Results for "scale"

Regulating access to large-scale compute.

A datastore optimized for similarity search over embeddings, enabling semantic retrieval at scale.

A model that assigns probabilities to sequences of tokens; often trained by next-token prediction.

Hardware resources used for training/inference; constrained by memory bandwidth, FLOPs, and parallelism.

A high-capacity language model trained on massive corpora, exhibiting broad generalization and emergent behaviors.

Optimization using curvature information; often expensive at scale.

Routes inputs to subsets of parameters for scalable capacity.

Capabilities that appear only beyond certain model sizes.

Chooses which experts process each token.

Incrementally deploying new models to reduce risk.

Increasing model capacity via compute.

Scaling law optimizing compute vs data.

Decomposes a matrix into orthogonal components; used in embeddings and compression.

Using limited human feedback to guide large models.

Maximum system processing rate.

Deep learning system for protein structure prediction.