nanogpt

A minimalist GPT implementation provides a compact PyTorch codebase to train GPT-2 from scratch. It covers transformer basics like masked self-attention and residuals, allowing you to reproduce the 124M model on OpenWebText or train on custom text corpora.

Tokenization and binary preprocessing convert raw text into optimized formats for transformer training pipelines. This Skill guides you through preparing character-level Shakespeare text or OpenWebText corpora into binary datasets for reproducible language modeling.

Yes, you can use multi-GPU DDP for GPT-2 reproduction. The training pipeline supports scalable distributed setups, allowing you to reproduce the 124M parameter model on OpenWebText or run larger educational experiments across multiple devices.

The best way to fine-tune pretrained GPT-2 weights is to load them into the configurable PyTorch architecture and apply your custom tokenized dataset. This Skill provides flexible starting points for fine-tuning alongside checkpointing and sampling workflows.

You do not need deep prior knowledge of masked self-attention to configure GPT hyperparameters, as the minimalist architecture demonstrates it. You can adjust layers, heads, and embedding size to experiment with transformer learning and observe the training mechanics directly.

GPT-2 training pipelines may fail during distributed runs due to misconfigured multi-GPU DDP settings or incorrect hyperparameters. This Skill provides troubleshooting for reproducible training, helping resolve checkpointing and sampling issues in larger multi-GPU workflows.