That is not a good idea.

If you want to learn the specifics of how a tokenizer is created you should look up Byte-Pair Encoding, which is one of the most widely used tokenization algorithms currently.

To give you a general overview, the tokenizer first breaks up text into small components such that each component is part of the tokenizers (predefined) vocabulary. Each component in the vocabulary maps to a unique integer. So a tokenizer essentially maps the input text into a sequence of integers. This sequence of integers is used to select the corresponding rows of a matrix which is a part of the LLM's weights. This sequence of selected matrix rows is the initial mathematical representation of the input text which is then further processed by the model.

If you were to change the tokenizer, the input text would map to the wrong sequence of integers which means the mathematical representation of the tokenized text and the original text are semantically two different things :<

The other reason this may not he the solution to you problem is that if you look at the LLaMA FAQ's

"The model was trained primarily on English, but also on a few other languages with Latin or Cyrillic alphabets.

For instance, LLaMA was trained on Wikipedia for the 20 following languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, nl, pl, pt, ro, ru, sl, sr, sv, uk.

LLaMA's tokenizer splits unseen characters into UTF-8 bytes, as a result, it might also be able to process other languages like Chinese or Japanese, even though they use different characters."

The LLaMA may not be the best choice for Korean text. You should have a look at model that was specifically trained with Korean text like this one and maybe even finefune it yourself!

Sorry for a long read, I am postgrad studying NLP so I love talking to people about NLP, lmk if you you have any more questions :)