GemmaBackbone
classkeras_hub.models.GemmaBackbone(
vocabulary_size,
num_layers,
num_query_heads,
num_key_value_heads,
hidden_dim,
intermediate_dim,
head_dim,
query_head_dim_normalize=True,
use_post_ffw_norm=False,
use_post_attention_norm=False,
attention_logit_soft_cap=None,
final_logit_soft_cap=None,
use_sliding_window_attention=False,
sliding_window_size=4096,
layer_norm_epsilon=1e-06,
dropout=0,
dtype=None,
**kwargs
)
Gemma core network with hyperparameters.
This backbone implements the base Transformer network for the Gemma model.
It includes the embedding lookups and transformer layers. This backbone
will output the final hidden states for each token, not generative
predictions over the vocabulary space. For a higher-level object for text
generation, see keras_hub.models.GemmaCausalLM
.
The default constructor gives a fully customizable, randomly initialized
Gemma model with any number of layers, heads, and embedding dimensions. To
load preset architectures and weights, use the from_preset
constructor.
Arguments
True
normalize the query before
attention with head_dim
. If False
, normalize the query with
hidden_dim / num_query_heads
. Defaults to True.keras.mixed_precision.DTypePolicy
. The dtype to use
for the models computations and weights. Note that some
computations, such as softmax and layer normalization will always
be done a float32 precision regardless of dtype.Example
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 Gemma decoder.
model = keras_hub.models.GemmaBackbone.from_preset("gemma_2b_en")
model(input_data)
# Randomly initialized Gemma decoder with custom config.
model = keras_hub.models.GemmaBackbone(
vocabulary_size=50257,
num_layers=12,
num_query_heads=12,
num_key_value_heads=1,
hidden_dim=768,
intermediate_dim=3072,
head_dim=64,
)
model(input_data)
from_preset
methodGemmaBackbone.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 |
---|---|---|
gemma_2b_en | 2.51B | 2 billion parameter, 18-layer, base Gemma model. |
gemma_instruct_2b_en | 2.51B | 2 billion parameter, 18-layer, instruction tuned Gemma model. |
gemma_1.1_instruct_2b_en | 2.51B | 2 billion parameter, 18-layer, instruction tuned Gemma model. The 1.1 update improves model quality. |
code_gemma_1.1_2b_en | 2.51B | 2 billion parameter, 18-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. The 1.1 update improves model quality. |
code_gemma_2b_en | 2.51B | 2 billion parameter, 18-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. |
gemma_7b_en | 8.54B | 7 billion parameter, 28-layer, base Gemma model. |
gemma_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned Gemma model. |
gemma_1.1_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned Gemma model. The 1.1 update improves model quality. |
code_gemma_7b_en | 8.54B | 7 billion parameter, 28-layer, CodeGemma model. This model has been trained on a fill-in-the-middle (FIM) task for code completion. |
code_gemma_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned CodeGemma model. This model has been trained for chat use cases related to code. |
code_gemma_1.1_instruct_7b_en | 8.54B | 7 billion parameter, 28-layer, instruction tuned CodeGemma model. This model has been trained for chat use cases related to code. The 1.1 update improves model quality. |
gemma2_2b_en | 2.61B | 2 billion parameter, 26-layer, base Gemma model. |
gemma2_instruct_2b_en | 2.61B | 2 billion parameter, 26-layer, instruction tuned Gemma model. |
gemma2_9b_en | 9.24B | 9 billion parameter, 42-layer, base Gemma model. |
gemma2_instruct_9b_en | 9.24B | 9 billion parameter, 42-layer, instruction tuned Gemma model. |
gemma2_27b_en | 27.23B | 27 billion parameter, 42-layer, base Gemma model. |
gemma2_instruct_27b_en | 27.23B | 27 billion parameter, 42-layer, instruction tuned Gemma model. |
shieldgemma_2b_en | 2.61B | 2 billion parameter, 26-layer, ShieldGemma model. |
shieldgemma_9b_en | 9.24B | 9 billion parameter, 42-layer, ShieldGemma model. |
shieldgemma_27b_en | 27.23B | 27 billion parameter, 42-layer, ShieldGemma model. |
token_embedding
propertykeras_hub.models.GemmaBackbone.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
methodGemmaBackbone.enable_lora(rank)
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.
get_layout_map
methodGemmaBackbone.get_layout_map(
device_mesh, model_parallel_dim_name="model", data_parallel_dim_name="batch"
)
Get a keras.distribution.LayoutMap
for model parallel distribution.
The returned LayoutMap
contains the sharding spec for the gemma
backbone weights, so that you can use it to distribute weights across
the accelerators.
Example
# Feel free to change the mesh shape to balance data and model parallelism
mesh = keras.distribution.DeviceMesh(
shape=(1, 8), axis_names=('batch', 'model'),
devices=keras.distribution.list_devices())
layout_map = GemmaBackbone.get_layout_map(
mesh, model_parallel_dim_name="model")
distribution = keras.distribution.ModelParallel(
layout_map=layout_map, batch_dim_name='batch')
with distribution.scope():
gemma_model = keras_hub.models.GemmaCausalLM.from_preset()
To see how the layout map was applied, load the model then run (for one decoder block):
embedding_layer = gemma_model.backbone.get_layer("token_embedding")
decoder_block_1 = gemma_model.backbone.get_layer('decoder_block_1')
for variable in embedding_layer.weights + decoder_block_1.weights:
print(f'{variable.path:<58} {str(variable.shape):<16} {str(variable.value.sharding.spec)}')
Arguments
keras.distribution.DeviceMesh
instance for
distribution.Return:
keras.distribution.LayoutMap
that contains the sharding spec
for all the model weights.