Small Language Models

Small Language Models (SLMs) are compact, efficient versions of large language models (LLMs) designed to run on resource-constrained devices like smartphones, edge devices, or local machines. They offer reduced computational requirements and faster inference times while maintaining reasonable performance for specific tasks, often through techniques like model pruning, quantization, or distillation. SLMs enable AI applications in scenarios where deploying massive models is impractical due to hardware, cost, or latency constraints.

Also known as: SLMs, Compact Language Models, Efficient Language Models, Lightweight Language Models, Tiny Language Models

Why learn Small Language Models?

Developers should learn about SLMs when building applications for edge computing, mobile devices, or environments with limited internet connectivity, as they allow for on-device AI processing without relying on cloud APIs. They are particularly useful for real-time applications like chatbots, translation tools, or content generation in low-resource settings, offering benefits in privacy, cost-efficiency, and reduced latency compared to cloud-based LLMs.

Learning Resources

• docs: Hugging Face Blog: Introduction to Small Language Models
• tutorial: Towards Data Science: A Guide to Small Language Models
• course: Coursera: Efficient Deep Learning for Edge Devices
• video: YouTube: Small Language Models Explained by AI Coffee Break
• book: Book: 'TinyML: Machine Learning with TensorFlow Lite' by Pete Warden and Daniel Situnayake