Small Language Models (SLMs)- Concepts, Advantages, Limitations and Applications

Abstract

This article explores Small Language Models (SLMs) as an efficient and sustainable alternative to Large LanguageModels (LLMs). While LLMs such as GPT-4, Llama 3, and Google Gemini demonstrate impressive capabilities,their billions or trillions of parameters demand massive computational and energy resources. SLMs, withparameters ranging from a few million to a few billion, offer comparable performance in specific domains, withsignificant advantages: faster and more economical training, 5 to 10 times faster inference, 10 to 20 times greaterenergy efficiency, and the ability to run on edge devices. The article examines definitions, training and inferencecharacteristics, competitive advantages, and known limitations.

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