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content/posts/25-09-29-nanodiffgpt/index.md

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[1] GitHub repo - nanoDiffGPT: https://github.com/hasithv/nanoDiffGPT
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[2] LLaDa paper: https://arxiv.org/abs/2502.09992
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[2] Diffusion notes: [{{< ref "../FlowDiffusion/FlowDiff.md" >}}]({{< relref "../FlowDiffusion/FlowDiff.md" >}})
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[3] Block diffusion paper: https://arxiv.org/abs/2503.09573
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[3] LLaDa paper: https://arxiv.org/abs/2502.09992
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[4] MMaDa paper: https://arxiv.org/abs/2505.15809
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[4] Block diffusion paper: https://arxiv.org/abs/2503.09573
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[5] MMaDa paper: https://arxiv.org/abs/2505.15809

public/archives/index.html

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<h3 class="archive-entry-title entry-hint-parent">Making Nano-GPT a Diffusion LLM
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<h3 class="archive-entry-title entry-hint-parent">Hacking Nano-GPT into a Diffusion LLM
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<div class="archive-meta"><span title='2025-09-29 19:45:01 -0500 CDT'>September 29, 2025</span>&nbsp;·&nbsp;10 min</div>
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<a class="entry-link" aria-label="post link to Making Nano-GPT a Diffusion LLM" href="https://hasithv.github.io/posts/25-09-29-nanodiffgpt/"></a>
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<a class="entry-link" aria-label="post link to Hacking Nano-GPT into a Diffusion LLM" href="https://hasithv.github.io/posts/25-09-29-nanodiffgpt/"></a>
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<h3 class="archive-entry-title entry-hint-parent">Lecture 2 - Constructing the Training Target

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<footer class="entry-footer"><span title='2025-09-29 19:45:01 -0500 CDT'>September 29, 2025</span>&nbsp;·&nbsp;10 min</footer>
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<a class="entry-link" aria-label="post link to Making Nano-GPT a Diffusion LLM" href="https://hasithv.github.io/posts/25-09-29-nanodiffgpt/"></a>
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<p>It was a fun little experiment to convert nanoGPT into a diffusion LLM. I&rsquo;ve been thinking of other experiments on how to hijack architectures to make them do what I want, and so doing this was a good proof of concept as to how well it would work. Additionally, I got to explore diffusion LLMs, an area which I think holds tons of promise.</p>
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<h2 id="references">References</h2>
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<p>[1] GitHub repo - nanoDiffGPT: <a href="https://github.com/hasithv/nanoDiffGPT">https://github.com/hasithv/nanoDiffGPT</a></p>
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<p>[2] LLaDa paper: <a href="https://arxiv.org/abs/2502.09992">https://arxiv.org/abs/2502.09992</a></p>
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<p>[3] Block diffusion paper: <a href="https://arxiv.org/abs/2503.09573">https://arxiv.org/abs/2503.09573</a></p>
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<p>[4] MMaDa paper: <a href="https://arxiv.org/abs/2505.15809">https://arxiv.org/abs/2505.15809</a></p>
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<p>[2] Diffusion notes: <a href="/posts/flowdiffusion/flowdiff/">https://hasithv.github.io/posts/flowdiffusion/flowdiff/</a></p>
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<p>[3] LLaDa paper: <a href="https://arxiv.org/abs/2502.09992">https://arxiv.org/abs/2502.09992</a></p>
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<p>[4] Block diffusion paper: <a href="https://arxiv.org/abs/2503.09573">https://arxiv.org/abs/2503.09573</a></p>
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<p>[5] MMaDa paper: <a href="https://arxiv.org/abs/2505.15809">https://arxiv.org/abs/2505.15809</a></p>
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<footer class="entry-footer"><span title='2025-09-29 19:45:01 -0500 CDT'>September 29, 2025</span>&nbsp;·&nbsp;10 min</footer>
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<a class="entry-link" aria-label="post link to Making Nano-GPT a Diffusion LLM" href="https://hasithv.github.io/posts/25-09-29-nanodiffgpt/"></a>
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<p>It was a fun little experiment to convert nanoGPT into a diffusion LLM. I&rsquo;ve been thinking of other experiments on how to hijack architectures to make them do what I want, and so doing this was a good proof of concept as to how well it would work. Additionally, I got to explore diffusion LLMs, an area which I think holds tons of promise.</p>
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<h2 id="references">References</h2>
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<p>[1] GitHub repo - nanoDiffGPT: <a href="https://github.com/hasithv/nanoDiffGPT">https://github.com/hasithv/nanoDiffGPT</a></p>
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<p>[2] LLaDa paper: <a href="https://arxiv.org/abs/2502.09992">https://arxiv.org/abs/2502.09992</a></p>
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<p>[3] Block diffusion paper: <a href="https://arxiv.org/abs/2503.09573">https://arxiv.org/abs/2503.09573</a></p>
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<p>[4] MMaDa paper: <a href="https://arxiv.org/abs/2505.15809">https://arxiv.org/abs/2505.15809</a></p>
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<p>[2] Diffusion notes: <a href="/posts/flowdiffusion/flowdiff/">https://hasithv.github.io/posts/flowdiffusion/flowdiff/</a></p>
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<p>[3] LLaDa paper: <a href="https://arxiv.org/abs/2502.09992">https://arxiv.org/abs/2502.09992</a></p>
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<p>[4] Block diffusion paper: <a href="https://arxiv.org/abs/2503.09573">https://arxiv.org/abs/2503.09573</a></p>
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<p>[5] MMaDa paper: <a href="https://arxiv.org/abs/2505.15809">https://arxiv.org/abs/2505.15809</a></p>
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<p>It was a fun little experiment to convert nanoGPT into a diffusion LLM. I&rsquo;ve been thinking of other experiments on how to hijack architectures to make them do what I want, and so doing this was a good proof of concept as to how well it would work. Additionally, I got to explore diffusion LLMs, an area which I think holds tons of promise.</p>
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<h2 id="references">References</h2>
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<p>[1] GitHub repo - nanoDiffGPT: <a href="https://github.com/hasithv/nanoDiffGPT">https://github.com/hasithv/nanoDiffGPT</a></p>
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<p>[2] LLaDa paper: <a href="https://arxiv.org/abs/2502.09992">https://arxiv.org/abs/2502.09992</a></p>
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<p>[3] Block diffusion paper: <a href="https://arxiv.org/abs/2503.09573">https://arxiv.org/abs/2503.09573</a></p>
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<p>[4] MMaDa paper: <a href="https://arxiv.org/abs/2505.15809">https://arxiv.org/abs/2505.15809</a></p>
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<p>[2] Diffusion notes: <a href="/posts/flowdiffusion/flowdiff/">https://hasithv.github.io/posts/flowdiffusion/flowdiff/</a></p>
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<p>[3] LLaDa paper: <a href="https://arxiv.org/abs/2502.09992">https://arxiv.org/abs/2502.09992</a></p>
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<p>[4] Block diffusion paper: <a href="https://arxiv.org/abs/2503.09573">https://arxiv.org/abs/2503.09573</a></p>
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<p>[5] MMaDa paper: <a href="https://arxiv.org/abs/2505.15809">https://arxiv.org/abs/2505.15809</a></p>
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