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  <title>DeepSeek Coder</title>
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          <h1 class="title is-2 publication-title">DeepSeek Coder:<br>Let the Code Write Itself</h1>
          <div class="is-size-4 publication-authors">
            <span class="author-block">
              Developed by <a href="https://www.deepseek.com/">DeepSeek AI</a>
          </div>
          <div class="column has-text-centered">
            <div class="publication-links">
              <!-- PDF Link. -->
              <span class="link-block">
                <a href="https://coder.deepseek.com" class="external-link button is-normal is-rounded is-light">
                  <span class="icon"> <img src="static/images/home.png" width="20px"> </span>
                  <span>Chat with DeepSeek Coder</span>
              </a>
              </span>
              <span class="link-block">
                <a href="https://arxiv.org/pdf/2401.14196.pdf" class="external-link button is-normal is-rounded is-light">
                  <span>📑 Technical Report</span>
                </a>
              </span>
              <span class="link-block">
                <a href="https://github.com/deepseek-ai/deepseek-coder/" class="external-link button is-normal is-rounded is-light">
                   <span class="icon">
                    <i class="fab fa-github"></i>
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                  <span>Github</span>
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                <a href="https://huggingface.co/deepseek-ai" class="external-link button is-normal is-rounded is-light">
                   <span class="icon"> <img src="static/images/huggingface_logo.svg" width="20px"> </span>
                  <span>HuggingFace</span>
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              <span class="link-block">
                <a href="https://discord.gg/Tc7c45Zzu5" class="external-link button is-normal is-rounded is-light">
                  <span>Discord</span>
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      <!--div class="hero-body">
        <h3 text-align="left">We introduce <b>DeepSeek Coder</b>, a series of large language model for coding:</h3>
        <ul>
          <li>
            -	Pretrained on <b>2 Trillion</b> tokens over more than 80 programming languages.
          </li>
          <li>
            -	Two versions, <b>Base</b> and <b>Instruct</b> and various model sizes to suit different needs: <b>1B</b>, <b>7B</b>, and <b>33B</b>.
          </li>
          <li>
            -	<b>Open source </b>and<b> free for research and commercial use</b>.
          </li>
          <li>
            -	Features a <b>16K window </b>size, supporting <b>repo-level</b> code completion and infilling.
          </li>
          <li>
            -	<b>State-of-the-Art</b> performance among open code LLMs. The 7B base model reaches the performance of CodeLLama-34B.
          </li>
        </ul>
        <img src="static/images/result3.png" alt="Description of the GIF" class="center" width="75%" />
        <h4 class="subtitle has-text-centered">
          State-of-the-art Perforamnce on Multilingual HumanEval
        </h4>
        <img src="static/images/completion_demo.gif" alt="Description of the GIF" />
        <h4 class="subtitle has-text-centered">
          DeepSeek Coder empowers users to code efficiently and solve various computer problems easily
        </h4>
      </div-->
    </div>
  </div>
</section>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            DeepSeek Coder comprises a series of code language models trained from scratch on both 87% code and 13% natural language in English and Chinese, with each model pre-trained on 2T tokens.
            We provide various sizes of the code model, ranging from 1B to 33B versions.
            Each model is pre-trained on repo-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, resulting in foundational models (DeepSeek-Coder-Base).
            We further fine-tune the base model with 2B tokens of instruction data to get instruction-tuned models, namedly DeepSeek-Coder-Instruct.
          </p>
          <!-- <p>
            For coding capabilities, DeepSeek-Coder-Base achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
            And Compared to GPT35-turbo, DeepSeek-Coder-Instruct demonstrates superior performance in human evaluation while maintaining comparable performance in MBPP.
          </p> -->
          <ul>
            <li>
              Pretrained on <b>2 Trillion</b> tokens over more than 80 programming languages.
            </li>
            <li>
              Various model sizes (<b>1.3B</b>, <b>5.7B</b>, <b>6.7B</b> and <b>33B</b>) to support different requirements.
            </li>
            <li>
              A window size of <b>16K window </b>size, supporting <b>project-level</b> code completion and infilling.
            </li>
            <li>
              <b>State-of-the-Art</b> performance among open code models.
            </li>
            <li>
              <b>Open source and free for research and commercial use</b>.
            </li>
          </ul>

        </div>
      </div>
    </div>
    <img src="static/images/code_chat.gif" alt="Description of the GIF" class="center" width="90%" >
    <!--/ Abstract. -->
  </div>
</section>

<section class="section">
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          <h4 class="title is-5">Code Generation</h4>
          <p>
            Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
            would be impossible without nerfies since it would require going through a wall.
          </p>
          <video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
            <source src="./static/videos/dollyzoom-stacked.mp4"
                    type="video/mp4">
          </video>
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        <h4 class="title is-5">Tests Generatation</h4>
        <div class="columns is-centered">
          <div class="column content">
            <p>
              As a byproduct of our method, we can also solve the matting problem by ignoring
              samples that fall outside of a bounding box during rendering.
            </p>
            <video id="matting-video" controls playsinline height="100%">
              <source src="./static/videos/matting.mp4"
                      type="video/mp4">
            </video>
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        </div>
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          <h2 class="title is-5">Demo Case 3</h2>
          <p>
            Using <i>nerfies</i> you can create fun visual effects. This Dolly zoom effect
            would be impossible without nerfies since it would require going through a wall.
          </p>
          <video id="dollyzoom" autoplay controls muted loop playsinline height="100%">
            <source src="./static/videos/dollyzoom-stacked.mp4"
                    type="video/mp4">
          </video>
        </div>
      </div>
      <div class="column">
        <h2 class="title is-5">Demo Case 4</h2>
        <div class="columns is-centered">
          <div class="column content">
            <p>
              As a byproduct of our method, we can also solve the matting problem by ignoring
              samples that fall outside of a bounding box during rendering.
            </p>
            <video id="matting-video" controls playsinline height="100%">
              <source src="./static/videos/matting.mp4"
                      type="video/mp4">
            </video>
          </div>
        </div>
      </div>
    </div> -->
    <div class="columns is-centered">
      <div class="column is-full-width">
        <h2 class="title is-3">Performance</h2>
        <p>
          We evaluate DeepSeek Coder on various coding-related benchmarks.
          The result shows that DeepSeek-Coder-Base-33B significantly outperforms existing open-source code LLMs.
          Compared with CodeLLama-34B, it leads by 7.9%, 9.3%, 10.8% and 5.9% respectively on HumanEval Python, HumanEval Multilingual, MBPP and DS-1000.
          Surprisingly, our DeepSeek-Coder-Base-7B reaches the performance of CodeLlama-34B.
          And the DeepSeek-Coder-Instruct-33B model after instruction tuning outperforms GPT-3.5-turbo on HumanEval and achieves comparable result with GPT-3.5-turbo on MBPP.
        </p>
        <br>
        <div class="image-container">
          <img src="static/images/table2.png" alt="图像2">
          <img src="static/images/result3.png" alt="图像1">
        </div>
        <br>
        <h4 class="subtitle has-text-centered">
          Fig. State-of-the-art Performance on various Coding Benchmarks and Multilingual HumanEval
        </h4>
        <br>
        <h3 class="title is-4">(1) Performance of different Code LLMs on Multilingual HumanEval Benchmark</h3>
        <img src="./static/images/HumanEval.png" alt="HumanEval" class="left" width="90%">
        <br>
        <br>
        <h3 class="title is-4">(2) Performance of different Code LLMs on MBPP Benchmark</h3>
        <img src="./static/images/MBPP.png" alt="MBPP" class="left" width="35%"/>
        <br>
        <br>
        <h3 class="title is-4">(3) Performance of different Code LLMs on DS-1000 Benchmark</h3>
        <img src="./static/images/DS-1000.png" alt="DS-1000" class="left" width="90%"/>
        <br>
        <br>
        <h3 class="title is-4">(4) Performance of different Code Models on Math-Reasoning Tasks.</h3>
        <img src="./static/images/Math.png" alt="math" class="left" width="90%"/>
      </div>
    </div>
    <!--/ Animation. -->

    <div class="columns is-centered">
      <div class="column is-full-width">
        <h2 class="title is-3">How to Use DeepSeek Coder</h2>
        <ul>
          <li>- Try now, please visit our <a href="https://coder.deepseek.com/chat">[<img src="static/images/home.png" width="20px"> DeepSeek-Coder]</a>.</li>
          <li>- More details and evaluations are available on our </i></span><a href="https://github.com/deepseek-ai/DeepSeek-Coder">[<img src="static/images/github-logo.png" width="16px"> Github]</a>.</li>
          <li>- Model weights are also available on <a href="https://huggingface.co/deepseek-ai">[🤗 Huggingface]</a> </li>
        </ul>
      </div>
    </div>
    <!-- Concurrent Work. -->
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        <h2 class="title is-3">Contact Us</h2>
        <div class="content has-text-justified">
          <p>
            If you have any questions, please raise an issue or contact us at <a href="mailto:agi_code@deepseek.com">agi_code@deepseek.com</a>.
          </p>
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