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keras/
gemma_instruct_7b_en

Name: gemma_instruct_7b_en
Rating: 2.5 (20 reviews)

keras-hub

text-generation-inference

text-classification

text-conversation

text-to-text-generation

text-generation

gemma

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Model Overview

Gemma is Google's family of lightweight, state-of-the art open models built from the same research and technology used to create the Gemini models. Gemma models are available with and without instruction tuning and come in two sizes: 2 billion and 7 billion parameters. Gemma 1.1 is the latest weights refresh. See the model card below for benchmarks, data sources, and intended use cases.

Weights are released under the Gemma License. Keras model code is released under the Apache 2 License.

Installation

Keras and KerasHub can be installed with:

pip install -U -q keras-hub
pip install -U -q keras>=3

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the Keras Getting Started page.

Presets

The following model checkpoints are provided by the Keras team. Full code examples for each are available below.

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.
`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.

Prompts

Gemma models are made available both pretrained and instruction tuned on turn by turn conversations. Base pretrained models (gemma_2b_en, gemma_7b_en) will complete sentences. The following are some example prompts:

"My favorite brownie recipe is "
"Why is the sky blue?"

Instruction tuned versions (suffixed with instruct) should be prompted with examples that precisely match the training data. Specifically, you must alternate user and assistant turns that begin and end with special tokens. New lines do matter. See the following for an example:

python

start_of_turn_user = "&lt;start_of_turn&gt;user\n"
start_of_turn_model = "&lt;start_of_turn&gt;model\n"
end_of_turn = "&lt;end_of_turn&gt;\n"
prompt = start_of_turn_user + "You are a friendly assistant. Say hi." + \
    end_of_turn + start_of_turn_model

Example Usage

python

!pip install -U keras-hub
!pip install -U keras

import keras
import keras_hub
import numpy as np

Use generate() to do text generation.

python

gemma_lm = keras_hub.models.GemmaCausalLM.from_preset("gemma_instruct_7b_en")
gemma_lm.generate("Keras is a", max_length=30)

# Generate with batched prompts.
gemma_lm.generate(["Keras is a", "I want to say"], max_length=30)

Compile the generate() function with a custom sampler.

python

gemma_lm = keras_hub.models.GemmaCausalLM.from_preset("gemma_instruct_7b_en")
gemma_lm.compile(sampler="top_k")
gemma_lm.generate("I want to say", max_length=30)

gemma_lm.compile(sampler= keras_hub.samplers.BeamSampler(num_beams=2))
gemma_lm.generate("I want to say", max_length=30)

Use generate() without preprocessing.

python

prompt = {
    # `2, 214064, 603` maps to the start token followed by "Keras is".
    "token_ids": np.array([[2, 214064, 603, 0, 0, 0, 0]] * 2),
    # Use `"padding_mask"` to indicate values that should not be overridden.
    "padding_mask": np.array([[1, 1, 1, 0, 0, 0, 0]] * 2),
}

gemma_lm = keras_hub.models.GemmaCausalLM.from_preset(
    "gemma_instruct_7b_en",
    preprocessor=None,
)
gemma_lm.generate(prompt)

Call fit() on a single batch.

python

features = ["The quick brown fox jumped.", "I forgot my homework."]
gemma_lm = keras_hub.models.GemmaCausalLM.from_preset("gemma_instruct_7b_en")
gemma_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

python

x = {
    "token_ids": np.array([[2, 214064, 603, 5271, 6044, 9581, 3, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 0]] * 2),
}
y = np.array([[214064, 603, 5271, 6044, 9581, 3, 0, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1, 1, 0, 0]] * 2)

gemma_lm = keras_hub.models.GemmaCausalLM.from_preset(
    "gemma_instruct_7b_en",
    preprocessor=None,
)
gemma_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)

Example Usage with Hugging Face URI

python

!pip install -U keras-hub
!pip install -U keras

import keras
import keras_hub
import numpy as np

Use generate() to do text generation.

python

gemma_lm = keras_hub.models.GemmaCausalLM.from_preset("hf://keras/gemma_instruct_7b_en")
gemma_lm.generate("Keras is a", max_length=30)

# Generate with batched prompts.
gemma_lm.generate(["Keras is a", "I want to say"], max_length=30)

Compile the generate() function with a custom sampler.

python

gemma_lm = keras_hub.models.GemmaCausalLM.from_preset("hf://keras/gemma_instruct_7b_en")
gemma_lm.compile(sampler="top_k")
gemma_lm.generate("I want to say", max_length=30)

gemma_lm.compile(sampler= keras_hub.samplers.BeamSampler(num_beams=2))
gemma_lm.generate("I want to say", max_length=30)

Use generate() without preprocessing.

python

prompt = {
    # `2, 214064, 603` maps to the start token followed by "Keras is".
    "token_ids": np.array([[2, 214064, 603, 0, 0, 0, 0]] * 2),
    # Use `"padding_mask"` to indicate values that should not be overridden.
    "padding_mask": np.array([[1, 1, 1, 0, 0, 0, 0]] * 2),
}

gemma_lm = keras_hub.models.GemmaCausalLM.from_preset(
    "hf://keras/gemma_instruct_7b_en",
    preprocessor=None,
)
gemma_lm.generate(prompt)

Call fit() on a single batch.

python

features = ["The quick brown fox jumped.", "I forgot my homework."]
gemma_lm = keras_hub.models.GemmaCausalLM.from_preset("hf://keras/gemma_instruct_7b_en")
gemma_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

python

x = {
    "token_ids": np.array([[2, 214064, 603, 5271, 6044, 9581, 3, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 0]] * 2),
}
y = np.array([[214064, 603, 5271, 6044, 9581, 3, 0, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1, 1, 0, 0]] * 2)

gemma_lm = keras_hub.models.GemmaCausalLM.from_preset(
    "hf://keras/gemma_instruct_7b_en",
    preprocessor=None,
)
gemma_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)

keras/gemma_instruct_7b_en

Model Overview

Links

Installation

Presets

Prompts

Example Usage

Example Usage with Hugging Face URI

keras/
gemma_instruct_7b_en