This repository provides a hands-on implementation of a GPT-like language model in PyTorch, building up from foundational concepts through tokenization, attention mechanisms, model architecture, and training procedures. Developers can use it to understand how large language models work under the hood by following step-by-step code examples that cover pretraining, fine-tuning, and instruction-tuning. It's useful for learning the practical details of transformer-based models and natural language processing rather than relying solely on high-level APIs.

4 open issues · last updated Jul 21, 2026