Results for "max tokens"

Predicts masked tokens in a sequence, enabling bidirectional context; often used for embeddings rather than generation.

Activation max(0, x); improves gradient flow and training speed in deep nets.

Detecting unauthorized model outputs or data leaks.

Samples from the k highest-probability tokens to limit unlikely outputs.

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

Samples from the smallest set of tokens whose probabilities sum to p, adapting set size by context.

Injects sequence order into Transformers, since attention alone is permutation-invariant.

Training objective where the model predicts the next token given previous tokens (causal modeling).

Generates sequences one token at a time, conditioning on past tokens.

Maximum number of tokens the model can attend to in one forward pass; constrains long-document reasoning.

How many requests or tokens can be processed per unit time; affects scalability and cost.

Prevents attention to future tokens during training/inference.

Encodes positional information via rotation in embedding space.

Encodes token position explicitly, often via sinusoids.

Attention mechanisms that reduce quadratic complexity.

A point where gradient is zero but is neither a max nor min; common in deep nets.

Nonlinear functions enabling networks to approximate complex mappings; ReLU variants dominate modern DL.

GNN framework where nodes iteratively exchange and aggregate messages from neighbors.

Mechanism that computes context-aware mixtures of representations; scales well and captures long-range dependencies.

Converting text into discrete units (tokens) for modeling; subword tokenizers balance vocabulary size and coverage.

The set of tokens a model can represent; impacts efficiency, multilinguality, and handling of rare strings.

Stochastic generation strategies that trade determinism for diversity; key knobs include temperature and nucleus sampling.

Techniques to handle longer documents without quadratic cost.

Limiting inference usage.

Architecture based on self-attention and feedforward layers; foundation of modern LLMs and many multimodal models.

Search algorithm for generation that keeps top-k partial sequences; can improve likelihood but reduce diversity.

Stores past attention states to speed up autoregressive decoding.

Transformer applied to image patches.

Cost to run models in production.