GRM Llama3.2 3B Rewardmodel Ft

🚀 GRM-Llama3.2-3B Reward Model

This is a state-of-the-art 3B reward model that outperforms many larger models and can serve as a strong judge, even surpassing gpt4/gemini.

🚀 Quick Start

This reward model is fine-tuned from the Ray2333/GRM-llama3.2-3B-sftreg using the decontaminated Skywork preference dataset v0.2. It achieved a score of 90.9 on the reward model benchmark, making it a SOTA 3B reward model that can outperform a series of 8B reward models and even surpass gpt4/gemini as a judge.

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

✨ Features

• High Performance: Achieved a high score on the reward model benchmark, outperforming many larger models.
• Generalizable: Can be used as a judge in various scenarios, surpassing gpt4/gemini in some cases.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

device = 'cuda:0'
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Ray2333/GRM-Llama3.2-3B-rewardmodel-ft')
reward_model = AutoModelForSequenceClassification.from_pretrained(
                'Ray2333/GRM-Llama3.2-3B-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)
# it will look like this: "<bos><start_of_turn>user\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?<end_of_turn>\n<start_of_turn>model\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?<end_of_turn>\n".

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

# The encode_plus may add another bos token though no impact on the final performance, but you can also avoid this by using the following code:
# tokens =  tokenizer.apply_chat_template(message, tokenize=True, return_dict=True, **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()

📚 Documentation

Evaluation

We evaluate GRM-Llama3.2-3B-rewardmodel-ft on the reward model benchmark, where it achieved strong performance among models smaller than 7B.

⚠️ Important Note

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

🔧 Technical Details

No technical details are provided in the original document, so this section is skipped.

📄 License

This project is licensed under the Apache-2.0 license.

📚 Citation

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

@inproceedings{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},
  booktitle={Advances in Neural Information Processing Systems},
  year={2024}
}