Multi-Head Self-Attention Function
The multi-head self-attention function operates on an input representation matrix, . Rather than using a single set of attention parameters, this mechanism employs multiple parallel 'attention heads'. Each head has its own unique set of learnable weight matrices for Query, Key, and Value projections. A scaled dot-product attention operation is performed independently within each head. The outputs from all heads are then concatenated and projected through a final linear transformation to produce the layer's output. This multi-headed approach enables the model to jointly attend to information from different representational subspaces at different positions.
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References
The Illustrated Transformer
Attention Is All You Need
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Reference of Foundations of Large Language Models Course
Tags
Data Science
Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.2 Generative Models - Foundations of Large Language Models
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