After adding tokens, the model doubles in size.

[supech]

l use the code below to add about 100 tokens to model and tokenizer,and the model doubles in size.

from transformers import  AutoTokenizer,AutoModel
tokenizer = AutoTokenizer.from_pretrained("llama-7b-model")
model = AutoModel.from_pretrained("llama-7b-model")
characters = [
  #100tokens
]
num_added_toks = tokenizer.add_tokens(characters)
tokenizer.save_pretrained("new-llama-7b-model")
model.save_pretrained("new-llama-7b-model")

In contrast, other language models such as BERT, BART, GPT-2, etc., that I have used previously do not exhibit such a significant increase in size.
I have also tried fine-tuning LLAMA1.3b, and after adding tokens, although the size nearly doubles, the entire fine-tuned model ends up slightly smaller than the original 1.3b model.
I am puzzled by this situation and unsure if it is normal. Do you have any insights into the reasons behind this?
Thank you.