Transformer
Definition
A neural network architecture that uses self-attention to process input sequences in parallel, enabling models to capture long-range dependencies and scale efficiently. The foundation of all modern LLMs.
Why It Matters
The Transformer architecture is the single most important innovation in modern AI. Every major language model (GPT, Claude, Gemini, Llama) is built on Transformers. Understanding this architecture is fundamental to understanding how LLMs work.
Before Transformers (2017), sequence models processed text token-by-token using recurrent neural networks (RNNs). This was slow and struggled with long-range dependencies. Transformers solved both problems: parallel processing enabled massive scaling, and self-attention let models relate any two positions in a sequence directly.
For AI engineers, you don’t need to implement Transformers from scratch. But understanding their core concepts (attention, positional encoding, feed-forward layers) helps you debug issues, optimize performance, and make informed architecture decisions.
Implementation Basics
Core Components
- Input Embedding: Convert tokens to dense vectors
- Positional Encoding: Add position information (since attention is position-agnostic)
- Multi-Head Attention: Learn different types of relationships between tokens
- Feed-Forward Network: Process each position independently
- Layer Normalization: Stabilize training
- Residual Connections: Enable deep networks
Architecture Variants
- Encoder-only: BERT (good for classification, embeddings)
- Decoder-only: GPT, Claude, Llama (good for generation)
- Encoder-Decoder: T5, BART (good for translation, summarization)
Key Properties
- Parallel processing: All tokens processed simultaneously
- Quadratic attention: O(n²) complexity with sequence length
- Fixed context window: Limited by positional encoding design
- Layer depth: More layers = more complex reasoning
Scale Matters Transformer capabilities emerge at scale:
- More parameters = better performance
- More training data = broader knowledge
- More compute = better convergence
This is why modern LLMs have billions of parameters and train on trillions of tokens.
Practical Implications
- Long sequences are computationally expensive
- Model size determines memory requirements
- Inference speed depends on model architecture
- Different sizes trade capability for cost
Source
The Transformer architecture, introduced in 'Attention Is All You Need', replaces recurrence with self-attention mechanisms, achieving superior performance and parallelization.
https://arxiv.org/abs/1706.03762