Understanding Transformer Architecture: From Attention to MoE
Deep-dive guide explaining self-attention, multi-head attention, and Mixture of Experts architectures with diagrams.
Key Highlights
- ✓ Self-attention mechanism explained
- ✓ Multi-head attention visualization
- ✓ Mixture of Experts architecture
- ✓ Positional encoding strategies
- ✓ Performance benchmarks across model sizes
Overview
A comprehensive guide to transformer architecture, from the original attention mechanism to modern Mixture of Experts (MoE) designs. Includes detailed diagrams, code examples, and performance comparisons.
What's Inside
Self-Attention Fundamentals
The self-attention mechanism allows each token in a sequence to attend to all other tokens, capturing long-range dependencies that RNNs struggle with. We break down the Q, K, V matrix operations with intuitive examples and show how attention weights are computed and applied.
Multi-Head Attention
Multi-head attention runs several attention operations in parallel, allowing the model to focus on different representation subspaces simultaneously. We visualize attention patterns across heads and explain how head specialization emerges during training.
Mixture of Experts
MoE architectures sparse-activate only a subset of parameters per token, enabling trillion-parameter models with manageable compute costs. We cover routing strategies, load balancing, expert specialization, and the training stability challenges unique to MoE models.
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