A simple and detailed explanation of multi-head attention, showing how multiple attention heads help transformer models understand context and relationships …

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Multi-head attention is the core mechanism in Transformer models like BERT and GPT that enables parallel processing of input sequences. It works by transforming embeddings into queries, keys, and values, then running multiple attention heads simultaneously to capture different relationships (grammar, semantics, long-range dependencies). Each head operates in a lower-dimensional subspace, computes attention scores through scaled dot-products, and applies softmax to weight the values. The outputs are concatenated and projected to form rich contextual representations. The article includes a detailed PyTorch implementation showing tensor shapes throughout the process, explains masked attention for autoregressive generation, and addresses common questions about scaling, interpretability, and computational complexity.

Multi-Head Attention Explained: Queries, Keys, and Values Made Simple

Good-to-Know Concepts in Multi-Head Attention

Multi-Head Attention in PyTorch (With Tensor Shapes)