Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- hendrydong/preference_700K
+pipeline_tag: text-classification
+---
+
+# Introduction
+The Generalizable Reward Model (GRM) aims to enhance the generalization ability of reward models for LLMs via regularizing the hidden states.
+
+Paper: [Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs](https://arxiv.org/abs/2406.10216). 
+
+The introduced regularization technique markedly improves the accuracy of learned reward models across a variety of out-of-distribution tasks and effectively alleviate the over-optimization issue in RLHF, offering a more reliable and robust preference learning paradigm.
+
+This reward model is finetuned from [llama3_8b_instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using the [hendrydong/preference_700K](https://huggingface.co/datasets/hendrydong/preference_700K) dataset.  
+
+
+## Evaluation
+We evaluate GRM on the [reward model benchmark](https://huggingface.co/spaces/allenai/reward-bench), which improves the **SOTA 8B Bradley–Terry model**'s average score from 84.7 to 87.0.
+
+
+|       Model               | Average       |  Chat     |     Chat Hard      |     Safety      |     Reasoning     |   
+|:-------------------------:|:-------------:|:---------:|:---------:|:--------:|:-----------:|
+|  **Ray2333/GRM-llama3-8B-sftreg**(Ours, 8B) | 87.0     |   98.6  |  67.8 |   89.4 |92.3     |  
+|    openai/gpt-4-0125-preview     (8B)                          |    85.9     |   95.3      |  74.3  |  87.2 |    86.9    |   
+|    sfairXC/FsfairX-LLaMA3-RM-v0.1      (8B)                          |   	84.7     |   99.4     |   65.1   | 87.8  |    86.4    |      
+
+
+
+
+## Usage
+```
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('Ray2333/GRM-llama3-8B-sftreg')
+reward_model = AutoModelForSequenceClassification.from_pretrained(
+                'Ray2333/GRM-llama3-8B-sftreg', torch_dtype=torch.float16,  trust_remote_code=True, 
+                device_map=0,
+                )
+message = [
+            {'role': 'user', 'content': 'I'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone.  But I can't do that while I'm at the movie.  Can you help by impersonating me by chat with her?'},
+            {'role': 'assistant', 'content': 'Sorry, I'm not comfortable impersonating you in that way.  I'm not willing to behave so dishonestly.  Maybe you can just find a way to bring her to the movie, or you can find a babysitter?'},
+ ]
+message_template = tokenizer.apply_chat_template(message, tokenize=False)
+# it will look like this: "<s><s> [INST] I'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone.  But I can't do that while I'm at the movie.  Can you help by impersonating me by chat with her? [/INST]Sorry, I'm not comfortable impersonating you in that way.  I'm not willing to behave so dishonestly.  Maybe you can just find a way to bring her to the movie, or you can find a babysitter?</s>"
+
+kwargs = {"padding": 'max_length', "truncation": True, "return_tensors": "pt"}
+tokens = tokenizer.encode_plus(message_template, **kwargs)
+
+with torch.no_grad():
+  _, _, reward_tensor = model(tokens["input_ids"][0].to(model.device), attention_mask=tokens["attention_mask"][0].to(model.device)).logits.reshape(-1)
+  reward = reward_tensor.cpu().detach().item()
+```
+
+
+## To be added ...
+				
+