<ul><li>In Part 1, we explored tokenizers like Byte-Pair Encoding and WordPiece, which help convert text into a sequence of integers for language models.</li><li>In Part 2, we discussed different methods of numerically representing the meaning of words using word embeddings, which function like lookup tables.</li><li>In Part 3, we learned about positional encoding, which adds contextual information to the language models by treating the order of words as absolute.</li><li>This article is part of a series that explains the internal components of large language models.</li></ul>

LLMs from the Inside 4: The Transformer Block

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