QWEN-4B-NAT 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.

🧠 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

⚙️ 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.

📊 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:

MedInjection/QWEN-4B-NAT

🩺 QWEN-4B-NAT

🧠 Model overview

⚙️ Training setup

📊 Evaluation summary

📚 Citation

MedInjection/
QWEN-4B-NAT