T5GemmaSeq2SeqLM model

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T5GemmaSeq2SeqLM class

keras_hub.models.T5GemmaSeq2SeqLM(backbone, preprocessor=None, **kwargs)

An end-to-end T5Gemma model for seq2seq language modeling.

A seq2seq language model (LM) is an encoder-decoder model which is used for conditional text generation. The encoder is given a "context" text (fed to the encoder), and the decoder predicts the next token based on both the encoder inputs and the previous tokens. You can finetune T5GemmaSeq2SeqLM to generate text for any seq2seq task (e.g., translation or summarization).

This model has a generate() method, which generates text based on a prompt. The generation strategy used is controlled by an additional sampler argument on compile(). You can recompile the model with different keras_hub.samplers objects to control the generation. By default, "greedy" sampling will be used.

This model can optionally be configured with a preprocessor layer, in which case it will automatically apply preprocessing to string inputs during fit(), predict(), evaluate() and generate(). This is done by default when creating the model with from_preset().

Arguments

• backbone: A keras_hub.models.T5GemmaBackbone instance.
• preprocessor: A keras_hub.models.T5GemmaSeq2SeqLMPreprocessor or None. If None, this model will not apply preprocessing, and inputs should be preprocessed before calling the model. Defaults to None.

Examples

Use generate() to do text generation.

import numpy as np
t5gemma_lm = keras_hub.models.T5GemmaSeq2SeqLM.from_preset(
    "t5gemma_b_b_prefixlm_it"
)
# Generate with encoder-only input.
t5gemma_lm.generate("The quick brown fox jumped.", max_length=30)

# Generate with batched encoder-only inputs.
t5gemma_lm.generate(
    ["The quick brown fox jumped.", "The whale."],
    max_length=30
)
# Generate with encoder and decoder inputs.
t5gemma_lm.generate(
    {
        "encoder_text": "The quick brown fox jumped.",
        "decoder_text": "A fast fox"
    },
    max_length=30
)

Compile the generate() function with a custom sampler.

t5gemma_lm = keras_hub.models.T5GemmaSeq2SeqLM.from_preset(
    "t5gemma_b_b_prefixlm_it"
)
t5gemma_lm.compile(sampler="top_k")
t5gemma_lm.generate("I want to say", max_length=30)

t5gemma_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
t5gemma_lm.generate("I want to say", max_length=30)

Use generate() without preprocessing.

# Preprocessed inputs, with encoder inputs corresponding to
# "The quick brown fox", and the decoder inputs to "A fast fox".
# Use `"padding_mask"` to indicate values that should not be overridden.
prompt = {
    "encoder_token_ids": np.array([[2, 10, 133, 2119, 6219, 23602, 1, 0]]),
    "encoder_padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 0]]),
    "decoder_token_ids": np.array([[2, 133, 1769, 1, 0, 0, 0]]),
    "decoder_padding_mask": np.array([[1, 1, 1, 1, 0, 0, 0]])
}

t5gemma_lm = keras_hub.models.T5GemmaSeq2SeqLM.from_preset(
    "t5gemma_b_b_prefixlm_it",
    preprocessor=None,
)
t5gemma_lm.generate(prompt)

Call fit() on a single batch.

features = {
    "encoder_text": ["The quick fox jumped.", "I forgot my homework."],
    "decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
}
t5gemma_lm = keras_hub.models.T5GemmaSeq2SeqLM.from_preset(
    "t5gemma_b_b_prefixlm_it"
)
t5gemma_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

x = {
    "encoder_token_ids": np.array([[2, 133, 2119, 1, 0]] * 2),
    "encoder_padding_mask": np.array([[1, 1, 1, 1, 0]] * 2),
    "decoder_token_ids": np.array([[2, 133, 1769, 1, 0]] * 2),
    "decoder_padding_mask": np.array([[1, 1, 1, 1, 1]] * 2),
}
y = np.array([[133, 1769, 1, 0, 0]] * 2)
sw = np.array([[1, 1, 1, 0, 0]] * 2)

t5gemma_lm = keras_hub.models.T5GemmaSeq2SeqLM.from_preset(
    "t5gemma_b_b_prefixlm_it",
    preprocessor=None,
)
t5gemma_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)

Custom backbone and vocabulary.

features = {
    "encoder_text": ["The quick fox jumped.", "I forgot my homework."],
    "decoder_text": ["The fast hazel fox leapt.", "I forgot my assignment."]
}
tokenizer = keras_hub.models.T5GemmaTokenizer(
    proto="proto.spm",
)
preprocessor = keras_hub.models.T5GemmaSeq2SeqLMPreprocessor(
    tokenizer=tokenizer,
    encoder_sequence_length=128,
    decoder_sequence_length=128,
)
backbone = keras_hub.models.T5GemmaBackbone(
    vocabulary_size=32000,
    # Encoder parameters.
    encoder_hidden_dim=256,
    encoder_intermediate_dim=512,
    encoder_num_layers=4,
    encoder_num_attention_heads=4,
    encoder_num_key_value_heads=2,
    encoder_head_dim=64,
    encoder_layer_types=["full_attention"] * 4,
    # Decoder parameters.
    decoder_hidden_dim=256,
    decoder_intermediate_dim=512,
    decoder_num_layers=4,
    decoder_num_attention_heads=4,
    decoder_num_key_value_heads=2,
    decoder_head_dim=64,
    decoder_layer_types=["full_attention"] * 4,
    # Common parameters.
    dropout_rate=0.1,
    rms_norm_eps=1e-6,
    query_pre_attn_scalar=1.0,
    attention_bias=False,
    hidden_activation="gelu_approximate",
)
t5gemma_lm = keras_hub.models.T5GemmaSeq2SeqLM(
    backbone=backbone,
    preprocessor=preprocessor,
)
t5gemma_lm.fit(x=features, batch_size=2)

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from_preset method

T5GemmaSeq2SeqLM.from_preset(preset, load_weights=True, **kwargs)

Instantiate a keras_hub.models.Task 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 Task 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 a task specific base class like keras_hub.models.CausalLM.from_preset(), or from a model class like keras_hub.models.BertTextClassifier.from_preset(). If calling from the a 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, saved weights will be loaded into the model architecture. If False, all weights will be randomly initialized.

Examples

# Load a Gemma generative task.
causal_lm = keras_hub.models.CausalLM.from_preset(
    "gemma_2b_en",
)

# Load a Bert classification task.
model = keras_hub.models.TextClassifier.from_preset(
    "bert_base_en",
    num_classes=2,
)

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generate method

T5GemmaSeq2SeqLM.generate(
    inputs, max_length=None, stop_token_ids="auto", strip_prompt=False
)

Generate text given prompt inputs.

This method generates text based on given inputs. The sampling method used for generation can be set via the compile() method.

If inputs are a tf.data.Dataset, outputs will be generated "batch-by-batch" and concatenated. Otherwise, all inputs will be handled as a single batch.

If a preprocessor is attached to the model, inputs will be preprocessed inside the generate() function and should match the structure expected by the preprocessor layer (usually raw strings). If a preprocessor is not attached, inputs should match the structure expected by the backbone. See the example usage above for a demonstration of each.

Arguments

• inputs: python data, tensor data, or a tf.data.Dataset. If a preprocessor is attached to the model, inputs should match the structure expected by the preprocessor layer. If a preprocessor is not attached, inputs should match the structure expected the backbone model.
• max_length: Optional. int. The max length of the generated sequence. Will default to the max configured sequence_length of the preprocessor. If preprocessor is None, inputs should be should be padded to the desired maximum length and this argument will be ignored.
• stop_token_ids: Optional. None, "auto", or tuple of token ids. Defaults to "auto" which uses the preprocessor.tokenizer.end_token_id. Not specifying a processor will produce an error. None stops generation after generating max_length tokens. You may also specify a list of token id's the model should stop on. Note that sequences of tokens will each be interpreted as a stop token, multi-token stop sequences are not supported.
• strip_prompt: Optional. By default, generate() returns the full prompt followed by its completion generated by the model. If this option is set to True, only the newly generated text is returned.

backbone property

keras_hub.models.T5GemmaSeq2SeqLM.backbone

A keras_hub.models.Backbone model with the core architecture.

preprocessor property

keras_hub.models.T5GemmaSeq2SeqLM.preprocessor

A keras_hub.models.Preprocessor layer used to preprocess input.