Small Language Models

Compact language models designed for efficient and specialised deployment.

Small language models contain fewer parameters than frontier-scale LLMs and are often trained or fine-tuned for narrower tasks. Their reduced size makes them less expensive to operate.

They are suitable for mobile devices, edge systems, private deployments and applications with strict latency requirements. Their capabilities may be narrower, but strong training data and task-specific optimisation can make them highly effective.

Also in

Diffusion Models Retrieval-Augmented Generation Prompt Engineering Context Engineering Few-Shot Learning Supervised Fine-Tuning

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