Revolutionizing Workflow with AELU: Crafting a High-Precision, Time-Efficient Custom GPT Model

This paper introduces a groundbreaking method to enhance the accuracy and efficiency of Generative Pre-trained Transformer (GPT) models by integrating BERT and a cus-tomized activation function (AF). Through the fusion of BERT's contextual understanding capabilities and the tailored non linear function ability to capture nuanced patterns in the data, our approach aims to surpass the limitations of traditional GPT models. Leveraging domain-specific knowledge and incorporating additional libraries and models. In extensive experiments, we evaluate the performance of our customized model against baseline BERT architectures and other state-of-the-art models, meticulously assessing accuracy, efficiency, and scalability across a spectrum of tasks and datasets. Our findings demonstrate a remarkable improvement in accu-racy, with an average enhancement of 95.6 percent over baseline GPT architectures. Additionally, our customized model exhibits a notable reduction in loss. These results underscore the effective-ness of our approach in elevating both the accuracy and efficiency of LLM model. Through iterative refinement, our customized model aspires to set new benchmarks in language understanding and generation, paving the way for enhanced performance in real-world applications. This research not only contributes to the advancement of customized language model development but also lays the groundwork for future improvements in accuracy, robustness, and versatility.