SLMs

Small language models are essentially more streamlined versions of LLMs, in regards to the size of their neural networks, and simpler architectures. 

Compared to LLMs, SLMs have fewer parameters and don’t need as much data and time to be trained — think minutes or a few hours of training time, versus many hours to even days to train a LLM. Because of their smaller size, SLMs are therefore generally more efficient and more straightforward to implement on-site, or on smaller devices.

Moreover, because SLMs can be tailored to more narrow and specific applications, that makes them more practical for companies that require a language model that is trained on more limited datasets, and can be fine-tuned for a particular domain.