Sentencepiece

You need to count tokens locally. When do you reach for tiktoken versus SentencePiece?

BPE grows the vocabulary by adding merges. Unigram does the opposite. Explain the difference.

Explain the Unigram LM tokenization algorithm and how its training differs fundamentally from BPE. What does stochastic tokenization enable?

Which statement correctly describes the Unigram Language Model tokenizer's training procedure?

Why does SentencePiece prepend a special space symbol (▁) to tokens, and what would break without it?

Which tokenizer can learn merges that span whitespace boundaries by design?

What are the practical differences between the sentencepiece library and HuggingFace tokenizers for serving a Llama model, and which is recommended?

For serving a Llama model in production with HuggingFace transformers, which tokenizer library is recommended?