FalconBackbone model

[source]

FalconBackbone class

keras_nlp.models.FalconBackbone(
    vocabulary_size,
    num_layers,
    num_attention_heads,
    hidden_dim,
    intermediate_dim,
    layer_norm_epsilon=1e-05,
    attention_dropout_rate=0,
    feedforward_dropout_rate=0,
    dtype=None,
    **kwargs
)

The Falcon core architecure.

This network implements a Transformer-based decoder-only network, Falcon.

Arguments

• vocabulary_size: int. The size of the token vocabulary.
• num_layers: int. The number of transformer layers.
• num_attention_heads: int. The number of attention heads for each transformer. The hidden size must be divisible by the number of attention heads.
• hidden_dim: int. The dimensionality of the embeddings and hidden states.
• intermediate_dim: int. The output dimension of the first Dense layer in the MLP network of each transformer.
• layer_norm_epsilon: float. Epsilon for the layer normalization layers in the transformer decoder.
• attention_dropout_rate: float. Dropout probability for the attention.
• feedforward_dropout_rate: flaot. Dropout probability for the feedforward.
• dtype: string or keras.mixed_precision.DTypePolicy. The dtype to use for model computations and weights. Note that some computations, such as softmax and layer normalization, will always be done at float32 precision regardless of dtype.

Examples

input_data = {
    "token_ids": np.ones(shape=(1, 12), dtype="int32"),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]]),
}

# Pretrained Falcon decoder.
# TODO: Update the preset.
model = keras_hub.models.FalconBackbone.from_preset("falcon_preset")
model(input_data)

# Randomly initialized Falcon decoder with a custom config.
model = keras_hub.models.FalconBackbone(
    vocabulary_size=10,
    num_layers=2,
    num_attention_heads=2,
    hidden_dim=32,
    intermediate_dim=32*4,
    layer_norm_epsilon=1e-5,
    attention_dropout_rate=0,
    feedforward_dropout_rate=0,
    dtype="float32",
)
model(input_data)

[source]

from_preset method

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

Instantiate a keras_hub.models.Backbone 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 a 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'

This constructor can be called in one of two ways. Either from the base class like keras_hub.models.Backbone.from_preset(), or from a model class like keras_hub.models.GemmaBackbone.from_preset(). If calling from the base class, the subclass of the returning object will be inferred from the config in the preset directory.

For any Backbone subclass, you can run cls.presets.keys() to list all built-in presets available on the class.

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, the weights will be loaded into the model architecture. If False, the weights will be randomly initialized.

Examples

# Load a Gemma backbone with pre-trained weights.
model = keras_hub.models.Backbone.from_preset(
    "gemma_2b_en",
)

# Load a Bert backbone with a pre-trained config and random weights.
model = keras_hub.models.Backbone.from_preset(
    "bert_base_en",
    load_weights=False,
)

token_embedding property

keras_nlp.models.FalconBackbone.token_embedding

A keras.layers.Embedding instance for embedding token ids.

This layer embeds integer token ids to the hidden dim of the model.