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akash2402/clinical-qna-lite-reasoning

sourceHugging Faceupdated 5mo agoView on Hugging Face
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Model Card

Model Card for clinical-qna-2026-05-06_18.26.19-lite-reasoning

This model is a fine-tuned version of meta-llama/Llama-3.2-3B. It has been trained using TRL.

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="akash2402/clinical-qna-2026-05-06_18.26.19-lite-reasoning", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, 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: 0.25.1
  • —Transformers: 5.7.0
  • —Pytorch: 2.5.1+cu121
  • —Datasets: 4.8.5
  • —Tokenizers: 0.22.2

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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}