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transformer-architecture-guide

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Guide to Transformer architectures for NLP and computer vision

skills/domains/ai-ml/transformer-architecture-guide/SKILL.md

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# 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 |