Karpathy's microgpt.py Dissected: Understanding GPT's Essence in 150 Lines
A line-by-line dissection of microgpt.py -- a pure Python GPT implementation with zero dependencies. Training, inference, and autograd in 150 lines.
Karpathy's microgpt.py Dissected: Understanding GPT's Essence in 150 Lines
Andrej Karpathy has released new code. This time, it is even more extreme than nanoGPT. A 150-line script that trains and runs inference on a GPT, using pure Python with no external libraries.
No PyTorch. No NumPy. Just three imports: os, math, random.
The comment at the top of the code says it all:
"This file is the complete algorithm. Everything else is just efficiency."
In this post, we dissect microgpt.py line by line. Follow along with the code, and you will see that the algorithm behind GPT is a surprisingly simple composition of mathematical operations.
Overall Structure
microgpt.py breaks down into roughly 6 parts:
| Part | Lines | Role |
|---|---|---|
| Data & Tokenizer | ~10 | Load name dataset, character-level tokenization |
| Value Class (Autograd) | ~35 | Scalar automatic differentiation engine |
| Parameter Initialization | ~15 | Weight matrix creation (4,192 parameters) |
| Model Architecture | ~40 | Embedding + Attention + MLP + RMSNorm |
| Training Loop | ~20 | Cross-entropy loss + Adam optimizer |
| Inference | ~15 | Name generation via temperature sampling |
Total parameters: 4,192. Compared to GPT-2 Small's 124M, that is roughly 30,000x smaller. But the algorithm is identical.
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