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https://github.com/keras-team/keras-hub

Pretrained model hub for Keras 3.
https://github.com/keras-team/keras-hub

deep-learning jax keras llm machine-learning natural-language-processing nlp python pytorch tensorflow

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Pretrained model hub for Keras 3.

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README 

        
# KerasHub: Multi-framework Pretrained Models

[![](https://github.com/keras-team/keras-hub/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/keras-hub/actions?query=workflow%3ATests+branch%3Amaster)

![Python](https://img.shields.io/badge/python-v3.9.0+-success.svg)

[![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/keras-team/keras-hub/issues)



> [!IMPORTANT]

> 📢 KerasNLP is now KerasHub! 📢 Read

> [the announcement](https://github.com/keras-team/keras-hub/issues/1831).



**KerasHub** is a pretrained modeling library that aims to be simple, flexible,

and fast. The library provides [Keras 3](https://keras.io/keras_3/)

implementations of popular model architectures, paired with a collection of

pretrained checkpoints available on [Kaggle Models](https://kaggle.com/models/).

Models can be used with text, image, and audio data for generation, classification,

and many other built in tasks.



KerasHub is an extension of the core Keras API; KerasHub components are provided

as `Layer` and `Model` implementations. If you are  familiar with Keras,

congratulations! You already understand most of KerasHub.



All models support JAX, TensorFlow, and PyTorch from a single model

definition and can be fine-tuned on GPUs and TPUs out of the box. Models can

be trained on individual accelerators with built-in PEFT techniques, or

fine-tuned at scale with model and data parallel training. See our

[Getting Started guide](https://keras.io/guides/keras_hub/getting_started)

to start learning our API.



## Quick Links



### For everyone



- [Home page](https://keras.io/keras_hub)

- [Getting started](https://keras.io/keras_hub/getting_started)

- [Guides](https://keras.io/keras_hub/guides)

- [API documentation](https://keras.io/keras_hub/api)

- [Pre-trained models](https://keras.io/keras_hub/presets/)



### For contributors



- [Call for Contributions](https://github.com/keras-team/keras-hub/issues/1835)

- [Roadmap](https://github.com/keras-team/keras-hub/issues/1836)

- [Contributing Guide](CONTRIBUTING.md)

- [Style Guide](STYLE_GUIDE.md)

- [API Design Guide](API_DESIGN_GUIDE.md)



## Quickstart



Choose a backend:



```python

import os

os.environ["KERAS_BACKEND"] = "jax"  # Or "tensorflow" or "torch"!

```



Import KerasHub and other libraries:



```python

import keras

import keras_hub

import numpy as np

import tensorflow_datasets as tfds

```



Load a resnet model and use it to predict a label for an image:



```python

classifier = keras_hub.models.ImageClassifier.from_preset(

    "resnet_50_imagenet",

    activation="softmax",

)

url = "https://upload.wikimedia.org/wikipedia/commons/a/aa/California_quail.jpg"

path = keras.utils.get_file(origin=url)

image = keras.utils.load_img(path)

preds = classifier.predict(np.array([image]))

print(keras_hub.utils.decode_imagenet_predictions(preds))

```



Load a Bert model and fine-tune it on IMDb movie reviews:



```python

classifier = keras_hub.models.BertClassifier.from_preset(

    "bert_base_en_uncased",

    activation="softmax",

    num_classes=2,

)

imdb_train, imdb_test = tfds.load(

    "imdb_reviews",

    split=["train", "test"],

    as_supervised=True,

    batch_size=16,

)

classifier.fit(imdb_train, validation_data=imdb_test)

preds = classifier.predict(["What an amazing movie!", "A total waste of time."])

print(preds)

```



## Installation



To install the latest KerasHub release with Keras 3, simply run:



```

pip install --upgrade keras-hub

```



To install the latest nightly changes for both KerasHub and Keras, you can use

our nightly package.



```

pip install --upgrade keras-hub-nightly

```



Currently, installing KerasHub will always pull in TensorFlow for use of the

`tf.data` API for preprocessing. When pre-processing with `tf.data`, training

can still happen on any backend.



Visit the [core Keras getting started page](https://keras.io/getting_started/)

for more information on installing Keras 3, accelerator support, and

compatibility with different frameworks.



## Configuring your backend



If you have Keras 3 installed in your environment (see installation above),

you can use KerasHub with any of JAX, TensorFlow and PyTorch. To do so, set the

`KERAS_BACKEND` environment variable. For example:



```shell

export KERAS_BACKEND=jax

```



Or in Colab, with:



```python

import os

os.environ["KERAS_BACKEND"] = "jax"



import keras_hub

```



> [!IMPORTANT]

> Make sure to set the `KERAS_BACKEND` **before** importing any Keras libraries;

> it will be used to set up Keras when it is first imported.



## Compatibility



We follow [Semantic Versioning](https://semver.org/), and plan to

provide backwards compatibility guarantees both for code and saved models built

with our components. While we continue with pre-release `0.y.z` development, we

may break compatibility at any time and APIs should not be considered stable.



## Disclaimer



KerasHub provides access to pre-trained models via the `keras_hub.models` API.

These pre-trained models are provided on an "as is" basis, without warranties

or conditions of any kind. The following underlying models are provided by third

parties, and subject to separate licenses:

BART, BLOOM, DeBERTa, DistilBERT, GPT-2, Llama, Mistral, OPT, RoBERTa, Whisper,

and XLM-RoBERTa.



## Citing KerasHub



If KerasHub helps your research, we appreciate your citations.

Here is the BibTeX entry:



```bibtex

@misc{kerashub2024,

  title={KerasHub},

  author={Watson, Matthew, and  Chollet, Fran\c{c}ois and Sreepathihalli,

  Divyashree, and Saadat, Samaneh and Sampath, Ramesh, and Rasskin, Gabriel and

  and Zhu, Scott and Singh, Varun and Wood, Luke and Tan, Zhenyu and Stenbit,

  Ian and Qian, Chen, and Bischof, Jonathan and others},

  year={2024},

  howpublished={\url{https://github.com/keras-team/keras-hub}},

}

```



## Acknowledgements



Thank you to all of our wonderful contributors!