Small Language Models for Smart Data Model Classification at the Edge: A Cost-Aware Hybrid Approach

The paper introduces a cost‑aware hybrid approach that uses small, open‑source language models to classify smart data models (SDMs) in Internet of Things (IoT) environments. It benchmarks general‑purpose, reasoning‑specialized, and code‑specialized models across domain‑specific datasets, highlighting their suitability for edge devices with limited resources. The study also compares these lightweight models to large language models and near‑zero‑cost baselines such as TF‑IDF and a lightweight sentence encoder to demonstrate practical performance gains.

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• llms
• benchmarks