How does embedding size influence the performance of models on tasks that require reasoning and inference?

Apr 8, 2023 ... The output dimension is mantained, since values coming from each attention head are concatenated and then projected with a matrix WO∈Rhdv×dmodel ... The first text embedding model with a 8192 context-length that outperforms OpenAI Ada-002 and text-embedding-3-small on both short and long context tasks. Embedding Dimension ... The embedding dimension is defined as the length m of the used single vector “butter embedding space” that can reconstruct the successive ... Feb 5, 2025 ... 1. Input Context Length · Rotary Position Embeddings (RoPE). RoPE encodes positional information using a rotation matrix, and YaRN modifies how ... Jun 4, 2024 ... Embedding dimensions: This is the length of the embedding vector. ... For common RAG applications, a good chunk size for an embedding is typically ... Model Choices Voyage currently provides the following text embedding models: Model Context Length (tokens) Embedding Dimension Description voyage-3-large ... Apr 8, 2024 ... What are embedding models? Embedding models are models that are trained specifically to generate vector embeddings ... Parameter Size. mxbai-embed ... Oct 1, 2024 ... I have a database on a Qdrant with a vector of 256 dimensions. The embedding was done using the “text-embedding-3-large” model with the dimensions parameter ... 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. The Granite Embedding lineup includes four different models of varying sizes: granite-embedding-30m-english: English only model that produces embedding vectors ...