GPTNeoXBackbone
classkeras_hub.models.GPTNeoXBackbone(
vocabulary_size,
num_layers,
num_heads,
hidden_dim,
intermediate_dim,
dropout=0.0,
rotary_percentage=0.25,
rotary_max_wavelength=10000,
layer_norm_epsilon=1e-05,
max_sequence_length=512,
dtype=None,
**kwargs
)
GPT-NeoX core network with hyperparameters.
This network implements a Transformer-based decoder network, Generative Pretrained Transformer-Neo-X (GPTNeoX), as described in "GPT-NeoX-20B: An Open-Source Autoregressive Language Model". It includes the embedding lookups and transformer layers.
The default constructor gives a fully customizable, randomly initialized GPT-NeoX model with any number of layers, heads, and embedding dimensions.
Disclaimer: Pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind. The underlying model is provided by a third party and subject to a separate license, available here.
Arguments
None
, max_sequence_length
uses the value from
sequence length. This determines the variable shape for positional
embeddings.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.from_preset
methodGPTNeoXBackbone.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:
'bert_base_en'
'kaggle://user/bert/keras/bert_base_en'
'hf://user/bert_base_en'
'./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
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
propertykeras_hub.models.GPTNeoXBackbone.token_embedding
A keras.layers.Embedding
instance for embedding token ids.
This layer embeds integer token ids to the hidden dim of the model.
enable_lora
methodGPTNeoXBackbone.enable_lora(rank, target_names=None)
Enable Lora on the backbone.
Calling this method will freeze all weights on the backbone,
while enabling Lora on the query & value EinsumDense
layers
of the attention layers.