nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predict subsequent elements.
The project distinguishes itself through a focus on high-speed data ingestion and hardware-accelerated performance. It includes a dedicated pipeline for transforming raw text into memory-mapped binary files, which enables efficient streaming during training. To maximize throughput, the system supports distributed data parallelism across multiple graphics processing units and employs just-in-time kernel compilation to optimize mathematical operations for specific hardware.
Beyond core training capabilities, the repository provides a command-line interface for generative text inference, allowing users to sample sequences from trained models using configurable parameters. It also includes integrated benchmarking tools to measure iteration speeds and identify hardware bottlenecks, ensuring efficient model development across various configurations.
