A lot of model-specific tokenizers have reference implementations ([0], [1]). Underlying them is a core algorithm like SentencePiece or Byte-pair encoding (BPE). Tiktoken and TokenDagger are BPE implementations. The wrapping "tokenizer" mostly deals with the quirks of the vocabulary and handling special tokens.

For this project, I think there is value in building some of these model-specific quirks into the library. Could see some minor performance gains and generally make it easier to integrate with. It's probably not too much work to keep up with newer models. Tokenizers change much less frequently.

[0] https://github.com/meta-llama/llama-models/blob/01dc8ce46fec...

[1] https://github.com/mistralai/mistral-common/tree/main/src/mi...

Gemini uses SentencePiece [1], and the proprietary Gemini models share the same tokenizer vocabulary as Gemma [2, 3, 4].

Out of the large proprietary western AI labs (OpenAI, Anthropic, Google), only Anthropic with Claude 3 and newer lack local tokenizers.

[1] https://github.com/google/sentencepiece

[2] https://github.com/googleapis/python-aiplatform/blob/main/ve...

[3] https://storage.googleapis.com/deepmind-media/gemma/gemma-2-...: "We inherit from the large Gemini vocabulary (256k entries)."

[4] https://storage.googleapis.com/deepmind-media/gemma/Gemma3Re...: "We use the same tokenizer as Gemini 2.0."

I thought Gemini used SentencePiece

https://github.com/google/sentencepiece