Total runs:
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Run Growth:
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Growth Rate:
59.21%
Sponsored by
APIMaster
edbeeching / Qwen3-4B-Instruct-2507-SFT-tr5
Model's Last Updated: March 23 2026
text-generation
Introduction of Qwen3-4B-Instruct-2507-SFT-tr5
Model Details of Qwen3-4B-Instruct-2507-SFT-tr5
Model Card for Qwen3-4B-Instruct-2507-SFT-tr5
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="edbeeching/Qwen3-4B-Instruct-2507-SFT-tr5", 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: 0.27.0.dev0
- Transformers: 5.3.0.dev0
- Pytorch: 2.10.0
- Datasets: 4.5.0
- Tokenizers: 0.22.2
Citations
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}}
}
Runs of edbeeching Qwen3-4B-Instruct-2507-SFT-tr5 on huggingface.co
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Total runs
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24-hour runs
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3-day runs
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30-day runs
More Information About Qwen3-4B-Instruct-2507-SFT-tr5 huggingface.co Model
Url of Qwen3-4B-Instruct-2507-SFT-tr5
Qwen3-4B-Instruct-2507-SFT-tr5 huggingface.co Url
Provider of Qwen3-4B-Instruct-2507-SFT-tr5 huggingface.co
Other API from edbeeching
Updated:June 30 2022
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