---
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 through regularizing the hidden states.

Paper: [Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs](https://arxiv.org/abs/2406.10216). 

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](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using the [hendrydong/preference_700K](https://huggingface.co/datasets/hendrydong/preference_700K) dataset.  

A distilled BT model using the features of this GRM can be found at [Ray2333/GRM-llama3-8B-distill](https://huggingface.co/Ray2333/GRM-llama3-8B-distill).

## 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     |  
|  [**Ray2333/GRM-llama3-8B-distill**](https://huggingface.co/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}
}
```