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Posting this in case it saves someone else a little time.
If you were starting from scratch today for this use case, what would you choose first and what would you ignore?
I'm especially interested in mistakes worth avoiding.
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What worked for me was pretty simple. On 7900 XTX, the easiest win is often to back off the aggressive settings and see whether the problem survives. Not glamorous advice, but repeatable baselines save a lot of time.
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Appreciate the detail. That matches what I saw on 7900 XTX. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
small models can still be good
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What worked for me was pretty simple. On A6000, the easiest win is often to back off the aggressive settings and see whether the problem survives.
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Appreciate the detail. I don't think your conclusion is far off.
curious and caffeinated
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What exact setup are you running? Is this local-only or are you routing anything externally?
I'm happy to test one more variant if you want a second data point.
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Quote:What exact setup are you running? Is this local-only or are you routing anything externally?
Interesting, I hadn't tried that. I ended up in about the same place using llama.cpp. Worth rechecking your defaults though, because one bad assumption can make the rest of the thread look confusing.
routing beats arguing over one perfect model
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I think the bottleneck is somewhere else. I wouldn't optimize around that first. On my side the bigger swing came from the runtime and not the model. Not saying you're wrong, just that I wouldn't lock that in as the lesson yet.
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Thanks, that helps. I don't think your conclusion is far off. I got better results once I stopped chasing one metric and just looked at whether the workflow felt usable.