Architecture
SwiGLU
SwiGLU is a gated activation used in the transformer feed-forward block: the input goes through two linear projections, one passed through the Swish function, and the two results are multiplied element-wise before the output projection.
In depth
SwiGLU is a gated activation used in the transformer feed-forward block: the input goes through two linear projections, one passed through the Swish function, and the two results are multiplied element-wise before the output projection. Noam Shazeer's 2020 paper tested several gated linear unit variants and found SwiGLU beat the usual ReLU and GELU activations. It has since become the default FFN activation in Llama and many other open models.
Primary source: arxiv.org
Example
In practice, developers reach for SwiGLU when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is SwiGLU?
SwiGLU is a gated activation used in the transformer feed-forward block: the input goes through two linear projections, one passed through the Swish function, and the two results are multiplied element-wise before the output projection.
Why does SwiGLU matter for AI developers?
SwiGLU sits in the Architecture part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about SwiGLU?
Developers Digest publishes tutorials and videos that cover Architecture topics including SwiGLU. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
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Sparse Autoencoder (SAE)
A sparse autoencoder (SAE) is a small neural network trained to rewrite a model's internal activations as a sparse mix of many learned features, so each feature tends to stand for one interpretable concept.
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Autoregressive Model
A model that generates a sequence one element at a time, feeding each output back in as input for the next step.
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Backpropagation
The algorithm that computes how much each weight in a neural network contributed to the error, so gradient descent knows how to adjust it.
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