Attention is the core innovation of the Transformer architecture. It allows the model to "focus" on relevant parts of a sequence when predicting the next word.
Remove noise, handle missing values, and redact sensitive information. build a large language model %28from scratch%29 pdf
Multiple attention mechanisms operate in parallel, allowing the model to attend to information from different representation subspaces at different positions. 3. Implementing the Architecture Attention is the core innovation of the Transformer
The quality of an LLM is largely determined by its training data. This stage involves transforming raw text into a format a machine can process. handle missing values
Enables the model to relate different positions of a single sequence to compute a representation of the sequence.