A Comprehensive Guide to LLM Fine-Tuning: Methods, Comparisons, and Best-Fit Scenarios
As enterprises build AI-powered applications, fine-tuning large language models (LLMs) has become essential for delivering customized capabilities. Over the years, fine-tuning techniques have evolved from traditional full-parameter training to efficient, low-cost approaches such as LoRA, QLoRA, Adapters, supervised fine-tuning (SFT), reward modeling (RM), and RLHF. This article provides a systematic overview of major fine-tuning methods, compares their strengths and weaknesses, and offers guidance on when each method is most suitable....