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Introduction

This reward model is finetuned from the Ray2333/GRM-llama3-8B-sftreg using the Skywork preference dataset.

Evaluation

We evluated this reward model on reward-bench (https://huggingface.co./spaces/allenai/reward-bench) with an average score of 91.5.

{'Chat': 0.9553072625698324, 'Chat Hard': 0.8618421052631579, 'Safety': 0.9116798876798876, 'Reasoning': 0.9361529437442025}

Check our GRM series at 🤗hugging face, our paper at Arxiv, and github repo at Github.

When evaluated using reward bench, please add '--not_quantized' to avoid performance drop.

Model Average Chat Chat Hard Safety Reasoning
GRM_Llama3.1_8B_rewardmodel-ft(8B) 92.6 95.0 87.7 91.4 96.4
GRM-Llama3-8B-rewardmodel-ft(8B) 91.5 95.5 86.2 90.8 93.6
GRM-Llama3.2-3B-rewardmodel-ft(ours, 3B) 90.9 91.6 84.9 92.7 94.6
GRM-gemma2-2B-rewardmodel-ft (Ours, 2B) 88.4 93.0 77.2 92.2 91.2
google/gemini-1.5-pro-0514 88.2 92.3 80.6 87.9 92.0
RLHFlow/pair-preference-model-LLaMA3-8B 87.1 98.3 65.8 89.7 94.7
GRM-llama3-8B-sftreg(ours, 8B) 87.0 98.6 67.8 89.2 92.3
google/gemini-1.5-pro-0924 86.8 94.1 77.0 85.8 90.2
openai/gpt-4o-2024-08-06 86.7 96.1 76.1 88.1 86.6
GRM-llama3.2-3B-sftreg(ours, 3B) 85.8 96.4 67.1 88.2 91.6
GRM-Gemma-2B-rewardmodel-ft (Ours, 2B) 84.7 89.4 75.2 85.5 88.8
openai/gpt-4o-2024-05-13 84.6 96.6 70.4 86.5 84.9
sfairXC/FsfairX-LLaMA3-RM-v0.1 (8B) 84.4 99.4 65.1 86.8 86.4
Nexusflow/Starling-RM-34B 82.6 96.9 57.2 87.7 88.5
GRM-Gemma2-2B-sftreg(Ours, 2B) 81.0 97.2 59.6 86.9 80.3
GRM-Gemma-2B-sftreg(Ours, 2B) 75.3 95.5 48.7 80.0 76.8
berkeley-nest/Starling-RM-7B-alpha (7B) 74.6 98 43.4 88.6 74.6
Gemma-2B-rewardmodel-baseline(Ours, 2B) 73.7 94.1 46.1 79.6 75.0
openbmb/UltraRM-13b (13B) 71.3 96.1 55.3 45.8 82

Usage

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

device = 'cuda:0'
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Ray2333/GRM-Llama3-8B-rewardmodel-ft')
reward_model = AutoModelForSequenceClassification.from_pretrained(
                'Ray2333/GRM-Llama3-8B-rewardmodel-ft', torch_dtype=torch.float16, 
                device_map=device,
                )
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)

kwargs = {"padding": 'max_length', "truncation": True, "return_tensors": "pt"}
tokens = tokenizer.encode_plus(message_template, **kwargs)

with torch.no_grad():
  reward_tensor = reward_model(tokens["input_ids"][0].view(1,-1).to(device), attention_mask=tokens["attention_mask"][0].view(1,-1).to(device))[0]
  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}
}
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