My lab found a way to migrate between embedding models with zero downtime.
This story is from 2026-09-08. It is preserved in the archive; the latest stories are on the live feed.
So I've been messinga round with embedding models for a bit, and I think they are interesting enough to experiment with. They are useful for rag, especially in a localllm sense because you can ground your answers in truth. But what happens if you have a billion documents, and you decide to upgrade…
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Timeline · 6 reports
- 2026-09-10 06:03 · r/learnmachinelearning
I made a way to migrate between embedding models without re-embedding your entire corpus - 2026-09-10 00:14 · r/MachineLearning
I made a way to migrate between embedding models without re-embedding your entire corpus [R] - 2026-09-10 00:12 · r/deeplearning
I made a way to migrate between embedding models without re-embedding your entire corpus - 2026-09-10 00:10 · r/LocalLLaMA
I made a way to migrate between embedding models without re-embedding your entire corpus - 2026-09-09 02:44 · r/huggingface
I tested 10 of the most common embedding models, and found a way to upgrade between them with zero downtime! - 2026-09-08 05:59 · r/deeplearning
My lab found a way to migrate between embedding models with zero downtime.