QWEN-4B-NAT-TRAD is a fine-tuned version of
Qwen-4B-Instruct trained on the
MedInjection-FR dataset, a French biomedical instruction corpus combining
native, synthetic, and translated medical question–answer pairs.
This model was fine-tuned using
Supervised Fine-Tuning (SFT) with
DoRA adapters, designed to study how the origin of supervision data influences model adaptation.
Evaluation was conducted on French biomedical benchmarks (MCQ, MCQU, OEQ).
Metrics include Exact Match (EM) and Hamming Score for multiple-choice tasks, and BLEU/ROUGE/BERTScore + LLM-as-a-judge for open-ended QA.
1@misc{belmadani2026medinjectionfrexploringrolenative,
2 title={MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning},
3 author={Ikram Belmadani and Oumaima El Khettari and Pacôme Constant dit Beaufils and Benoit Favre and Richard Dufour},
4 year={2026},
5 eprint={2603.06905},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL},
8 url={https://arxiv.org/abs/2603.06905},
9}
