Tiny Language Model

Tiny language models (TLMs) aim to achieve high performance in natural language processing tasks while drastically reducing the computational cost and energy consumption associated with larger models. Current research focuses on optimizing model architectures, training strategies (including knowledge distillation from larger models), and efficient tokenization techniques to create effective TLMs with significantly fewer parameters. This pursuit of efficient and accessible language models has significant implications for deploying AI applications on resource-constrained devices and expanding access to advanced NLP capabilities across diverse languages and settings.