Fine-Tuning

Questions about Low-Rank Adaptation (LoRA) are becoming increasingly common. Interviewers love asking what LoRA is and how it differs from traditional fine-tuning methods in large language models because it probes your understanding of cutting-edge parameter-efficient fine-tuning (PEFT) techniques and your ability to contrast them with classical approaches. This is a popular question because LoRA is a recent innovation that significantly lowers the cost of fine-tuning huge models and reveals whether you stay up-to-date with modern ML practices. As large language models (LLMs) like GPT, Claude, and Llama, etc., soar into the billions and maybe trillions of parameters, companies want engineers who know how to adapt these models efficiently without always resorting to brute-force retraining of all parameters.