In today's podcast we deep dive into the anatomy of the AI Small Language Model (SLM), which is fundamentally built upon a simplified version of the powerful transformer architecture. This architecture processes input text by breaking it into numerical representations called word embeddings and running them through an encoder and decoder structure, utilizing the self-attention mechanism to prioritize the most relevant parts of the input sequence. Distinguished by their scale, SLMs typically contain parameters ranging from tens of millions to a few hundred million, usually staying under the 10 billion threshold, making them vastly smaller than Large Language Models (LLMs) which may have billions or even trillions of parameters. To attain efficiency, SLMs often undergo sophisticated compression techniques such as knowledge distillation, where a smaller "student" model learns the behaviors of a larger "teacher" model, and quantization, which reduces model size by mapping weights to lower bit precision, like 4-bit. Further structural optimizations, such as Grouped-Query Attention (GQA) and Sliding Window Attention (SWA), enhance inference speed and memory efficiency, enabling models like Phi-3 mini and Mistral 7B to deliver high performance on resource-constrained edge devices.