Ray2333/GRM-llama3-8B-sftreg

Introduction

The Generalizable Reward Model (GRM) aims to enhance the generalization ability of reward models for LLMs through regularizing the hidden states.

Paper: Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs.

The introduced text generation regularization 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 (even with corrupted preference data), offering a more reliable and robust preference learning paradigm.

This reward model is finetuned from llama3_8b_instruct using the hendrydong/preference_700K dataset.

A distilled BT model using the features of this GRM can be found at Ray2333/GRM-llama3-8B-distill.

Evaluation

We evaluate GRM on the reward model benchmark, 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
Ray2333/GRM-llama3-8B-distill(Ours, 8B)	86.1	98.3	68.4	86.1	91.3
openai/gpt-4-0125-preview	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

Note 1: Please download the model.py file from this repository to ensure the structure is loaded correctly and verify that the v_head is properly initialized.

Note 2: loading llama3 model into 8 bit could lead to performance degradation.

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: "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nI'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?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nSorry, 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?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n".

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()

Citation

If you find this model helpful for your research, please cite GRM

@article{yang2024regularizing,
  title={Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs},
  author={Yang, Rui and Ding, Ruomeng and Lin, Yong and Zhang, Huan and Zhang, Tong},
  journal={arXiv preprint arXiv:2406.10216},
  year={2024}
}