GPT in 60 Lines of NumPy
FreeGPT implementation in 60 lines of NumPy
FreeFree tier
Inputs: textOutputs: text
About GPT in 60 Lines of NumPy
This blog post provides a step-by-step implementation of a GPT (Generative Pre-trained Transformer) from scratch using just 60 lines of NumPy. The implementation is designed for educational purposes, emphasizing clarity over performance. It loads pretrained GPT-2 model weights from OpenAI and demonstrates autoregressive text generation. The complete code is available on GitHub under picoGPT. The tutorial covers tokenization, embedding layers, the decoder stack (including multi-head causal self-attention and position-wise feedforward networks), and sampling. Readers need basic Python, NumPy, and neural network knowledge.
Key Features
Full GPT implementation in ~60 lines of NumPy
Supports loading pretrained GPT-2 weights from OpenAI
Autoregressive text generation with temperature sampling
Token embeddings and positional embeddings
Multi-head causal self-attention mechanism
GELU activation and layer normalization
Decoder-only transformer architecture
Simple, readable code intended for learning
Pros & Cons
Pros
- Minimal, easy-to-read code for learning
- Free and open-source (MIT license)
- No GPU required; runs on CPU with NumPy
- Loads actual GPT-2 weights for realistic outputs
- Well-documented blog post with clear explanations
Cons
- Not optimized for performance or large-scale use
- Lacks backpropagation and training support
- No batching or inference optimizations
- Missing many practical features (e.g., stop tokens, fine-tuning)
- Only supports single-sequence inference
Best For
Educational resource to understand GPT internalsExperimenting with transformer architecture modificationsGenerating text from a pretrained GPT-2 modelTeaching deep learning and neural network concepts
FAQ
What is GPT?
GPT stands for Generative Pre-trained Transformer. It is a decoder-only transformer neural network that generates text. It is 'generative' because it produces text, 'pre-trained' because it is trained on large text corpora, and 'transformer' because it uses the transformer architecture.
How does the GPT generate text?
The GPT uses autoregressive sampling: it takes a sequence of tokens as input, predicts the next token probabilities, selects a token (via temperature-controlled sampling), appends it to the input, and repeats the process until a stopping condition (e.g., maximum length or end token) is met.
What is the purpose of this implementation?
This implementation is purely educational, demonstrating how a GPT works internally. It sacrifices performance and completeness for simplicity and clarity, using only NumPy.