"The use of focused datasets makes SLMs particularly well-suited to domain-specific functions and small-scale applications such as mobile applications, edge computing, and environments with limited compute resources."
"As training techniques improve, SLMs with fewer parameters are becoming more accurate and can have a much faster processing time."
"By definition, SLMs are less expensive and energy-intensive to run, as they need far less computing power than LLMs; nor do they need expensive infrastructure."
"Another advantage is that it's easier for them to meet regulatory requirements. Not only is it more straightforward to obtain licenses for training material, but they avoid stringent obligations as they don't meet the computing threshold."
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