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NANOGPT
Train and fine-tune GPT models from scratch with minimal boilerplate
MIT
ABOUT
Training GPT models from scratch typically requires complex distributed training setups, deep knowledge of transformer internals, and thousands of lines of boilerplate code. NanoGPT distills GPT training into a single ~300-line training script that fits on a single GPU. Researchers and developers can reproduce GPT-2 scale models, experiment with architectural variations, or fine-tune on custom datasets without wading through production-scale training infrastructure or multi-node orchestration.
INTEGRATION GUIDE
1. Train a GPT-2 scale (124M parameter) language model from scratch on a single consumer GPU
2. Fine-tune a pre-trained GPT model on domain-specific data like code, legal documents, or medical texts
3. Experiment with transformer architecture modifications without writing training infrastructure from scratch
4. Reproduce research results for transformer-based language models using a minimal, readable codebase
TAGS
pythongpttransformertrainingfine-tuningpytorchresearch