this post was submitted on 07 Aug 2024
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From what I've heard the general idea is to run AI search on your browsing history, which is a very useful feature. I'm not deep into AI tech at all but to me it looks like that would involve local finetuning, ingesting all that history during inference sounds like a bad idea. It also wouldn't be necessary to generate stuff, only answer "Can you find that article about how nature makes blue feathers" and it's going to spit out previously-read links that match that kind of thing. Also, tl;dr-bot it.
Oh and there's already AI, as in ML, in firefox, in the form of machine translation. Language detection seems to be built-in, translating requires downloading a model per language pair, 16M parameters. Trained on workstations with 8GPUs. Which is all to say: You don't need gigantic GPU farms if you aren't training gazillion parameter models on the whole internet.
It shoudn't be finetuning, if anything it should be RAG with an embeddings model + regular inference.
This is kinda cool, but it still doesn't seem to justify bogging down a machine with a huge LLM. And I am speaking as a massive local LLM enthusiast who uses them every day.