If i have multiple 7b models where each model is trained on one specific topic (e.g. roleplay, math, coding, history, politic…) and i have an interface which decides depending on the context which model to use. Could this outperform bigger models while being faster?
Does this use of mixture-of-experts mean that multiple 70b models would perform ?better than multiple 7b models
the question was if multiple small models can beat a single big model but also having the speed advantage, and answer is yes, and an example of that is MOE, which is a collection of small models all inside a single big model,
https://huggingface.co/google/switch-c-2048 is a such example
Thank you for sharing, I understand now
big is an understatement. Please do correct me if I got it wildly wrong, but it appears to be a 3.6TB colossus.