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@@ -177,7 +177,7 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
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<li class="sidebar-item">
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<div class="sidebar-item-container">
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<a href="../docs/cli.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">CLI Reference</span></a>
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<span class="menu-text">Command Line Interface (CLI)</span></a>
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</div>
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</li>
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<li class="sidebar-item">
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@@ -185,6 +185,12 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
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<a href="../docs/config.html" class="sidebar-item-text sidebar-link">
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<span class="menu-text">Config Reference</span></a>
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</div>
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</li>
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<li class="sidebar-item">
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<div class="sidebar-item-container">
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<a href="../docs/api" class="sidebar-item-text sidebar-link">
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<span class="menu-text">API Reference</span></a>
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</div>
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</li>
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@@ -644,7 +650,8 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
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<p>See https://github.com/cognitivecomputations/spectrum</p>
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<section id="overview" class="level3">
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<h3 class="anchored" data-anchor-id="overview">Overview</h3>
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<p>Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models. By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.</p>
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<p>Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.
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By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.</p>
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</section>
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<section id="usage-5" class="level3">
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<h3 class="anchored" data-anchor-id="usage-5">Usage</h3>
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