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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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<title>KIMI K2: Open Agentic Intelligence</title> |
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:root { |
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display: grid; |
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background: rgba(30, 64, 175, 0.2); |
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transform: translateY(-5px); |
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box-shadow: 0 10px 20px -5px rgba(0, 0, 0, 0.2); |
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} |
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.tech-icon { |
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color: var(--accent); |
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font-weight: 600; |
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margin-bottom: 0.5rem; |
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} |
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.tech-desc { |
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font-size: 0.9rem; |
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opacity: 0.8; |
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@media (max-width: 768px) { |
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.content { |
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grid-template-columns: 1fr; |
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} |
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.title { |
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font-size: 2.5rem; |
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} |
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.stats, .tech-grid { |
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grid-template-columns: 1fr; |
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} |
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} |
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</style> |
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</head> |
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<body> |
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<div class="poster"> |
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<div class="header"> |
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<h1 class="title">KIMI K2</h1> |
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<h2 class="subtitle">Open Agentic Intelligence</h2> |
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<p class="team">Technical Report · Kimi Team</p> |
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</div> |
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<div class="content"> |
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<div class="abstract"> |
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<h3 class="abstract-title">ABSTRACT</h3> |
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<p class="abstract-text">We introduce Kimi K2, a Mixture-of-Experts (MoE) large language model with 32 billion activated parameters and 1 trillion total parameters. We propose the MuonClip optimizer, which improves upon Muon with a novel QK-clip technique to address training instability while enjoying the advanced token efficiency of Muon.</p> |
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<p class="abstract-text">Kimi K2 achieves state-of-the-art performance among open-source non-thinking models, with strengths in agentic capabilities. Notably, K2 obtains 66.1 on Tau2-Bench, 76.5 on ACEBench (En), 65.8 on SWE-Bench Verified, and 47.3 on SWE-Bench Multilingual — surpassing most open and closed-sourced baselines in non-thinking settings.</p> |
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</div> |
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<div class="highlights"> |
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<h3 class="highlights-title">KEY HIGHLIGHTS</h3> |
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<div class="highlight-item"> |
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<div class="highlight-icon"><i class="fas fa-brain"></i></div> |
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<div> |
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<strong>1.04 Trillion Parameters</strong> - MoE architecture with 32B activated parameters |
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</div> |
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</div> |
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<div class="highlight-item"> |
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<div class="highlight-icon"><i class="fas fa-chart-line"></i></div> |
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<div> |
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<strong>15.5 Trillion Tokens</strong> - Pre-trained with zero loss spikes |
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</div> |
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</div> |
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<div class="highlight-item"> |
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<div class="highlight-icon"><i class="fas fa-robot"></i></div> |
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<div> |
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<strong>Agentic Capabilities</strong> - Superior performance in software engineering and agentic tasks |
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</div> |
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</div> |
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</div> |
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</div> |
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<div class="stats"> |
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<div class="stat-item"> |
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<div class="stat-value">66.1</div> |
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<div class="stat-label">Tau2-Bench</div> |
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</div> |
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<div class="stat-item"> |
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<div class="stat-value">76.5</div> |
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<div class="stat-label">ACEBench (En)</div> |
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</div> |
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<div class="stat-item"> |
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<div class="stat-value">65.8</div> |
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<div class="stat-label">SWE-Bench Verified</div> |
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</div> |
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<div class="stat-item"> |
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<div class="stat-value">47.3</div> |
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<div class="stat-label">SWE-Bench Multilingual</div> |
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</div> |
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</div> |
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<div class="image-container"> |
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<img src="https://cdn.vansin.top/picgo/2301e0231d5e471d6d6231ca53eaccfb06089892a33c2d2311d5805ee440c93a.jpg" alt="Kimi K2 Results"> |
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</div> |
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<div class="tech-grid"> |
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<div class="tech-item"> |
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<div class="tech-icon"><i class="fas fa-cogs"></i></div> |
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<h4 class="tech-title">MuonClip Optimizer</h4> |
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<p class="tech-desc">Novel QK-clip technique for stable training</p> |
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</div> |
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<div class="tech-item"> |
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<div class="tech-icon"><i class="fas fa-database"></i></div> |
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<h4 class="tech-title">Agentic Data Pipeline</h4> |
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<p class="tech-desc">Large-scale synthesis of tool-use demonstrations</p> |
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</div> |
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<div class="tech-item"> |
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<div class="tech-icon"><i class="fas fa-robot"></i></div> |
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<h4 class="tech-title">RL Framework</h4> |
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<p class="tech-desc">Combines verifiable rewards with self-critique</p> |
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</div> |
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</div> |
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<div class="footer"> |
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Created by <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank">AnyCoder</a> |
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</div> |
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</div> |
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</body> |
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</html> |
