Minor refactorings to the LFM2.5 cookbook for accuracy (#28072)
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@@ -256,12 +256,12 @@ in the GitHub search bar.
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>LFM2</strong> (350M, 1.2B)</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>LiquidAI/LFM2.5-1.2B-Instruct</code></td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Liquid AI's hybrid attention + short convolution language model.</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Liquid AI's hybrid language model combining gated short convolutions with a small number of grouped query attention (GQA) blocks.</td>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>LFM2-MoE</strong> (8B-A1B, 24B-A2B)</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>LiquidAI/LFM2-8B-A1B</code></td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Liquid AI's Mixture-of-Experts variant with sigmoid routing and top-k expert selection.</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Liquid AI's sparse Mixture-of-Experts variant of the LFM2 hybrid backbone, with SwiGLU experts, normalized sigmoid routing, and top-k expert selection.</td>
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>Falcon-H1</strong> (0.5B–34B)</td>
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@@ -198,7 +198,7 @@ in the GitHub search bar.
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<td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><strong>LFM2-VL</strong></td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}><code>LiquidAI/LFM2.5-VL-1.6B</code></td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Liquid AI's vision-language model combining a SigLip2 vision encoder (NaFlex variable-resolution) with the LFM2 hybrid attention + short convolution language model. Supports multi-image inputs.</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}>Liquid AI's vision-language model combining a SigLIP2 NaFlex vision encoder (variable resolution, native aspect ratio) with the LFM2 hybrid gated short conv + GQA language model. Supports multi-image inputs.</td>
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<td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}></td>
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