Qwen3-0.6B-Diagnosis-Expert

This project performs full fine-tuning on the Qwen3-0.6B language model to enhance its clinical diagnosis interpretation and reasoning capabilities. The model was optimized using the bfloat16 (bf16) data type.

Training Procedure

1. Dataset Preparation

   • Dataset: Containing paired clinical patient histories and step-by-step diagnostic conclusions.

2. Model Loading and Configuration

   • Base model: Qwen3-0.6B, loaded with the unsloth library in bf16 precision.
   • Full fine-tuning (full_finetuning=True) applied to all layers to adapt the model for medical diagnostic tasks.

3. Supervised Fine-Tuning (SFT)

   • Utilized the Hugging Face TRL library with the Supervised Fine-Tuning approach.

   • The model was trained to generate both intermediate reasoning steps and final diagnostic statements.

   • Training hyperparameters:

     • Epochs: 2
     • Learning rate: 2e-5
     • Batch size: 8

Purpose and Outcome

• Significantly improved the model’s ability to interpret clinical information and propose accurate, structured diagnoses.

Evaluation

• Performance was measured on a held-out validation set with the following metric:

  • Diagnostic Similarity: 71.68% similarity compared to DeepSeek V3-0324 baseline.

License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.