KerasHub: Pretrained Models
Getting started Developer guides API documentation Modeling API Model Architectures Tokenizers Preprocessing Layers Modeling Layers Samplers Metrics Pretrained models list
► KerasHub: Pretrained Models / API documentation / Model Architectures / Gemma4 / Gemma4CausalLM model

Gemma4CausalLM model

[source]

Gemma4CausalLM class

keras_hub.models.Gemma4CausalLM(preprocessor, backbone, **kwargs)

An end-to-end multimodal Gemma4 model for causal language modeling.

A causal language model (LM) predicts the next token based on previous tokens. This task setup can be used to train the model unsupervised on images and plain text inputs, or to autoregressively generate plain text similar to the data used for training. Note that the model is image-text in, text out.

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

[source]

from_preset method

Gemma4CausalLM.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,
)
Preset Parameters Description
gemma4_2b 5.10B Gemma 4 E2B base model: 2.3B effective parameters (5.1B total with Per-Layer Embeddings), 35-layer, audio+vision+text pretrained Gemma4 model. The 'E' denotes effective parameters — PLE gives each decoder layer its own token embedding table, maximizing parameter efficiency for on-device deployment.
gemma4_instruct_2b 5.10B Gemma 4 E2B instruction-tuned model: 2.3B effective parameters (5.1B total with Per-Layer Embeddings), 35-layer, audio+vision+text instruction-tuned Gemma4 model. The 'E' denotes effective parameters — PLE gives each decoder layer its own token embedding table, maximizing parameter efficiency for on-device deployment.
gemma4_4b 7.90B Gemma 4 E4B base model: 4.5B effective parameters (7.9B total with Per-Layer Embeddings), 42-layer, audio+vision+text pretrained Gemma4 model. The 'E' denotes effective parameters — PLE gives each decoder layer its own token embedding table, maximizing parameter efficiency for on-device deployment.
gemma4_instruct_4b 7.90B Gemma 4 E4B instruction-tuned model: 4.5B effective parameters (7.9B total with Per-Layer Embeddings), 42-layer, audio+vision+text instruction-tuned Gemma4 model. The 'E' denotes effective parameters — PLE gives each decoder layer its own token embedding table, maximizing parameter efficiency for on-device deployment.
gemma4_26b_a4b 26.00B Gemma 4 26B A4B base model: Mixture-of-Experts (MoE) model with 26B total parameters and only 4B active parameters per forward pass, 30-layer, vision+text pretrained Gemma4 model. The 'A' denotes active parameters — by activating only a 4B subset during inference, this MoE model runs nearly as fast as a dense 4B model.
gemma4_instruct_26b_a4b 26.00B Gemma 4 26B A4B instruction-tuned model: Mixture-of-Experts (MoE) model with 26B total parameters and only 4B active parameters per forward pass, 30-layer, vision+text instruction-tuned Gemma4 model. The 'A' denotes active parameters — by activating only a 4B subset during inference, this MoE model runs nearly as fast as a dense 4B model.
gemma4_31b 31.00B Gemma 4 31B base model: 31B parameter, 60-layer, dense vision+text pretrained Gemma4 model. The dense model in the Gemma 4 family, offering maximum quality for deployments where inference speed is less of a constraint.
gemma4_instruct_31b 31.00B Gemma 4 31B instruction-tuned model: 31B parameter, 60-layer, dense vision+text instruction-tuned Gemma4 model. The dense model in the Gemma 4 family, offering maximum quality for deployments where inference speed is less of a constraint.

[source]

generate method

Gemma4CausalLM.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.Gemma4CausalLM.backbone

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

preprocessor property

keras_hub.models.Gemma4CausalLM.preprocessor

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

Gemma4CausalLM model
Gemma4CausalLM class
from_preset method
generate method
backbone property
preprocessor property