transformer Archives

A Vision Transformer applies transformer attention to image patches for visual classification, recognition, and representation learning.

Jul 22, 2026 by Marcus Lee • 1 min read

Seq2Seq models transform one sequence into another, supporting translation, summarization, transcription, and similar tasks.

Jul 16, 2026 by Ethan Caldwell • 1 min read

A transformer is a neural network architecture built on attention and used in modern language, vision, audio, video, and multimodal AI models.

Jul 15, 2026 by Daniel Mercer • 1 min read

By Ethan Caldwell Jul 12, 2026 • 1 min read

Self-attention lets each element in a sequence weigh and combine information from other elements in that same sequence.

By Ethan Caldwell Jun 9, 2026 • 1 min read

Multi-head attention runs several attention operations in parallel so a model can capture different relationships in the same input.

By Marcus Lee May 21, 2026 • 1 min read

XLM-R is a multilingual transformer encoder that supports cross-lingual classification, entity recognition, and language understanding tasks.

By Marcus Lee Apr 29, 2026 • 1 min read

GPT is a family of transformer language models pre-trained to predict text and adapted to generate useful responses from prompts.

By Ethan Caldwell Apr 16, 2026 • 1 min read

An encoder transforms raw input into a compact representation that preserves information useful for another model component or task.

By Ethan Caldwell Mar 3, 2026 • 1 min read

Causal language modeling trains a model to predict each next token without accessing future tokens in the sequence.

By Ethan Caldwell Feb 6, 2026 • 1 min read

An attention mask tells a transformer which token positions it may consider and which positions should be ignored.

#Transformer