  This study presents a comprehensive analysis and comparison of two
predominant fine-tuning methodologies - full-parameter fine-tuning and
parameter-efficient tuning - within the context of medical Large Language
Models (LLMs). We developed and refined a series of LLMs, based on the Llama-2
architecture, specifically designed to enhance medical knowledge retrieval,
reasoning, and question-answering capabilities. Our experiments systematically
evaluate the effectiveness of these tuning strategies across various well-known
medical benchmarks. Notably, our medical LLM Med42 showed an accuracy level of
72% on the US Medical Licensing Examination (USMLE) datasets, setting a new
standard in performance for openly available medical LLMs. Through this
comparative analysis, we aim to identify the most effective and efficient
method for fine-tuning LLMs in the medical domain, thereby contributing
significantly to the advancement of AI-driven healthcare applications.
