Add Keras NNX guide

[divyashreepathihalli]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @divyashreepathihalli, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

I've added a new guide that details the integration of Keras with Flax's NNX module system. This guide aims to show how Keras can leverage NNX for enhanced variable handling and advanced training capabilities within the JAX ecosystem. It covers the setup, demonstrates the core integration of Keras variables with NNX, and illustrates both the classic Keras model.fit workflow and custom training loops using NNX. The guide also includes sections on model saving/loading and a practical example of fine-tuning a Gemma model.

Highlights

• Introduction of Keras-NNX Integration Guide: I've added a new comprehensive guide that details the integration of Keras with Flax's NNX module system, explaining the benefits of combining Keras's user-friendliness with JAX's performance and NNX's explicit state management.
• Demonstration of Keras Variable Compatibility with NNX: The guide demonstrates how keras.Variable instances are fully recognized and managed by NNX's state management, allowing for seamless mixing of Keras and NNX components.
• Dual Training Workflow Support: I've included examples for both the familiar model.fit() API for high-level training and custom JAX/NNX training loops for fine-grained control over the gradient and update process.
• Model Persistence and Real-World Application: The guide covers standard model serialization (saving and loading) with NNX enabled and provides a real-world application example of loading and fine-tuning a Gemma model from KerasHub.

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[gemini-code-assist]

Code Review

This pull request introduces a new guide on using Keras with the Flax NNX backend. The guide is comprehensive and covers enabling NNX mode, using Keras Variables within NNX, training with both model.fit and custom loops, saving/loading models, and a real-world example with Gemma. My review includes suggestions to improve code style by consolidating imports, correcting the usage of nnx.grad to align with the public API, and fixing a typo and formatting in the conclusion to enhance clarity.

[hertschuh]

Can you run autogen and add the ipynb and md files?

[abheesht17]

Thanks!