Phi3Backbone model

[source]

Phi3Backbone class

keras_nlp.models.Phi3Backbone(
    vocabulary_size,
    num_layers,
    hidden_dim,
    intermediate_dim,
    num_query_heads,
    num_key_value_heads,
    layer_norm_epsilon=1e-06,
    dropout=0.0,
    max_sequence_length=4096,
    pretraining_sequence_length=4096,
    rope_max_wavelength=10000,
    rope_scaling_type=None,
    rope_scaling_short_factor=None,
    rope_scaling_long_factor=None,
    dtype=None,
    **kwargs
)

Phi-3 core network with hyperparameters.

This network implements a Transformer-based decoder network, Phi-3, as described in "Phi-3 Technical Report". It includes the embedding lookups and transformer layers.

The default constructor gives a fully customizable, randomly initialized phi-3 model with any number of layers, heads, and embedding dimensions. To load preset architectures and weights, use the from_preset constructor.

Arguments

• vocabulary_size (int): The size of the token vocabulary.
• num_layers (int): The number of transformer layers.
• hidden_dim (int): The size of the embeddings and the hidden states of the transformer layers.
• intermediate_dim (int): The output dimension of the first Dense layer in a three-layer feedforward network for each transformer.
• num_query_heads (int): The number of query attention heads for each transformer layer.
• num_key_value_heads (int): The number of key and value attention heads for each transformer layer.
• layer_norm_epsilon (float, optional): Epsilon for the RMS layernorm layers in the transformer decoder. Defaults to 1e-6.
• dropout: (float, optional): Dropout probability for the Transformer decoder.
• max_sequence_length (int, optional): The maximum sequence length that this model might ever be used with. Defaults to 4096.
• pretraining_sequence_length (int, optional): The maximum sequence length that the model was pretrained with. Defaults to 4096.
• rope_max_wavelength (int, optional): The maximum angular wavelength of the sine/cosine curves, for rotary embeddings. Defaults to 10000.
• rope_scaling_type (str, optional): The type of the rope scaling. Can be either None or "su". None is for no rope scaling, "su" is for SuScaled rope, "su" is used when max_sequence_length is larger than original_max_sequence_length. Defaults to None.
• rope_scaling_short_factor List[float]: List of factors used to adjust rope frequencies when the rope_scaling_type is "su". List must be of length hidden_dim//num_query_heads//2. It is used when sequence_length is smaller than original_max_sequence_length. Defaults to None.
• rope_scaling_long_factor List[float]: List of factors used to adjust rope frequencies when the rope_scaling_type is "su". List must be of length hidden_dim//num_query_heads//2. It is used when sequence_length is larger than original_max_sequence_length. Defaults to None.
• 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 Phi3 decoder.
model = keras_hub.models.Phi3Backbone.from_preset(
    "phi3_mini_4k_instruct_en"
)
model(input_data)

# Randomly initialized Phi3 decoder with custom config.
model = keras_hub.models.Phi3Backbone(
    vocabulary_size=10,
    num_layers=2,
    hidden_dim=512,
    intermediate_dim=1024,
    num_query_heads=32,
    num_key_value_heads=8,
    layer_norm_epsilon=1e-6,
    dtype="float32"
)
model(input_data)

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from_preset method

Phi3Backbone.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.Phi3Backbone.token_embedding

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

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