BUILDING AGENTS FOR SMALL LANGUAGE MODELS: A DEEP DIVE INTO LIGHTWEIGHT AI

Summary from the TLDR AI newsletter

Small language models are models ranging from 270M to 32B parameters that run efficiently on CPUs or modest GPUs. They offer immense potential: privacy through local deployment, predictable costs, and full control thanks to open weights. They also present unique challenges that require a shift in how agent architectures are designed. This article reviews lessons learned from hands-on experimentation, debugging, and optimizing of inference pipelines for small language models.