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| <div class="hero">
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| <div class="cat Lion">Lion Optimized</div>
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| <h1>AXL-Code-1B-Lion</h1>
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| <p class="desc">The largest Lion model. 318M params trained in 20 min. PPL 1.90. Context 256 bytes.</p>
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| </div>
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| <div class="ms">
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| <div class="mc"><div class="v">318M</div><div class="l">Parameters</div></div>
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| <div class="mc"><div class="v">1.90</div><div class="l">Perplexity</div></div>
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| <div class="mc"><div class="v">20 min</div><div class="l">Training</div></div>
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| <div class="mc"><div class="v">636 MB</div><div class="l">GGUF</div></div>
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| <input type="radio" name="t" id="t1" checked><label for="t1" class="tl">Specs</label>
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| <input type="radio" name="t" id="t2"><label for="t2" class="tl">Training</label>
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| <input type="radio" name="t" id="t3"><label for="t3" class="tl">Usage</label>
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| <div class="p p1">
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| <table>
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| <tr><th>Property</th><th>Value</th></tr>
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| <tr><td>Architecture</td><td>Multi-Scale Transformer</td></tr>
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| <tr><td>d_model</td><td>?</td></tr>
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| <tr><td>Attention Heads</td><td>?</td></tr>
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| <tr><td>Layers per Scale</td><td>?</td></tr>
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| <tr><td>Context Window</td><td>256 bytes</td></tr>
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| <tr><td>Downsample Factors</td><td>[1, 2, 4]</td></tr>
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| <tr><td>Vocab Size</td><td>258 (byte-level)</td></tr>
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| <tr><td>Optimizer</td><td>Lion</td></tr>
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| </table>
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| <div class="story">Trained on 50MB real HF Python code. 421 steps, 20 min. Lion vs SGD: PPL 1.90 vs 31.22.</div>
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| <table>
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| <tr><th>Metric</th><th>Value</th></tr>
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| <tr><td>Final Loss</td><td>0.6338</td></tr>
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| <tr><td>Perplexity</td><td>1.90</td></tr>
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| <tr><td>Training Steps</td><td>421</td></tr>
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| <tr><td>Training Time</td><td>20 min</td></tr>
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| </table>
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| </div>
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| <div class="p p3">
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| <h3 style="color:#fff;margin-bottom:12px">Usage</h3>
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| <pre><code>ollama create axl-code-1b-lion -f Modelfile
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| ollama run axl-code-1b-lion "def fibonacci():"</code></pre>
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| <div class="note">Best overall code generation. General-purpose code completion.</div>
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| </div>
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| <div class="p p4">
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| <table>
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| <tr><th>File</th><th>Size</th><th>Format</th></tr>
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| <tr><td>F16 GGUF</td><td>636 MB</td><td>Full precision</td></tr>
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| <tr><td>Q4_K_M GGUF</td><td>197 MB</td><td>4-bit quantized</td></tr>
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| </table>
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| <div class="note" style="margin-top:16px">GGUF files work with Ollama and llama.cpp. Q4_K_M is about 3x smaller than F16.</div>
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