+> output=model.generate(input_ids,do_sample=False)
+> judgement=tokenizer.decode(output[0][input_ids.shape[1]:],skip_special_tokens=True)
+> print("Model judgement: ",judgement)
+> ```