---
library_name: keras-hub
---
### Model Overview
Llama 2 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 2 Community License](https://ai.meta.com/llama/license/). Keras model code is released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).

## Links

* [Llama 2 Quickstart Notebook](https://www.kaggle.com/code/matthewdwatson/llama2-quickstart)
* [Llama 2 API Documentation](https://keras.io/api/keras_hub/models/llama2/)
* [Llama 2 Model Card & Prompt Formats](https://llama.meta.com/docs/model-cards-and-prompt-formats/other-models/)
* [KerasHub Beginner Guide](https://keras.io/guides/keras_hub/getting_started/)
* [KerasHub Model Publishing Guide](https://keras.io/guides/keras_hub/upload/)

## 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](https://keras.io/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   |
|-----------------------|------------|---------------|
|` llama2_7b_en` | 6.74B | 7 billion parameter, 32-layer, base LLaMA 2 model. |
|` llama2_7b_en_int8` | 6.74B | 7 billion parameter, 32-layer, base LLaMA 2 model with activation and weights quantized to int8. |
| `llama2_instruct_7b_en` | 6.74B | 7 billion parameter, 32-layer, instruction tuned LLaMA 2 model. |
| `llama2_instruct_7b_en_int8` | 6.74B | 7 billion parameter, 32-layer, instruction tuned LLaMA 2 model with activation and weights quantized to int8. |


## Prompts

Llama-2 "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 = """<s>[INST] <
```

### Example Usage
```python
import keras
import keras_hub
import numpy as np
```

Use `generate()` to do text generation.
```python
llama_lm = keras_hub.models.LlamaCausalLM.from_preset("llama2_7b_en")
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.LlamaCausalLM.from_preset("llama2_7b_en")
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 = {
    # `1` maps to the start token followed by "I want to say".
    "token_ids": np.array([[1, 306, 864, 304, 1827, 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, 1, 0, 0, 0, 0, 0]] * 2),
}

llama_lm = keras_hub.models.LlamaCausalLM.from_preset(
    "llama2_7b_en",
    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.LlamaCausalLM.from_preset("llama2_7b_en")
llama_lm.fit(x=features, batch_size=2)
```

Call `fit()` without preprocessing.
```python
x = {
    "token_ids": np.array([[1, 450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0]] * 2),
}
y = np.array([[450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2)

llama_lm = keras_hub.models.LlamaCausalLM.from_preset(
    "llama2_7b_en",
    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.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en")
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.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en")
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 = {
    # `1` maps to the start token followed by "I want to say".
    "token_ids": np.array([[1, 306, 864, 304, 1827, 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, 1, 0, 0, 0, 0, 0]] * 2),
}

llama_lm = keras_hub.models.LlamaCausalLM.from_preset(
    "hf://keras/llama2_7b_en",
    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.LlamaCausalLM.from_preset("hf://keras/llama2_7b_en")
llama_lm.fit(x=features, batch_size=2)
```

Call `fit()` without preprocessing.
```python
x = {
    "token_ids": np.array([[1, 450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0]] * 2),
    "padding_mask": np.array([[1, 1, 1, 1, 1, 1, 1, 1, 1, 0]] * 2),
}
y = np.array([[450, 4996, 17354, 1701, 29916, 12500, 287, 29889, 0, 0]] * 2)
sw = np.array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0]] * 2)

llama_lm = keras_hub.models.LlamaCausalLM.from_preset(
    "hf://keras/llama2_7b_en",
    preprocessor=None,
    dtype="bfloat16"
)
llama_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
```