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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 --> | |
| <div class="card paper-card"> | |
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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. | |
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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. | |
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| <!-- Continue with other papers... --> | |
| <div class="card paper-card"> | |
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| <h3 class="title is-4"> | |
| DeepSeek-V3 Technical Report | |
| <span class="coming-soon-badge">Deep Dive Coming Soon</span> | |
| </h3> | |
| <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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| <!-- Add remaining papers following the same pattern --> | |
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