Transformers

Attention-based architecture behind most modern language models.

Transformers process tokens in parallel and use self-attention to model relationships across sequence positions.

Core Components

• Token embeddings
• Multi-head self-attention
• Feed-forward layers
• Residual connections and layer normalization

Why It Matters

Transformer scaling unlocked large language models, multimodal systems, and modern AI assistants.