qwen2.5-coder-3b-distilled-from-14b

Model Card for qwen2.5-coder-3b-distilled-from-14b

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-3B-Instruct. 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="Harsha901/qwen2.5-coder-3b-distilled-from-14b", 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 Distillation, a method introduced in On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes.

Framework versions

• TRL: 1.4.0
• Transformers: 5.0.0
• Pytorch: 2.10.0+cu128
• Datasets: 4.8.5
• Tokenizers: 0.22.2

Citations

Cite Distillation as:

@inproceedings{agarwal2024on-policy,
    title        = {{On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes}},
    author       = {Rishabh Agarwal and Nino Vieillard and Yongchao Zhou and Piotr Stanczyk and Sabela Ramos Garea and Matthieu Geist and Olivier Bachem},
    year         = 2024,
    booktitle    = {The Twelfth International Conference on Learning Representations, {ICLR} 2024, Vienna, Austria, May 7-11, 2024},
    publisher    = {OpenReview.net},
    url          = {https://openreview.net/forum?id=3zKtaqxLhW},
}

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}
}