<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Model Architecture on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/model-architecture/</link><description>Recent content in Model Architecture on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 30 Jan 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/model-architecture/index.xml" rel="self" type="application/rss+xml"/><item><title>Chapter 6: Understanding Tunix Model Architectures and State Management</title><link>https://ai-blog.noorshomelab.dev/tunix-mastery-2026/06-model-architecture/</link><pubDate>Fri, 30 Jan 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/tunix-mastery-2026/06-model-architecture/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome back, future LLM expert! In our previous chapters, we laid the groundwork by setting up Tunix and understanding its core philosophy. Now, it&amp;rsquo;s time to peek under the hood and explore how Tunix, built on the powerful JAX ecosystem, handles the intricate dance of model architectures and their ever-evolving state.&lt;/p&gt;
&lt;p&gt;Understanding how your Large Language Model (LLM) is represented and how its parameters (the &amp;ldquo;knowledge&amp;rdquo; it holds) are managed is absolutely crucial for effective post-training. Unlike traditional imperative frameworks where model state might be implicitly updated, JAX operates on a functional paradigm. This means state management is explicit, predictable, and incredibly powerful when you know how to wield it. Tunix leverages this power, often integrating with libraries like Flax NNX, to give you granular control over your LLM&amp;rsquo;s internal workings.&lt;/p&gt;</description></item></channel></rss>