đ Qwen3-0.6B-Medical-Expert
This project fine-tunes the Qwen3-0.6B language model to boost its medical reasoning and clinical understanding. It uses the FreedomIntelligence/medical-o1-reasoning-SFT dataset for training.
đ Quick Start
This project conducts full fine - tuning on the Qwen3 - 0.6B language model. The goal is to enhance its medical reasoning and clinical understanding capabilities. Training is carried out on the FreedomIntelligence/medical-o1-reasoning-SFT dataset with bfloat16 (bf16) precision for efficient optimization.
⨠Features
- Enhanced Medical Reasoning: The model's ability to interpret medical instructions and generate step - by - step clinical reasoning has been significantly improved.
- Factual and Transparent Responses: It produces responses that combine factual accuracy with transparent reasoning, which is useful in educational and assistive medical AI contexts.
đĻ Installation
No installation steps are provided in the original document, so this section is skipped.
đģ Usage Examples
No code examples are provided in the original document, so this section is skipped.
đ Documentation
Training Procedure
1. Dataset Preparation
- The
FreedomIntelligence/medical-o1-reasoning-SFT dataset was used.
- Each example in the dataset 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.
đ§ Technical Details
No specific technical details (more than 50 - word description) are provided in the original document, so this section is skipped.
đ License
This project is licensed under the Apache License 2.0. See the LICENSE file for details.
Support
| Property |
Details |
| Model Type |
Qwen3 - 0.6B |
| Training Data |
FreedomIntelligence/medical - o1 - reasoning - SFT |
| Pipeline Tag |
text - generation |
| Library Name |
transformers |
| Base Model |
Qwen/Qwen3 - 0.6B |
| License |
Apache License 2.0 |
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