The Architecture That Changed Everything
The Transformer architecture, introduced in the 2017 paper "Attention Is All You Need," revolutionized NLP. Its key innovation — self-attention — allows every token to directly attend to every other token, enabling parallel processing and capturing long-range dependencies.
A Room Full of Experts
Imagine a room where every word in a sentence is a person. In old systems (RNNs), they had to pass notes one by one down a line. With Transformers, everyone can talk to everyone simultaneously — dramatically faster and no information gets lost along the way.
96
Layers (GPT-3)
12,288
Hidden Dim
96
Attention Heads
175B
Parameters
Self-Attention Visualized
👆 Try it yourself!
When processing "it", the model looks at all other words. Stronger colors = higher attention:
Layer-by-Layer Breakdown
🔤
Token Embedding
Convert tokens to vectors
📍
Positional Encoding
Add position information
👀
Self-Attention
Tokens attend to each other
⚡
Feed-Forward Network
Process attended info
⚖️
Layer Normalization
Stabilize activations
🔗
Residual Connection
Skip connections for gradient flow
📊
Output Projection
Project to vocabulary size
🎯
Softmax
Convert to probabilities
✅
Key Takeaways
- Self-attention enables parallel processing of all tokens
- Multi-head attention captures different relationship types
- Residual connections enable training of very deep networks
- Modern LLMs stack 32-128 transformer layers