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Learn to construct a miniature GPT language model from the ground up using PyTorch in this comprehensive beginner-friendly tutorial. Discover how large language models like GPT function internally by implementing a simplified version that predicts the next word in a sequence. Master the probability formulas behind next-word prediction and understand the fundamental mechanics of transformer-based language models. Create and prepare your own text dataset, then implement tokenization to convert words into numerical identifiers that neural networks can process. Build the complete model architecture using PyTorch, including attention mechanisms and neural network layers essential for language understanding. Train your mini GPT model on your prepared dataset and learn to make predictions with the trained system. Gain practical hands-on experience with the core concepts underlying modern AI language models while working through each implementation step systematically. Access the complete source code through the provided GitHub repository to follow along and experiment with your own modifications.
