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---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: transformers
model_name: DeepSeek-R1-Distill-Qwen-1.5B-Medical-QLoRA
tags:
- generated_from_trainer
- trl
- sft
licence: license
license: mit
datasets:
- FreedomIntelligence/medical-o1-reasoning-SFT
language:
- en
pipeline_tag: text-generation
---
# DeepSeek-R1-Distill-Qwen-1.5B-Medical-QLoRA
This is an adapter of [MilyaShams/DeepSeek-R1-Distill-Qwen-1.5B-Medical](https://huggingface.co/MilyaShams/DeepSeek-R1-Distill-Qwen-1.5B-Medical), fine-tuned using [QLoRA](https://huggingface.co/docs/peft/main/en/conceptual_guides/lora) with [TRL](https://github.com/huggingface/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
```python
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"/>](https://wandb.ai/miliusha2801-innopolis-university/Deepseek-R1-Qwen-1.5b%20SFT%20on%20medical%20dataset%20full%201%20epoch%20v.0/runs/7q51lr76)
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:
```bibtex
@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}}
}
``` |