nvidia/HelpSteer2
Viewer • Updated • 21.4k • 5.14k • 452
How to use AIPlans/Qwen3-0.6B-RM-hs2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="AIPlans/Qwen3-0.6B-RM-hs2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("AIPlans/Qwen3-0.6B-RM-hs2")
model = AutoModelForSequenceClassification.from_pretrained("AIPlans/Qwen3-0.6B-RM-hs2")This model is a fine-tuned version of Qwen/Qwen3-0.6B-Base. It has been trained using TRL. Intended Use: Research on model diffing, preference fine-tuning, and evaluation of lightweight LLM behavior changes. It was developed for use in the Model Diffing project of AI-Plans.
This model is a reward model and was trained using prompts with a chosen response >=3 only. It took about 1h 20mins with an A100(40 GB).
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
Base model
Qwen/Qwen3-0.6B-Base