The post discusses the evolving definition of small language models (LMs) in the context of machine learning and AI. Key highlights include an exploration of the varying classifications of language models based on parameter counts and deployment capabilities. It mentions that terms like 'small' are relative and can encompass various sizes, including models that operate on edge devices (like Raspberry Pi) to those that function effectively on high-performance laptops. The comments emphasize the need for practical impact over parameter size, and users express desire for models that can run locally in browsers without heavy resources. The conversation reveals a trend towards accessible, lightweight models that retain efficiency and utility in a wide range of applications.