Are there any techniques for compressing or quantizing large embedding matrices without significant loss of performance?

Apr 16, 2024 ... Explore Snowflake Arctic's new open-source embedding models, delivering top retrieval performance with smaller sizes for faster deployment ... Sep 6, 2023 ... The authors of word2vec are somewhat vague, recommending somewhere between 100 and 1000 dimensions. Other authors pick numbers like 100 or 300. Jul 22, 2024 ... This paper proposes to learn Scalable Lightweight Embeddings for streaming recommendation, called SCALL, which can adaptively adjust the embedding sizes of ... Model Choices Voyage currently provides the following text embedding models: Model Context Length (tokens) Embedding Dimension Description voyage-3-large ... Apr 2, 2025 ... Resource and data size limits​ ; Embedding dimension. Per index. 4096 ; Indexes. Per endpoint. 50 ; Columns. Per index. 50 ; Columns. Supported ... Jun 12, 2024 ... How models can be used to generate vector embeddings. How vector embeddings represent data. In machine learning, the “dimensions” of data do not ... The Granite Embedding lineup includes four different models of varying sizes: granite-embedding-30m-english: English only model that produces embedding vectors ... Oct 23, 2023 ... Paper: Towards General Text Embeddings with Multi-stage Contrastive Learning; Embedding Dimension: 768; Model Size: 219 MB. 2. GTE-Large. Model ... text-embedding-3-small is OpenAI's small text embedding model, creating embeddings with 1536 dimensions. Model size: the size of a model indicates how easy it will be to run. All ... embeddings # openai embedding function def embed(docs: list[str]) -> list ...