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This article breaks down Andrej Karpathy’s zero-dependency, 243-line GPT implementation in plain Python. It explains how each part—tokenizer, autograd engine, embeddings, attention mechanism, residual connections, and MLP—mirrors a full-scale transformer on a tiny dataset of baby names.
- Karpathy's microGPT implements a full GPT—tokenizer, autograd engine, transformer, training loop—in just 243 lines of pure Python with zero external dependencies beyond os, math, random and argparse.
- A ~40-line custom autograd engine (Value class) replicates PyTorch's backward-pass mechanics via topological graph traversal.
- The toy model trains on baby names using a tiny architecture (16-dim embeddings, seq length 8, single layer, 4 attention heads) totaling about 4,000 parameters.
- The same core transformer math—embeddings, RMSNorm, attention, MLP—scales up unchanged to power trillion-parameter models like GPT-4.