Data-driven fine-tuning has emerged as a transformative approach in the field of deep learning, enabling substantial improvements in the performance of pre-trained language models. SD FQ, a prominent technique within this realm, leverages massive datasets to enhance the parameters of existing models, resulting in specialized solutions for diverse t