metadata

library_name: transformers
tags:
  - gemma
  - chatml

ChatML Tokenizer for Gemma

This repository includes a fast tokenizer for google/gemma-7b with the ChatML format. The Tokenizer was created by replacing the string values of original tokens with id 106 (<start_of_turn>) and 107 (<end_of_turn>) with the chatML tokens <|im_start|> and <|im_end|>.

No new tokens were added during that process to ensure that the original model's embedding doesn't need to be modified.

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("philschmid/gemma-tokenizer-chatml")

messages = [
  {"role": "system", "content": "You are Gemma."},
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
]

chatml = tokenizer.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)

# <|im_start|>system
# You are Gemma.<|im_end|>
# <|im_start|>user
# Hello, how are you?<|im_end|>
# <|im_start|>assistant
# I'm doing great. How can I help you today?<|im_end|>

Test

tokenizer = AutoTokenizer.from_pretrained("philschmid/gemma-tokenizer-chatml")
original_tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b-it")

# get special tokens
print(tokenizer.special_tokens_map)
print(original_tokenizer.special_tokens_map)

# check length of vocab
assert len(tokenizer) == len(original_tokenizer), "tokenizer are not having the same length"

# tokenize messages 
messages = [
  {"role": "user", "content": "Hello, how are you?"},
  {"role": "assistant", "content": "I'm doing great. How can I help you today?"},
]

chatml = tokenizer.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)
google_format = original_tokenizer.apply_chat_template(messages, add_generation_prompt=False, tokenize=False)

print(f"ChatML: \n{chatml}\n-------------------\nGoogle: \n{google_format}")