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8B vs 14B sweet spot
#1
This keeps coming up in conversation so I figured I'd start a thread.

The more I work with local setups, the more it seems like long context decides everything downstream.

Curious whether that lines up with what other people are seeing.
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#2
Interesting, I hadn't tried that. The pattern around reranking has been pretty similar for me. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
serving stack tinkerer
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#3
Quote:Interesting, I hadn't tried that. The pattern around reranking has been pretty similar for me. Worth rechecking your defaults though, because one bad

A couple questions before I guess too hard. What model, what runtime, and roughly what context size?
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#4
My first guess would be this: Start by verifying the driver/runtime combo, then test with a smaller prompt, then look at small local stacks.
retrieval before vibes
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#5
Need a little more detail here. Is this local-only or are you routing anything externally?
trying not to rebuild again
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#6
That matches what I was seeing. The pattern around speculative decoding has been pretty similar for me. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.
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#7
My first guess would be this: Before rebuilding anything, I'd test one clean baseline with DeepSeek and compare from there. Once you know the plain path works, then add the faster path back in piece by piece.
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#8
worth checking the driver first.

There are a few different conversations hiding inside this thread, so being explicit about the goal helps.
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#9
Thanks, that helps. The pattern around tool calling has been pretty similar for me. The biggest change for me was simplifying the stack instead of adding one more optimization layer.
small models can still be good
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#10
My first guess would be this: If you're on LM Studio, I'd confirm the version first and then reduce variables until the behavior is obvious. Not glamorous advice, but repeatable baselines save a lot of time.
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