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Could multiple 7b models outperform 70b models? (alien.top)

submitted 1 year ago by freehuntx@alien.top to c/localllama@poweruser.forum

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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?

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[–] wishtrepreneur@alien.top 1 points 1 year ago (1 children)

You can use a general LLM to “classify” a prompt and then route the entire prompt to a downstream LLM.

why can't you just train the "router" LLM on which downstream LLM to use and pass the activations to the downstream LLMs? Can't you have "headless" (without encoding layer) downstream LLMs? So inference could use a (6.5B+6.5B) params model with the generalizability of a 70B model.

[–] feynmanatom@alien.top 1 points 1 year ago

Hmm, not sure if I track what an encoding layer is? The encoding phase involves filling the KV cache across the depth of the model. I don’t think there’s an activation you could just pass across without model surgery + additional fine tuning.