🧠 GPT in 243 lines. No libraries. No PyTorch. Just pure Python.
Andrej Karpathy released microGPT: the most minimalist possible implementation of a functional GPT transformer. No external dependencies — just the essential mathematics.
“Train and inference GPT in 243 lines of pure, dependency-free Python. This is the full algorithmic content of what is needed. Everything else is just for efficiency. I cannot simplify this any further.” — Andrej Karpathy, Feb 2026
What’s inside those 243 lines?
- Autograd engine → backpropagation implemented from scratch
- GPT architecture → multi-head self-attention, feed-forward, layer norm
- Training loop → tokenization, forward pass, loss calculation, backprop, update
Why does it matter?
Most transformer tutorials today use PyTorch or TensorFlow — which hide the implementation. microGPT removes all those abstraction layers and shows exactly how an LLM works under the hood.
Ideal for:
- Students who want to truly understand transformers
- Researchers who want a minimal baseline
- Anyone who wants to see the “algorithmic core” of GPT without noise
💡 Explanation in a nutshell#
Modern transformers (GPT, BERT, etc.) are essentially: attention (which words look at which other words), feed-forward (processing that information), and normalization. microGPT implements exactly that — without production optimizations — so every line of code is understandable and debuggable.
More information at the link 👇
