Adds Quantization documentation

[JyotinderSingh]

Adds a "Quantization in Keras" overview page, which includes:

1. Basic intro to quantization
2. Introduction to the Keras Quantization toolkit
3. Code Examples
4. Tips and recommendations.

[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 quantization in Keras. It covers the fundamental concepts of quantization, explains the various supported modes (int8, float8, int4, and GPTQ) with their mechanisms and benefits, and provides clear guidance and code examples for applying quantization at both the model and layer levels. The new guide aims to help users optimize Keras models for reduced memory footprint and improved inference performance.

Highlights

• Quantization Overview: Introduces the fundamental concepts of quantization, highlighting its benefits such as reduced model size, lower VRAM/RAM footprint, and increased memory bandwidth for improved inference performance.
• Keras Quantization Toolkit: Details the first-class post-training quantization (PTQ) workflows available in Keras, supporting various modes including int4, int8, float8, and weight-only GPTQ.
• Quantization Modes Explained: Provides in-depth explanations for each quantization mode (int8, float8, int4, and GPTQ), covering their operational mechanisms, use cases, and expected outcomes.
• Model and Layer-wise Quantization: Demonstrates how to apply quantization to entire models and individual layers with practical code examples, outlining typical workflows and scenarios for each approach.
• Layer Coverage and Practical Guidance: Lists the core Keras layers that are supported by the quantization framework and offers practical advice for effectively utilizing GPTQ and measuring performance metrics.

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[gemini-code-assist]

Code Review

This pull request adds comprehensive documentation for quantization in Keras, covering various modes like int8, float8, int4, and GPTQ. The new guide is provided in notebook, markdown, and python script formats. The changes are mostly good, but I've found a couple of minor issues in the documentation text that could be clarified.

More importantly, this PR introduces a file guides/quantization_overview.py which seems to be an alternative or older draft of the quantization guide. It describes a different API and is not integrated with the rest of the documentation. To avoid confusion, this file should be removed from the pull request. My specific comments are attached to the relevant files.

[JyotinderSingh]

/gemini review

[gemini-code-assist]

Code Review

This pull request adds comprehensive documentation for quantization in Keras. The new guide is provided in Jupyter notebook, Markdown, and Python script formats, and is added to the main guides index. The documentation covers the introduction to quantization, different modes supported by Keras (int8, float8, int4, GPTQ), API usage with code examples, and practical tips. The content is well-structured and informative. I've provided a couple of suggestions to improve the consistency of markdown formatting and comments across the different file formats.

[hertschuh]

Thanks for the PR! This is really useful.