NeuCLIR Query Preference Learning
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How to use selink/Qwen3-4B-subdim_maxcov-fa with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="selink/Qwen3-4B-subdim_maxcov-fa") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("selink/Qwen3-4B-subdim_maxcov-fa")
model = AutoModelForSequenceClassification.from_pretrained("selink/Qwen3-4B-subdim_maxcov-fa", device_map="auto")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-subdim_maxcov-fa", device="cuda")
output = rewarder(text)[0]
print(output["score"])
This model was trained with Reward.
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}