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ai-agent-book 精选快照(<2MB 代码与文档,来自 github.com/bojieli/ai-agent-book)
2026-08-20 13:12:50 +00:00

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<text x="390" y="130" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="16" fill="#666666" text-anchor="middle" dominant-baseline="central" font-weight="normal">d × d</text>
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<text x="520" y="140" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="24" fill="#333333" text-anchor="middle" dominant-baseline="central" font-weight="bold">+</text>
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<text x="610" y="108" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="14" fill="#666666" text-anchor="middle" dominant-baseline="central" font-weight="normal">d × r</text>
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<text x="610" y="163" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="16" fill="#333333" text-anchor="middle" dominant-baseline="central" font-weight="bold">Matriz A</text>
<text x="610" y="183" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="14" fill="#666666" text-anchor="middle" dominant-baseline="central" font-weight="normal">r × d</text>
<text x="610" y="197" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="14" fill="#666666" text-anchor="middle" dominant-baseline="central" font-weight="normal">8×4096</text>
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<text x="695" y="140" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="16" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="bold">ΔW = αBA</text>
<text x="280" y="140" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="20" fill="#333333" text-anchor="middle" dominant-baseline="central" font-weight="normal">x →</text>
<text x="695" y="165" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="14" fill="#666666" text-anchor="start" dominant-baseline="central" font-weight="normal">rango r = 8</text>
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<text x="390" y="252" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="20" fill="#333333" text-anchor="middle" dominant-baseline="central" font-weight="bold">Hallazgos prácticos clave (Thinking Machines Lab)</text>
<text x="50" y="282" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="16" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="bold">▸ Tasa de aprendizaje óptima</text>
<text x="50" y="300" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="14" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="normal">LR de LoRA = LR de ajuste fino completo × 10 (aprox. 15× para entrenamiento corto)</text>
<text x="50" y="317" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="16" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="bold">▸ Alcance de aplicación</text>
<text x="50" y="335" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="11" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="normal">Debe aplicarse a todas las capas (especialmente MLP/MoE; aplicarlo solo a las capas de atención degrada significativamente el rendimiento)</text>
<text x="50" y="352" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="16" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="bold">▸ Escenario de RL</text>
<text x="50" y="370" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="14" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="normal">Incluso rank=1 puede lograr el rendimiento de ajuste fino completo (solo O(1) bits absorbidos por episodio)</text>
<text x="50" y="387" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="16" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="bold">▸ Eficiencia computacional</text>
<text x="50" y="405" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="14" fill="#666666" text-anchor="start" dominant-baseline="central" font-weight="normal">FLOPs ≈ 2/3 del ajuste fino completo (2N² vs 3N²); LoRA es mejor al graficar el rendimiento frente a los FLOPs</text>
<text x="50" y="422" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="16" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="bold">▸ Ventajas de inferencia</text>
<text x="50" y="440" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="14" fill="#666666" text-anchor="start" dominant-baseline="central" font-weight="normal">Servicio multitenant: vLLM/SGLang admiten la carga simultánea de múltiples adaptadores</text>
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