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bge-m3 is a good candidate since it also supports sparse embedding model. In the e2e pipeline, we found the process turning text into embedding took most of the time. (text->embedding through openai API costs 100ms+ while vector search part only needs 10ms). It would be nice to have an efficient embedding model at local
Overview
This is a global tracking issue to bring generic sentence embedding models to MLCEngine.
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