DeepSeek-R1-Distill-Qwen-1.5B-medical-sft-adapter

DeepSeek-R1-Distill-Qwen-1.5B-Medical-QLoRA

This is an adapter of MilyaShams/DeepSeek-R1-Distill-Qwen-1.5B-Medical, fine-tuned using QLoRA with TRL.

The adapter preserves the medical domain specialization of the base model while optimizing memory efficiency and training speed through low-rank adaptation.

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="MilyaShams/DeepSeek-R1-Distill-Qwen-1.5B-Medical-QLoRA", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=1024, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>

This model was trained with SFT.

Framework versions

• TRL: 0.15.2
• Transformers: 4.47.0
• Pytorch: 2.5.1+cu121
• Datasets: 3.3.1
• Tokenizers: 0.21.0

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}}
}