transformer-architecture-guide
Transformer Architecture Guide
Understand, implement, and adapt Transformer architectures for NLP, computer vision, and multimodal research, from the original attention mechanism to modern variants.
The Original Transformer
The Transformer (Vaswani et al., 2017, "Attention Is All You Need") replaced recurrence and convolution with self-attention as the primary sequence modeling mechanism.
Core Components
| Component | Function | Key Parameters |
|---|---|---|
| Multi-Head Self-Attention | Computes attention weights across all positions | d_model, n_heads, d_k, d_v |
| Feed-Forward Network | Position-wise nonlinear transformation | d_model, d_ff |
| Positional Encoding | Injects sequence order information | Sinusoidal or learned |
| Layer Normalization | Stabilizes training | Pre-norm or post-norm |
| Residual Connections | Enables gradient flow in deep networks | Add before or after norm |
Self-Attention Mechanism
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