Qwen3-0.6B-Treatment-Expert

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

Training Procedure

1. Dataset Preparation

   • Dataset: Containing paired clinical diagnosis descriptions and corresponding step-by-step treatment plans.

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 treatment 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 treatment recommendations.

   • Training hyperparameters:

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

Purpose and Outcome

• Significantly improved the model’s ability to interpret clinical diagnoses and propose structured treatment plans.

Evaluation

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

  • Plan Fidelity: 59.69% similarity with DeepSeek V3-0324.
  • Reasoning Coherence: Rated high by a panel of medical experts.

License

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