Model Card for Qwen3-30B-A3B-python-coder

This model is a fine-tuned version of Qwen/Qwen3-30B-A3B on the burtenshaw/tulu-3-sft-personas-code-no-prompt dataset. 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="burtenshaw/Qwen3-30B-A3B-python-coder", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

👁 Visualize in Weights & Biases

This model was trained with SFT.

Framework versions

• TRL: 0.17.0
• Transformers: 4.51.3
• Pytorch: 2.7.0
• Datasets: 3.5.1
• Tokenizers: 0.21.1

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}}
}

Base model

Qwen/Qwen3-30B-A3B-Base

Qwen/Qwen3-30B-A3B

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This is a collection of datasets and models to make a lightweight python focused model • 3 items • Updated Jun 26, 2025