A Chinese technical blog post provides a clear, in-depth explanation of the SwiGLU activation function, a critical component in modern large language models (LLMs) such as Llama and PaLM. The author contrasts SwiGLU with earlier activations like ReLU and GELU, highlighting how its gating mechanism allows for more expressive transformations in the feed-forward network (FFN) layers of transformers. The article is part of a series on core LLM architecture, covering normalization (RMSNorm) and now activation functions. For ML engineers and researchers, this serves as a solid reference for understanding why SwiGLU has become a default choice, offering insights into the trade-offs between computational cost and model quality. The piece is technically rigorous without being overly academic, making it accessible to practitioners.
A technical deep-dive into the SwiGLU activation function, explaining its role in improving LLM performance over older designs.