Model Overview Card for DeepSeek R1

Basic Information

• Model Name: DeepSeek R1
• Developer/Creator: DeepSeek AI
• Release Date: January 21, 2025
• Version: 1.0
• Model Type: Large Language Model (LLM) focused on reasoning

Description

Overview:

DeepSeek R1 is a cutting-edge reasoning model developed by DeepSeek AI, designed to excel in complex problem-solving, mathematical reasoning, and programming assistance. Leveraging a Mixture-of-Experts (MoE) architecture, the model activates only a subset of its parameters for each token processed, allowing for efficient computation while maintaining high performance across various tasks.

Key Features:

• Mixture-of-Experts Architecture: Activates 37 billion out of 671 billion parameters per token, optimizing resource usage.
• Chain-of-Thought Reasoning: Capable of breaking down complex problems into smaller, manageable steps for enhanced clarity and accuracy.
• High Performance on Benchmarks: Achieves remarkable scores on various benchmarks such as 91.6% on the MATH benchmark and competitive ratings on coding challenges.
• Reinforcement Learning Training: Utilizes pure reinforcement learning for training without extensive supervised fine-tuning, enhancing its reasoning capabilities.
• Open Source Licensing: Available under the MIT license, allowing for unrestricted use and modification.

Intended Use:

DeepSeek R1 is intended for software developers, data scientists, and researchers who require advanced reasoning capabilities in their applications. It is particularly useful for tasks involving mathematical computations, coding challenges, and logical problem-solving.

Language Support:

The model primarily supports English but can accommodate multiple languages depending on user requirements.

Technical Details

Architecture:

DeepSeek R1 employs a Mixture-of-Experts (MoE) architecture that allows it to activate only a portion of its parameters during each forward pass. This design choice significantly reduces computational costs while maintaining high performance levels.

Training Data:

The model was trained on a large dataset consisting of diverse programming languages and mathematical problems.

• Data Source and Size: The training dataset includes 14.8 trillion tokens sourced from various publicly available code repositories and mathematical texts.
• Diversity and Bias: The training data was curated to minimize biases while maximizing diversity in topics and styles, ensuring robust performance across different scenarios.

Performance Metrics:

Usage

Code Samples:

The model is available on the AI/ML API platform as "DeepSeek R1 .

API Documentation:

Detailed API Documentation is available here.

Ethical Guidelines

DeepSeek AI emphasizes ethical considerations in AI development by promoting transparency regarding the model's capabilities and limitations. The organization encourages responsible usage to prevent misuse or harmful applications of generated content.

Licensing

DeepSeek R1 is available under an open-source MIT license that allows both research and commercial usage rights while ensuring compliance with ethical standards regarding creator rights

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Get DeepSeek R1 API here.