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adapter stacking for DeepSeek - Printable Version +- Inference (https://www.datasculpting.ai/inference) +-- Forum: Optimization & Performance (https://www.datasculpting.ai/inference/forumdisplay.php?fid=10) +--- Forum: Benchmarks (https://www.datasculpting.ai/inference/forumdisplay.php?fid=43) +--- Thread: adapter stacking for DeepSeek (/showthread.php?tid=850) Pages:
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Re: adapter stacking for DeepSeek - griffinloom66 - 10-17-2026 A couple things I'd check first. If you're on vLLM, I'd confirm the version first and then reduce variables until the behavior is obvious. A lot of these threads end up being one bad config plus a pile of conclusions drawn from it. Re: adapter stacking for DeepSeek - cudascope - 10-17-2026 I'd start with the simpler stack. Re: adapter stacking for DeepSeek - circuitworks90 - 10-17-2026 Appreciate the detail. I ended up in about the same place using MLX. The biggest change for me was simplifying the stack instead of adding one more optimization layer. Re: adapter stacking for DeepSeek - cudascope - 10-17-2026 What worked for me was pretty simple. If you're on MLX, I'd confirm the version first and then reduce variables until the behavior is obvious. Once you know the plain path works, then add the faster path back in piece by piece. Re: adapter stacking for DeepSeek - batchsize - 10-17-2026 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. Not glamorous advice, but repeatable baselines save a lot of time. |