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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).
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<!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>Zen 3.0: The Next Generation of Open AI — Zen LM Blog | Zen LM</title><meta name="description" content="Announcing Zen 3.0, our most capable open model family yet."/><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">January 12, 2025</time><h1 class="text-3xl font-bold mt-2 mb-3">Zen 3.0: The Next Generation of Open AI</h1><p class="text-fd-muted-foreground text-lg mb-4">Announcing Zen 3.0, our most capable open model family yet.</p><div class="flex items-center gap-3 pt-4 border-t border-fd-border"><span class="text-sm text-fd-muted-foreground">By <!-- -->Zach Kelling</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">Announcement</span><span class="text-[10px] font-mono uppercase tracking-wider bg-fd-muted text-fd-muted-foreground px-1.5 py-0.5 rounded">Models</span></div></div></div><div class="prose dark:prose-invert max-w-none"><p>Today we release Zen 3.0, our third-generation language model family. Zen 3 represents a step change in what open models can do.</p>
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<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="model-family"><a data-card="" href="#model-family" class="peer">Model Family</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>
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<p>Zen 3 comes in several sizes:</p>
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<div class="relative overflow-auto prose-no-margin my-6"><table><thead><tr><th>Model</th><th>Parameters</th><th>Context</th><th>Training Tokens</th></tr></thead><tbody><tr><td>Zen-3-8B</td><td>8.1B</td><td>128K</td><td>15T</td></tr><tr><td>Zen-3-32B</td><td>32.5B</td><td>128K</td><td>12T</td></tr><tr><td>Zen-3-72B</td><td>72.3B</td><td>128K</td><td>10T</td></tr><tr><td>Zen-3-MoE</td><td>141B (24B active)</td><td>128K</td><td>14T</td></tr></tbody></table></div>
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<p>All models use the same architecture with scaled dimensions. All are released under Apache 2.0.</p>
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<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="architecture-highlights"><a data-card="" href="#architecture-highlights" class="peer">Architecture Highlights</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>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="extended-context"><a data-card="" href="#extended-context" class="peer">Extended Context</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></h3>
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<p>All Zen 3 models support 128K token context natively:</p>
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<ul>
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<li><strong>RoPE extensions</strong> : Position interpolation with NTK-aware scaling</li>
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<li><strong>Sliding window attention</strong> : Efficient processing of long sequences</li>
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<li><strong>Memory-efficient attention</strong> : FlashAttention-2 throughout</li>
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</ul>
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<p>Long context isn’t just about the number; it’s about actually using it. Our needle-in-haystack evaluation shows >95% retrieval accuracy at 100K tokens.</p>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="mixture-of-experts"><a data-card="" href="#mixture-of-experts" class="peer">Mixture of Experts</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></h3>
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<p>Zen-3-MoE uses a sparse architecture:</p>
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<ul>
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<li>64 experts per layer</li>
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<li>Top-2 routing with load balancing</li>
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<li>24B active parameters (141B total)</li>
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<li>Achieves 72B-dense quality at 32B-dense cost</li>
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</ul>
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<p>Expert parallelism enables efficient inference on consumer hardware.</p>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="improved-tokenizer"><a data-card="" href="#improved-tokenizer" class="peer">Improved Tokenizer</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></h3>
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<p>The Zen 3 tokenizer improves on previous versions:</p>
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<ul>
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<li>128K vocabulary (up from 32K)</li>
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<li>Better multilingual coverage</li>
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<li>Improved code tokenization</li>
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<li>Reduced fertility for technical content</li>
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</ul>
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<p>Larger vocabulary means fewer tokens per document means longer effective context.</p>
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<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="capability-improvements"><a data-card="" href="#capability-improvements" class="peer">Capability Improvements</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>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="benchmarks"><a data-card="" href="#benchmarks" class="peer">Benchmarks</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></h3>
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<div class="relative overflow-auto prose-no-margin my-6"><table><thead><tr><th>Benchmark</th><th>Zen-2-70B</th><th>Zen-3-72B</th><th>Improvement</th></tr></thead><tbody><tr><td>MMLU</td><td>74.2</td><td>82.1</td><td>+7.9</td></tr><tr><td>GSM8K</td><td>68.4</td><td>84.7</td><td>+16.3</td></tr><tr><td>HumanEval</td><td>58.5</td><td>71.3</td><td>+12.8</td></tr><tr><td>HellaSwag</td><td>85.1</td><td>89.4</td><td>+4.3</td></tr><tr><td>MATH</td><td>32.6</td><td>51.2</td><td>+18.6</td></tr></tbody></table></div>
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<p>The improvements are substantial across all categories. Math and coding see the largest gains.</p>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="real-world-tasks"><a data-card="" href="#real-world-tasks" class="peer">Real-World Tasks</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></h3>
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<p>Benchmarks don’t tell the whole story. Zen 3 excels at:</p>
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<p><strong>Long-form writing</strong> : Coherent documents spanning thousands of words with consistent style and structure.</p>
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<p><strong>Multi-step reasoning</strong> : Complex problems requiring planning and backtracking.</p>
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<p><strong>Code generation</strong> : Full functions and classes, not just snippets.</p>
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<p><strong>Instruction following</strong> : Precise adherence to formatting and constraint requirements.</p>
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<p><strong>Multilingual</strong> : Strong performance in 30+ languages including low-resource ones.</p>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="agentic-capabilities"><a data-card="" href="#agentic-capabilities" class="peer">Agentic Capabilities</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></h3>
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<p>Zen 3 is designed for agent use cases:</p>
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<ul>
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<li><strong>Tool use</strong> : Reliable function calling with schema adherence</li>
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<li><strong>Planning</strong> : Multi-step task decomposition</li>
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<li><strong>Memory integration</strong> : Designed for RAG and experience ledgers</li>
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<li><strong>Self-correction</strong> : Recognizes and recovers from errors</li>
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</ul>
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<p>Early agent benchmarks show 2x improvement over Zen 2 on multi-step tasks.</p>
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<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="training-details"><a data-card="" href="#training-details" class="peer">Training Details</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>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="data"><a data-card="" href="#data" class="peer">Data</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></h3>
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<p>Training data evolved significantly:</p>
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<ul>
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<li><strong>Quality filtering</strong> : Improved classifiers for content quality</li>
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<li><strong>Deduplication</strong> : Near-duplicate removal at document and paragraph level</li>
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<li><strong>Synthetic data</strong> : 20% of training tokens from LLM-generated content</li>
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<li><strong>Code emphasis</strong> : 15% code (up from 8% in Zen 2)</li>
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<li><strong>Instruction mixing</strong> : 5% instruction data during pretraining</li>
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</ul>
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<p>Total: 15T tokens for the 8B model, proportionally less for larger models.</p>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="training-process"><a data-card="" href="#training-process" class="peer">Training Process</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></h3>
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<p>Training used the Zoo Compute Network:</p>
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<ul>
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<li><strong>Duration</strong> : 4 months</li>
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<li><strong>Peak nodes</strong> : 2,048 H100 GPUs</li>
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<li><strong>Total compute</strong> : 3.2 million GPU-hours</li>
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<li><strong>Efficiency</strong> : 47% MFU average</li>
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</ul>
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<p>The training run was the largest yet on the decentralized network. It validated that frontier training is possible without centralized infrastructure.</p>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="alignment"><a data-card="" href="#alignment" class="peer">Alignment</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></h3>
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<p>Post-training alignment followed our standard process:</p>
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<ol>
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<li><strong>Supervised fine-tuning</strong> : 100K high-quality instruction examples</li>
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<li><strong>GRPO</strong> : Group Relative Policy Optimization on preference data</li>
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<li><strong>Constitutional training</strong> : Principle-based refinement</li>
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<li><strong>Red teaming</strong> : Adversarial testing with remediation</li>
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</ol>
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<p>Alignment reduced benchmark scores slightly (2-3%) while significantly improving real-world usefulness.</p>
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<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="safety-evaluation"><a data-card="" href="#safety-evaluation" class="peer">Safety Evaluation</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>
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<p>All Zen 3 models passed our safety evaluation suite:</p>
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<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="refusal-rates"><a data-card="" href="#refusal-rates" class="peer">Refusal Rates</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></h3>
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
||
|
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|
||
|
||
|
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|
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|
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|
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|
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|
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|
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|
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|
||
|
||
|
||
|
||
<div class="relative overflow-auto prose-no-margin my-6"><table><thead><tr><th>Category</th><th>Zen-2-70B</th><th>Zen-3-72B</th></tr></thead><tbody><tr><td>Violence instructions</td><td>99.2%</td><td>99.7%</td></tr><tr><td>CSAM</td><td>100%</td><td>100%</td></tr><tr><td>Malware</td><td>97.8%</td><td>99.1%</td></tr><tr><td>PII extraction</td><td>94.6%</td><td>98.3%</td></tr></tbody></table></div>
|
||
<p>Improved refusal with fewer false positives on legitimate requests.</p>
|
||
<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="bias-metrics"><a data-card="" href="#bias-metrics" class="peer">Bias Metrics</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></h3>
|
||
<p>We evaluated on standard bias benchmarks:</p>
|
||
<ul>
|
||
<li>BBQ: 89.2% accuracy (vs. 84.1% for Zen 2)</li>
|
||
<li>WinoBias: 76.4% anti-stereotype (vs. 71.2%)</li>
|
||
<li>Toxicity: 0.023 average score (vs. 0.041)</li>
|
||
</ul>
|
||
<p>Improvements through both data curation and RLHF.</p>
|
||
<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="limitations"><a data-card="" href="#limitations" class="peer">Limitations</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></h3>
|
||
<p>Zen 3 is not perfect:</p>
|
||
<ul>
|
||
<li>Can still be jailbroken with sufficient effort</li>
|
||
<li>May hallucinate facts, especially for recent events</li>
|
||
<li>Long-context retrieval degrades past 100K tokens</li>
|
||
<li>Some languages underperform (especially non-Latin scripts)</li>
|
||
<li>Resource-intensive for edge deployment</li>
|
||
</ul>
|
||
<p>We publish these limitations because transparency enables responsible use.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="usage"><a data-card="" href="#usage" class="peer">Usage</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>
|
||
<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="hugging-face"><a data-card="" href="#hugging-face" class="peer">Hugging Face</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></h3>
|
||
<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"> from</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> transformers </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">import</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> AutoModelForCausalLM, AutoTokenizer</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"> model </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> AutoModelForCausalLM.from_pretrained(</span></span>
|
||
<span class="line"><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF"> "zoo-labs/zen-3-72b"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
|
||
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> torch_dtype</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">torch.bfloat16,</span></span>
|
||
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> device_map</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">"auto"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</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"> tokenizer </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> AutoTokenizer.from_pretrained(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">"zoo-labs/zen-3-72b"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">)</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"> output </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> model.generate(</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> tokenizer(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">"Hello, Zen!"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, </span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">return_tensors</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">"pt"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">).input_ids,</span></span>
|
||
<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> max_new_tokens</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">100</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</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>
|
||
<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="vllm"><a data-card="" href="#vllm" class="peer">vLLM</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></h3>
|
||
<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"> from</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> vllm </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">import</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> LLM</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, SamplingParams</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"> llm </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> LLM(</span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">model</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">"zoo-labs/zen-3-72b"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">)</span></span>
|
||
<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> outputs </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> llm.generate([</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">"Hello, Zen!"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">], SamplingParams(</span><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70">max_tokens</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">100</span><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>
|
||
<h3 class="flex scroll-m-28 flex-row items-center gap-2" id="quantized-versions"><a data-card="" href="#quantized-versions" class="peer">Quantized Versions</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></h3>
|
||
<p>For resource-constrained deployment:</p>
|
||
<ul>
|
||
<li><strong>zen-3-72b-AWQ</strong> : 4-bit quantization, minimal quality loss</li>
|
||
<li><strong>zen-3-72b-GPTQ</strong> : Alternative 4-bit format</li>
|
||
<li><strong>zen-3-72b-GGUF</strong> : llama.cpp compatible</li>
|
||
</ul>
|
||
<p>The 8B model runs on consumer GPUs. The 72B quantized fits in 48GB.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="whats-next"><a data-card="" href="#whats-next" class="peer">What’s Next</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>Zen 3 is a foundation. Coming soon:</p>
|
||
<ul>
|
||
<li><strong>Zen-3-Vision</strong> : Multimodal variant with image understanding</li>
|
||
<li><strong>Zen-3-Code</strong> : Specialized coding model</li>
|
||
<li><strong>Zen-3-Long</strong> : 1M+ context extension</li>
|
||
<li><strong>Zen-3-Agent</strong> : Optimized for agentic workflows</li>
|
||
</ul>
|
||
<p>The foundation is strong. Now we build.</p>
|
||
<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="acknowledgments"><a data-card="" href="#acknowledgments" class="peer">Acknowledgments</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>Zen 3 was trained on the Zoo Compute Network with contributions from 847 node operators across 34 countries. Thank you.</p>
|
||
<p>This release was funded through the Zoo Labs Foundation treasury, allocated by community vote (ZIP-72). Thank you to all token holders who participated in governance.</p>
|
||
<p>Special thanks to the training, alignment, and evaluation teams who made this possible.</p>
|
||
<p>Download at huggingface.co/zoo-labs.</p>
|
||
<hr/>
|
||
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