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<!DOCTYPE html>
<html>
<head>
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  <meta name="description" content="DeepSeek: Advancing Open-Source Language Models">
  <meta name="keywords" content="DeepSeek, LLM, AI">
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  <title>DeepSeek: Advancing Open-Source Language Models</title>

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<section class="hero">
  <div class="hero-body">
    <div class="container is-max-desktop">
      <div class="columns is-centered">
        <div class="column has-text-centered">
          <h1 class="title is-1 publication-title">DeepSeek: Advancing Open-Source Language Models</h1>
          <div class="is-size-5 publication-authors">
            A collection of groundbreaking research papers in AI and language models
          </div>
        </div>
      </div>
    </div>
  </div>
</section>

<section class="section">
  <div class="container is-max-desktop">
    <!-- Abstract. -->
    <div class="columns is-centered has-text-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Overview</h2>
        <div class="content has-text-justified">
          <p>
            DeepSeek has released a series of significant papers detailing advancements in large language models (LLMs). 
            Each paper represents a step forward in making AI more capable, efficient, and accessible.
          </p>
        </div>
      </div>
    </div>
    <!--/ Abstract. -->

    <!-- Paper Collection -->
    <div class="columns is-centered has-text-centered">
      <div class="column is-four-fifths">
        <h2 class="title is-3">Research Papers</h2>
        
        <!-- Paper 1 -->
        <div class="publication-block">
          <div class="publication-header">
            <h3 class="title is-4">DeepSeekLLM: Scaling Open-Source Language Models with Longer-termism</h3>
            <span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
            <div class="is-size-5 publication-authors">
              Released: November 29, 2023
            </div>
          </div>
          <div class="content has-text-justified">
            <p>This foundational paper explores scaling laws and the trade-offs between data and model size, 
            establishing the groundwork for subsequent models.</p>
          </div>
        </div>

        <!-- Paper 2 -->
        <div class="publication-block">
          <div class="publication-header">
            <h3 class="title is-4">DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model</h3>
            <span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
            <div class="is-size-5 publication-authors">
              Released: May 2024
            </div>
          </div>
          <div class="content has-text-justified">
            <p>Introduces a Mixture-of-Experts (MoE) architecture, enhancing performance while reducing 
            training costs by 42%.</p>
          </div>
        </div>

        <!-- Additional papers following same structure -->
        <div class="publication-block">
          <div class="publication-header">
            <h3 class="title is-4">DeepSeek-V3 Technical Report</h3>
            <span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
            <div class="is-size-5 publication-authors">
              Released: December 2024
            </div>
          </div>
          <div class="content has-text-justified">
            <p>Discusses the scaling of sparse MoE networks to 671 billion parameters.</p>
          </div>
        </div>

        <div class="publication-block">
          <div class="publication-header">
            <h3 class="title is-4">DeepSeek-R1: Incentivizing Reasoning Capability in LLMs</h3>
            <span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
            <div class="is-size-5 publication-authors">
              Released: January 20, 2025
            </div>
          </div>
          <div class="content has-text-justified">
            <p>Enhances reasoning capabilities through large-scale reinforcement learning.</p>
          </div>
        </div>

        <div class="publication-block">
          <div class="publication-header">
            <h3 class="title is-4">DeepSeekMath: Pushing the Limits of Mathematical Reasoning</h3>
            <span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
            <div class="is-size-5 publication-authors">
              Released: April 2024
            </div>
          </div>
          <div class="content has-text-justified">
            <p>Presents methods to improve mathematical reasoning in LLMs.</p>
          </div>
        </div>

        <div class="publication-block">
          <div class="publication-header">
            <h3 class="title is-4">DeepSeek-Prover: Advancing Theorem Proving in LLMs</h3>
            <span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
          </div>
          <div class="content has-text-justified">
            <p>Focuses on enhancing theorem proving capabilities using synthetic data for training.</p>
          </div>
        </div>

        <div class="publication-block">
          <div class="publication-header">
            <h3 class="title is-4">DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models</h3>
            <span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
          </div>
          <div class="content has-text-justified">
            <p>Details advancements in code-related tasks with emphasis on open-source methodologies.</p>
          </div>
        </div>

        <div class="publication-block">
          <div class="publication-header">
            <h3 class="title is-4">DeepSeekMoE: Advancing Mixture-of-Experts Architecture</h3>
            <span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
          </div>
          <div class="content has-text-justified">
            <p>Discusses the integration and benefits of the Mixture-of-Experts approach.</p>
          </div>
        </div>
      </div>
    </div>
  </div>
</section>

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