Fine-tuning large language models (LLMs) on niche text corpora has emerged as a crucial step in enhancing their performance on technical tasks. This study investigates various fine-tuning strategies for LLMs when applied to research text. We analyze the impact of different parameters, such as dataset size, model design, and hyperparameter tuning, o