MistralTekkenTokenizer

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MistralTekkenTokenizer class

keras_hub.tokenizers.MistralTekkenTokenizer(
    vocabulary=None, merges=None, split_pattern=None, **kwargs
)

Mistral Tekken tokenizer layer based on byte-level BPE.

This tokenizer class handles Mistral's Tekken (tiktoken-style byte-level BPE) vocabulary, used by presets such as Magistral. It is based on keras_hub.tokenizers.BytePairTokenizer, but uses the Tekken pre-tokenization regex instead of the GPT-2/Llama3 pattern hardcoded in the base class, and checks for the special tokens needed by Mistral models.

The vocabulary and merges are usually produced from a tekken.json file by the Hugging Face conversion path; see keras_hub.src.utils.transformers.convert_mistral.

If input is a batch of strings (rank > 0), the layer will output a tf.RaggedTensor where the last dimension of the output is ragged.

If input is a scalar string (rank == 0), the layer will output a dense tf.Tensor with static shape [None].

Arguments

• vocabulary: A dict mapping token strings to integer ids, or a path to a vocabulary JSON file.
• merges: A list of BPE merge rules, or a path to a merges file.
• split_pattern: str. The Tekken pre-tokenization regex.

Examples

tokenizer = keras_hub.models.MistralTekkenTokenizer.from_preset(
    "hf://mistralai/Magistral-Small-2506",
)
tokenizer("The quick brown fox jumped.")
tokenizer.detokenize(tokenizer("The quick brown fox jumped."))

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from_preset method

MistralTekkenTokenizer.from_preset(preset, config_file="tokenizer.json", **kwargs)

Instantiate a keras_hub.models.Tokenizer from a model preset.

A preset is a directory of configs, weights and other file assets used to save and load a pre-trained model. The preset can be passed as one of:

1. a built-in preset identifier like 'bert_base_en'
2. a Kaggle Models handle like 'kaggle://user/bert/keras/bert_base_en'
3. a Hugging Face handle like 'hf://user/bert_base_en'
4. a path to a local preset directory like './bert_base_en'

For any Tokenizer subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

This constructor can be called in one of two ways. Either from the base class like keras_hub.models.Tokenizer.from_preset(), or from a model class like keras_hub.models.GemmaTokenizer.from_preset(). If calling from the base class, the subclass of the returning object will be inferred from the config in the preset directory.

Arguments

• preset: string. A built-in preset identifier, a Kaggle Models handle, a Hugging Face handle, or a path to a local directory.
• load_weights: bool. If True, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a preset tokenizer.
tokenizer = keras_hub.tokenizer.Tokenizer.from_preset("bert_base_en")

# Tokenize some input.
tokenizer("The quick brown fox tripped.")

# Detokenize some input.
tokenizer.detokenize([5, 6, 7, 8, 9])