Binary and Scalar Embedding Quantization for Significantly Faster & Cheaper Retrieval

Improving scalability There are several ways to approach the challenges of scaling embeddings. The most common approach is dimensionality reduction, such as PCA. However, classic dimensionality reduction -- like PCA methods -- tends to perform poorly when used with embeddings. In recent news, Matryoshka Representation Learning (blogpost) (MRL) as used by OpenAI also allows for cheaper embeddings.

[secondlife]

sentence transformer で int8 量子化方法ととその評価。モデルによってはスコア低下がほぼない。