Fine-tuning large language models (LLMs) on domain-specific text corpora has emerged as a crucial step in enhancing their performance on research tasks. This paper investigates various fine-tuning methods for LLMs when applied to research text. We analyze the impact of different variables, such as training, architecture, and configuration settings,