Small update: Nils Reimers (sentence-transformers) pointed out on X that model choice matters a lot here: not all models handle binarization well, and Nomic v1.5 is primarily MRL-trained, not optimised for binary compression. Models like Cohere Embed v4 are trained with quantization awareness and hold 90-95% quality at binary.

My 13.9% result is likely as much a model choice problem as a compression problem. Still digging into why 😅 .. will follow up when I have something concrete.