Gemma4VideoConverter

[source]

Gemma4VideoConverter class

keras_hub.layers.Gemma4VideoConverter(
    patch_size=16, max_soft_tokens=70, pooling_kernel_size=3, num_frames=32, **kwargs
)

Video converter for Gemma4.

This layer handles video inputs by sampling frames and delegating to Gemma4ImageConverter for frame-level processing.

Arguments

• patch_size: int. Size of each square patch in pixels. Defaults to 16.
• max_soft_tokens: int. Maximum number of pooled soft tokens per video frame. Defaults to 70.
• pooling_kernel_size: int. Spatial pooling kernel size applied after the vision encoder. Defaults to 3.
• num_frames: int. Number of frames to uniformly sample from the video. Defaults to 32.

[source]

from_preset method

Gemma4VideoConverter.from_preset(preset, **kwargs)

Instantiate a keras_hub.layers.VideoConverter 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 'gemma4_2b_it'
2. a Kaggle Models handle like 'kaggle://user/gemma4/keras/gemma4_2b_it'
3. a Hugging Face handle like 'hf://google/gemma-4-2b-it'
4. a path to a local preset directory like './gemma4_2b_it'

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.VideoConverter.from_preset(), or from a model class like keras_hub.models.Gemma4VideoConverter.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.

Examples

# Load a video converter from a preset.
converter = keras_hub.layers.VideoConverter.from_preset(
    "hf://google/gemma-4-2b-it"
)