Adds 4-bit Integer Quantization Documentation

[JyotinderSingh]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @JyotinderSingh, 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!

This pull request introduces comprehensive documentation for 4-bit integer quantization (INT4) within the Keras framework. The new guide explains the principles, benefits, and practical application of INT4 quantization, including code examples for both custom and KerasHub models. It also provides detailed performance benchmarks across various model types and offers guidance on its appropriate use and limitations, thereby enhancing Keras's capabilities for efficient model deployment.

Highlights

• New INT4 Quantization Guide: A comprehensive guide for 4-bit integer quantization (INT4) in Keras has been added, detailing its principles, benefits, and practical application.
• Practical Examples: The guide includes code examples demonstrating how to quantize both minimal functional models and KerasHub models (e.g., Gemma3), along with saving and reloading quantized models.
• Performance Benchmarks: Detailed micro-benchmarks are provided for various models (DistilBERT, Falcon, Gemma3, Llama 3.2, OPT), showcasing the impact of INT4 quantization on accuracy, model size, GPU memory, latency, and throughput.
• Usage Guidance and Limitations: The documentation offers clear advice on when to prefer INT4 over INT8, practical tips for implementation, and outlines the current limitations of INT4 quantization in Keras.
• Documentation Integration: The new INT4 quantization guide has been integrated into the main Keras documentation structure, making it accessible to users.

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[gemini-code-assist]

Code Review

This pull request adds excellent and thorough documentation for INT4 quantization in Keras. The guide is well-written, covering the topic from theory to practice with useful code examples and performance benchmarks. This is a great contribution to the Keras documentation. I have only one minor suggestion for a grammatical correction to enhance clarity.

[hertschuh]

Are these links going stay in the long run?

[JyotinderSingh]

I'll keep them live, but I'm going to replace the links with a permanent version as soon as I figure out the best long-term solution.

[hertschuh]

@JyotinderSingh Can you rebase?

I don't know which guide you want first in the menu, the int8 one or the int4 one?

[JyotinderSingh]

@JyotinderSingh Can you rebase?

I don't know which guide you want first in the menu, the int8 one or the int4 one?

Resolved the conflicts