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selink/Qwen3-4B-fa
Qwen3-4B-fa is a text classification model from selink. Use it when you need a label for a piece of text. It is set up for transformers.
This model is a fine-tuned version of Qwen/Qwen3-4B. It has been trained using TRL.
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From the Hugging Face model README
This model is a fine-tuned version of Qwen/Qwen3-4B. It has been trained using TRL.
from transformers import pipeline
text = "The capital of France is Paris."
rewarder = pipeline(model="selink/Qwen3-4B-fa", device="cuda")
output = rewarder(text)[0]
print(output["score"])
This model was trained with Reward.
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}}
}