This technical report, "The Ultimate Guide to Fine-Tuning LLMs," offers a comprehensive review of techniques for adapting large language models. It traces the evolution of LLMs, distinguishes between various fine-tuning methodologies, and outlines a seven-stage pipeline for the process. The report also examines critical aspects like data preparation, training setup, evaluation, deployment, and ongoing maintenance. Furthermore, it addresses emerging challenges and research directions, including multimodal LLMs, scalability, ethical considerations, and integration with other technologies, aiming to be a valuable resource for both researchers and practitioners. https://arxiv.org/pdf/2408.13296
