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Ty for the reminder, been so busy dealing with last minute polish for text selection that I hadn't played with it yet Sadly, even with a 64 gb M2 Max running i

Ty for the reminder, been so busy dealing with last minute polish for text selection that I hadn't played with it yet Sadly, even with a 64 gb M2 Max running it at q4, it takes like 3-5 minutes to answer a q. I'd have to do an API for a full eval It got the first med one wrong, TL;Dr woman was in an accident and likely braindead, what do we do to confirm? Model lands on EEG, but, answer is corneal reflex. Meaningless, but figured I'd share the one answer I got at least :p In general o1 series is really really _really_ nice for RAG, I imagine this is too, at least with the approach where you have the Reasoner think out loud and Summarizer give the output to user Fun to see a full on, real, reasoning trace too: https://docs.google.com/document/d/1pMUO1XuFCr0nBmWNyOMp8ky4jfAFC7_CQNB2xyqDbSU/edit?usp=drivesdk https://docs.google.com/document/d/1pMUO1XuFCr0nBmWNyOMp8ky4...

moffkalast 2y ago
Ha as a layman I'd probably say EEG to that too, how can eyes reliably show the state of the entire brain? But I guess it's standard practice. Should be more interesting if everything related to "diagnosing brain death" from several textbooks is retrieved and thrown into the context, I would imagine it might even get it right. I've found its thought process really interesting while throwing it at fairly meaningless stuff like code optimization or drawing conclusions from unstructured data and its size and slowness coupled with the way it works is really a problem. Maybe you can try it with Qwen-2.5-1.5B as a draft predictor to speed it up, but I think that'll have limited gains on a Mac.