Glossary

The numeric output of a layer after its linear transform and nonlinearity, passed forward through the network.

Post-training steps that steer a model toward helpful, honest, and policy-compliant behavior—often via preference data, reward models, or safety filters.

The blueprint that defines how a model's layers, modules, and data flow connect during inference.

A generation paradigm that produces outputs one discrete step at a time, each step conditioned on everything generated so far.

The backward pass that applies the chain rule on a computational graph to compute gradients for every differentiable parameter.

A finer-grained part inside a module, such as an attention head or projection layer.

A record of which operations produced each tensor so automatic differentiation can run backward.

Extra inputs—text prompts, class labels, images, or guidance weights—that steer a generative model without retraining the full parameter stack each time.

The maximum number of tokens a model can attend over in one forward pass, distinct from training sequence length and how many tokens it may emit in a session.

The repeated next-token stage that reuses the KV cache and turns inter-token latency into the main reader experience after prefill finishes.

A network stack that turns internal representations into outputs—tokens, pixels, or structured predictions—often one step at a time with attention to prior context.

A generation paradigm that starts from noisy latents and iteratively removes noise—or adds structure—until a clean sample emerges.

A generative model family that learns to reverse a noise corruption process through many iterative denoising steps.

A model trained to score, rank, or classify inputs into labels or preferences rather than synthesize new content.

A dense vector that represents a token or other discrete item so the model can run continuous math on it.

Qualitative capability jumps that appear only past certain model scale or training thresholds—distinct from smooth metric improvements tracked by scaling laws.

A network stack that maps raw or patched inputs into internal representations—tokens, latents, or context vectors—for downstream decoders or heads.

An architecture that compresses input into representations with an encoder, then generates outputs with a decoder—often via cross-attention between the two stacks.

A measure of how spread out or uncertain a probability distribution is over next-token choices.

A large pretrained model intended as a shared starting point for many downstream tasks via fine-tuning or prompting.

How well a model performs on data it was not explicitly trained to memorize—validation sets, new domains, and live traffic are the practical tests.

A model trained to produce new outputs such as text, images, or audio rather than only assign labels to fixed categories.

The direction and size of change each parameter should take to reduce the training loss.

A deterministic next-token rule that always picks the highest-probability option, making output stable and repeatable but usually less diverse.

The width of a model's internal vectors—the number of dimensions in each token embedding and each token's per-position hidden state before the vocabulary projection.

The serving latency metric that measures the delay between streamed output tokens after generation has started.

The saved key and value tensors a decoder keeps from earlier tokens so later tokens can reuse attention state instead of recomputing the full prefix.

A compressed internal code that summarizes input structure for reconstruction, generation, or downstream tasks.

A raw, unnormalized score for each vocabulary item before softmax turns scores into probabilities.

A scalar score that measures how wrong the model's predictions are so training knows which direction to improve.

The kind of input or output data a model is built to handle, such as text, images, audio, or video.

A trained machine learning system that maps inputs to outputs using learned parameters.

How much function a model can represent—driven by parameter count, width, depth, and training data—before optimization and regularization constrain what it actually learns.

A reusable building block inside a model architecture, such as attention or a feed-forward network.

A model family that accepts or produces more than one data modality, such as text paired with images or audio.

Layers that rescale hidden vectors inside each transformer block so activations stay stable as depth and batch composition change.

Extra memory an adaptive optimizer keeps beyond model weights, such as momentum and variance estimates for each parameter.

When a model fits training noise so closely that held-out or production data perform worse—the classic gap between memorization and useful generalization.

A learnable numeric value stored in the model weights that training updates to fit data.

A fixed-size chunk of input data—pixels, spectrogram frames, or grouped tokens—that an encoder turns into a representation vector.

A standard language-model evaluation metric: exponentiated average cross-entropy over tokens, lower is better when comparing models on the same corpus.

An internal encoding of input data that downstream layers or tasks use instead of raw features.

Skip paths that add each sublayer's output back onto the running hidden stream so deep transformer stacks stay trainable.

The next-token decision step that turns a probability distribution into one chosen token, shaping how diverse, stable, or controllable a model's output feels.

Empirical power-law relationships between model scale, compute, data, and downstream loss or capability—used to forecast training budgets and benchmark trends.

A broader shortcut path that carries information around one or more layers, including the residual adds used inside transformers.

A function that turns a vector of logits into a probability distribution that sums to one.

Reserved tokenizer markers such as beginning-of-sequence, end-of-sequence, padding, and separators that carry structural meaning instead of ordinary text meaning.

A positive scaling factor applied to logits before softmax to make the next-token distribution sharper or flatter.

A multi-dimensional array of numbers that carries activations, weights, or gradients through the network.

The serving tradeoff between aggregate completed work over time and the wait experienced by an individual request under concurrency.

The serving latency metric that measures how long a reader waits from request start until the first generated token appears.

The smallest unit of text a language model reads and predicts—usually a word piece, not always a whole word. Each token ID maps to a dense vector through vector embedding of model hidden size before attention runs.

Tokens per second (tokens/s, tok/s, or TPS) is a throughput rate: how many model tokens a serving system generates per second over the measurement window you choose.

A next-token rule that keeps only the k highest-probability options before drawing one, trading some stability for more bounded diversity than greedy decoding.

A next-token rule that keeps the smallest probability mass above a threshold before drawing one token, adapting the candidate count from step to step.

A model family built from stacked attention and feed-forward blocks that mix information across token positions in parallel.

An ordered list of numbers that represents a point or direction in continuous space—embeddings and activations are vectors at different stages of the model.

The total number of token IDs a tokenizer can emit, including ordinary text pieces and reserved control tokens that occupy part of the same table.

A model family that learns environment dynamics or state transitions so it can predict or simulate what happens next.