LlamaTokenizer
classkeras_nlp.tokenizers.LlamaTokenizer(proto, **kwargs)
Llama tokenizer layer based on SentencePiece.
This tokenizer class will tokenize raw strings into integer sequences and
is based on keras_hub.tokenizers.SentencePieceTokenizer
. Unlike the
underlying tokenizer, it will check for all special tokens needed by
Llama models and provides a from_preset()
method to automatically
download a matching vocabulary for a Llama preset.
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
string
path to a SentencePiece proto file, or a
bytes
object with a serialized SentencePiece proto. See the
SentencePiece repository
for more details on the format.Examples
# Unbatched input.
tokenizer = keras_hub.models.LlamaTokenizer.from_preset(
"llama_7b_en",
)
tokenizer("The quick brown fox jumped.")
# Batched input.
tokenizer(["The quick brown fox jumped.", "The fox slept."])
# Detokenization.
tokenizer.detokenize(tokenizer("The quick brown fox jumped."))
from_preset
methodLlamaTokenizer.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:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./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
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])
Preset name | Parameters | Description |
---|---|---|
llama2_7b_en | 6.74B | 7 billion parameter, 32-layer, base LLaMA 2 model. |
llama2_7b_en_int8 | 6.74B | 7 billion parameter, 32-layer, base LLaMA 2 model with activation and weights quantized to int8. |
llama2_instruct_7b_en | 6.74B | 7 billion parameter, 32-layer, instruction tuned LLaMA 2 model. |
llama2_instruct_7b_en_int8 | 6.74B | 7 billion parameter, 32-layer, instruction tuned LLaMA 2 model with activation and weights quantized to int8. |
vicuna_1.5_7b_en | 6.74B | 7 billion parameter, 32-layer, instruction tuned Vicuna v1.5 model. |
llama3_8b_en | 8.03B | 8 billion parameter, 32-layer, base LLaMA 3 model. |
llama3_8b_en_int8 | 8.03B | 8 billion parameter, 32-layer, base LLaMA 3 model with activation and weights quantized to int8. |
llama3_instruct_8b_en | 8.03B | 8 billion parameter, 32-layer, instruction tuned LLaMA 3 model. |
llama3_instruct_8b_en_int8 | 8.03B | 8 billion parameter, 32-layer, instruction tuned LLaMA 3 model with activation and weights quantized to int8. |