Large Language Models (LLMs) have already started revolutionizing industries through their wide range of applications and capabilities. However, their size and huge computational requirements often make it difficult to use them in a lot of places making their accessibility and sustainability a bigger challenge.
But with the advancements in Small Language Models (SLMs), we have got comprehensive solution. SLMs can achieve comparable and even better performance than LLMs with significantly fewer parameters resulting in cost effectiveness, better efficiency, accessibility, and faster processing.
Therefore, SLMs are well suited for deployment in resource-constrained devices like smartphones or IoT devices. They also greatly enhance the privacy of user data by eliminating the need to send it to external servers. Moreover, they are easily customizable and can be fine-tuned for niche applications.
Check out our detailed guide on Small Language Models (SLMs) and understand if they are better alternatives to Large Language Models (LLMs) or not. Learn about various techniques used to build SLMS, the major differences between small and large language models, their key features, applications, use cases, and more.
Understanding the underlying principles of these newer technologies will help you plan and execute your AI career goals properly and select the appropriate AI certification to boost your career.
Download your copy now to learn Small Language Models in detail.
