Param-efficient fine-tuning has emerged as a critical technique in the field of natural language processing (NLP). It enables us to modify large language models (LLMs) for targeted tasks while controlling the number of parameters that are tuned. This strategy offers several benefits, including reduced computational costs, faster calibration times,