[belladoreai]

> "What " might be a different token than "What" but the total token count shouldn't increment, would just be a different token, right?

The input string "What" (without trailing space) tokenizes into 1 token. The input string "What " tokenizes into 2 tokens. In theory, one might have a tokenizer that would simply tokenize "What " into a single token, but the actual tokenizers we have will tokenize that into at least 2 tokens.

> Curious then why this is called "LLaMA 3 tokenizer" what does it have to do with llama3?

When you input text into any of the LLaMA 3 models, the first step in the process is tokenizing your input. This library is called "LLaMA 3 tokenizer", because it produces the same tokenization as the official LLaMA 3 repo.

When I said that different models use different tokenization schemes, I am talking in comparison to other models, such as LLaMA 1, or GPT-4. Different models use different tokenizers, so the same text is tokenized into different tokens depending on if you're using GPT-4 or LLaMA 3 or what not.

[_akhe]

Thanks for clarifying, this is exactly where I was confused.

I just read about how both sentencepiece and tiktoken tokenize.

Thanks for making this (in JavaScript no less!) and putting it online! I'm going to use it in my auto-completion library (here: https://github.com/bennyschmidt/next-token-prediction/blob/m...) instead of just `.split(' ')` as I'm pretty sure it will be more nuanced :)

Awesome work!

[_akhe]

Well I installed your npm and tried to integrate it, but no matter what every token is always " word" with a leading space, and it's isolating foreign symbols as standalone tokens. I tried different options to strip those or to not include preceding spaces but it's always that way. It's probably how llama3 tokenizes text but I can't get use out of it for my autocomplete library unfortunately. I would need more-or-less the tokens to be words or occasional phrases.

I really love that it is 0 deps and that you provided the npm, and would love to defer this part of my work to an efficient library like this.

[belladoreai]

I don't think I really understand your use case.

My library solves the following problem: how to tokenize text in a way that is compatible with llama3.

If you don't have any particular constraint (as in "tokenize text in a way that is compatible to model X"), then you can just write your own tokenization that tokenizes the text however you want. It doesn't really make sense to use a complicated tokenization scheme from some LLM model if you don't need to be compatible with that model.

If you really want each word to be its own token, you can easily do that by just splitting on whitespace and punctuation (though that will lead to a huge vocabulary).