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Remove noise, handle missing values, and redact sensitive information.

Breaking down raw text into smaller units called tokens. Modern models often use Byte-Pair Encoding (BPE) to handle a vast vocabulary efficiently. build a large language model %28from scratch%29 pdf

Tokens are converted into numeric vectors (embeddings) that represent the semantic meaning of the words. Remove noise, handle missing values, and redact sensitive

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. handle missing values

Multiple attention mechanisms operate in parallel, allowing the model to attend to information from different representation subspaces at different positions. 3. Implementing the Architecture