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

MedQA is an innovative project that fine-tunes the Qwen3-1.7B language model for medical question answering using AMD hardware and ROCm, highlighting a significant departure from the conventional reliance on NVIDIA's CUDA. The model, trained on the MedMCQA dataset, uniquely provides both the correct answer and an explanation for its choices, enhancing its clinical utility. The project showcases the feasibility of using AMD's Instinct MI300X, with its substantial 192 GB HBM3 memory, to train large models without resorting to quantization, thereby ensuring cleaner training outputs. Utilizing LoRA for fine-tuning, MedQA manages to effectively train only a small fraction of the model's parameters, significantly reducing memory usage and training time. The project further demonstrates the compatibility of the HuggingFace ecosystem with ROCm, proving that the same training code can be effortlessly adapted from CUDA to ROCm with minor environment adjustments. As a result, MedQA not only establishes the practical applicability of AMD hardware for complex AI tasks but also emphasizes the importance of explanatory outputs in medical AI, setting a precedent for future developments in this field.