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keras/
llama3.1_guard_8b

Name: llama3.1_guard_8b
Availability: InStock

keras-hub

text-generation

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Model Overview

Llama 3 is a set of large language models published by Meta. Both pretrained and instruction tuned models are available, and range in size from 7 billion to 70 billion parameters. See the model card below for benchmarks, data sources, and intended use cases.

Weights are released under the Llama 3 Community 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

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
`llama3_8b_en`	8.03B	8 billion parameter, 32-layer, base LLaMA 3 model.
`llama3_8b_en_int8`	8.03B	8 billion parameter, 32-layer, base LLaMA 3 model with activation and weights quantized to int8.
`llama3_instruct_8b_en`	8.03B	8 billion parameter, 32-layer, instruction tuned LLaMA 3 model.
`llama3_instruct_8b_en_int8`	8.03B	8 billion parameter, 32-layer, instruction tuned LLaMA 3 model with activation and weights quantized to int8.
`llama3.1_8b`	8.03B	8 billion parameter, 32-layer, based LLaMA 3.1 model.
`llama3.1_guard_8b`	8.03B	8 billion parameter, 32-layer, LLaMA 3.1 fine-tuned for consent safety classification.
`llama3.1_instruct_8b`	8.03B	8 billion parameter, 32-layer, instruction tuned LLaMA 3.1.
`llama3.2_1b`	1.5B	1 billion parameter, 16-layer, based LLaMA 3.2 model.
`llama3.2_3b`	3.6B	3 billion parameter, 26-layer, based LLaMA 3.2 model.
`llama3.2_guard_1b`	1.5B	1 billion parameter, 16-layer, based LLaMA 3.2 model fine-tuned for consent safety classification.
`llama3.2_instruct_1b`	1.5B	1 billion parameter, 16-layer, instruction tuned LLaMA 3.2.
`llama3.2_instruct_3b`	3.6B	3 billion parameter, 28-layer, instruction tuned LLaMA 3.2.

Prompts

Llama-3 "instruct" models are instruction tuned on turn by turn conversations and 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

prompt = """&lt;|start_header_id|&gt;system&lt;|end_header_id|&gt;

You are a helpful AI assistant for travel tips and recommendations&lt;|eot_id|&gt;&lt;|start_header_id|&gt;user&lt;|end_header_id|&gt;

What can you help me with?&lt;|eot_id|&gt;&lt;|start_header_id|&gt;assistant&lt;|end_header_id|&gt;
"""

For more details, please refer to this link: Llama 3 Model Card & Prompt Formats.

Base models (without instruct in the name) have no specific prompting structure, and should usually be fine-tuned for a specific task.

Example Usage

python

import keras
import keras_hub
import numpy as np

Use generate() to do text generation.

python

llama_lm = keras_hub.models.Llama3CausalLM.from_preset("llama3.1_guard_8b")
llama_lm.generate("What is Keras?", max_length=500)

# Generate with batched prompts.
llama_lm.generate(["What is Keras?", "Give me your best brownie recipe."], max_length=500)

Compile the generate() function with a custom sampler.

python

llama_lm = keras_hub.models.Llama3CausalLM.from_preset("llama3.1_guard_8b")
llama_lm.compile(sampler="greedy")
llama_lm.generate("I want to say", max_length=30)

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

Use generate() without preprocessing.

python

prompt = {
    "token_ids": np.array([[306, 864, 304, 1827, 0, 0, 0, 0, 0, 0]] * 2),
    # Use `"padding_mask"` to indicate values that should not be overridden.
    "padding_mask": np.array([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0]] * 2),
}

llama_lm = keras_hub.models.Llama3CausalLM.from_preset(
    "llama3.1_guard_8b",
    preprocessor=None,
    dtype="bfloat16"
)
llama_lm.generate(prompt)

Call fit() on a single batch.

python

features = ["The quick brown fox jumped.", "I forgot my homework."]
llama_lm = keras_hub.models.Llama3CausalLM.from_preset("llama3.1_guard_8b")
llama_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

python

x = {
    "token_ids": np.array([[450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
y = np.array([[4996, 17354, 1701, 29916, 12500, 287, 29889, 0, 0, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1, 1, 1, 0, 0, 0]] * 2)

llama_lm = keras_hub.models.Llama3CausalLM.from_preset(
    "llama3.1_guard_8b",
    preprocessor=None,
    dtype="bfloat16"
)
llama_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)

Example Usage with Hugging Face URI

python

import keras
import keras_hub
import numpy as np

Use generate() to do text generation.

python

llama_lm = keras_hub.models.Llama3CausalLM.from_preset("hf://keras/llama3.1_guard_8b")
llama_lm.generate("What is Keras?", max_length=500)

# Generate with batched prompts.
llama_lm.generate(["What is Keras?", "Give me your best brownie recipe."], max_length=500)

Compile the generate() function with a custom sampler.

python

llama_lm = keras_hub.models.Llama3CausalLM.from_preset("hf://keras/llama3.1_guard_8b")
llama_lm.compile(sampler="greedy")
llama_lm.generate("I want to say", max_length=30)

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

Use generate() without preprocessing.

python

prompt = {
    "token_ids": np.array([[306, 864, 304, 1827, 0, 0, 0, 0, 0, 0]] * 2),
    # Use `"padding_mask"` to indicate values that should not be overridden.
    "padding_mask": np.array([[1, 1, 1, 1, 0, 0, 0, 0, 0, 0]] * 2),
}

llama_lm = keras_hub.models.Llama3CausalLM.from_preset(
    "hf://keras/llama3.1_guard_8b",
    preprocessor=None,
    dtype="bfloat16"
)
llama_lm.generate(prompt)

Call fit() on a single batch.

python

features = ["The quick brown fox jumped.", "I forgot my homework."]
llama_lm = keras_hub.models.Llama3CausalLM.from_preset("hf://keras/llama3.1_guard_8b")
llama_lm.fit(x=features, batch_size=2)

Call fit() without preprocessing.

python

x = {
    "token_ids": np.array([[450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2),
}
y = np.array([[4996, 17354, 1701, 29916, 12500, 287, 29889, 0, 0, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1, 1, 1, 0, 0, 0]] * 2)

llama_lm = keras_hub.models.Llama3CausalLM.from_preset(
    "hf://keras/llama3.1_guard_8b",
    preprocessor=None,
    dtype="bfloat16"
)
llama_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)

keras/llama3.1_guard_8b

Model Overview

Links

Installation

Presets

Prompts

Example Usage

Example Usage with Hugging Face URI

keras/
llama3.1_guard_8b