ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）
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<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>
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<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>
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<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">rank 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">Temuan Praktis Utama (Thinking Machines Lab)</text>
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<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">▸ Learning rate optimal</text>
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<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">LoRA LR = Full fine-tuning LR × 10 (sekitar 15× untuk pelatihan singkat)</text>
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<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">▸ Lingkup penerapan</text>
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<text x="50" y="335" font-family="Arial, 'Helvetica Neue', Helvetica, 'PingFang SC', 'Microsoft YaHei', sans-serif" font-size="13" fill="#333333" text-anchor="start" dominant-baseline="central" font-weight="normal">Harus diterapkan pada semua lapisan (terutama MLP/MoE; hanya menerapkan pada lapisan attention akan menurunkan kinerja secara signifikan)</text>
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<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">▸ Skenario RL</text>
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<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">Bahkan rank=1 dapat mencapai kinerja full fine-tuning (hanya O(1) bit yang diserap per episode)</text>
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<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">▸ Efisiensi komputasi</text>
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<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 dari full fine-tuning (2N² vs 3N²); LoRA lebih baik ketika memplot kinerja terhadap FLOPs</text>
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<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">▸ Keuntungan inferensi</text>
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<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">Layanan multi-tenant: vLLM/SGLang mendukung pemuatan beberapa adaptor secara bersamaan</text>
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