README.md

---
library_name: keras-hub
license: llama2
language:
- en
tags:
- text-generation-inference
pipeline_tag: text-generation
---
### Model Overview
Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.Weights are release under the [Llama 2 Community License Agreement ](https://ai.meta.com/llama/license/) and Keras model code are released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).

Model type: An auto-regressive language model based on the transformer architecture.

Fine tuned from model: Llama 2

Uses: 
The primary use of Vicuna is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

## Links

* [Vicuna Quickstart Notebook](https://www.kaggle.com/code/laxmareddypatlolla/vicuna-quickstart-notebook)
* [Vicuna API Documentation](coming soon)
* [Vicuna Model Card](https://huggingface.co/lmsys/vicuna-7b-v1.5#vicuna-model-card)
* [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
```

Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instruction 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   |
|-----------------------|------------|---------------|
|` vicuna_1.5_7b_en ` | 6.74B | 7 billion parameter, 32-layer, instruction tuned Vicuna v1.5 model.|


Paper: https://arxiv.org/abs/2306.05685

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

Use `generate()` to do text generation.
```python
vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("vicuna_1.5_7b_en")
vicuna_lm.generate("### HUMAN:\nWhat is Keras? \n### RESPONSE:\n", max_length=500)

# Generate with batched prompts.
vicuna_lm.generate([
    "### HUMAN:\nWhat is ML? \n### RESPONSE:\n",
    "### HUMAN:\nGive me your best brownie recipe.\n### RESPONSE:\n",
],max_length=500)
```

Compile the `generate()` function with a custom sampler.
```python
vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("vicuna_1.5_7b_en")
vicuna_lm.compile(sampler="greedy")
vicuna_lm.generate("I want to say", max_length=30)

vicuna_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
vicuna_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),
}

vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset(
    "vicuna_1.5_7b_en",
    preprocessor=None,
    dtype="bfloat16"
)
vicuna_lm.generate(prompt)
```

Call `fit()` on a single batch.
```python
features = ["The quick brown fox jumped.", "I forgot my homework."]
vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("vicuna_1.5_7b_en")
vicuna_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)

vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset(
    "vicuna_1.5_7b_en",
    preprocessor=None,
    dtype="bfloat16"
)
vicuna_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
vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/vicuna_1.5_7b_en")
vicuna_lm.generate("### HUMAN:\nWhat is Keras? \n### RESPONSE:\n", max_length=500)

# Generate with batched prompts.
vicuna_lm.generate([
    "### HUMAN:\nWhat is ML? \n### RESPONSE:\n",
    "### HUMAN:\nGive me your best brownie recipe.\n### RESPONSE:\n",
],max_length=500)
```

Compile the `generate()` function with a custom sampler.
```python
vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/vicuna_1.5_7b_en")
vicuna_lm.compile(sampler="greedy")
vicuna_lm.generate("I want to say", max_length=30)

vicuna_lm.compile(sampler=keras_hub.samplers.BeamSampler(num_beams=2))
vicuna_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),
}

vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset(
    "hf://keras/vicuna_1.5_7b_en",
    preprocessor=None,
    dtype="bfloat16"
)
vicuna_lm.generate(prompt)
```

Call `fit()` on a single batch.
```python
features = ["The quick brown fox jumped.", "I forgot my homework."]
vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset("hf://keras/vicuna_1.5_7b_en")
vicuna_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)

vicuna_lm = keras_hub.models.LlamaCausalLM.from_preset(
    "hf://keras/vicuna_1.5_7b_en",
    preprocessor=None,
    dtype="bfloat16"
)
vicuna_lm.fit(x=x, y=y, sample_weight=sw, batch_size=2)
```