Qwen/Qwen3.5-0.8B fine-tuned directly (no LoRA/adapters) on medical reasoning data, to turn a general-purpose model into a domain-focused one. Overfitting to the training data was an accepted trade-off of this experiment; the goal was to study the training/validation loss behaviour. Loss is computed on the assistant response only (reasoning + answer); the prompt is masked. Best validation loss at epoch. See losscurve.png for train vs validation loss. - Research into domain adaptation, partial fine-tuning, and overfitting behaviour of small models. - Offline experimentation with medical-style question answering and reasoning formats. - Clinical decision support, diagnosis, treatment…
Independent publisher
Aditya G
adityag-india
Models in Library2
Datasets in Library0
Models on Hugging Face14
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Published tasks: Text generation.
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Models
Qwen/Qwen3.5-0.8B fully fine-tuned (no LoRA) on FreedomIntelligence/medical-o1-reasoning-SFT (en). Only the MLP blocks of layers [0, 1, 2, 3, 4] were trained; everything else is frozen. Checkpoint: best.pt. Split 80/10/10, 10 epochs, lr 1e-05.