Year: 2025
Type: article
Abstract: This series of papers introduces EasyGPT, a minimalistic, flexible and novel deep learning implementation of the Transformer architecture for the simulation and testing of Natural Language Processing (NLP) applications. Built to open industry standards, our model combines a customizable modular design which enables, among other things, model selection, hyperparameter configuration and user-selectable tokenization engine plugins. In this first paper in the series, we discuss the overall system design of EasyGPT, and evaluate its performance by fine-tuning the DistilGPT2 model on the DailyDialog dataset. Our work provides both a simple way for those starting in AI research to experience ChatGTP-like chatbot technologies at the coding level, as well as providing a foundation for the transition towards more realistic and complex model-building and experimentation.
Authors: Saira Arif *, F. N. Alavi
Institutions: Yanbu University College, Virtual University of Pakistan
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409966843