OPTBackbone
classkeras_hub.models.OPTBackbone(
vocabulary_size,
num_layers,
num_heads,
hidden_dim,
intermediate_dim,
dropout=0.1,
max_sequence_length=2048,
dtype=None,
**kwargs
)
An OPT decoder network.
This class implements a Transformer-based decoder model as described in
"OPT: Open Pre-trained Transformer Language Models".
The default constructor gives a fully customizable, randomly initialized OPT
model with any number of layers, heads, and embedding dimensions. To load
preset architectures and weights, use the from_preset()
constructor.
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.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 OPT decoder
model = keras_hub.models.OPTBackbone.from_preset("opt_125m_en")
model(input_data)
# Randomly initialized OPT decoder model with a custom config
model = keras_hub.models.OPTBackbone(
vocabulary_size=50265,
num_layers=4,
num_heads=4,
hidden_dim=256,
intermediate_dim=512,
max_sequence_length=128,
)
model(input_data)
from_preset
methodOPTBackbone.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,
)
Preset name | Parameters | Description |
---|---|---|
opt_125m_en | 125.24M | 12-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora. |
opt_1.3b_en | 1.32B | 24-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora. |
opt_2.7b_en | 2.70B | 32-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora. |
opt_6.7b_en | 6.70B | 32-layer OPT model where case in maintained. Trained on BookCorpus, CommonCrawl, Pile, and PushShift.io corpora. |
token_embedding
propertykeras_hub.models.OPTBackbone.token_embedding
A keras.layers.Embedding
instance for embedding token ids.
This layer embeds integer token ids to the hidden dim of the model.