Editing Machine Learning at the Edge (section)

==='''Utilizing Small Language Models (SLMs)'''===
Large Language Models (LLMs) have become a prevalent system recently and are able to do and help with a variety of tasks. However, running and training an LLM requires a significant amount of computational resources which is not feasible when working with edge devices. Most modern LLMs are cloud based, but this may lead to high latency and increased network traffic, especially when working with a large subsystem of nodes. 

One way that a similar system can be achieved on edge devices is by using SLMs. These are not as accurate and do not have the vast knowledge of LLMs or the amount of data they are trained on, but for the purposes of basic applications and edge devices, they can be sufficient to accomplish many tasks. They are also often fine-tuned and trained to accomplish the specific tasks which they are deployed for and are much faster and resource-efficient than LLMs. They can also provide much more privacy because they are able to be run on local devices without sharing user data to the cloud. This can be useful for a wide variety of edge applications. If needed and privacy constraints permit, they can query and LLM as needed for more complicated tasks. This means that not every prompt leads to a query, and thus network traffic, privacy, and latency constraints are still preserved.