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<h1 class="title is-1 publication-title">DeepSeek Papers</h1>
<h2 class="subtitle is-3">Advancing Open-Source Language Models</h2>
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<!-- DeepSeekLLM -->
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<h3 class="title is-4">
DeepSeekLLM: Scaling Open-Source Language Models with Longer-termism
<span class="coming-soon-badge">Deep Dive Coming Soon</span>
</h3>
<p class="release-date">Released: November 29, 2023</p>
<p class="paper-description">
This foundational paper explores scaling laws and the trade-offs between data and model size,
establishing the groundwork for subsequent models.
</p>
</div>
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<!-- DeepSeek-V2 -->
<div class="card paper-card">
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<h3 class="title is-4">
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
<span class="coming-soon-badge">Deep Dive Coming Soon</span>
</h3>
<p class="release-date">Released: May 2024</p>
<p class="paper-description">
Introduces a Mixture-of-Experts (MoE) architecture, enhancing performance while reducing
training costs by 42%. Emphasizes strong performance characteristics and efficiency improvements.
</p>
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<h3 class="title is-4">
DeepSeek-V3 Technical Report
<span class="coming-soon-badge">Deep Dive Coming Soon</span>
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<p class="release-date">Released: December 2024</p>
<p class="paper-description">
Discusses the scaling of sparse MoE networks to 671 billion parameters, utilizing mixed precision
training and high-performance computing (HPC) co-design strategies.
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