Small Language Models (SLMs) are compact versions of traditional Large Language Models (LLMs), designed to operate efficiently on standard hardware. Typically ranging from a few million to a few billion parameters, SLMs are tailored for specific tasks, making them faster, more cost-effective, and easier to deploy. They are particularly suitable for edge devices like smartphones and IoT sensors, allowing for real-time applications without the need for extensive computational resources. As the demand for AI solutions grows, SLMs represent a significant shift towards more accessible and efficient AI technologies, focusing on quality and performance rather than sheer size.