--- license: apache-2.0 tags: - unsloth - trl - sft - medical - reasoning datasets: - FreedomIntelligence/medical-o1-reasoning-SFT language: - en base_model: - Qwen/Qwen3-0.6B pipeline_tag: text-generation library_name: transformers --- # Qwen3-0.6B-Medical-Expert This project performs full fine-tuning on the **Qwen3-0.6B** language model to enhance its **medical reasoning** and **clinical understanding** capabilities. Training was conducted on the `FreedomIntelligence/medical-o1-reasoning-SFT` dataset using bfloat16 (bf16) precision for efficient optimization. ## 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. ## License This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details. ## Support