The problem is that once you say “fine tuned” then you have immediately slashed the user base down to virtually nothing. You need to fine-tune per-task and usually per-user (or org). There is no good way to scale that.
Apple can fine-tune a local LLM to respond to a catalog of common interactions and requests but it’s hard to see anyone else deploying fine-tuned models for non-technical audiences or even for their own purposes when most of their needs are one-off and not recurring cases of the same thing.
Not necessarily, you can fine tune on a general domain of knowledge (people already do this and open source the results) then use on device RAG to give it specific knowledge in the domain.
Apple can fine-tune a local LLM to respond to a catalog of common interactions and requests but it’s hard to see anyone else deploying fine-tuned models for non-technical audiences or even for their own purposes when most of their needs are one-off and not recurring cases of the same thing.