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rrvaswin / qwen3_4b_rldecomp
Model's Last Updated: February 24 2026
text-generation
Introduction of qwen3_4b_rldecomp
Model Details of qwen3_4b_rldecomp
Model Card for sefcom
This model is a fine-tuned version of Qwen/Qwen3-4B-Instruct-2507 . It has been trained using TRL .
Quick start
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 0.28.0
- Transformers: 5.2.0
- Pytorch: 2.10.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2
Citations
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}
}
Runs of rrvaswin qwen3_4b_rldecomp on huggingface.co
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More Information About qwen3_4b_rldecomp huggingface.co Model
Url of qwen3_4b_rldecomp
qwen3_4b_rldecomp huggingface.co Url
Provider of qwen3_4b_rldecomp huggingface.co
Other API from rrvaswin
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