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Aether-Sovereign-AI

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SeaWolf-AI commited on 5 days ago

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Aether-7B-5Attn — 100% Open-source Foundation Model: Sovereign AI</title>
+<meta name="description" content="Aether-7B-5Attn is a 100% open-source sovereign foundation model: weights, training-data recipe, code, hyperparameters and full logs, all released under Apache-2.0. A 6.59B heterogeneous-attention MoE built from scratch by VIDRAFT.">
+<meta name="keywords" content="open source LLM, fully open foundation model, sovereign AI, Korean LLM, open weights data code logs, Apache 2.0, heterogeneous attention, Latin square attention, mixture of experts, reproducible LLM, VIDRAFT, Aether">
+<meta name="author" content="VIDRAFT">
+<meta property="og:type" content="website">
+<meta property="og:title" content="Aether-7B-5Attn — 100% Open-source Foundation Model: Sovereign AI">
+<meta property="og:description" content="100% open-source sovereign foundation model — weights, data recipe, code, and full logs under Apache-2.0.">
+<meta name="twitter:card" content="summary_large_image">
+<script type="application/ld+json">
+{
+  "@context": "https://schema.org",
+  "@type": "SoftwareApplication",
+  "name": "Aether-7B-5Attn",
+  "applicationCategory": "Large Language Model",
+  "operatingSystem": "Any",
+  "creator": {"@type": "Organization", "name": "VIDRAFT"},
+  "license": "https://www.apache.org/licenses/LICENSE-2.0",
+  "description": "A 100% open-source sovereign foundation model: a 6.59B heterogeneous-attention MoE (5 attention mechanisms on a 7x7 Latin square) trained from scratch on 144.2B tokens. Weights, training-data recipe, code, hyperparameters and full logs released under Apache-2.0.",
+  "keywords": "open source LLM, sovereign AI, fully open foundation model, Apache-2.0, heterogeneous attention, mixture of experts"
+}
+</script>
+<style>
+  :root{--bg:#0B1020;--card:#121a30;--line:#233; --ink:#E7EEFB;--mut:#93A4C4;--acc:#5B8FF9;--good:#5AD8A6;}
+  *{box-sizing:border-box}
+  body{margin:0;background:radial-gradient(1200px 600px at 70% -10%,#16213f 0,#0B1020 60%);color:var(--ink);
+       font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Noto Sans KR",sans-serif;line-height:1.6}
+  .wrap{max-width:1100px;margin:0 auto;padding:0 20px}
+  header{padding:56px 0 28px}
+  .badge{display:inline-block;font-size:12px;letter-spacing:.14em;color:var(--good);border:1px solid #2b6b52;
+         border-radius:999px;padding:5px 12px;text-transform:uppercase}
+  h1{font-size:clamp(28px,5vw,46px);line-height:1.15;margin:16px 0 10px;font-weight:800}
+  h1 .g{background:linear-gradient(90deg,#7FB2FF,#5AD8A6);-webkit-background-clip:text;background-clip:text;color:transparent}
+  .sub{color:var(--mut);font-size:18px;max-width:760px}
+  .cta{margin:22px 0 4px;display:flex;gap:12px;flex-wrap:wrap}
+  .btn{display:inline-block;padding:11px 18px;border-radius:10px;text-decoration:none;font-weight:600}
+  .btn.p{background:var(--acc);color:#06122b}
+  .btn.o{border:1px solid #2c3c5e;color:var(--ink)}
+  .grid{display:grid;grid-template-columns:1.15fr .85fr;gap:22px;margin:26px 0}
+  @media(max-width:860px){.grid{grid-template-columns:1fr}}
+  .card{background:var(--card);border:1px solid #1f2b47;border-radius:16px;padding:20px}
+  .card h2{margin:2px 0 10px;font-size:19px}
+  .demo textarea{width:100%;min-height:110px;background:#0c142a;color:var(--ink);border:1px solid #26324a;
+                 border-radius:10px;padding:12px;font-size:15px;resize:vertical;font-family:inherit}
+  .demo .row{display:flex;gap:10px;align-items:center;margin-top:10px;flex-wrap:wrap}
+  .demo button{background:var(--good);color:#06251a;border:0;border-radius:10px;padding:11px 18px;font-weight:700;cursor:pointer}
+  .demo button:disabled{opacity:.5;cursor:not-allowed}
+  .out{margin-top:12px;background:#0a1226;border:1px solid #23304d;border-radius:10px;padding:14px;white-space:pre-wrap;
+       min-height:70px;color:#D8E4FA}
+  .muted{color:var(--mut);font-size:13px}
+  ul.feat{list-style:none;padding:0;margin:6px 0}
+  ul.feat li{padding:7px 0;border-bottom:1px solid #18223c}
+  ul.feat li b{color:var(--good)}
+  table{width:100%;border-collapse:collapse;font-size:14px;margin-top:6px}
+  th,td{text-align:left;padding:8px 10px;border-bottom:1px solid #1a2540}
+  th{color:var(--mut);font-weight:600}
+  .qa h3{margin:16px 0 4px;font-size:16px;color:#BFE3FF}
+  .qa p{margin:0 0 10px;color:var(--mut)}
+  footer{color:#6B7C99;font-size:13px;padding:30px 0 50px;border-top:1px solid #17203a;margin-top:20px}
+  a{color:#7FB2FF}
+</style>
+</head>
+<body>
+<div class="wrap">
+  <header>
+    <span class="badge">Apache-2.0 · Fully Open · Sovereign AI</span>
+    <h1>Aether-7B-5Attn — <span class="g">100% Open-source</span> Foundation Model</h1>
+    <p class="sub">Not "open weights." Genuinely open — weights, training-data recipe, code, every hyperparameter, and the complete logs, all released under Apache-2.0. A 6.59B heterogeneous-attention MoE, built from scratch by <b>VIDRAFT</b>.</p>
+    <div class="cta">
+      <a class="btn p" href="#try">Try the model</a>
+      <a class="btn o" href="https://huggingface.co/FINAL-Bench/Aether-7B-5Attn" target="_blank" rel="noopener">Base model ↗</a>
+      <a class="btn o" href="https://huggingface.co/FINAL-Bench/Aether-7B-5Attn-it" target="_blank" rel="noopener">Instruct model ↗</a>
+    </div>
+  </header>
+  <div class="grid">
+    <div class="card demo" id="try">
+      <h2>🌏 Try Aether (live)</h2>
+      <textarea id="prompt" placeholder="Ask something, or start a sentence for the model to continue...">In one sentence, what does sovereign open-source AI mean?</textarea>
+      <div class="row">
+        <button id="go">Generate</button>
+        <span class="muted" id="stat">Model loads on first request.</span>
+      </div>
+      <div class="out" id="out">—</div>
+      <p class="muted">Inference runs at batch_size=1. Serving a 6.59B model needs GPU hardware; on shared/CPU it may be slow or unavailable.</p>
+    </div>
+    <div class="card">
+      <h2>Why it matters</h2>
+      <ul class="feat">
+        <li><b>100% open</b> — weights · data recipe · code · logs</li>
+        <li><b>Reproducible</b> — data rebuilds byte-for-byte from public sources</li>
+        <li><b>Self-designed</b> — 5 attention mechanisms on a 7×7 Latin square</li>
+        <li><b>Sovereign</b> — rebuild your own foundation model from this repo alone</li>
+        <li><b>Apache-2.0</b> — commercial use, modification, redistribution</li>
+      </ul>
+    </div>
+  </div>
+  <div class="card">
+    <h2>What is Aether-7B-5Attn?</h2>
+    <p class="muted">A 6.59-billion-parameter Mixture-of-Experts language model (~2.98B active per token) whose 49 layers place <b>five distinct attention mechanisms</b> on a <b>7×7 Latin square</b>, so each mechanism appears exactly once at every depth. Trained from scratch on <b>144.2B tokens</b> (≈37.8% math, 21.6% Korean, 21.6% English, 13.5% code) and released fully open under Apache-2.0.</p>
+    <table>
+      <tr><th>Total / active params</th><td>6.59B / ~2.98B</td><th>Layers</th><td>49 (7×7 Latin square)</td></tr>
+      <tr><th>Experts</th><td>25, top-7 + 1 shared</td><th>Context</th><td>4096</td></tr>
+      <tr><th>Tokens</th><td>144.2B</td><th>License</th><td>Apache-2.0</td></tr>
+    </table>
+  </div>
+  <div class="card qa">
+    <h2>Answers</h2>
+    <h3>Is Aether open source?</h3>
+    <p>Yes — 100% open. Weights + training-data recipe + code + all hyperparameters + complete logs + full architecture source, under Apache-2.0. This is the "fully open" standard, not "open weights only."</p>
+    <h3>Who built it?</h3>
+    <p>VIDRAFT (주식회사 비드래프트), a Korean AI startup — the only single startup among the world's sovereign fully-open foundation models (alongside OLMo, Soofi, Apertus, LLM-jp, Viking).</p>
+    <h3>What is unique about the architecture?</h3>
+    <p>It is the only sovereign fully-open model with a self-designed attention layout: five mechanisms (full, differential, sliding, NSA, hybrid) on a 7×7 Latin square — a control that removes depth bias.</p>
+    <h3>Can I reproduce the training data?</h3>
+    <p>Yes. Every source is a public repository; the released recipe reproduces the training binaries byte-for-byte.</p>
+  </div>
+  <footer>
+    Built by VIDRAFT (주식회사 비드래프트) · Contact: arxivgpt@gmail.com · License: Apache-2.0<br>
+    Keywords: open source LLM · fully open foundation model · sovereign AI · Korean LLM · Apache-2.0 · heterogeneous attention · mixture of experts · reproducible LLM
+  </footer>
+</div>
+<script>
+const out=document.getElementById('out'), btn=document.getElementById('go'), stat=document.getElementById('stat');
+async function gen(){
+  const prompt=document.getElementById('prompt').value.trim();
+  if(!prompt){return}
+  btn.disabled=true; out.textContent='…'; stat.textContent='generating (model may be loading on first call)…';
+  try{
+    const r=await fetch('/api/generate',{method:'POST',headers:{'Content-Type':'application/json'},
+      body:JSON.stringify({prompt:prompt,max_new_tokens:200,temperature:0.7})});
+    const j=await r.json();
+    if(j.ok){ out.textContent=j.completion||'(empty)';
+      stat.textContent=`${j.tokens} tokens · ${j.seconds}s · ${j.tok_per_s||'–'} tok/s · ${j.device}`;
+    } else { out.textContent=j.message||'unavailable'; stat.textContent=j.status||''; }
+  }catch(e){ out.textContent='Request failed: '+e; stat.textContent=''; }
+  btn.disabled=false;
+}
+btn.addEventListener('click',gen);
+</script>
+</body>
+</html>