Qwen3-0.6B-Medical-Expert (Abliterated)

Training Procedure

1. Dataset Preparation
   • The FreedomIntelligence/medical-o1-reasoning-SFT dataset was used.
   • Each example consists of medically relevant instructions or questions paired with detailed, step-by-step clinical reasoning responses.
   • Prompts were structured to encourage safe, factual, and coherent medical reasoning chains.
2. Model Loading and Configuration
   • Qwen3 base model weights were loaded via the unsloth library in bf16 precision.
   • All model layers were fully updated (full_finetuning=True) to effectively adapt the model to medical reasoning and decision-making tasks.
3. Supervised Fine-Tuning
   • Fine-tuning was conducted using the Hugging Face TRL library with the Supervised Fine-Tuning (SFT) approach.
   • The model was trained to follow clinical instructions, interpret symptoms, and generate reasoned diagnoses or treatment suggestions.

Purpose and Outcome

• The model’s ability to interpret medical instructions and generate step-by-step clinical reasoning has been significantly enhanced.
• It produces responses that combine factual accuracy with transparent reasoning, making it useful in educational and assistive medical AI contexts.

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