SwiGLU
Also known as: Swish-Gated Linear Unit
Gated activation in transformer MLP blocks (Swish gate × linear up-projection), used in LLaMA and friends.
An activation function used in the MLP block of modern transformers (LLaMA, PaLM). Combines a Swish gate with a linear up-projection; slightly more expressive than ReLU/GeLU at the cost of one extra projection.
In practice
Small architectural choice with measurable quality gain. Worth knowing for any model-architecture deep-dive question.
Related topics
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Related terms
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How a model decides which input tokens to weight when computing each output token.
Causal Mask
Attention mask that hides future tokens so each position can only see itself and prior tokens.
Context Window
The max number of tokens a model can attend to at once.
Decoder-Only
Single autoregressive transformer stack: the shape of every modern frontier LLM.
Encoder-Decoder
Transformer with separate encoder + decoder stacks; strong for translation and structured seq2seq tasks.
Layer Normalization
Per-sample, per-position normalization. The glue that keeps transformer training stable.