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Keep 2-bit on one line, stack result tables
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index.html
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@@ -125,8 +125,8 @@ tr.ours .name{color:var(--gold);font-weight:700}
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tr.ours .pbar span{background:linear-gradient(90deg,var(--gold-deep),var(--gold))}
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.tag{display:inline-block;font-size:11px;letter-spacing:.06em;text-transform:uppercase;border:1px solid var(--line-strong);color:var(--muted);border-radius:99px;padding:2px 8px;margin-left:6px;vertical-align:1px}
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.up{color:var(--gold);font-weight:700}
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.two{display:grid;grid-template-columns:1fr
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.note{font-size:14px;color:var(--muted);margin:16px 2px 0}
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.note b{color:var(--text)}
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<div class="hero">
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<span class="eyebrow"><i></i>Pingala-8 · 2-bit GGUF for llama.cpp</span>
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<h1>85% smaller LLMs. <em>Better than llama.cpp's own 2-bit</em> at the same size.</h1>
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<p class="lede">We compress models you already have to 2 bits, with <b>no retraining</b>. Qwen3-4B goes from <b>8.05 GB to 1.24 GB</b>, and at that size its WikiText-2 perplexity is <b>16.64 against 28.61</b> for llama.cpp's IQ2_XXS (lower is better).</p>
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<div class="cta">
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<a class="btn primary" href="https://github.com/Genrius-AI/pingala8-llm/releases/tag/v2.4-oneclick" target="_blank" rel="noopener">
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<div>
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<h3 style="margin:0 0 12px;font-size:18px">Zero-shot quiz scores, Qwen3-4B</h3>
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<div class="tbl">
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<table style="min-width:
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<thead><tr><th>Test</th><th>8-bit original</th><th>IQ2_XXS</th><th>IQ2_KT</th><th>Ours</th><th>vs IQ2_XXS</th></tr></thead>
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<tbody>
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<tr><td>ARC-Easy</td><td class="num">66.1</td><td class="num">43.9</td><td class="num">53.3</td><td class="num up">60.9</td><td class="num up">+39%</td></tr>
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<div>
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<h3 style="margin:0 0 12px;font-size:18px">Llama-3.1-8B, pre-registered</h3>
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<div class="tbl">
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<table style="min-width:
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<thead><tr><th>Format</th><th>Size</th><th>PPL, BOS</th><th>PPL, strided</th></tr></thead>
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<tbody>
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<tr><td class="name">fp16 original</td><td class="num">16.08 GB</td><td class="num">-</td><td class="num">-</td></tr>
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tr.ours .pbar span{background:linear-gradient(90deg,var(--gold-deep),var(--gold))}
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.tag{display:inline-block;font-size:11px;letter-spacing:.06em;text-transform:uppercase;border:1px solid var(--line-strong);color:var(--muted);border-radius:99px;padding:2px 8px;margin-left:6px;vertical-align:1px}
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.up{color:var(--gold);font-weight:700}
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.two{display:grid;grid-template-columns:1fr;gap:28px}
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.nw{white-space:nowrap}
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.note{font-size:14px;color:var(--muted);margin:16px 2px 0}
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.note b{color:var(--text)}
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<div class="hero">
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<span class="eyebrow"><i></i>Pingala-8 · 2-bit GGUF for llama.cpp</span>
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<h1>85% smaller LLMs. <em>Better than llama.cpp's own <span class="nw">2-bit</span></em> at the same size.</h1>
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<p class="lede">We compress models you already have to 2 bits, with <b>no retraining</b>. Qwen3-4B goes from <b>8.05 GB to 1.24 GB</b>, and at that size its WikiText-2 perplexity is <b>16.64 against 28.61</b> for llama.cpp's IQ2_XXS (lower is better).</p>
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<div class="cta">
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<a class="btn primary" href="https://github.com/Genrius-AI/pingala8-llm/releases/tag/v2.4-oneclick" target="_blank" rel="noopener">
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<div>
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<h3 style="margin:0 0 12px;font-size:18px">Zero-shot quiz scores, Qwen3-4B</h3>
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<div class="tbl">
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<table style="min-width:540px">
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<thead><tr><th>Test</th><th>8-bit original</th><th>IQ2_XXS</th><th>IQ2_KT</th><th>Ours</th><th>vs IQ2_XXS</th></tr></thead>
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<tbody>
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<tr><td>ARC-Easy</td><td class="num">66.1</td><td class="num">43.9</td><td class="num">53.3</td><td class="num up">60.9</td><td class="num up">+39%</td></tr>
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<div>
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<h3 style="margin:0 0 12px;font-size:18px">Llama-3.1-8B, pre-registered</h3>
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<div class="tbl">
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<table style="min-width:480px">
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<thead><tr><th>Format</th><th>Size</th><th>PPL, BOS</th><th>PPL, strided</th></tr></thead>
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<tbody>
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<tr><td class="name">fp16 original</td><td class="num">16.08 GB</td><td class="num">-</td><td class="num">-</td></tr>
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