nanoGPT, created by Andrej Karpathy, is the simplest and fastest repository for training and fine-tuning medium-sized GPT models. With a compact, highly readable codebase and minimal dependencies, it makes Transformer training workflows, data preprocessing, and optimization techniques accessible to learners and practitioners alike.
Minimal Training Pipeline
- Strips the GPT training pipeline to its essentials, making every line of code understandable without sacrificing correctness
- Both training from scratch and fine-tuning on smaller datasets are supported out of the box for quick experimentation
- Example configurations and training scripts ensure reproducibility, letting anyone replicate published training workflows
Education and Prototyping
- Widely used as a teaching tool for building deep, hands-on understanding of GPT architecture and the full training pipeline
- Researchers rely on it for rapid prototyping of training experiments and benchmarking optimization techniques in controlled settings
- Small teams explore model capabilities and data processing strategies without the overhead of large-scale training frameworks
Clean and Readable Codebase
- The Python codebase prioritizes readability and experimentability, approachable from beginner to intermediate levels
- Released under the MIT License with an active community that contributes improvements and extensions
- One of the most widely referenced repositories in AI education and small-scale model exploration