The article discusses small language models (SLMs) designed for high-quality machine intelligence on resource-constrained devices like smartphones and wearables. It highlights innovations in architectural designs, datasets, and training algorithms that enhance SLMs' efficiency and performance, making AI more accessible.
This article explores NuExtract, a family of Small Language Models (SLMs) for extracting structured data from text. The author, Fabio Matricardi, discusses using NuExtract to process candidate CVs for a database and highlights its benefits for privacy protection and running on less powerful computers.
Explores recent trends in LLM research, including multi-modal LLMs, open-source LLMs, domain-specific LLMs, LLM agents, smaller LLMs, and Non-Transformer LLMs. Mentions examples such as OpenAI's Sora, LLM360, BioGPT, StarCoder, and Mamba.
