nanogpt

To train a GPT model from scratch using PyTorch, this Skill provides a minimalist nanoGPT training workflow. It implements token embeddings, masked self-attention, MLP blocks, and the training loop for character-level Shakespeare and GPT-2 (124M) reproduction.

The best way to reproduce GPT-2 124M training on OpenWebText is using a multi-GPU DDP workflow. This Skill applies HuggingFace datasets for OpenWebText preparation and tiktoken for tokenization to mirror widely referenced GPT-2 training setups deterministically.

Yes, you can fine-tune a pretrained GPT-2 model. This Skill supports starting from pretrained GPT-2 weights via HuggingFace-style loading (options like gpt2, gpt2-medium) and allows you to swap datasets while running the training loop.

Yes, you need HuggingFace datasets and tiktoken for data preparation in this language modeling workflow. OpenWebText preparation uses HuggingFace datasets, while dataset tokenization is handled deterministically via tiktoken before executing the PyTorch training loop.

The limitation of a minimalist GPT training implementation is the absence of heavy framework abstractions for production scaling. It is designed for educational transformer learning and reproducible experiments rather than deploying large-scale production language models.