Activation functions play a crucial role in the success of deep neural networks, particularly in natural language processing (NLP) tasks. In recent years, the Swish-Gated Linear Unit (SwiGLU) activation function has gained popularity among researchers due to its ability to effectively capture complex relationships between input features and output variables. In this blog post, we'll delve into the technical aspects of SwiGLU, discuss its advantages over traditional activation functions, and demonstrate its application in large language models. Mathematical Definition of SwiGLU SwiGLU stands for Swish-Gated Linear Unit, which is a type of activation function used in deep neural networks. It is designed to capture non-linearity in the input data and produce a non-linear output. The SwiGLU activation function is defined as follows: SwiGLU(x) = x \* sigmoid(β \* x + γ) Where x is the input to the function, β and γ are learnable parameters, and sigmoid is the standard sigmoid functio