hey @sML, in general fine-tuning will not update the vocabulary of the tokenizer as the vocabulary is specific to the corpus that the model was pretrained on.
if your domain is not too different from the corpus used to pretrain the model, then i would just try fine-tuning the LM (following the tutorial you lined to) to see what kind of results you get.
one alternative would be to manually add new tokens to the tokenizer’s vocabulary, e.g.
tokenizer = ...
model = ...

new_toks = [tok1, tok2, ..., tokN]
# docs: https://huggingface.co/transformers/internal/tokenization_utils.html?highlight=add_tokens#transformers.tokenization_utils_base.SpecialTokensMixin.add_tokens
added_toks = tokenizer.add_tokens(new_toks)
# you need to resize the dimension of the token embeddings to match the new vocab size
model.resize_token_embeddings(len(tokenizer))

you can find a more sophisticated approach described in this paper: https://www.aclweb.org/anthology/2020.findings-emnlp.129.pdf 6
hth!