RLHF Workflow: From Reward Modeling to Online RLHF
Paper • 2405.07863 • Published • 71
How to use sfairXC/FsfairX-Gemma2-RM-v0.1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="sfairXC/FsfairX-Gemma2-RM-v0.1") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sfairXC/FsfairX-Gemma2-RM-v0.1")
model = AutoModelForSequenceClassification.from_pretrained("sfairXC/FsfairX-Gemma2-RM-v0.1", device_map="auto")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This is a Vanilla BT based Reward model based on Gemma-2-9B. The recipes are from RLHF Workflow.
We have the reward-bench result:
Chat: 98.04
Chat Hard: 65.35
Safety: 89.54
Reasoning: 92.31
Please refer to
@misc{dong2024rlhf,
title={RLHF Workflow: From Reward Modeling to Online RLHF},
author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang},
year={2024},
eprint={2405.07863},
archivePrefix={arXiv},
primaryClass={cs.LG}
}