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  <!DOCTYPE html>
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  <head>
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- <meta charset="UTF-8">
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- <meta name="viewport" content="width=device-width, initial-scale=1.0">
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- <title>DeepSeek Papers</title>
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  </head>
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  <body>
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- <header>
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- <h1>DeepSeek Papers</h1>
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- </header>
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- <div class="container">
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- <h2>DeepSeek Research Contributions</h2>
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- <p>Below is a list of significant papers by DeepSeek detailing advancements in large language models (LLMs). Each paper includes a brief description and highlights upcoming deep dives.</p>
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- <!-- Paper List -->
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- <div class="paper">
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- <a href="#">DeepSeekLLM: Scaling Open-Source Language Models with Longer-termism</a>
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- <span class="coming-soon">[Deep Dive Coming Soon]</span>
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- <p><strong>Release Date:</strong> November 29, 2023<br>
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- This foundational paper explores scaling laws and the trade-offs between data and model size, establishing the groundwork for subsequent models.</p>
74
- </div>
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- <div class="paper">
76
- <a href="#">DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model</a>
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- <span class="coming-soon">[Deep Dive Coming Soon]</span>
78
- <p><strong>Release Date:</strong> May 2024<br>
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- This paper introduces a Mixture-of-Experts (MoE) architecture, enhancing performance while reducing training costs by 42%.</p>
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- </div>
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- <div class="paper">
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- <a href="#">DeepSeek-V3 Technical Report</a>
83
- <span class="coming-soon">[Deep Dive Coming Soon]</span>
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- <p><strong>Release Date:</strong> December 2024<br>
85
- This report discusses the scaling of sparse MoE networks to 671 billion parameters, utilizing mixed precision training and HPC co-design strategies.</p>
86
- </div>
87
- <div class="paper">
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- <a href="#">DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning</a>
89
- <span class="coming-soon">[Deep Dive Coming Soon]</span>
90
- <p><strong>Release Date:</strong> January 20, 2025<br>
91
- The R1 model enhances reasoning capabilities through large-scale reinforcement learning, competing directly with leading models like OpenAI's o1.</p>
92
  </div>
93
- <div class="paper">
94
- <a href="#">DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models</a>
95
- <span class="coming-soon">[Deep Dive Coming Soon]</span>
96
- <p><strong>Release Date:</strong> April 2024<br>
97
- This paper presents methods to improve mathematical reasoning in LLMs, introducing the Group Relative Policy Optimization (GRPO) algorithm.</p>
98
- </div>
99
- <div class="paper">
100
- <a href="#">DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data</a>
101
- <span class="coming-soon">[Deep Dive Coming Soon]</span>
102
- <p>Focuses on enhancing theorem proving capabilities in language models using synthetic data for training.</p>
 
 
 
 
 
 
 
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  </div>
104
- <div class="paper">
105
- <a href="#">DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence</a>
106
- <span class="coming-soon">[Deep Dive Coming Soon]</span>
107
- <p>This paper details advancements in code-related tasks with an emphasis on open-source methodologies, improving upon earlier coding models.</p>
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  </div>
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- <div class="paper">
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- <a href="#">DeepSeekMoE</a>
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- <span class="coming-soon">[Deep Dive Coming Soon]</span>
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- <p>Discusses the integration and benefits of the Mixture-of-Experts approach within the DeepSeek framework.</p>
 
 
 
 
 
 
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  </div>
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  </div>
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- <footer>
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- &copy; 2025 DeepSeek Research. All rights reserved.
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- </footer>
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  </body>
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  </html>
 
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  <!DOCTYPE html>
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+ <html>
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  <head>
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+ <meta charset="utf-8">
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+ <meta name="description" content="DeepSeek: Advancing Open-Source Language Models">
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+ <meta name="keywords" content="DeepSeek, LLM, AI">
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+ <meta name="viewport" content="width=device-width, initial-scale=1">
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+ <title>DeepSeek: Advancing Open-Source Language Models</title>
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+
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+ <link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro" rel="stylesheet">
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+ <link rel="stylesheet" href="./static/css/bulma.min.css">
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+ <link rel="stylesheet" href="./static/css/bulma-carousel.min.css">
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+ <link rel="stylesheet" href="./static/css/bulma-slider.min.css">
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+ <link rel="stylesheet" href="./static/css/index.css">
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+ <link rel="icon" href="./static/images/favicon.svg">
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+
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+ <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
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+ <script defer src="./static/js/fontawesome.all.min.js"></script>
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  <body>
 
 
 
 
 
 
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+ <section class="hero">
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+ <div class="hero-body">
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+ <div class="container is-max-desktop">
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+ <div class="columns is-centered">
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+ <div class="column has-text-centered">
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+ <h1 class="title is-1 publication-title">DeepSeek Papers</h1>
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+ <div class="is-size-5 publication-authors">
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+ Advancing Open-Source Language Models
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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>
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+ </section>
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+
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+ <section class="section">
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+ <div class="container is-max-desktop">
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+ <!-- Abstract. -->
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+ <div class="columns is-centered has-text-centered">
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+ <div class="column is-four-fifths">
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+ <h2 class="title is-3">DeepSeek Research Contributions</h2>
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+ <div class="content has-text-justified">
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+ <p>
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+ Below is a list of significant papers by DeepSeek detailing advancements in large language models (LLMs),
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+ ordered by release date from most recent to oldest. Each paper includes a brief description and highlights
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+ upcoming deep dives.
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+ </p>
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+ </div>
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+ </div>
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  </div>
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+ <!--/ Abstract. -->
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+
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+ <!-- Paper Collection -->
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+ <div class="columns is-centered">
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+ <div class="column is-four-fifths">
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+ <div class="content">
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+ <div class="publication-list">
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+ <!-- Papers in chronological order -->
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+ <div class="publication-item">
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+ <div class="publication-title">
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+ <a href="#">DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning</a>
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+ <span class="tag is-info is-light">[Deep Dive Coming Soon]</span>
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+ </div>
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+ <div class="publication-info">
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+ <strong>Release Date:</strong> January 20, 2025
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+ </div>
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+ <div class="publication-description">
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+ The R1 model enhances reasoning capabilities through large-scale reinforcement learning, competing
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+ directly with leading models like OpenAI's o1.
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+ </div>
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+ </div>
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+
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+ <div class="publication-item">
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+ <div class="publication-title">
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+ <a href="#">DeepSeek-V3 Technical Report</a>
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+ <span class="tag is-info is-light">[Deep Dive Coming Soon]</span>
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+ </div>
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+ <div class="publication-info">
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+ <strong>Release Date:</strong> December 2024
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+ </div>
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+ <div class="publication-description">
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+ This report discusses the scaling of sparse MoE networks to 671 billion parameters, utilizing mixed
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+ precision training and HPC co-design strategies.
90
+ </div>
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+ </div>
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+
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+ <div class="publication-item">
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+ <div class="publication-title">
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+ <a href="#">DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model</a>
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+ <span class="tag is-info is-light">[Deep Dive Coming Soon]</span>
97
+ </div>
98
+ <div class="publication-info">
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+ <strong>Release Date:</strong> May 2024
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+ </div>
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+ <div class="publication-description">
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+ This paper introduces a Mixture-of-Experts (MoE) architecture, enhancing performance while reducing
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+ training costs by 42%.
104
+ </div>
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+ </div>
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+
107
+ <div class="publication-item">
108
+ <div class="publication-title">
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+ <a href="#">DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models</a>
110
+ <span class="tag is-info is-light">[Deep Dive Coming Soon]</span>
111
+ </div>
112
+ <div class="publication-info">
113
+ <strong>Release Date:</strong> April 2024
114
+ </div>
115
+ <div class="publication-description">
116
+ This paper presents methods to improve mathematical reasoning in LLMs, introducing the Group
117
+ Relative Policy Optimization (GRPO) algorithm.
118
+ </div>
119
+ </div>
120
+
121
+ <div class="publication-item">
122
+ <div class="publication-title">
123
+ <a href="#">DeepSeekLLM: Scaling Open-Source Language Models with Longer-termism</a>
124
+ <span class="tag is-info is-light">[Deep Dive Coming Soon]</span>
125
+ </div>
126
+ <div class="publication-info">
127
+ <strong>Release Date:</strong> November 29, 2023
128
+ </div>
129
+ <div class="publication-description">
130
+ This foundational paper explores scaling laws and the trade-offs between data and model size,
131
+ establishing the groundwork for subsequent models.
132
+ </div>
133
+ </div>
134
+
135
+ <div class="publication-item">
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+ <div class="publication-title">
137
+ <a href="#">DeepSeek-Prover: Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data</a>
138
+ <span class="tag is-info is-light">[Deep Dive Coming Soon]</span>
139
+ </div>
140
+ <div class="publication-description">
141
+ Focuses on enhancing theorem proving capabilities in language models using synthetic data for training.
142
+ </div>
143
+ </div>
144
+
145
+ <div class="publication-item">
146
+ <div class="publication-title">
147
+ <a href="#">DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence</a>
148
+ <span class="tag is-info is-light">[Deep Dive Coming Soon]</span>
149
+ </div>
150
+ <div class="publication-description">
151
+ This paper details advancements in code-related tasks with an emphasis on open-source methodologies,
152
+ improving upon earlier coding models.
153
+ </div>
154
+ </div>
155
+
156
+ <div class="publication-item">
157
+ <div class="publication-title">
158
+ <a href="#">DeepSeekMoE</a>
159
+ <span class="tag is-info is-light">[Deep Dive Coming Soon]</span>
160
+ </div>
161
+ <div class="publication-description">
162
+ Discusses the integration and benefits of the Mixture-of-Experts approach within the DeepSeek framework.
163
+ </div>
164
+ </div>
165
+ </div>
166
+ </div>
167
+ </div>
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  </div>
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+ </div>
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+ </section>
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+
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+ <footer class="footer">
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+ <div class="container">
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+ <div class="content has-text-centered">
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+ <p>
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+ This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
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+ Commons Attribution-ShareAlike 4.0 International License</a>.
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+ </p>
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  </div>
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+ </footer>
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+
 
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  </html>