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labml.ai Annotated Paper Implementations

Explained, line-by-line code for dozens of well-known AI research papers, shown with notes side by side.

What it does

This collection rebuilds the ideas from many famous deep-learning papers in simple code, with explanations written next to each part. A companion website shows the code and notes side by side.

Topics include transformers (the design behind modern chat AI), diffusion models used for image generation, GANs, LoRA fine-tuning, reinforcement learning and more. Its makers say they add new implementations regularly.

Who it's for

Students and engineers who want to understand how AI models work by reading clear, annotated code.

Can I use it without coding?

Yes. You can read everything on the companion website. Running the code needs Python. New to this? Start here.

Can I run it?

Cost
FreeNothing to pay or sign up for.
Runs on
Web browser
Computer
An ordinary computer is fine
API key
Not needed

Try it

  1. Visit the project's website to read each implementation with notes side by side.
  2. Pick a topic, such as transformers or diffusion models, and follow the explanations.

Using Claude Code, Cursor or another AI coding assistant? Paste the prompt and it will walk you through the setup.New to this?

Before you install

  • Only download from the official GitHub page. Copies with similar names exist for many popular projects.
  • License (MIT): Very permissive: free to use, change and share, including for business.
  • More on staying safe

Where people are talking about it