Gemma 3 12b It Codeforces SFT

🚀 gemma-3-12b-it-codeforces-SFT

This is a fine - tuned model based on the Transformer architecture, trained on the Codeforces dataset to enhance text - generation capabilities.

🚀 Quick Start

Basic Usage

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="qgallouedec/gemma-3-12b-it-codeforces-SFT", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

✨ Features

This model is a fine - tuned version of google/gemma-3-12b-it on the open-r1/codeforces-cots dataset. It has been trained using TRL.

📚 Documentation

Training procedure

This model was trained with SFT.

Framework versions

• TRL: 0.16.0.dev0
• Transformers: 4.50.0.dev0
• Pytorch: 2.6.0
• Datasets: 3.0.0
• Tokenizers: 0.21.0

📄 License

The model is under the specified license.

📚 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 Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}