GemmaTokenizer

class torchtune.models.gemma.GemmaTokenizer(path: str)

Gemma’s implementation of the SentencePiece tokenizer

Parameters:

path (str) – Path to pretrained tokenizer file.

Examples

>>> tokenizer = GemmaTokenizer("/path/to/spm_model")
>>> tokenized_text = tokenizer.encode("Hello world!", add_bos=True, add_eos=True)
>>> print(tokenized_text)
[1, 31587, 29644, 102, 2]

tokenize_messages(messages: List[Message], max_seq_len: Optional[int] = None) → Tuple[List[int], List[bool]]

Tokenize a list of messages one at a time then concatenate them, returning a list of tokens and a list of masks.

Example

>>> tokenizer = GemmaTokenizer(tokenizer_path)
>>> messages = [
    Message(role="system", content="system message\n", masked=True),
    Message(role="user", content="user prompt\n", masked=True),
    Message(role="assistant", content="assistant response\n"),
]
# tokenize_messages encodes messages separately and concats
>>> tokenizer.tokenize_messages(messages, max_seq_len)[0]
[1, 1788, 2643, 13, 1792, 9508, 13, 465, 22137, 2933, 2]

# Same result as encoding the full string in one go >>> tokenizer.encode(‘’.join([message.content for message in messages])) [1, 1788, 2643, 13, 1792, 9508, 13, 465, 22137, 2933, 2]

Parameters:

• messages (List[Message]) – A list of messages, each containing role, content, and masked attributes.

• max_seq_len (Optional[int]) – A max sequence length to truncate tokens to. Default: None

Returns:

The tokenized messages

Return type:

Tuple[List[int], List[bool]]