ANALYSIS OF THE EFFICIENCY OF GPT-2 MODEL APPLICATION WITH ADAPTED TRANSFER LEARNING ON VARIOUS HARDWARE ARCHITECTURES

Abstract

This paper conducts an analysis of the efficiency in implementing the GPT-2 model, one of the advanced artificial intelligence models for text generation, through adapted transfer learning, focusing particularly on the utilization of various GPU architectures. The primary goal of this research is to examine the impact of adapted transfer learning on the performance of the GPT-2 model exclusively on various GPU architectures, assessing how different GPU strengths enhance or influence the model's efficiency. The work relies on an experimental method to evaluate and compare the model's performance in terms of accuracy, processing speed, and energy efficiency on each of the tested platforms. Special attention is given to analysing how different characteristics of hardware architectures, such as processing power and memory capacity, affect the efficiency of the transfer learning process. This study provides important insights into the potential for optimizing the GPT-2 model for specific hardware platforms, which is crucial for its application in a wide range of real-world scenarios. The results of this research offer valuable information for researchers in the fields of artificial intelligence and machine learning, providing a foundation for further development and improvement of AI technologies.