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?
No, several sources include Microsoft have said GPT 3.5 Turbo is 20B. GPT 3 was 175B, and GPT 3.5 Turbo was about 10x cheaper on the API than GPT 3 when it came out so it makes sense.
Yeah if that’s the case, I can see gpt-4 requiring about 220-250B of loaded parameters to do token decoding