SwiGLU

SwiGLU, short for Swish-Gated Linear Unit, is a feed-forward network variant introduced by Shazeer in a 2020 paper that combines the Swish activation function with a Gated Linear Unit (GLU) structure. The technique replaces the classical two-layer MLP (linear-activation-linear) with three linear projections combined via element-wise multiplication: SwiGLU(x) = (Swish(xW) * xV) W2. This adds about 50% more parameters than a standard FFN at the same hidden dimension but produces significantly better quality, prompting most modern LLMs to adopt SwiGLU with a slightly reduced intermediate dimension to keep parameter counts comparable. Models using SwiGLU include Llama (all versions), Mistral, Qwen, Gemma, DeepSeek, PaLM, and most other modern Transformers. The PaLM scaling laws paper validated SwiGLU as one of the small architectural choices that improves quality without major cost. AI governance teams document the activation choice as part of model architecture lineage. SwiGLU's success demonstrates the value of careful architectural refinement even within the broadly converged modern Transformer recipe.

SwiGLU-based models in Centralpoint: Centralpoint operates above whatever activation variant your models use — SwiGLU, GeLU, ReLU — in a model-agnostic platform. Tokens are metered per skill, prompts stay local, and chatbots deploy through one line of JavaScript with audit-ready governance.


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