5 Small Language Models for Agentic Tool Calling

Take a curated look at the best small language models currently available on Hugging Face, what each one is actually good at, the benchmark numbers that back those claims up, and the code to get started with each one.

Three key advantages of SLMs Division of labor: Modern AI architecture uses routers to send routine tasks to 7B-parameter SLMs, reserving trillion-parameter LLMs only for complex reasoning. Economic efficiency: For high-volume, repetitive tasks, SLMs can reduce cloud inference costs by up to 90% while providing near-instant latency. Privacy at the edge: Because SLMs can run locally on-device or on-premises, they reduce the data leakage risks inherent in sending sensitive telemetry to the public cloud. Large language models (LLMs) are the workhorses of AI, supporting ever more sophisticated capabilities and workflows, and approaching near-human level performance. But sometimes more isn’t always better — it’s just more. Specialized data and limited capabilities are just fine for some workflows. This realization is driving the evolution of small language models (SLMs), rather than one-size-fits-all LLMs. SLMs — coming in the form of domain-specific models, statistical langua

The AI boom has hit across industries, and public sector organizations are facing pressure to accelerate adoption. At the same time, government institutions face distinct constraints around security, governance, and operations that set them apart from their business counterparts. For this reason, purpose-built small language models (SLMs) offer a promising path to operationalize AI in…