Update README.md

README.md

@@ -18,8 +18,7 @@ library_name: transformers
 
 # 🚀 Can we cast reward modeling as a reasoning task?
 
-**RM-R1** is a training framework for *Reasoning Reward Model* (ReasRM) that judges two candidate answers by first **thinking out loud**—generating structured rubrics or reasoning traces—then emitting its preference.  
-Compared to traditional scalar or generative reward models, RM-R1 delivers **state-of-the-art performance** on public RM benchmarks while offering fully interpretable justifications.
+**RM-R1** is a training framework for *Reasoning Reward Model* (ReasRM) that judges two candidate answers by first **thinking out loud**—generating structured rubrics or reasoning traces—then emitting its preference. Compared to traditional scalar or generative reward models, RM-R1 delivers **state-of-the-art performance** on public RM benchmarks on average while offering fully interpretable justifications.
 
 
 ## 🧠 TL;DR
@@ -106,7 +105,7 @@ INSTRUCT_SINGLE_USER_PROMPT_TEMPLATE = (
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "gaotang/RM-R1-Qwen2.5-Instruct-32B"
+model_name = "gaotang/RM-R1-Qwen2.5-Instruct-14B"
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     torch_dtype="auto",