Hands-On with Transformers: A Step-by-Step Guide to Building Language Models from Scratch

The advent of the Transformer architecture has revolutionized Natural Language Processing (NLP), rendering traditional recurrent neural networks (RNNs) obsolete through innovations like self-attention and parallelization. This review paper serves a dual purpose: (1) to provide a theoretical deep-dive into the Transformer’s architecture, dissecting its core components—self-attention mechanisms, positional encoding, and encoder-decoder frameworks—and (2) to deliver practical, educational value through a modular, open-source PyTorch implementation. By translating theory into executable code, we demystify the "black box" of Transformers, enabling researchers, educators, and developers to experiment with state-of-the-art NLP tools even under hardware constraints.Our implementation, tested on an English-Hindi translation task. Beyond technical analysis, this paper emphasizes the social impact of democratizing AI education, showing how accessible frameworks can empower global communities to address challenges in language preservation, healthcare, and education. By bridging the gap between theoretical understanding and hands-on application.