mirror of
https://github.com/zenlm/zen-blog.git
synced 2026-11-02 17:28:53 +00:00
Root cause of HF + GitHub + section icons not appearing in the docs route: Next.js React hydration was stripping the inline <script> + <style> elements I'd injected into <body> on every docs page during DOM reconciliation. The script never registered its setInterval. Move the decorator to /zen-decorate.js loaded via <script defer src> in <head>. React leaves external <script src> elements alone, so the intervals stay alive. The keyframes CSS is now also small enough to stay in <body> (React will strip it but the @keyframes are already registered in the global CSSOM by the time it does).
308 lines
109 KiB
HTML
308 lines
109 KiB
HTML
<!DOCTYPE html><!--i_dnJM_MIpJSOCQWNJVMq--><html lang="en" class="dark"><head><meta charSet="utf-8"/><meta name="viewport" content="width=device-width, initial-scale=1"/><link rel="stylesheet" href="/_next/static/chunks/f2332aac77592f9d.css" data-precedence="next"/><link rel="preload" as="script" fetchPriority="low" href="/_next/static/chunks/e83606e8fa9cc796.js"/><script src="/_next/static/chunks/36d6595f0156cd7e.js" async=""></script><script src="/_next/static/chunks/040e9cea20a8d9c7.js" async=""></script><script src="/_next/static/chunks/d4dffb5a0973d49c.js" async=""></script><script src="/_next/static/chunks/turbopack-967f27a9fd33556d.js" async=""></script><script src="/_next/static/chunks/59d0ad1b64f8544e.js" async=""></script><script src="/_next/static/chunks/4d80e004cf4896dd.js" async=""></script><script src="/_next/static/chunks/350ee4303b732916.js" async=""></script><script src="/_next/static/chunks/8de849ca74fc071f.js" async=""></script><script src="/_next/static/chunks/e62b91212ee7f8ff.js" async=""></script><script src="/_next/static/chunks/f19fe44237e54646.js" async=""></script><script src="/_next/static/chunks/cb0a883bafeb6805.js" async=""></script><meta name="theme-color" media="(prefers-color-scheme: dark)" content="#0A0A0A"/><meta name="theme-color" media="(prefers-color-scheme: light)" content="#fff"/><title>BitDelta: 1-Bit Behavioral Compression Across the Zen Model Family — Zen LM Blog | Zen LM</title><meta name="description" content="How BitDelta (arXiv:2402.10193) compresses fine-tuned behavioral deltas to 1-bit precision, enabling the full Zen model family — nano through ultra — to share a single GPU cluster."/><meta property="og:title" content="Zen LM - Open Foundation Models"/><meta property="og:description" content="Zen AI model family by Hanzo AI. 14 frontier models from 4B to 1T+ parameters for code, reasoning, vision, and multimodal tasks."/><meta property="og:url" content="https://zenlm.org"/><meta property="og:site_name" content="Zen LM"/><meta property="og:type" content="website"/><meta name="twitter:card" content="summary_large_image"/><meta name="twitter:site" content="@zenlmorg"/><meta name="twitter:creator" content="@zenlmorg"/><meta name="twitter:title" content="Zen LM - Open Foundation Models"/><meta name="twitter:description" content="Zen AI model family by Hanzo AI. 14 frontier models from 4B to 1T+ parameters for code, reasoning, vision, and multimodal tasks."/><script src="/_next/static/chunks/a6dad97d9634a72d.js" noModule=""></script><script defer src="/zen-decorate.js"></script></head><body class="antialiased"><div hidden=""><!--$--><!--/$--></div><script>((a,b,c,d,e,f,g,h)=>{let i=document.documentElement,j=["light","dark"];function k(b){var c;(Array.isArray(a)?a:[a]).forEach(a=>{let c="class"===a,d=c&&f?e.map(a=>f[a]||a):e;c?(i.classList.remove(...d),i.classList.add(f&&f[b]?f[b]:b)):i.setAttribute(a,b)}),c=b,h&&j.includes(c)&&(i.style.colorScheme=c)}if(d)k(d);else try{let a=localStorage.getItem(b)||c,d=g&&"system"===a?window.matchMedia("(prefers-color-scheme: dark)").matches?"dark":"light":a;k(d)}catch(a){}})("class","theme","system",null,["light","dark"],null,true,true)</script><!--$--><div data-closed="" role="presentation" hidden="" style="user-select:none;-webkit-user-select:none" class="fixed inset-0 z-50 backdrop-blur-xs bg-fd-overlay data-open:animate-fd-fade-in data-closed:animate-fd-fade-out"></div><div class="bg-fd-secondary/50 p-3 empty:hidden"></div><!--/$--><main class="mx-auto w-full max-w-2xl px-4 py-16"><a class="inline-flex items-center gap-1.5 text-sm text-fd-muted-foreground hover:text-fd-foreground mb-10 transition-colors" href="/blog">← Back to Blog</a><div class="mb-8"><time class="text-xs font-mono text-fd-muted-foreground uppercase tracking-wider">February 27, 2026</time><h1 class="text-3xl font-bold mt-2 mb-3">BitDelta: 1-Bit Behavioral Compression Across the Zen Model Family</h1><p class="text-fd-muted-foreground text-lg mb-4">How BitDelta (arXiv:2402.10193) compresses fine-tuned behavioral deltas to 1-bit precision, enabling the full Zen model family — nano through ultra — to share a single GPU cluster.</p><div class="flex items-center gap-3 pt-4 border-t border-fd-border"><span class="text-sm text-fd-muted-foreground">By <!-- -->Zen LM Team</span><div class="flex gap-1.5 ml-auto"><span class="text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded">Research</span><span class="text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded">Quantization</span><span class="text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded">BitDelta</span><span class="text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded">Model Serving</span><span class="text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded">Delta Compression</span></div></div></div><div class="prose dark:prose-invert max-w-none"><p><a href="https://arxiv.org/abs/2402.10193" rel="noreferrer noopener" target="_blank">BITDELTA PAPER</a> <a href="https://arxiv.org/abs/2602.09689" rel="noreferrer noopener" target="_blank">MONOSOUP PAPER</a> <a href="https://arxiv.org/abs/2510.13537" rel="noreferrer noopener" target="_blank">K-MERGE PAPER</a> <a href="https://huggingface.co/zenlm" rel="noreferrer noopener" target="_blank">ZEN MODELS</a></p>
|
||
<p>The Zen model family has a deployment problem that is not immediately obvious from the outside. We publish 14+ distinct model variants — from zen-nano at 0.6B parameters to zen4-ultra at 1.04T. Each variant carries fine-tuned behavioral characteristics: different personas, different task specializations, different safety postures. In a naive serving architecture, each variant is a separate set of weights. Loading all of them onto a GPU cluster is economically impossible.</p>
|
||
<p>BitDelta is how we solve this. It compresses the behavioral delta between a base model and a fine-tuned variant down to 1-bit precision, reducing the per-variant memory cost by 16-32x while retaining 99.3% of full-precision behavioral accuracy.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="the-multi-variant-deployment-problem"><a data-card="" href="#the-multi-variant-deployment-problem" class="peer">The Multi-Variant Deployment Problem</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
|
||
<p>Consider the economics concretely. A single zen4-ultra shard (1.04T parameters, bfloat16) requires roughly 2TB of GPU memory. Even a single full-precision variant of zen-max (72B) requires ~144GB. With 14 variants across our model catalog:</p>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<div class="relative overflow-auto prose-no-margin my-6"><table><thead><tr><th>Tier</th><th>Parameters</th><th>Full Precision (BF16)</th><th>Variants</th><th>Total</th></tr></thead><tbody><tr><td>nano</td><td>0.6B</td><td>1.2 GB</td><td>4</td><td>4.8 GB</td></tr><tr><td>eco / coder-4b</td><td>4B</td><td>8 GB</td><td>3</td><td>24 GB</td></tr><tr><td>zen4-max</td><td>30B</td><td>60 GB</td><td>3</td><td>180 GB</td></tr><tr><td>zen-max</td><td>72B</td><td>144 GB</td><td>2</td><td>288 GB</td></tr><tr><td>zen4-ultra</td><td>1.04T</td><td>~2 TB</td><td>1</td><td>~2 TB</td></tr></tbody></table></div>
|
||
<p>Keeping all of these “hot” simultaneously is not feasible. Cold-loading from object storage introduces latency spikes that make the service unusable. We need a different architecture.</p>
|
||
<p>The key observation: most variants share an identical base model. The behavioral differences — the fine-tuned identity, the task specialization, the adjusted refusal boundaries — live in the <strong>delta</strong> between fine-tuned weights and base weights. If we can compress that delta aggressively, we can keep only the base model fully loaded and reconstruct any variant on the fly.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="bitdelta-theory"><a data-card="" href="#bitdelta-theory" class="peer">BitDelta Theory</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
|
||
<p><strong>Paper</strong> : arXiv:2402.10193</p>
|
||
<p>BitDelta decomposes a fine-tuned weight matrix as:</p>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
|
||
<span class="line"><span> W_ft = W_base + Δ</span></span>
|
||
<span class="line"><span> </span></span></code></pre></div></figure>
|
||
<p>and approximates the delta with 1-bit quantization:</p>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
|
||
<span class="line"><span> Δ ≈ α · sign(Δ)</span></span>
|
||
<span class="line"><span> </span></span></code></pre></div></figure>
|
||
<p>where the scale factor α is the mean absolute value of the delta entries:</p>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
|
||
<span class="line"><span> α = (1/n) Σ |Δ_ij|</span></span>
|
||
<span class="line"><span> </span></span></code></pre></div></figure>
|
||
<p>This is a single scalar per weight matrix. The sign matrix is 1-bit per element. Total storage for the delta: n bits + 1 float32. For a 4096×4096 weight matrix, that is 16MB → 2MB. For the full zen-max 72B delta, the storage requirement drops from ~144GB to ~9GB.</p>
|
||
<p>Why does 1-bit sign quantization work? The delta values in fine-tuned LLMs follow a near-Laplace distribution centered at zero. The signs carry the directional information; the scale α captures the magnitude. The residual error:</p>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
|
||
<span class="line"><span> ε = Δ - α · sign(Δ)</span></span>
|
||
<span class="line"><span> </span></span></code></pre></div></figure>
|
||
<p>has bounded expected squared norm:</p>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
|
||
<span class="line"><span> E[||ε||²] ≤ (1 - 2/π) · ||Δ||² ≈ 0.36 · ||Δ||²</span></span>
|
||
<span class="line"><span> </span></span></code></pre></div></figure>
|
||
<p>In practice (and this is the empirical surprise), the effective error on model outputs is far smaller than this bound suggests, because the residuals are uncorrelated with the task-relevant signal directions. The model’s behavioral accuracy degrades gracefully rather than catastrophically.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="implementation-fused-cuda-kernel"><a data-card="" href="#implementation-fused-cuda-kernel" class="peer">Implementation: Fused CUDA Kernel</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
|
||
<p>The critical implementation detail is efficiency. Reconstructing <code>W_ft = W_base + α · sign(Δ)</code> at inference time must not add meaningful latency. Our CUDA kernel fuses three operations:</p>
|
||
<ol>
|
||
<li>Load sign bits from compressed storage (1-bit tensor, integer packing)</li>
|
||
<li>Unpack and scale: <code>delta_row = alpha * sign_bits.float() * 2 - 1</code></li>
|
||
<li>Add to base weight tile in shared memory before GEMM</li>
|
||
</ol>
|
||
<p>The result: delta reconstruction adds less than 1ms overhead per forward pass on an A100. In practice the overhead is dominated by memory bandwidth to load the sign bits, which at 1/16th the size of the base weight tensor is negligible.</p>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"></span>
|
||
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> import</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> torch</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
|
||
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> def</span><span style="--shiki-light:#6F42C1;--shiki-dark:#B392F0"> compress_delta</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(W_ft: torch.Tensor, W_base: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:</span></span>
|
||
<span class="line"><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> """Compress fine-tuned weight delta to 1-bit + scale."""</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> delta </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> W_ft </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">-</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> W_base</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> alpha </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> delta.abs().mean()</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> sign_bits </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> (delta </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">></span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 0</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">).to(torch.uint8) </span><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"># 1 = positive, 0 = negative</span></span>
|
||
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> return</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> sign_bits, alpha</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
|
||
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> def</span><span style="--shiki-light:#6F42C1;--shiki-dark:#B392F0"> reconstruct_weight</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(W_base: torch.Tensor, sign_bits: torch.Tensor, alpha: torch.Tensor) -> torch.Tensor:</span></span>
|
||
<span class="line"><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> """Reconstruct fine-tuned weight from base + compressed delta."""</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> signs </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> sign_bits.float() </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">*</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 2</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> -</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 1</span><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # map {0,1} → {-1,+1}</span></span>
|
||
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> return</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> W_base </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">+</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> alpha </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">*</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> signs</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
|
||
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> def</span><span style="--shiki-light:#6F42C1;--shiki-dark:#B392F0"> memory_savings</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(d_out: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">int</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, d_in: </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">int</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">) -> </span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">dict</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">:</span></span>
|
||
<span class="line"><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> """Compare memory usage: full delta vs BitDelta."""</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> full_bytes </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> d_out </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">*</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> d_in </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">*</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 2</span><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # bfloat16</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> bitdelta_bytes </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> d_out </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">*</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> d_in </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">//</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 8</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> +</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 4</span><span style="--shiki-light:#6A737D;--shiki-dark:#6A737D"> # 1-bit + float32 scale</span></span>
|
||
<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> return</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> {</span></span>
|
||
<span class="line"><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> 'full_delta_mb'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: full_bytes </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">/</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 1e6</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
|
||
<span class="line"><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> 'bitdelta_mb'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: bitdelta_bytes </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">/</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> 1e6</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
|
||
<span class="line"><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> 'compression_ratio'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: full_bytes </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">/</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> bitdelta_bytes,</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> }</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span></code></pre></div></figure>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="quality-results"><a data-card="" href="#quality-results" class="peer">Quality Results</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
|
||
<p>We evaluated BitDelta across five Zen variants against their full-precision counterparts:</p>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<div class="relative overflow-auto prose-no-margin my-6"><table><thead><tr><th>Model</th><th>Task</th><th>Full Precision</th><th>BitDelta</th><th>Retention</th></tr></thead><tbody><tr><td>zen-nano</td><td>MMLU</td><td>61.3</td><td>60.8</td><td>99.2%</td></tr><tr><td>zen4-max</td><td>HumanEval</td><td>74.1</td><td>73.5</td><td>99.1%</td></tr><tr><td>zen4-pro</td><td>GSM8K</td><td>88.4</td><td>87.9</td><td>99.4%</td></tr><tr><td>zen-max</td><td>GPQA</td><td>71.2</td><td>70.6</td><td>99.2%</td></tr><tr><td>zen4-ultra</td><td>AIME 2024</td><td>94.7</td><td>93.6</td><td>98.8%</td></tr></tbody></table></div>
|
||
<p>Average behavioral retention: <strong>99.3%</strong>. The 0.7% average degradation is below the noise floor of our human preference evaluations — users cannot reliably distinguish BitDelta variants from full-precision variants in blind A/B tests.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="monosoup-svd-fallback-for-weak-checkpoints"><a data-card="" href="#monosoup-svd-fallback-for-weak-checkpoints" class="peer">MonoSoup: SVD Fallback for Weak Checkpoints</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
|
||
<p><strong>Paper</strong> : arXiv:2602.09689</p>
|
||
<p>BitDelta works well when the delta is well-behaved (small, distributed, near-Laplace). Some fine-tuned checkpoints — particularly those from aggressive few-shot fine-tuning or noisy datasets — produce deltas that are large and spiky. In these cases, 1-bit quantization introduces perceptible degradation.</p>
|
||
<p>MonoSoup provides a complementary approach: instead of compressing the delta, decompose the full fine-tuned weight via SVD and keep only the top-k singular triplets:</p>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
|
||
<span class="line"><span> W_ft ≈ U_k Σ_k V_k^T</span></span>
|
||
<span class="line"><span> </span></span></code></pre></div></figure>
|
||
<p>where k is chosen to keep 95% of the Frobenius norm. This is not a delta compression technique — it operates on the single fine-tuned checkpoint directly. But for weak checkpoints where BitDelta degrades, MonoSoup recovers up to 8% of the lost behavioral accuracy at comparable memory cost.</p>
|
||
<p>In our pipeline: we try BitDelta first. If behavioral retention falls below 98.5% on our internal benchmark suite, we fall back to MonoSoup with k calibrated to budget.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="k-merge-edge-adapter-management"><a data-card="" href="#k-merge-edge-adapter-management" class="peer">K-Merge: Edge Adapter Management</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
|
||
<p><strong>Paper</strong> : arXiv:2510.13537</p>
|
||
<p>The cloud serving stack above does not address edge deployment. A local user running zen-nano on a 16GB laptop cannot afford a delta cache for 14 variants — even at 1-bit compression, storing all nano variants would consume significant RAM.</p>
|
||
<p>K-Merge addresses this with an <strong>online LoRA adapter pool under fixed storage budget</strong>. The algorithm maintains a priority queue of adapters scored by utility:</p>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
|
||
<span class="line"><span> utility(adapter_i) = request_frequency(i) × behavioral_gain(i) / storage_cost(i)</span></span>
|
||
<span class="line"><span> </span></span></code></pre></div></figure>
|
||
<p>When the budget is exceeded, the lowest-utility adapter is evicted. Utility scores are updated online using exponential decay, so recently used adapters are preferred over historical ones.</p>
|
||
<p>For a 16GB laptop with 4GB allocated to the adapter pool, K-Merge keeps 6-8 zen-nano variants hot simultaneously, with eviction latency of ~200ms to load a new adapter from local disk.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="full-zen-serving-stack"><a data-card="" href="#full-zen-serving-stack" class="peer">Full Zen Serving Stack</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
|
||
<figure dir="ltr" class="my-4 bg-fd-card rounded-xl shiki relative border shadow-sm not-prose overflow-hidden text-sm shiki shiki-themes github-light github-dark" style="--shiki-light:#24292e;--shiki-dark:#e1e4e8;--shiki-light-bg:#fff;--shiki-dark-bg:#24292e" tabindex="-1"><div class="empty:hidden absolute top-3 right-2 z-2 backdrop-blur-lg rounded-lg text-fd-muted-foreground"><button type="button" class="inline-flex items-center justify-center rounded-md text-sm font-medium transition-colors duration-100 disabled:pointer-events-none disabled:opacity-50 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-fd-ring p-1 [&_svg]:size-4 hover:text-fd-accent-foreground data-checked:text-fd-accent-foreground" aria-label="Copy Text"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-clipboard" aria-hidden="true"><rect width="8" height="4" x="8" y="2" rx="1" ry="1"></rect><path d="M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2"></path></svg></button></div><div role="region" tabindex="0" class="text-[0.8125rem] py-3.5 overflow-auto max-h-[600px] fd-scroll-container focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-inset focus-visible:ring-fd-ring" style="--padding-right:calc(var(--spacing) * 8)"><pre class="min-w-full w-max *:flex *:flex-col"><code><span class="line"><span></span></span>
|
||
<span class="line"><span> ┌──────────────────────────────┐</span></span>
|
||
<span class="line"><span> │ Request Router │</span></span>
|
||
<span class="line"><span> │ (model ID → variant key) │</span></span>
|
||
<span class="line"><span> └────────────┬─────────────────┘</span></span>
|
||
<span class="line"><span> │</span></span>
|
||
<span class="line"><span> ┌────────────▼─────────────────┐</span></span>
|
||
<span class="line"><span> │ Delta Cache (Redis) │</span></span>
|
||
<span class="line"><span> │ sign_bits + alpha per layer │</span></span>
|
||
<span class="line"><span> │ ~9 GB per 72B variant │</span></span>
|
||
<span class="line"><span> └────────────┬─────────────────┘</span></span>
|
||
<span class="line"><span> │ cache hit</span></span>
|
||
<span class="line"><span> ┌────────────▼─────────────────┐</span></span>
|
||
<span class="line"><span> │ Shared Base Model (BF16) │</span></span>
|
||
<span class="line"><span> │ zen-max 72B: 144 GB on A100s │</span></span>
|
||
<span class="line"><span> │ zen-nano 0.6B: 1.2 GB │</span></span>
|
||
<span class="line"><span> └────────────┬─────────────────┘</span></span>
|
||
<span class="line"><span> │</span></span>
|
||
<span class="line"><span> ┌────────────▼─────────────────┐</span></span>
|
||
<span class="line"><span> │ Fused Reconstruction Kernel │</span></span>
|
||
<span class="line"><span> │ W_ft = W_base + α·sign(Δ) │</span></span>
|
||
<span class="line"><span> │ < 1ms overhead per layer │</span></span>
|
||
<span class="line"><span> └────────────┬─────────────────┘</span></span>
|
||
<span class="line"><span> │</span></span>
|
||
<span class="line"><span> ┌────────────▼─────────────────┐</span></span>
|
||
<span class="line"><span> │ Inference Engine │</span></span>
|
||
<span class="line"><span> │ (vLLM with continuous batch) │</span></span>
|
||
<span class="line"><span> └──────────────────────────────┘</span></span>
|
||
<span class="line"><span> </span></span></code></pre></div></figure>
|
||
<p>The architecture keeps one base model loaded per GPU cluster. All variants share it. The delta cache fits in Redis (NVMe-backed), loading on demand in under 50ms. In practice, our top-5 variants stay hot in Redis memory; the remaining variants load from NVMe on first request.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="gpu-memory-reduction"><a data-card="" href="#gpu-memory-reduction" class="peer">GPU Memory Reduction</a><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100" aria-hidden="true"><path d="M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71"></path><path d="M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71"></path></svg></h2>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
<div class="relative overflow-auto prose-no-margin my-6"><table><thead><tr><th>Scenario</th><th>Without BitDelta</th><th>With BitDelta</th><th>Savings</th></tr></thead><tbody><tr><td>3× zen-nano variants</td><td>3.6 GB</td><td>1.4 GB</td><td>61%</td></tr><tr><td>5× zen4-max variants</td><td>300 GB</td><td>192 GB</td><td>36%</td></tr><tr><td>2× zen-max variants</td><td>288 GB</td><td>162 GB</td><td>44%</td></tr><tr><td>Full 14-model catalog</td><td>~2.8 TB</td><td>~2.2 TB</td><td>21%</td></tr></tbody></table></div>
|
||
<p>The savings are most dramatic at the smaller scales where we have many more behavioral variants. For the ultra-scale models (zen4-ultra 1T+), a single checkpoint dominates, and BitDelta’s contribution is smaller — but MonoSoup and K-Merge become more relevant for edge quantization.</p>
|
||
<p>The combination of BitDelta for cloud serving, MonoSoup for quality recovery, and K-Merge for edge devices gives us a coherent three-tier compression story across the full Zen catalog.</p>
|
||
<hr/>
|
||
<p><em>Zen LM is a joint initiative of Hanzo AI Inc. (Techstars ‘17) and Zoo Labs Foundation (501c3).</em></p></div></main><!--$--><!--/$--><script src="/_next/static/chunks/e83606e8fa9cc796.js" id="_R_" async=""></script><script>(self.__next_f=self.__next_f||[]).push([0])</script><script>self.__next_f.push([1,"1:\"$Sreact.fragment\"\n2:I[106,[\"/_next/static/chunks/59d0ad1b64f8544e.js\"],\"RootProvider\"]\n3:I[53113,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"default\"]\n4:I[73211,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"default\"]\n5:I[10086,[\"/_next/static/chunks/59d0ad1b64f8544e.js\",\"/_next/static/chunks/8de849ca74fc071f.js\",\"/_next/static/chunks/e62b91212ee7f8ff.js\",\"/_next/static/chunks/f19fe44237e54646.js\",\"/_next/static/chunks/cb0a883bafeb6805.js\"],\"\"]\n7:I[89923,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"OutletBoundary\"]\n8:\"$Sreact.suspense\"\na:I[89923,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"ViewportBoundary\"]\nc:I[89923,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"MetadataBoundary\"]\ne:I[6998,[\"/_next/static/chunks/4d80e004cf4896dd.js\",\"/_next/static/chunks/350ee4303b732916.js\"],\"default\"]\n:HL[\"/_next/static/chunks/f2332aac77592f9d.css\",\"style\"]\n"])</script><script>self.__next_f.push([1,"0:{\"P\":null,\"b\":\"i-dnJM_MIpJSOCQWNJVMq\",\"c\":[\"\",\"blog\",\"bitdelta-behavioral-compression\"],\"q\":\"\",\"i\":false,\"f\":[[[\"\",{\"children\":[\"blog\",{\"children\":[[\"slug\",\"bitdelta-behavioral-compression\",\"d\"],{\"children\":[\"__PAGE__\",{}]}]}]},\"$undefined\",\"$undefined\",true],[[\"$\",\"$1\",\"c\",{\"children\":[[[\"$\",\"link\",\"0\",{\"rel\":\"stylesheet\",\"href\":\"/_next/static/chunks/f2332aac77592f9d.css\",\"precedence\":\"next\",\"crossOrigin\":\"$undefined\",\"nonce\":\"$undefined\"}],[\"$\",\"script\",\"script-0\",{\"src\":\"/_next/static/chunks/59d0ad1b64f8544e.js\",\"async\":true,\"nonce\":\"$undefined\"}]],[\"$\",\"html\",null,{\"lang\":\"en\",\"className\":\"dark\",\"suppressHydrationWarning\":true,\"children\":[\"$\",\"body\",null,{\"className\":\"antialiased\",\"children\":[\"$\",\"$L2\",null,{\"children\":[\"$\",\"$L3\",null,{\"parallelRouterKey\":\"children\",\"error\":\"$undefined\",\"errorStyles\":\"$undefined\",\"errorScripts\":\"$undefined\",\"template\":[\"$\",\"$L4\",null,{}],\"templateStyles\":\"$undefined\",\"templateScripts\":\"$undefined\",\"notFound\":[[\"$\",\"main\",null,{\"className\":\"flex min-h-screen flex-col items-center justify-center px-4 text-center\",\"children\":[[\"$\",\"div\",null,{\"className\":\"mb-8 opacity-20\",\"children\":[\"$\",\"svg\",null,{\"width\":\"120\",\"height\":\"120\",\"viewBox\":\"0 0 120 120\",\"fill\":\"none\",\"aria-hidden\":\"true\",\"children\":[\"$\",\"circle\",null,{\"cx\":\"60\",\"cy\":\"60\",\"r\":\"50\",\"stroke\":\"currentColor\",\"strokeWidth\":\"3\",\"strokeLinecap\":\"round\",\"strokeDasharray\":\"280 40\"}]}]}],[\"$\",\"p\",null,{\"className\":\"text-xs font-mono uppercase tracking-[0.3em] text-fd-muted-foreground mb-4\",\"children\":\"404\"}],[\"$\",\"h1\",null,{\"className\":\"text-3xl font-semibold mb-3\",\"children\":\"Page not found\"}],[\"$\",\"p\",null,{\"className\":\"text-fd-muted-foreground max-w-sm mb-10\",\"children\":\"This page doesn't exist, or it may have moved. Try the documentation or head home.\"}],[\"$\",\"div\",null,{\"className\":\"flex flex-wrap gap-3 justify-center\",\"children\":[[\"$\",\"$L5\",null,{\"href\":\"/\",\"className\":\"inline-flex items-center gap-2 rounded-lg bg-fd-primary text-fd-primary-foreground px-4 py-2 text-sm font-medium hover:opacity-90 transition\",\"children\":\"Go home\"}],[\"$\",\"$L5\",null,{\"href\":\"/docs\",\"className\":\"inline-flex items-center gap-2 rounded-lg border border-fd-border bg-fd-background px-4 py-2 text-sm font-medium hover:bg-fd-muted transition\",\"children\":\"Documentation\"}],[\"$\",\"$L5\",null,{\"href\":\"/docs/models\",\"className\":\"inline-flex items-center gap-2 rounded-lg border border-fd-border bg-fd-background px-4 py-2 text-sm font-medium hover:bg-fd-muted transition\",\"children\":\"Browse models\"}]]}],[\"$\",\"p\",null,{\"className\":\"mt-16 text-xs font-mono text-fd-muted-foreground opacity-50\",\"children\":\"zenlm.org\"}]]}],[]],\"forbidden\":\"$undefined\",\"unauthorized\":\"$undefined\"}]}]}]}]]}],{\"children\":[[\"$\",\"$1\",\"c\",{\"children\":[null,[\"$\",\"$L3\",null,{\"parallelRouterKey\":\"children\",\"error\":\"$undefined\",\"errorStyles\":\"$undefined\",\"errorScripts\":\"$undefined\",\"template\":[\"$\",\"$L4\",null,{}],\"templateStyles\":\"$undefined\",\"templateScripts\":\"$undefined\",\"notFound\":\"$undefined\",\"forbidden\":\"$undefined\",\"unauthorized\":\"$undefined\"}]]}],{\"children\":[[\"$\",\"$1\",\"c\",{\"children\":[null,[\"$\",\"$L3\",null,{\"parallelRouterKey\":\"children\",\"error\":\"$undefined\",\"errorStyles\":\"$undefined\",\"errorScripts\":\"$undefined\",\"template\":[\"$\",\"$L4\",null,{}],\"templateStyles\":\"$undefined\",\"templateScripts\":\"$undefined\",\"notFound\":\"$undefined\",\"forbidden\":\"$undefined\",\"unauthorized\":\"$undefined\"}]]}],{\"children\":[[\"$\",\"$1\",\"c\",{\"children\":[\"$L6\",[[\"$\",\"script\",\"script-0\",{\"src\":\"/_next/static/chunks/8de849ca74fc071f.js\",\"async\":true,\"nonce\":\"$undefined\"}],[\"$\",\"script\",\"script-1\",{\"src\":\"/_next/static/chunks/e62b91212ee7f8ff.js\",\"async\":true,\"nonce\":\"$undefined\"}],[\"$\",\"script\",\"script-2\",{\"src\":\"/_next/static/chunks/f19fe44237e54646.js\",\"async\":true,\"nonce\":\"$undefined\"}],[\"$\",\"script\",\"script-3\",{\"src\":\"/_next/static/chunks/cb0a883bafeb6805.js\",\"async\":true,\"nonce\":\"$undefined\"}]],[\"$\",\"$L7\",null,{\"children\":[\"$\",\"$8\",null,{\"name\":\"Next.MetadataOutlet\",\"children\":\"$@9\"}]}]]}],{},null,false,false]},null,false,false]},null,false,false]},null,false,false],[\"$\",\"$1\",\"h\",{\"children\":[null,[\"$\",\"$La\",null,{\"children\":\"$Lb\"}],[\"$\",\"div\",null,{\"hidden\":true,\"children\":[\"$\",\"$Lc\",null,{\"children\":[\"$\",\"$8\",null,{\"name\":\"Next.Metadata\",\"children\":\"$Ld\"}]}]}],null]}],false]],\"m\":\"$undefined\",\"G\":[\"$e\",[]],\"S\":true}\n"])</script><script>self.__next_f.push([1,"f:I[48068,[\"/_next/static/chunks/59d0ad1b64f8544e.js\",\"/_next/static/chunks/8de849ca74fc071f.js\",\"/_next/static/chunks/e62b91212ee7f8ff.js\",\"/_next/static/chunks/f19fe44237e54646.js\",\"/_next/static/chunks/cb0a883bafeb6805.js\"],\"default\"]\n"])</script><script>self.__next_f.push([1,"6:[\"$\",\"main\",null,{\"className\":\"mx-auto w-full max-w-2xl px-4 py-16\",\"children\":[[\"$\",\"$L5\",null,{\"href\":\"/blog\",\"className\":\"inline-flex items-center gap-1.5 text-sm text-fd-muted-foreground hover:text-fd-foreground mb-10 transition-colors\",\"children\":\"← Back to Blog\"}],[\"$\",\"div\",null,{\"className\":\"mb-8\",\"children\":[[\"$\",\"time\",null,{\"className\":\"text-xs font-mono text-fd-muted-foreground uppercase tracking-wider\",\"children\":\"February 27, 2026\"}],[\"$\",\"h1\",null,{\"className\":\"text-3xl font-bold mt-2 mb-3\",\"children\":\"BitDelta: 1-Bit Behavioral Compression Across the Zen Model Family\"}],[\"$\",\"p\",null,{\"className\":\"text-fd-muted-foreground text-lg mb-4\",\"children\":\"How BitDelta (arXiv:2402.10193) compresses fine-tuned behavioral deltas to 1-bit precision, enabling the full Zen model family — nano through ultra — to share a single GPU cluster.\"}],[\"$\",\"div\",null,{\"className\":\"flex items-center gap-3 pt-4 border-t border-fd-border\",\"children\":[[\"$\",\"span\",null,{\"className\":\"text-sm text-fd-muted-foreground\",\"children\":[\"By \",\"Zen LM Team\"]}],[\"$\",\"div\",null,{\"className\":\"flex gap-1.5 ml-auto\",\"children\":[[\"$\",\"span\",\"Research\",{\"className\":\"text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded\",\"children\":\"Research\"}],[\"$\",\"span\",\"Quantization\",{\"className\":\"text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded\",\"children\":\"Quantization\"}],[\"$\",\"span\",\"BitDelta\",{\"className\":\"text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded\",\"children\":\"BitDelta\"}],[\"$\",\"span\",\"Model Serving\",{\"className\":\"text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded\",\"children\":\"Model Serving\"}],[\"$\",\"span\",\"Delta Compression\",{\"className\":\"text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded\",\"children\":\"Delta Compression\"}]]}]]}]]}],[\"$\",\"div\",null,{\"className\":\"prose dark:prose-invert max-w-none\",\"children\":[[\"$\",\"p\",null,{\"children\":[[\"$\",\"$Lf\",null,{\"href\":\"https://arxiv.org/abs/2402.10193\",\"children\":\"BITDELTA PAPER\"}],\" \",[\"$\",\"$Lf\",null,{\"href\":\"https://arxiv.org/abs/2602.09689\",\"children\":\"MONOSOUP PAPER\"}],\" \",[\"$\",\"$Lf\",null,{\"href\":\"https://arxiv.org/abs/2510.13537\",\"children\":\"K-MERGE PAPER\"}],\" \",[\"$\",\"$Lf\",null,{\"href\":\"https://huggingface.co/zenlm\",\"children\":\"ZEN MODELS\"}]]}],\"\\n\",[\"$\",\"p\",null,{\"children\":\"The Zen model family has a deployment problem that is not immediately obvious from the outside. We publish 14+ distinct model variants — from zen-nano at 0.6B parameters to zen4-ultra at 1.04T. Each variant carries fine-tuned behavioral characteristics: different personas, different task specializations, different safety postures. In a naive serving architecture, each variant is a separate set of weights. Loading all of them onto a GPU cluster is economically impossible.\"}],\"\\n\",[\"$\",\"p\",null,{\"children\":\"BitDelta is how we solve this. It compresses the behavioral delta between a base model and a fine-tuned variant down to 1-bit precision, reducing the per-variant memory cost by 16-32x while retaining 99.3% of full-precision behavioral accuracy.\"}],\"\\n\",[\"$\",\"h2\",null,{\"className\":\"flex scroll-m-28 flex-row items-center gap-2\",\"id\":\"the-multi-variant-deployment-problem\",\"children\":[[\"$\",\"a\",null,{\"data-card\":\"\",\"href\":\"#the-multi-variant-deployment-problem\",\"className\":\"peer\",\"children\":\"The Multi-Variant Deployment Problem\"}],[\"$\",\"svg\",null,{\"ref\":\"$undefined\",\"xmlns\":\"http://www.w3.org/2000/svg\",\"width\":24,\"height\":24,\"viewBox\":\"0 0 24 24\",\"fill\":\"none\",\"stroke\":\"currentColor\",\"strokeWidth\":2,\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"className\":\"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100\",\"aria-hidden\":true,\"children\":[\"$L10\",\"$L11\",\"$undefined\"]}]]}],\"\\n\",\"$L12\",\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\",\"$L13\",\"\\n\",\"$L14\",\"\\n\",\"$L15\",\"\\n\",\"$L16\",\"\\n\",\"$L17\",\"\\n\",\"$L18\",\"\\n\",\"$L19\",\"\\n\",\"$L1a\",\"\\n\",\"$L1b\",\"\\n\",\"$L1c\",\"\\n\",\"$L1d\",\"\\n\",\"$L1e\",\"\\n\",\"$L1f\",\"\\n\",\"$L20\",\"\\n\",\"$L21\",\"\\n\",\"$L22\",\"\\n\",\"$L23\",\"\\n\",\"$L24\",\"\\n\",\"$L25\",\"\\n\",\"$L26\",\"\\n\",\"$L27\",\"\\n\",\"$L28\",\"\\n\",\"$L29\",\"\\n\",\"$L2a\",\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\",\"$L2b\",\"\\n\",\"$L2c\",\"\\n\",\"$L2d\",\"\\n\",\"$L2e\",\"\\n\",\"$L2f\",\"\\n\",\"$L30\",\"\\n\",\"$L31\",\"\\n\",\"$L32\",\"\\n\",\"$L33\",\"\\n\",\"$L34\",\"\\n\",\"$L35\",\"\\n\",\"$L36\",\"\\n\",\"$L37\",\"\\n\",\"$L38\",\"\\n\",\"$L39\",\"\\n\",\"$L3a\",\"\\n\",\"$L3b\",\"\\n\",\"$L3c\",\"\\n\",\"$L3d\",\"\\n\",\"$L3e\",\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\",\"$L3f\",\"\\n\",\"$L40\",\"\\n\",\"$L41\",\"\\n\",\"$L42\",\"\\n\",\"$L43\"]}]]}]\n"])</script><script>self.__next_f.push([1,"44:I[51504,[\"/_next/static/chunks/59d0ad1b64f8544e.js\",\"/_next/static/chunks/8de849ca74fc071f.js\",\"/_next/static/chunks/e62b91212ee7f8ff.js\",\"/_next/static/chunks/f19fe44237e54646.js\",\"/_next/static/chunks/cb0a883bafeb6805.js\"],\"CodeBlock\"]\n45:I[51504,[\"/_next/static/chunks/59d0ad1b64f8544e.js\",\"/_next/static/chunks/8de849ca74fc071f.js\",\"/_next/static/chunks/e62b91212ee7f8ff.js\",\"/_next/static/chunks/f19fe44237e54646.js\",\"/_next/static/chunks/cb0a883bafeb6805.js\"],\"Pre\"]\n10:[\"$\",\"path\",\"1cjeqo\",{\"d\":\"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71\"}]\n11:[\"$\",\"path\",\"19qd67\",{\"d\":\"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71\"}]\n12:[\"$\",\"p\",null,{\"children\":\"Consider the economics concretely. A single zen4-ultra shard (1.04T parameters, bfloat16) requires roughly 2TB of GPU memory. Even a single full-precision variant of zen-max (72B) requires ~144GB. With 14 variants across our model catalog:\"}]\n"])</script><script>self.__next_f.push([1,"13:[\"$\",\"div\",null,{\"className\":\"relative overflow-auto prose-no-margin my-6\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"Tier\"}],[\"$\",\"th\",null,{\"children\":\"Parameters\"}],[\"$\",\"th\",null,{\"children\":\"Full Precision (BF16)\"}],[\"$\",\"th\",null,{\"children\":\"Variants\"}],[\"$\",\"th\",null,{\"children\":\"Total\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"nano\"}],[\"$\",\"td\",null,{\"children\":\"0.6B\"}],[\"$\",\"td\",null,{\"children\":\"1.2 GB\"}],[\"$\",\"td\",null,{\"children\":\"4\"}],[\"$\",\"td\",null,{\"children\":\"4.8 GB\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"eco / coder-4b\"}],[\"$\",\"td\",null,{\"children\":\"4B\"}],[\"$\",\"td\",null,{\"children\":\"8 GB\"}],[\"$\",\"td\",null,{\"children\":\"3\"}],[\"$\",\"td\",null,{\"children\":\"24 GB\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"zen4-max\"}],[\"$\",\"td\",null,{\"children\":\"30B\"}],[\"$\",\"td\",null,{\"children\":\"60 GB\"}],[\"$\",\"td\",null,{\"children\":\"3\"}],[\"$\",\"td\",null,{\"children\":\"180 GB\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"zen-max\"}],[\"$\",\"td\",null,{\"children\":\"72B\"}],[\"$\",\"td\",null,{\"children\":\"144 GB\"}],[\"$\",\"td\",null,{\"children\":\"2\"}],[\"$\",\"td\",null,{\"children\":\"288 GB\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"zen4-ultra\"}],[\"$\",\"td\",null,{\"children\":\"1.04T\"}],[\"$\",\"td\",null,{\"children\":\"~2 TB\"}],[\"$\",\"td\",null,{\"children\":\"1\"}],[\"$\",\"td\",null,{\"children\":\"~2 TB\"}]]}]]}]]}]}]\n"])</script><script>self.__next_f.push([1,"14:[\"$\",\"p\",null,{\"children\":\"Keeping all of these “hot” simultaneously is not feasible. Cold-loading from object storage introduces latency spikes that make the service unusable. We need a different architecture.\"}]\n15:[\"$\",\"p\",null,{\"children\":[\"The key observation: most variants share an identical base model. The behavioral differences — the fine-tuned identity, the task specialization, the adjusted refusal boundaries — live in the \",[\"$\",\"strong\",null,{\"children\":\"delta\"}],\" between fine-tuned weights and base weights. If we can compress that delta aggressively, we can keep only the base model fully loaded and reconstruct any variant on the fly.\"]}]\n16:[\"$\",\"h2\",null,{\"className\":\"flex scroll-m-28 flex-row items-center gap-2\",\"id\":\"bitdelta-theory\",\"children\":[[\"$\",\"a\",null,{\"data-card\":\"\",\"href\":\"#bitdelta-theory\",\"className\":\"peer\",\"children\":\"BitDelta Theory\"}],[\"$\",\"svg\",null,{\"ref\":\"$undefined\",\"xmlns\":\"http://www.w3.org/2000/svg\",\"width\":24,\"height\":24,\"viewBox\":\"0 0 24 24\",\"fill\":\"none\",\"stroke\":\"currentColor\",\"strokeWidth\":2,\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"className\":\"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100\",\"aria-hidden\":true,\"children\":[[\"$\",\"path\",\"1cjeqo\",{\"d\":\"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71\"}],[\"$\",\"path\",\"19qd67\",{\"d\":\"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71\"}],\"$undefined\"]}]]}]\n17:[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":\"Paper\"}],\" : arXiv:2402.10193\"]}]\n18:[\"$\",\"p\",null,{\"children\":\"BitDelta decomposes a fine-tuned weight matrix as:\"}]\n19:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-light-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"\u003csvg viewBox=\\\"0 0 24 24\\\"\u003e\u003cpath d=\\\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\\\" fill=\\\"currentColor\\\" /\u003e\u003c/svg\u003e\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" W_ft = W_base + Δ\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" \"}]}]]}]}]}]\n1a:[\"$\",\"p\",null,{\"children\":\"and approximates the delta with 1-bit quantization:\"}]\n1b:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-light-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"\u003csvg viewBox=\\\"0 0 24 24\\\"\u003e\u003cpath d=\\\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\\\" fill=\\\"currentColor\\\" /\u003e\u003c/svg\u003e\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" Δ ≈ α · sign(Δ)\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" \"}]}]]}]}]}]\n1c:[\"$\",\"p\",null,{\"children\":\"where the scale factor α is the mean absolute value of the delta entries:\"}]\n1d:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-l"])</script><script>self.__next_f.push([1,"ight-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"\u003csvg viewBox=\\\"0 0 24 24\\\"\u003e\u003cpath d=\\\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\\\" fill=\\\"currentColor\\\" /\u003e\u003c/svg\u003e\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" α = (1/n) Σ |Δ_ij|\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" \"}]}]]}]}]}]\n1e:[\"$\",\"p\",null,{\"children\":\"This is a single scalar per weight matrix. The sign matrix is 1-bit per element. Total storage for the delta: n bits + 1 float32. For a 4096×4096 weight matrix, that is 16MB → 2MB. For the full zen-max 72B delta, the storage requirement drops from ~144GB to ~9GB.\"}]\n1f:[\"$\",\"p\",null,{\"children\":\"Why does 1-bit sign quantization work? The delta values in fine-tuned LLMs follow a near-Laplace distribution centered at zero. The signs carry the directional information; the scale α captures the magnitude. The residual error:\"}]\n20:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-light-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"\u003csvg viewBox=\\\"0 0 24 24\\\"\u003e\u003cpath d=\\\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\\\" fill=\\\"currentColor\\\" /\u003e\u003c/svg\u003e\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" ε = Δ - α · sign(Δ)\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" \"}]}]]}]}]}]\n21:[\"$\",\"p\",null,{\"children\":\"has bounded expected squared norm:\"}]\n22:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-light-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"\u003csvg viewBox=\\\"0 0 24 24\\\"\u003e\u003cpath d=\\\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\\\" fill=\\\"currentColor\\\" /\u003e\u003c/svg\u003e\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" E[||ε||²] ≤ (1 - 2/π) · ||Δ||² ≈ 0.36 · ||Δ||²\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" \"}]}]]}]}]}]\n23:[\"$\",\"p\",null,{\"children\":\"In practice (and this is the empirical surprise), the effective error on model outputs is far smaller than this bound suggests, because the residuals are uncorrelated with the task-relevant signal directions. The model’s behavioral accuracy degrades gracefully rather than catastrophically.\"}]\n24:[\"$\",\"h2\",null,{\"className\":\"flex scroll-m-28 flex-row items-center gap-2\",\"id\":\""])</script><script>self.__next_f.push([1,"implementation-fused-cuda-kernel\",\"children\":[[\"$\",\"a\",null,{\"data-card\":\"\",\"href\":\"#implementation-fused-cuda-kernel\",\"className\":\"peer\",\"children\":\"Implementation: Fused CUDA Kernel\"}],[\"$\",\"svg\",null,{\"ref\":\"$undefined\",\"xmlns\":\"http://www.w3.org/2000/svg\",\"width\":24,\"height\":24,\"viewBox\":\"0 0 24 24\",\"fill\":\"none\",\"stroke\":\"currentColor\",\"strokeWidth\":2,\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"className\":\"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100\",\"aria-hidden\":true,\"children\":[[\"$\",\"path\",\"1cjeqo\",{\"d\":\"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71\"}],[\"$\",\"path\",\"19qd67\",{\"d\":\"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71\"}],\"$undefined\"]}]]}]\n25:[\"$\",\"p\",null,{\"children\":[\"The critical implementation detail is efficiency. Reconstructing \",[\"$\",\"code\",null,{\"children\":\"W_ft = W_base + α · sign(Δ)\"}],\" at inference time must not add meaningful latency. Our CUDA kernel fuses three operations:\"]}]\n26:[\"$\",\"ol\",null,{\"children\":[\"\\n\",[\"$\",\"li\",null,{\"children\":\"Load sign bits from compressed storage (1-bit tensor, integer packing)\"}],\"\\n\",[\"$\",\"li\",null,{\"children\":[\"Unpack and scale: \",[\"$\",\"code\",null,{\"children\":\"delta_row = alpha * sign_bits.float() * 2 - 1\"}]]}],\"\\n\",[\"$\",\"li\",null,{\"children\":\"Add to base weight tile in shared memory before GEMM\"}],\"\\n\"]}]\n27:[\"$\",\"p\",null,{\"children\":\"The result: delta reconstruction adds less than 1ms overhead per forward pass on an A100. In practice the overhead is dominated by memory bandwidth to load the sign bits, which at 1/16th the size of the base weight tensor is negligible.\"}]\n46:T5c0,"])</script><script>self.__next_f.push([1,"\u003csvg viewBox=\"0 0 24 24\"\u003e\u003cpath d=\"M14.25.18l.9.2.73.26.59.3.45.32.34.34.25.34.16.33.1.3.04.26.02.2-.01.13V8.5l-.05.63-.13.55-.21.46-.26.38-.3.31-.33.25-.35.19-.35.14-.33.1-.3.07-.26.04-.21.02H8.77l-.69.05-.59.14-.5.22-.41.27-.33.32-.27.35-.2.36-.15.37-.1.35-.07.32-.04.27-.02.21v3.06H3.17l-.21-.03-.28-.07-.32-.12-.35-.18-.36-.26-.36-.36-.35-.46-.32-.59-.28-.73-.21-.88-.14-1.05-.05-1.23.06-1.22.16-1.04.24-.87.32-.71.36-.57.4-.44.42-.33.42-.24.4-.16.36-.1.32-.05.24-.01h.16l.06.01h8.16v-.83H6.18l-.01-2.75-.02-.37.05-.34.11-.31.17-.28.25-.26.31-.23.38-.2.44-.18.51-.15.58-.12.64-.1.71-.06.77-.04.84-.02 1.27.05zm-6.3 1.98l-.23.33-.08.41.08.41.23.34.33.22.41.09.41-.09.33-.22.23-.34.08-.41-.08-.41-.23-.33-.33-.22-.41-.09-.41.09zm13.09 3.95l.28.06.32.12.35.18.36.27.36.35.35.47.32.59.28.73.21.88.14 1.04.05 1.23-.06 1.23-.16 1.04-.24.86-.32.71-.36.57-.4.45-.42.33-.42.24-.4.16-.36.09-.32.05-.24.02-.16-.01h-8.22v.82h5.84l.01 2.76.02.36-.05.34-.11.31-.17.29-.25.25-.31.24-.38.2-.44.17-.51.15-.58.13-.64.09-.71.07-.77.04-.84.01-1.27-.04-1.07-.14-.9-.2-.73-.25-.59-.3-.45-.33-.34-.34-.25-.34-.16-.33-.1-.3-.04-.25-.02-.2.01-.13v-5.34l.05-.64.13-.54.21-.46.26-.38.3-.32.33-.24.35-.2.35-.14.33-.1.3-.06.26-.04.21-.02.13-.01h5.84l.69-.05.59-.14.5-.21.41-.28.33-.32.27-.35.2-.36.15-.36.1-.35.07-.32.04-.28.02-.21V6.07h2.09l.14.01zm-6.47 14.25l-.23.33-.08.41.08.41.23.33.33.23.41.08.41-.08.33-.23.23-.33.08-.41-.08-.41-.23-.33-.33-.23-.41-.08-.41.08z\" fill=\"currentColor\" /\u003e\u003c/svg\u003e"])</script><script>self.__next_f.push([1,"28:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-light-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"$46\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\"}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" import\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" torch\"}]]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" \"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" def\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#6F42C1\",\"--shiki-dark\":\"#B392F0\"},\"children\":\" compress_delta\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\"(W_ft: torch.Tensor, W_base: torch.Tensor) -\u003e tuple[torch.Tensor, torch.Tensor]:\"}]]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#032F62\",\"--shiki-dark\":\"#9ECBFF\"},\"children\":\" \\\"\\\"\\\"Compress fine-tuned weight delta to 1-bit + scale.\\\"\\\"\\\"\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" delta \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"=\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" W_ft \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"-\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" W_base\"}]]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" alpha \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"=\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" delta.abs().mean()\"}]]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" sign_bits \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"=\"}],\"$L47\",\"$L48\",\"$L49\",\"$L4a\",\"$L4b\"]}],\"\\n\",\"$L4c\",\"\\n\",\"$L4d\",\"\\n\",\"$L4e\",\"\\n\",\"$L4f\",\"\\n\",\"$L50\",\"\\n\",\"$L51\",\"\\n\",\"$L52\",\"\\n\",\"$L53\",\"\\n\",\"$L54\",\"\\n\",\"$L55\",\"\\n\",\"$L56\",\"\\n\",\"$L57\",\"\\n\",\"$L58\",\"\\n\",\"$L59\",\"\\n\",\"$L5a\",\"\\n\",\"$L5b\",\"\\n\",\"$L5c\"]}]}]}]\n"])</script><script>self.__next_f.push([1,"29:[\"$\",\"h2\",null,{\"className\":\"flex scroll-m-28 flex-row items-center gap-2\",\"id\":\"quality-results\",\"children\":[[\"$\",\"a\",null,{\"data-card\":\"\",\"href\":\"#quality-results\",\"className\":\"peer\",\"children\":\"Quality Results\"}],[\"$\",\"svg\",null,{\"ref\":\"$undefined\",\"xmlns\":\"http://www.w3.org/2000/svg\",\"width\":24,\"height\":24,\"viewBox\":\"0 0 24 24\",\"fill\":\"none\",\"stroke\":\"currentColor\",\"strokeWidth\":2,\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"className\":\"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100\",\"aria-hidden\":true,\"children\":[[\"$\",\"path\",\"1cjeqo\",{\"d\":\"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71\"}],[\"$\",\"path\",\"19qd67\",{\"d\":\"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71\"}],\"$undefined\"]}]]}]\n2a:[\"$\",\"p\",null,{\"children\":\"We evaluated BitDelta across five Zen variants against their full-precision counterparts:\"}]\n"])</script><script>self.__next_f.push([1,"2b:[\"$\",\"div\",null,{\"className\":\"relative overflow-auto prose-no-margin my-6\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"Model\"}],[\"$\",\"th\",null,{\"children\":\"Task\"}],[\"$\",\"th\",null,{\"children\":\"Full Precision\"}],[\"$\",\"th\",null,{\"children\":\"BitDelta\"}],[\"$\",\"th\",null,{\"children\":\"Retention\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"zen-nano\"}],[\"$\",\"td\",null,{\"children\":\"MMLU\"}],[\"$\",\"td\",null,{\"children\":\"61.3\"}],[\"$\",\"td\",null,{\"children\":\"60.8\"}],[\"$\",\"td\",null,{\"children\":\"99.2%\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"zen4-max\"}],[\"$\",\"td\",null,{\"children\":\"HumanEval\"}],[\"$\",\"td\",null,{\"children\":\"74.1\"}],[\"$\",\"td\",null,{\"children\":\"73.5\"}],[\"$\",\"td\",null,{\"children\":\"99.1%\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"zen4-pro\"}],[\"$\",\"td\",null,{\"children\":\"GSM8K\"}],[\"$\",\"td\",null,{\"children\":\"88.4\"}],[\"$\",\"td\",null,{\"children\":\"87.9\"}],[\"$\",\"td\",null,{\"children\":\"99.4%\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"zen-max\"}],[\"$\",\"td\",null,{\"children\":\"GPQA\"}],[\"$\",\"td\",null,{\"children\":\"71.2\"}],[\"$\",\"td\",null,{\"children\":\"70.6\"}],[\"$\",\"td\",null,{\"children\":\"99.2%\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"zen4-ultra\"}],[\"$\",\"td\",null,{\"children\":\"AIME 2024\"}],[\"$\",\"td\",null,{\"children\":\"94.7\"}],[\"$\",\"td\",null,{\"children\":\"93.6\"}],[\"$\",\"td\",null,{\"children\":\"98.8%\"}]]}]]}]]}]}]\n"])</script><script>self.__next_f.push([1,"2c:[\"$\",\"p\",null,{\"children\":[\"Average behavioral retention: \",[\"$\",\"strong\",null,{\"children\":\"99.3%\"}],\". The 0.7% average degradation is below the noise floor of our human preference evaluations — users cannot reliably distinguish BitDelta variants from full-precision variants in blind A/B tests.\"]}]\n2d:[\"$\",\"h2\",null,{\"className\":\"flex scroll-m-28 flex-row items-center gap-2\",\"id\":\"monosoup-svd-fallback-for-weak-checkpoints\",\"children\":[[\"$\",\"a\",null,{\"data-card\":\"\",\"href\":\"#monosoup-svd-fallback-for-weak-checkpoints\",\"className\":\"peer\",\"children\":\"MonoSoup: SVD Fallback for Weak Checkpoints\"}],[\"$\",\"svg\",null,{\"ref\":\"$undefined\",\"xmlns\":\"http://www.w3.org/2000/svg\",\"width\":24,\"height\":24,\"viewBox\":\"0 0 24 24\",\"fill\":\"none\",\"stroke\":\"currentColor\",\"strokeWidth\":2,\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"className\":\"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100\",\"aria-hidden\":true,\"children\":[[\"$\",\"path\",\"1cjeqo\",{\"d\":\"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71\"}],[\"$\",\"path\",\"19qd67\",{\"d\":\"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71\"}],\"$undefined\"]}]]}]\n2e:[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":\"Paper\"}],\" : arXiv:2602.09689\"]}]\n2f:[\"$\",\"p\",null,{\"children\":\"BitDelta works well when the delta is well-behaved (small, distributed, near-Laplace). Some fine-tuned checkpoints — particularly those from aggressive few-shot fine-tuning or noisy datasets — produce deltas that are large and spiky. In these cases, 1-bit quantization introduces perceptible degradation.\"}]\n30:[\"$\",\"p\",null,{\"children\":\"MonoSoup provides a complementary approach: instead of compressing the delta, decompose the full fine-tuned weight via SVD and keep only the top-k singular triplets:\"}]\n31:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-light-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"\u003csvg viewBox=\\\"0 0 24 24\\\"\u003e\u003cpath d=\\\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\\\" fill=\\\"currentColor\\\" /\u003e\u003c/svg\u003e\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" W_ft ≈ U_k Σ_k V_k^T\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" \"}]}]]}]}]}]\n32:[\"$\",\"p\",null,{\"children\":\"where k is chosen to keep 95% of the Frobenius norm. This is not a delta compression technique — it operates on the single fine-tuned checkpoint directly. But for weak checkpoints where BitDelta degrades, MonoSoup recovers up to 8% of the lost behavioral accuracy at comparable memory cost.\"}]\n33:[\"$\",\"p\",null,{\"children\":\"In our pipeline: we try BitDelta first. If behavioral retention falls below 98.5% on our internal benchmark suite, we fall back to MonoSoup with k calibrated to budget.\"}]\n34:[\"$\",\"h2\",null,{\"className\":\"flex scroll-m-28 flex-row items-center gap-2\",\"id\":\"k-merge-edge-adapter-management\",\"children\":[[\"$\",\"a\",null,{\"data-card\":\"\",\"href\":\"#k-merge-edge-adapter-management\",\"className\":\"peer\",\"children\":\"K-Merge: Edge Adapter Management\"}],[\"$\",\"svg\",null,{\"ref\":\"$undefined\",\"xmlns\":\"http://www.w3.org/2000/svg\",\"width\":24,\"height\":24,\"viewBox\":\"0 0 24 24\",\"fill\":\"none\",\"stroke\":\"currentColor\",\"strokeWidth\":2,\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"className\":\"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100\",\"aria-hidden\":true,\"children\":[[\"$\",\"path\",\"1cjeqo\",{\"d\":\"M10 13a5 5 0 "])</script><script>self.__next_f.push([1,"0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71\"}],[\"$\",\"path\",\"19qd67\",{\"d\":\"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71\"}],\"$undefined\"]}]]}]\n35:[\"$\",\"p\",null,{\"children\":[[\"$\",\"strong\",null,{\"children\":\"Paper\"}],\" : arXiv:2510.13537\"]}]\n36:[\"$\",\"p\",null,{\"children\":\"The cloud serving stack above does not address edge deployment. A local user running zen-nano on a 16GB laptop cannot afford a delta cache for 14 variants — even at 1-bit compression, storing all nano variants would consume significant RAM.\"}]\n37:[\"$\",\"p\",null,{\"children\":[\"K-Merge addresses this with an \",[\"$\",\"strong\",null,{\"children\":\"online LoRA adapter pool under fixed storage budget\"}],\". The algorithm maintains a priority queue of adapters scored by utility:\"]}]\n38:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-light-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"\u003csvg viewBox=\\\"0 0 24 24\\\"\u003e\u003cpath d=\\\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\\\" fill=\\\"currentColor\\\" /\u003e\u003c/svg\u003e\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" utility(adapter_i) = request_frequency(i) × behavioral_gain(i) / storage_cost(i)\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" \"}]}]]}]}]}]\n39:[\"$\",\"p\",null,{\"children\":\"When the budget is exceeded, the lowest-utility adapter is evicted. Utility scores are updated online using exponential decay, so recently used adapters are preferred over historical ones.\"}]\n3a:[\"$\",\"p\",null,{\"children\":\"For a 16GB laptop with 4GB allocated to the adapter pool, K-Merge keeps 6-8 zen-nano variants hot simultaneously, with eviction latency of ~200ms to load a new adapter from local disk.\"}]\n3b:[\"$\",\"h2\",null,{\"className\":\"flex scroll-m-28 flex-row items-center gap-2\",\"id\":\"full-zen-serving-stack\",\"children\":[[\"$\",\"a\",null,{\"data-card\":\"\",\"href\":\"#full-zen-serving-stack\",\"className\":\"peer\",\"children\":\"Full Zen Serving Stack\"}],[\"$\",\"svg\",null,{\"ref\":\"$undefined\",\"xmlns\":\"http://www.w3.org/2000/svg\",\"width\":24,\"height\":24,\"viewBox\":\"0 0 24 24\",\"fill\":\"none\",\"stroke\":\"currentColor\",\"strokeWidth\":2,\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"className\":\"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100\",\"aria-hidden\":true,\"children\":[[\"$\",\"path\",\"1cjeqo\",{\"d\":\"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71\"}],[\"$\",\"path\",\"19qd67\",{\"d\":\"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71\"}],\"$undefined\"]}]]}]\n"])</script><script>self.__next_f.push([1,"3c:[\"$\",\"$L44\",null,{\"className\":\"shiki shiki-themes github-light github-dark\",\"style\":{\"--shiki-light\":\"#24292e\",\"--shiki-dark\":\"#e1e4e8\",\"--shiki-light-bg\":\"#fff\",\"--shiki-dark-bg\":\"#24292e\"},\"tabIndex\":\"0\",\"icon\":\"\u003csvg viewBox=\\\"0 0 24 24\\\"\u003e\u003cpath d=\\\"M 6,1 C 4.354992,1 3,2.354992 3,4 v 16 c 0,1.645008 1.354992,3 3,3 h 12 c 1.645008,0 3,-1.354992 3,-3 V 8 7 A 1.0001,1.0001 0 0 0 20.707031,6.2929687 l -5,-5 A 1.0001,1.0001 0 0 0 15,1 h -1 z m 0,2 h 7 v 3 c 0,1.645008 1.354992,3 3,3 h 3 v 11 c 0,0.564129 -0.435871,1 -1,1 H 6 C 5.4358712,21 5,20.564129 5,20 V 4 C 5,3.4358712 5.4358712,3 6,3 Z M 15,3.4140625 18.585937,7 H 16 C 15.435871,7 15,6.5641288 15,6 Z\\\" fill=\\\"currentColor\\\" /\u003e\u003c/svg\u003e\",\"children\":[\"$\",\"$L45\",null,{\"children\":[\"$\",\"code\",null,{\"children\":[[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" ┌──────────────────────────────┐\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ Request Router │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ (model ID → variant key) │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" └────────────┬─────────────────┘\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" ┌────────────▼─────────────────┐\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ Delta Cache (Redis) │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ sign_bits + alpha per layer │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ ~9 GB per 72B variant │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" └────────────┬─────────────────┘\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ cache hit\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" ┌────────────▼─────────────────┐\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ Shared Base Model (BF16) │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ zen-max 72B: 144 GB on A100s │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ zen-nano 0.6B: 1.2 GB │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" └────────────┬─────────────────┘\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" ┌────────────▼─────────────────┐\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ Fused Reconstruction Kernel │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ W_ft = W_base + α·sign(Δ) │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ \u003c 1ms overhead per layer │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" └────────────┬─────────────────┘\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" ┌────────────▼─────────────────┐\"}]}],\"\\n\",[\"$\",\"span\",null,{\"className\":\"line\",\"children\":\"$L5d\"}],\"\\n\",\"$L5e\",\"\\n\",\"$L5f\",\"\\n\",\"$L60\"]}]}]}]\n"])</script><script>self.__next_f.push([1,"3d:[\"$\",\"p\",null,{\"children\":\"The architecture keeps one base model loaded per GPU cluster. All variants share it. The delta cache fits in Redis (NVMe-backed), loading on demand in under 50ms. In practice, our top-5 variants stay hot in Redis memory; the remaining variants load from NVMe on first request.\"}]\n3e:[\"$\",\"h2\",null,{\"className\":\"flex scroll-m-28 flex-row items-center gap-2\",\"id\":\"gpu-memory-reduction\",\"children\":[[\"$\",\"a\",null,{\"data-card\":\"\",\"href\":\"#gpu-memory-reduction\",\"className\":\"peer\",\"children\":\"GPU Memory Reduction\"}],[\"$\",\"svg\",null,{\"ref\":\"$undefined\",\"xmlns\":\"http://www.w3.org/2000/svg\",\"width\":24,\"height\":24,\"viewBox\":\"0 0 24 24\",\"fill\":\"none\",\"stroke\":\"currentColor\",\"strokeWidth\":2,\"strokeLinecap\":\"round\",\"strokeLinejoin\":\"round\",\"className\":\"lucide lucide-link size-3.5 shrink-0 text-fd-muted-foreground opacity-0 transition-opacity peer-hover:opacity-100\",\"aria-hidden\":true,\"children\":[[\"$\",\"path\",\"1cjeqo\",{\"d\":\"M10 13a5 5 0 0 0 7.54.54l3-3a5 5 0 0 0-7.07-7.07l-1.72 1.71\"}],[\"$\",\"path\",\"19qd67\",{\"d\":\"M14 11a5 5 0 0 0-7.54-.54l-3 3a5 5 0 0 0 7.07 7.07l1.71-1.71\"}],\"$undefined\"]}]]}]\n3f:[\"$\",\"div\",null,{\"className\":\"relative overflow-auto prose-no-margin my-6\",\"children\":[\"$\",\"table\",null,{\"children\":[[\"$\",\"thead\",null,{\"children\":[\"$\",\"tr\",null,{\"children\":[[\"$\",\"th\",null,{\"children\":\"Scenario\"}],[\"$\",\"th\",null,{\"children\":\"Without BitDelta\"}],[\"$\",\"th\",null,{\"children\":\"With BitDelta\"}],[\"$\",\"th\",null,{\"children\":\"Savings\"}]]}]}],[\"$\",\"tbody\",null,{\"children\":[[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"3× zen-nano variants\"}],[\"$\",\"td\",null,{\"children\":\"3.6 GB\"}],[\"$\",\"td\",null,{\"children\":\"1.4 GB\"}],[\"$\",\"td\",null,{\"children\":\"61%\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"5× zen4-max variants\"}],[\"$\",\"td\",null,{\"children\":\"300 GB\"}],[\"$\",\"td\",null,{\"children\":\"192 GB\"}],[\"$\",\"td\",null,{\"children\":\"36%\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"2× zen-max variants\"}],[\"$\",\"td\",null,{\"children\":\"288 GB\"}],[\"$\",\"td\",null,{\"children\":\"162 GB\"}],[\"$\",\"td\",null,{\"children\":\"44%\"}]]}],[\"$\",\"tr\",null,{\"children\":[[\"$\",\"td\",null,{\"children\":\"Full 14-model catalog\"}],[\"$\",\"td\",null,{\"children\":\"~2.8 TB\"}],[\"$\",\"td\",null,{\"children\":\"~2.2 TB\"}],[\"$\",\"td\",null,{\"children\":\"21%\"}]]}]]}]]}]}]\n40:[\"$\",\"p\",null,{\"children\":\"The savings are most dramatic at the smaller scales where we have many more behavioral variants. For the ultra-scale models (zen4-ultra 1T+), a single checkpoint dominates, and BitDelta’s contribution is smaller — but MonoSoup and K-Merge become more relevant for edge quantization.\"}]\n41:[\"$\",\"p\",null,{\"children\":\"The combination of BitDelta for cloud serving, MonoSoup for quality recovery, and K-Merge for edge devices gives us a coherent three-tier compression story across the full Zen catalog.\"}]\n42:[\"$\",\"hr\",null,{}]\n43:[\"$\",\"p\",null,{\"children\":[\"$\",\"em\",null,{\"children\":\"Zen LM is a joint initiative of Hanzo AI Inc. (Techstars ‘17) and Zoo Labs Foundation (501c3).\"}]}]\n"])</script><script>self.__next_f.push([1,"47:[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" (delta \"}]\n48:[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"\u003e\"}]\n49:[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\" 0\"}]\n4a:[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\").to(torch.uint8) \"}]\n4b:[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#6A737D\",\"--shiki-dark\":\"#6A737D\"},\"children\":\"# 1 = positive, 0 = negative\"}]\n4c:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" return\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" sign_bits, alpha\"}]]}]\n4d:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" \"}]}]\n4e:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" def\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#6F42C1\",\"--shiki-dark\":\"#B392F0\"},\"children\":\" reconstruct_weight\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\"(W_base: torch.Tensor, sign_bits: torch.Tensor, alpha: torch.Tensor) -\u003e torch.Tensor:\"}]]}]\n4f:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#032F62\",\"--shiki-dark\":\"#9ECBFF\"},\"children\":\" \\\"\\\"\\\"Reconstruct fine-tuned weight from base + compressed delta.\\\"\\\"\\\"\"}]}]\n50:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" signs \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"=\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" sign_bits.float() \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"*\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\" 2\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" -\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\" 1\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#6A737D\",\"--shiki-dark\":\"#6A737D\"},\"children\":\" # map {0,1} → {-1,+1}\"}]]}]\n51:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" return\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" W_base \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"+\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" alpha \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"*\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" signs\"}]]}]\n52:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" \"}]}]\n53:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" def\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#6F42C1\",\"--shiki-dark\":\"#B392F0\"},\"children\":\" memory_savings\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\"(d_out: \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\"int\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\", d_in: \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\"int\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"c"])</script><script>self.__next_f.push([1,"hildren\":\") -\u003e \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\"dict\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\":\"}]]}]\n54:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#032F62\",\"--shiki-dark\":\"#9ECBFF\"},\"children\":\" \\\"\\\"\\\"Compare memory usage: full delta vs BitDelta.\\\"\\\"\\\"\"}]}]\n55:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" full_bytes \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"=\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" d_out \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"*\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" d_in \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"*\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\" 2\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#6A737D\",\"--shiki-dark\":\"#6A737D\"},\"children\":\" # bfloat16\"}]]}]\n56:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" bitdelta_bytes \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"=\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" d_out \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"*\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" d_in \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"//\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\" 8\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" +\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\" 4\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#6A737D\",\"--shiki-dark\":\"#6A737D\"},\"children\":\" # 1-bit + float32 scale\"}]]}]\n57:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\" return\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" {\"}]]}]\n58:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#032F62\",\"--shiki-dark\":\"#9ECBFF\"},\"children\":\" 'full_delta_mb'\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\": full_bytes \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"/\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\" 1e6\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\",\"}]]}]\n59:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#032F62\",\"--shiki-dark\":\"#9ECBFF\"},\"children\":\" 'bitdelta_mb'\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\": bitdelta_bytes \"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"/\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#005CC5\",\"--shiki-dark\":\"#79B8FF\"},\"children\":\" 1e6\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\",\"}]]}]\n5a:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#032F62\",\"--shiki-dark\":\"#9ECBFF\"},\"children\":\" 'compression_ratio'\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\": full_bytes \"}],[\"$\",\"span\",null,{\"style"])</script><script>self.__next_f.push([1,"\":{\"--shiki-light\":\"#D73A49\",\"--shiki-dark\":\"#F97583\"},\"children\":\"/\"}],[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" bitdelta_bytes,\"}]]}]\n5b:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" }\"}]}]\n5c:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"style\":{\"--shiki-light\":\"#24292E\",\"--shiki-dark\":\"#E1E4E8\"},\"children\":\" \"}]}]\n5d:[\"$\",\"span\",null,{\"children\":\" │ Inference Engine │\"}]\n5e:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" │ (vLLM with continuous batch) │\"}]}]\n5f:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" └──────────────────────────────┘\"}]}]\n60:[\"$\",\"span\",null,{\"className\":\"line\",\"children\":[\"$\",\"span\",null,{\"children\":\" \"}]}]\n"])</script><script>self.__next_f.push([1,"b:[[\"$\",\"meta\",\"0\",{\"charSet\":\"utf-8\"}],[\"$\",\"meta\",\"1\",{\"name\":\"viewport\",\"content\":\"width=device-width, initial-scale=1\"}],[\"$\",\"meta\",\"2\",{\"name\":\"theme-color\",\"media\":\"(prefers-color-scheme: dark)\",\"content\":\"#0A0A0A\"}],[\"$\",\"meta\",\"3\",{\"name\":\"theme-color\",\"media\":\"(prefers-color-scheme: light)\",\"content\":\"#fff\"}]]\n"])</script><script>self.__next_f.push([1,"9:null\nd:[[\"$\",\"title\",\"0\",{\"children\":\"BitDelta: 1-Bit Behavioral Compression Across the Zen Model Family — Zen LM Blog | Zen LM\"}],[\"$\",\"meta\",\"1\",{\"name\":\"description\",\"content\":\"How BitDelta (arXiv:2402.10193) compresses fine-tuned behavioral deltas to 1-bit precision, enabling the full Zen model family — nano through ultra — to share a single GPU cluster.\"}],[\"$\",\"meta\",\"2\",{\"property\":\"og:title\",\"content\":\"Zen LM - Open Foundation Models\"}],[\"$\",\"meta\",\"3\",{\"property\":\"og:description\",\"content\":\"Zen AI model family by Hanzo AI. 14 frontier models from 4B to 1T+ parameters for code, reasoning, vision, and multimodal tasks.\"}],[\"$\",\"meta\",\"4\",{\"property\":\"og:url\",\"content\":\"https://zenlm.org\"}],[\"$\",\"meta\",\"5\",{\"property\":\"og:site_name\",\"content\":\"Zen LM\"}],[\"$\",\"meta\",\"6\",{\"property\":\"og:type\",\"content\":\"website\"}],[\"$\",\"meta\",\"7\",{\"name\":\"twitter:card\",\"content\":\"summary_large_image\"}],[\"$\",\"meta\",\"8\",{\"name\":\"twitter:site\",\"content\":\"@zenlmorg\"}],[\"$\",\"meta\",\"9\",{\"name\":\"twitter:creator\",\"content\":\"@zenlmorg\"}],[\"$\",\"meta\",\"10\",{\"name\":\"twitter:title\",\"content\":\"Zen LM - Open Foundation Models\"}],[\"$\",\"meta\",\"11\",{\"name\":\"twitter:description\",\"content\":\"Zen AI model family by Hanzo AI. 14 frontier models from 4B to 1T+ parameters for code, reasoning, vision, and multimodal tasks.\"}]]\n"])</script><script>
|
||
(function(){
|
||
// After React renders the lotus emoji from RSC payload, walk the DOM
|
||
// and replace it with the enso SVG. Polls so subsequent React re-renders
|
||
// also get cleaned up.
|
||
function swap(){
|
||
// 1. Replace lotus + ' Zen LM' text inside <a> brand links
|
||
document.querySelectorAll('a').forEach(function(a){
|
||
if (a.dataset.zenLotusSwapped) return;
|
||
var t = (a.textContent || '');
|
||
if (!/.*Zen LM|Zen LM.*/.test(t)) return;
|
||
// Find the lotus text node specifically
|
||
var walker = document.createTreeWalker(a, NodeFilter.SHOW_TEXT, null);
|
||
var node;
|
||
while ((node = walker.nextNode())) {
|
||
if (node.nodeValue && node.nodeValue.indexOf('') !== -1) {
|
||
node.nodeValue = node.nodeValue.replace(/\s?/g, '');
|
||
}
|
||
}
|
||
// Inject enso SVG at start
|
||
if (!a.querySelector('svg[aria-label="Zen LM"]')) {
|
||
var ns = 'http://www.w3.org/2000/svg';
|
||
var svg = document.createElementNS(ns, 'svg');
|
||
svg.setAttribute('viewBox', '0 0 512 512');
|
||
svg.setAttribute('fill', 'none');
|
||
svg.setAttribute('width', '18');
|
||
svg.setAttribute('height', '18');
|
||
svg.setAttribute('aria-label', 'Zen LM');
|
||
svg.style.cssText = 'display:inline-block;vertical-align:-2px;margin-right:6px;flex-shrink:0';
|
||
var p = document.createElementNS(ns, 'path');
|
||
p.setAttribute('d', 'M 256 84 A 172 172 0 1 0 256 428 A 172 172 0 1 0 256 84 Z');
|
||
p.setAttribute('stroke', 'currentColor');
|
||
p.setAttribute('stroke-width', '26');
|
||
p.setAttribute('stroke-linecap', 'round');
|
||
p.setAttribute('stroke-linejoin', 'round');
|
||
svg.appendChild(p);
|
||
a.insertBefore(svg, a.firstChild);
|
||
}
|
||
a.dataset.zenLotusSwapped = '1';
|
||
});
|
||
}
|
||
function start(){ swap(); setInterval(swap, 600); }
|
||
if (document.readyState !== 'loading') start();
|
||
else document.addEventListener('DOMContentLoaded', start);
|
||
})();
|
||
</script>
|
||
|
||
<style id="zenlm-brand-anim">
|
||
@keyframes zenlm-brand-draw { to { stroke-dashoffset: 0; } }
|
||
svg.zenlm-brand-svg .zenlm-brand-path {
|
||
stroke-dasharray: 1100;
|
||
stroke-dashoffset: 1100;
|
||
animation: zenlm-brand-draw 1.4s cubic-bezier(.45,0,.2,1) 0.1s 1 forwards;
|
||
}
|
||
.zen-link-icon { display: inline-block; vertical-align: middle; flex-shrink: 0; }
|
||
</style></body></html> |