How does NanoGPT compare to HuggingFace Transformers?

While HuggingFace is designed for production and features thousands of pre-built models, NanoGPT is optimized for education and hackability, keeping the entire model logic in roughly 300 lines of code.

Can I use my own custom dataset for training?

Yes, the skill provides a template for data preparation (prepare.py) that allows you to load any text file, create character mappings, and convert it into the binary format used by the trainer.

Can I run NanoGPT on a standard laptop CPU?

Yes, NanoGPT is designed to be lightweight. The character-level Shakespeare model can be prepared and trained on a standard CPU in approximately 5 minutes for educational purposes.

Does this skill support multi-GPU training?

Absolutely. It includes configurations and scripts for Distributed Data Parallel (DDP) to scale training across multiple GPUs, which is required for reproducing the GPT-2 124M model.

NanoGPT Model Training

Name: NanoGPT Model Training
Author: zechenzhangAGI

byzechenzhangAGI

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384

Data Science & ML

Implements and trains minimalist GPT architectures for educational and research purposes using Andrej Karpathy's clean, hackable codebase.

About

NanoGPT provides a lightweight, pure PyTorch implementation of the GPT-2 architecture designed for maximum transparency and hackability. It enables users to understand the inner workings of Transformers by building them from scratch, offering workflows for training character-level models on CPUs or reproducing full GPT-2 (124M) models on multi-GPU setups. Whether you are a student learning AI architecture or a researcher prototyping new transformer variants, this skill provides the essential scripts, configurations, and best practices to get models running efficiently without the complexity of heavy abstractions found in large-scale frameworks.

Key Features

Custom dataset preparation scripts for tokenizing and formatting private text data.
Pre-configured workflows for character-level training (Shakespeare) and large-scale datasets (OpenWebText).
Support for multi-GPU training with Distributed Data Parallel (DDP) and PyTorch 2.0 compilation.
384 GitHub stars
Minimalist GPT-2 implementation in ~300 lines of clean, readable PyTorch code.
Seamless fine-tuning capabilities starting from pretrained OpenAI GPT-2 checkpoints.

Use Cases

Training small-scale, domain-specific language models on custom text datasets with limited compute.
Prototyping and experimenting with architectural modifications to GPT blocks or attention mechanisms.
Learning the fundamental internal architecture of Transformer-based Large Language Models.

About

Key Features

Custom dataset preparation scripts for tokenizing and formatting private text data.
Pre-configured workflows for character-level training (Shakespeare) and large-scale datasets (OpenWebText).
Support for multi-GPU training with Distributed Data Parallel (DDP) and PyTorch 2.0 compilation.
384 GitHub stars
Minimalist GPT-2 implementation in ~300 lines of clean, readable PyTorch code.
Seamless fine-tuning capabilities starting from pretrained OpenAI GPT-2 checkpoints.

Use Cases

Training small-scale, domain-specific language models on custom text datasets with limited compute.
Prototyping and experimenting with architectural modifications to GPT blocks or attention mechanisms.
Learning the fundamental internal architecture of Transformer-based Large Language Models.