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The mapping shifts those problem bytes into the higher Unicode range so the merge table only ever sees printable code points
from the top result below
https://gpt-news.net/inside-the-bpe-tokenizer-how-gpt-splits-words-into-subword-units
To understand how the GPT BPE tokenizer works, you have to look at three layers most write-ups skip: the regex that pre-cuts text before any merging happens, the byte-level fallback that keeps emoji and Chinese from breaking the merge table, and the rank-priority merge loop that turns 13 raw bytes into the integer IDs