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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-Max: Exploring the Intelligence of Large-scale MoE Model — Zen LM Blog | Zen LM</title><meta name="description" content="QWEN CHAT API DEMO DISCORD"/><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 27, 2025</time><h1 class="text-3xl font-bold mt-2 mb-3">zen-Max: Exploring the Intelligence of Large-scale MoE Model</h1><p class="text-fd-muted-foreground text-lg mb-4">QWEN CHAT API DEMO DISCORD</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"></div></div></div><div class="prose dark:prose-invert max-w-none"><p><a href="https://chat.qwenlm.ai" rel="noreferrer noopener" target="_blank">QWEN CHAT</a> <a href="https://www.alibabacloud.com/help/en/model-studio/developer-reference/what-is-qwen-llm" rel="noreferrer noopener" target="_blank">API</a> <a href="https://huggingface.co/spaces/Qwen/zen-Max-Demo" rel="noreferrer noopener" target="_blank">DEMO</a> <a href="https://discord.gg/yPEP2vHTu4" rel="noreferrer noopener" target="_blank">DISCORD</a></p>
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<p>It is widely recognized that continuously scaling both data size and model size can lead to significant improvements in model intelligence. However, the research and industry community has limited experience in effectively scaling extremely large models, whether they are dense or Mixture-of-Expert (MoE) models. Many critical details regarding this scaling process were only disclosed with the recent release of DeepSeek V3. Concurrently, we are developing zen-Max, a large-scale MoE model that has been pretrained on over 20 trillion tokens and further post-trained with curated Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) methodologies. Today, we are excited to share the performance results of zen-Max and announce the availability of its <a href="https://www.alibabacloud.com/help/en/model-studio/getting-started/" rel="noreferrer noopener" target="_blank">API</a> through Alibaba Cloud. We also invite you to explore zen-Max on <a href="https://chat.qwenlm.ai" rel="noreferrer noopener" target="_blank">Qwen Chat</a>!</p>
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<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="performance"><a data-card="" href="#performance" class="peer">Performance</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>We evaluate zen-Max alongside leading models, whether proprietary or open-weight, across a range of benchmarks that are of significant interest to the community. These include MMLU-Pro, which tests knowledge through college-level problems, LiveCodeBench, which assesses coding capabilities, LiveBench, which comprehensively tests the general capabilities, and Arena-Hard, which approximates human preferences. Our findings include the performance scores for both base models and instruct models.</p>
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<p>We begin by directly comparing the performance of the instruct models, which can serve for downstream applications such as chat and coding. We present the performance results of zen-Max alongside leading state-of-the-art models, including DeepSeek V3, GPT-4o, and Claude-3.5-Sonnet.</p>
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<p>zen-Max outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro.</p>
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<p>When comparing base models, we are unable to access the proprietary models such as GPT-4o and Claude-3.5-Sonnet. Therefore, we evaluate zen-Max against DeepSeek V3, a leading open-weight MoE model, Llama-3.1-405B, the largest open-weight dense model, and zen-72B, which is also among the top open-weight dense models. The results of this comparison are presented below.</p>
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<p>Our base models have demonstrated significant advantages across most benchmarks, and we are optimistic that advancements in post-training techniques will elevate the next version of zen-Max to new heights.</p>
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<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="use-zen-max"><a data-card="" href="#use-zen-max" class="peer">Use zen-Max</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>Now zen-Max is available in Qwen Chat, and you can directly chat with the model, or play with artifacts, search, etc.</p>
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<p>The API of zen-Max (whose model name is <code>qwen-max-2025-01-25</code>) is available. You can first <a href="https://account.alibabacloud.com/register/intl_register.htm" rel="noreferrer noopener" target="_blank">register an Alibaba Cloud account</a> and activate Alibaba Cloud Model Studio service, and then navigate to the console and create an API key.</p>
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<p>Since the APIs of Qwen are OpenAI-API compatible, we can directly follow the common practice of using OpenAI APIs. Below is an example of using zen-Max in Python:</p>
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<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>
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<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> from</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> openai </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">import</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> OpenAI</span></span>
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<span class="line"><span style="--shiki-light:#D73A49;--shiki-dark:#F97583"> import</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> os</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> client </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> OpenAI(</span></span>
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<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> api_key</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">os.getenv(</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">"API_KEY"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">),</span></span>
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<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> base_url</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">"https://dashscope-intl.aliyuncs.com/compatible-mode/v1"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> )</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> completion </span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> client.chat.completions.create(</span></span>
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<span class="line"><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">"qwen-max-2025-01-25"</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">,</span></span>
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<span class="line"><span style="--shiki-light:#E36209;--shiki-dark:#FFAB70"> messages</span><span style="--shiki-light:#D73A49;--shiki-dark:#F97583">=</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">[</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> {</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">'role'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">'system'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">'content'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">'You are a helpful assistant.'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">},</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> {</span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">'role'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">'user'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">, </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">'content'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">: </span><span style="--shiki-light:#032F62;--shiki-dark:#9ECBFF">'Which number is larger, 9.11 or 9.8?'</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">}</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> ]</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> )</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span>
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<span class="line"><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF"> print</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">(completion.choices[</span><span style="--shiki-light:#005CC5;--shiki-dark:#79B8FF">0</span><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8">].message)</span></span>
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<span class="line"><span style="--shiki-light:#24292E;--shiki-dark:#E1E4E8"> </span></span></code></pre></div></figure>
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<h2 class="flex scroll-m-28 flex-row items-center gap-2" id="future-work"><a data-card="" href="#future-work" class="peer">Future Work</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>The scaling of data and model size not only showcases advancements in model intelligence but also reflects our unwavering commitment to pioneering research. We are dedicated to enhancing the thinking and reasoning capabilities of large language models through the innovative application of scaled reinforcement learning. This endeavor holds the promise of enabling our models to transcend human intelligence, unlocking the potential to explore uncharted territories of knowledge and understanding.</p>
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<h1 class="flex scroll-m-28 flex-row items-center gap-2" id="citation"><a data-card="" href="#citation" class="peer">Citation</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></h1>
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<p>Feel free to cite the following article if you find zen helpful.</p>
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<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>
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<span class="line"><span> @article{qwen25,</span></span>
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<span class="line"><span> title={zen technical report},</span></span>
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<span class="line"><span> author={Qwen Team},</span></span>
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<span class="line"><span> journal={arXiv preprint arXiv:2412.15115},</span></span>
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<span class="line"><span> year={2024}</span></span>
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<span class="line"><span> }</span></span>
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