A compact language model, typically with a few hundred million to around ten billion parameters, built to run efficiently on modest hardware.
Small language models trade some breadth of knowledge and reasoning for speed, lower cost, and the ability to run on laptops, phones, or edge devices. Techniques such as knowledge distillation, careful training data selection, and quantization have made them far more capable than their size once suggested.
A common use is on-device text processing. A note-taking app might use a small model to summarize meeting notes directly on a laptop, so the content never leaves the device and works offline. Small models perform best on focused tasks such as classification, extraction, rewriting, or answering questions from supplied documents. For open-ended reasoning or broad world knowledge, larger cloud models usually still perform better, which is why many products route requests between the two.
A training technique in which a smaller student model learns to imitate a larger teacher model, keeping much of its skill at lower cost.
Large Language ModelAn AI model trained on vast amounts of text to understand and generate human-like language, code, and other content.
On-Device AIAI that runs directly on a phone, laptop, or other device instead of sending data to remote cloud servers for processing.
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How large language models work in plain English, from tokens and training to next-word prediction, with a simple diagram and no math.