AI GitHub
nanoGPT for studying a small GPT training loop
Read and run a compact implementation of transformer language-model training.
Key Takeaways
- The repository separates data preparation, model definition and training.
- Start with a documented small dataset and training configuration that fits your device.
- Run a short character-level experiment and watch training and validation loss.
- The repository is a learning reference with recipes from a specific period.
How it works
The repository separates data preparation, model definition and training. Its small examples make it possible to trace tokens from a dataset through a loss calculation and into sampled output.
What you need
Start with a documented small dataset and training configuration that fits your device. Read the data preparation code and training loop side by side.
What to try first
Run a short character-level experiment and watch training and validation loss. Change one model dimension and inspect parameter count and sample output.
Print the sampled training windows alongside the target tokens. This makes the shift between input and label visible before you inspect model quality.
Where to look for failures
The repository is a learning reference with recipes from a specific period. Adapt dependencies and budgets for your environment and treat small-corpus generation as a demonstration.
Related Franklin pages
Use very small inputs when learning the implementation. Inspect the actual token indices, tensor shapes and gradients before relying on a larger training run to show what went wrong.
- Trace gradients through scalar operations in a deliberately small implementation. micrograd for learning reverse-mode differentiation
- Follow runnable chapter code for language-model components and training. LLMs from Scratch for building a transformer step by step
For the role of multilayer networks and how they learn, see Franklin’s glossary. Deep learning
Project documentation
Try it for your workflow
Explore the project’s documentation for setup and usage, or save this page to a collection for later.
Read the project documentation ↗