Param-efficient fine-tuning has emerged as a essential technique in the field of natural language processing (NLP). It enables us to train large language models (LLMs) for specific tasks while minimizing the number of weights that are adjusted. This methodology offers several advantages, including reduced computational costs, faster fine-tuning … Read More