llm’s are random text generators. each one is a different architecture, different training data, or different training recipe.
first off: you probably dont need to fill up your harddrive with every good model in existance. often a url link is sufficient.(unless the owner deletes it. but quants are often still there)
optional second questions: how many links do you have to decent models(excluding low value experiments) how many would you estimate that you have?
optional third question: how much is your divirsity of model usage? assuming you had the compute, how many tested models would you utilize for a single query across different models? average. max.
fourth question: do you still rely on inference compute for non-locally hosted models?
i wish there were more specialized models (domains… in addition to styles).
Formation of the Open Secure AI Alliance
there are very real usecases for alternative models.
reddit locallama: reason for uncensored models
its a double edge sword. i compare it to marijuana decriminalization/legalization and, less positively, firearm rights. but actually, i think its more like linux being open-source.