Tiny LLM

A category of language models with extremely small parameter counts, typically in the range of hundreds of millions to a few billion. Achieved through compression techniques such as quantization, pruning, and knowledge distillation, these models are capable of running locally on edge devices or in browsers, and are suited for privacy-sensitive, low-latency, and low-cost deployment scenarios.