🩺 QWEN-4B-ALL

QWEN-4B-ALL 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.

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🧠 Model overview

Property	Description
Base model	Qwen3-4B-Instruct-2507
Fine-tuning method	DoRA (Weight-Decomposed Low-Rank Adaptation)
Architecture size	~4B parameters
Language	French 🇫🇷
Domain	Biomedical, Clinical, Health
Intended use	Research on instruction tuning and domain adaptation
Caution	Not for clinical or diagnostic use

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⚙️ Training setup

Fine-tuning was performed on 30k multiple-choice (MCQ and MCQU) examples for each configuration, using:

• 10 epochs
• Batch size: 12
• Learning rate: 1e-4
• Gradient accumulation: 8
• Cosine scheduler with 5% warmup
• LoRA rank: 16, α = 16, dropout = 0.05
• Adapters applied to: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

All runs used identical hyperparameters to isolate the effect of data provenance.

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📊 Evaluation summary

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.

See MedInjection-FR GitHub for full results and plots.

📚 Citation

If you use this model, please cite:

@misc{belmadani2026medinjectionfrexploringrolenative,
      title={MedInjection-FR: Exploring the Role of Native, Synthetic, and Translated Data in Biomedical Instruction Tuning}, 
      author={Ikram Belmadani and Oumaima El Khettari and Pacôme Constant dit Beaufils and Benoit Favre and Richard Dufour},
      year={2026},
      eprint={2603.06905},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.06905}, 
}