This is an educational implementation that builds a generative pre-trained transformer (GPT) language model from scratch using PyTorch. The project is structured as a step-by-step tutorial, walking through the construction of a decoder-only transformer architecture and its training loop with clean git commits and an accompanying video lecture for a hands-on learning experience.
What sets this implementation apart is its focus on practical reproduction: it provides a workflow to train a 124-million-parameter model from scratch in about one hour on cloud GPU hardware, costing under ten dollars. The tutorial covers both the architecture construction and the full training pipeline, making it suitable for those who want to understand the inner workings of a GPT-scale model without relying on pre-built frameworks.
The technical implementation covers the core components of a decoder-only transformer, including causal masked self-attention where each token attends only to preceding tokens, cross-entropy loss minimization for next-token prediction, weight-decay regularization to prevent overfitting, and GPU-accelerated training through PyTorch for large-scale computation. While the project is small in scale, it mirrors the architectural patterns used in larger language models.
