Cohere int8 & binary Embeddings - Scale Your Vector Database to Large Datasets (via) Jo Kristian Bergum told me “The accuracy retention [of binary embedding vectors] is sensitive to whether the model has been using this binarization as part of the loss function.”
Cohere provide an API for embeddings, and last week added support for returning binary vectors specifically tuned in this way.
250M embeddings (Cohere provide a downloadable dataset of 250M embedded documents from Wikipedia) at float32 (4 bytes) is 954GB.
Cohere claim that reducing to 1 bit per dimension knocks that down to 30 GB (954/32) while keeping “90-98% of the original search quality”.
Recent articles
- Trying out llama.cpp's new vision support - 10th May 2025
- Saying "hi" to Microsoft's Phi-4-reasoning - 6th May 2025
- Feed a video to a vision LLM as a sequence of JPEG frames on the CLI (also LLM 0.25) - 5th May 2025