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+# KerasHub: Multi-framework 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).
+>
+> We have renamed the repo to KerasHub in preparation for the release, but have not yet
+> released the new package. Follow the announcement for news.
+
+KerasHub is a library that supports natural language processing, computer
+vision, audio, and multimodal backbones and task models, working natively with
+TensorFlow, JAX, or PyTorch. KerasHub provides a repository of pre-trained
+models and a collection of lower-level building blocks for these tasks. Built
+on Keras 3, models can be trained and serialized in any framework and re-used
+in another without costly migrations.
+
+This library is an extension of the core Keras API; all high-level modules are
+Layers and Models that receive that same level of polish as core Keras.
+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. Browse our models on
+[Kaggle](https://www.kaggle.com/organizations/keras/models).
+We welcome contributions.
+
+## Quick Links
+
+### For everyone
+
+- [Home Page](https://keras.io/keras_hub)
+- [Developer Guides](https://keras.io/guides/keras_hub)
+- [API Reference](https://keras.io/api/keras_hub)
+- [Pre-trained Models](https://www.kaggle.com/organizations/keras/models)
+
+### For contributors
+
+- [Contributing Guide](CONTRIBUTING.md)
+- [Roadmap](ROADMAP.md)
+- [Style Guide](STYLE_GUIDE.md)
+- [API Design Guide](API_DESIGN_GUIDE.md)
+- [Call for Contributions](https://github.com/keras-team/keras-hub/issues?q=is%3Aissue+is%3Aopen+label%3A%22contributions+welcome%22)
+
+## Quickstart
+
+Fine-tune a BERT classifier on IMDb movie reviews:
+
+```python
+import os
+os.environ["KERAS_BACKEND"] = "jax"  # Or "tensorflow" or "torch"!
+
+import keras_hub
+import tensorflow_datasets as tfds
+
+imdb_train, imdb_test = tfds.load(
+    "imdb_reviews",
+    split=["train", "test"],
+    as_supervised=True,
+    batch_size=16,
+)
+
+# Load a BERT model.
+classifier = keras_hub.models.Classifier.from_preset(
+    "bert_base_en",
+    num_classes=2,
+    activation="softmax",
+)
+
+# Fine-tune on IMDb movie reviews.
+classifier.fit(imdb_train, validation_data=imdb_test)
+# Predict two new examples.
+classifier.predict(["What an amazing movie!", "A total waste of my time."])
+```
+
+Try it out [in a colab](https://colab.research.google.com/drive/1gSWkh3yOLwmKAaNh2dQQ6kQIlnGte7P2?usp=sharing).
+For more in depth guides and examples, visit
+[keras.io/keras_hub](https://keras.io/keras_hub/).
+
+## Installation
+
+To try out the latest version of KerasHub, you can use
+our nightly package:
+
+```bash
+pip install keras-hub
+```
+
+KerasHub currently requires TensorFlow to be installed for use of the
+`tf.data` API for preprocessing. Even when pre-processing with `tf.data`,
+training can still happen on any backend.
+
+Read [Getting started with Keras](https://keras.io/getting_started/) for more
+information on installing Keras 3 and compatibility with different frameworks.
+
+> [!IMPORTANT]
+> We recommend using KerasHub with TensorFlow 2.16 or later, as TF 2.16 packages
+> Keras 3 by default.
+
+## 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!
+
+<a href="https://github.com/keras-team/keras-hub/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=keras-team/keras-hub" />
+</a>