MedQA: Fine-Tuning a Clinical AI on AMD ROCm — No CUDA Required

This article demonstrates LoRA fine-tuning of Qwen3-1.7B on MedMCQA using AMD MI300X with ROCm, enabling clinical question-answering without CUDA.

• •HuggingFace ecosystem runs seamlessly on ROCm with only three environment variables, requiring no code changes or custom kernels
• •AMD MI300X's 192 GB HBM3 memory enables full fp16 training without quantization, eliminating 4-bit/8-bit quantization needs
• •Training used ~2.2 million trainable parameters (0.15% of total) via LoRA, completing 2,000 samples in ~5 minutes
• •Model generates both correct multiple-choice answer and clinical reasoning explanation
• •Adapter weights publicly available on HuggingFace Hub (HK2184/medqa-qwen3-lora) with live demo on Spaces

This summary was automatically generated by AI based on the original article and may not be fully accurate.