GemmaCausalLMPreprocessor
classkeras_nlp.models.GemmaCausalLMPreprocessor(
tokenizer, sequence_length=1024, add_start_token=True, add_end_token=True, **kwargs
)
Gemma Causal LM preprocessor.
This preprocessing layer is meant for use with
keras_hub.models.GemmaCausalLM
. By default, it will take in batches of
strings, and return outputs in a (x, y, sample_weight)
format, where the
y
label is the next token id in the x
sequence.
For use with generation, the layer also exposes two methods
generate_preprocess()
and generate_postprocess()
. When this preprocessor
is attached to a keras_hub.models.GemmaCausalLM
instance, these methods
will be called implicitly in generate()
. They can also be called
standalone (e.g. to precompute preprocessing inputs for generation in a
separate process).
Arguments
keras_hub.models.GemmaTokenizer
instance.True
, the preprocessor will prepend the tokenizer
start token to each input sequence.True
, the preprocessor will append the tokenizer
end token to each input sequence.Call arguments
tf.Tensor
or list of python strings.None
as the layer generates labels.None
as the layer
generates label weights.sequence_length
of
the layer.Examples
# Load the preprocessor from a preset.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
"gemma_2b_en"
)
# Tokenize and pack a single sentence.
preprocessor("The quick brown fox jumped.")
# Tokenize a batch of sentences.
preprocessor(["The quick brown fox jumped.", "Call me Ishmael."])
# Apply tokenization to a [`tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset).
features = tf.constant(["The quick brown fox.", "Call me Ishmael."])
ds = tf.data.Dataset.from_tensor_slices(features)
ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
# Prepare tokens for generation (no end token).
preprocessor.generate_preprocess(["The quick brown fox jumped."])
# Map generation outputs back to strings.
preprocessor.generate_postprocess({
'token_ids': np.array([[2, 714, 4320, 8426, 25341, 32292, 235265, 0]]),
'padding_mask': np.array([[ 1, 1, 1, 1, 1, 1, 1, 0]]),
})
from_preset
methodGemmaCausalLMPreprocessor.from_preset(
preset, config_file="preprocessor.json", **kwargs
)
Instantiate a keras_hub.models.Preprocessor
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 Preprocessor
subclass, you can run cls.presets.keys()
to
list all built-in presets available on the class.
As there are usually multiple preprocessing classes for a given model,
this method should be called on a specific subclass like
keras_hub.models.BertTextClassifierPreprocessor.from_preset()
.
Arguments
Examples
# Load a preprocessor for Gemma generation.
preprocessor = keras_hub.models.GemmaCausalLMPreprocessor.from_preset(
"gemma_2b_en",
)
# Load a preprocessor for Bert classification.
preprocessor = keras_hub.models.BertTextClassifierPreprocessor.from_preset(
"bert_base_en",
)
Preset name | Parameters | Description |
---|---|---|
gemma_2b_en | 2.51B | 2 billion parameter, 18-layer, base Gemma model. |
gemma_instruct_2b_en | 2.51B | 2 billion parameter, 18-layer, instruction tuned Gemma model. |
gemma_1.1_instruct_2b_en | 2.51B | 2 billion parameter, 18-layer, instruction tuned Gemma model. The 1.1 update improves model quality. |
code_gemma_1.1_2b_en | 2.51B | 2 billion parameter, 18-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. The 1.1 update improves model quality. |
code_gemma_2b_en | 2.51B | 2 billion parameter, 18-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. |
gemma_7b_en | 8.54B | 7 billion parameter, 28-layer, base Gemma model. |
gemma_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned Gemma model. |
gemma_1.1_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned Gemma model. The 1.1 update improves model quality. |
code_gemma_7b_en | 8.54B | 7 billion parameter, 28-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. |
code_gemma_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned CodeGemma model. This model has been trained for chat use cases related to code. |
code_gemma_1.1_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned CodeGemma model. This model has been trained for chat use cases related to code. The 1.1 update improves model quality. |
gemma2_2b_en | 2.61B | 2 billion parameter, 26-layer, base Gemma model. |
gemma2_instruct_2b_en | 2.61B | 2 billion parameter, 26-layer, instruction tuned Gemma model. |
gemma2_9b_en | 9.24B | 9 billion parameter, 42-layer, base Gemma model. |
gemma2_instruct_9b_en | 9.24B | 9 billion parameter, 42-layer, instruction tuned Gemma model. |
gemma2_27b_en | 27.23B | 27 billion parameter, 42-layer, base Gemma model. |
gemma2_instruct_27b_en | 27.23B | 27 billion parameter, 42-layer, instruction tuned Gemma model. |
shieldgemma_2b_en | 2.61B | 2 billion parameter, 26-layer, ShieldGemma model. |
shieldgemma_9b_en | 9.24B | 9 billion parameter, 42-layer, ShieldGemma model. |
shieldgemma_27b_en | 27.23B | 27 billion parameter, 42-layer, ShieldGemma model. |
tokenizer
propertykeras_nlp.models.GemmaCausalLMPreprocessor.tokenizer
The tokenizer used to tokenize strings.