|
--- |
|
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 |
|
``` |
|
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() |
|
``` |
|
|
|
**Note: loading llama3 model into 8 bit could lead to performance degradation.** |
|
|
|
## 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} |
|
} |
|
``` |
|
|
|
|
|
|
