Results for "embeddings"
Rotary Positional Embeddings
IntermediateEncodes positional information via rotation in embedding space.
Rotary positional embeddings are a clever way for AI models to understand where words are in a sentence. Instead of just giving each word a fixed position, this method uses a kind of rotation to show how words relate to each other. Imagine you’re arranging books on a shelf; the position of each b...
Encodes positional information via rotation in embedding space.
A continuous vector encoding of an item (word, image, user) such that semantic similarity corresponds to geometric closeness.
A datastore optimized for similarity search over embeddings, enabling semantic retrieval at scale.
Predicts masked tokens in a sequence, enabling bidirectional context; often used for embeddings rather than generation.
Automatically learning useful internal features (latent variables) that capture salient structure for downstream tasks.
Mechanisms for retaining context across turns/sessions: scratchpads, vector memories, structured stores.
AI subfield dealing with understanding and generating human language, including syntax, semantics, and pragmatics.
Decomposes a matrix into orthogonal components; used in embeddings and compression.
Models trained to decide when to call tools.