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For my personal retrieval-augmented generation (RAG) study, I needed a local model to answer questions from documents. My laptop has an RTX 3060 Laptop GPU with 6 GiB of VRAM, so memory use mattered alongside speed. I compared qwen3:4b and qwen3:0.
Most smart products already have a name. The hard part is making the device hear that name reliably without sending every conversation to the cloud. This is the product question behind a custom wake word: can a device recognize one short phrase locally, then start the next interaction?
This is from MV2's open notebook on Kakeya-type problems ( https://github. com/munimtechnologies/kakeya-small-fields ). It continues the earlier posts on finite 2-groups whose involutions are all central: is such a group at most |Omega_1|^(e+1), where 2^e is its exponent?
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The page says chunking has degraded retrieval quality in the internal knowledge-base bot. What that actually means is explained by the trace: on call sees a plausible response, a successful vector query, and citations that point to the right handbook but the wrong passage. Nothing is down.