Model Overview Card for DeepSeek-V3

Basic Information

• Model Name: DeepSeek-V3
• Developer/Creator: DeepSeek AI
• Release Date: December 26, 2024
• Version: 1.0
• Model Type: Large Language Model (LLM)

Description

Overview:

DeepSeek-V3 is a state-of-the-art large language model developed by DeepSeek AI, designed to deliver exceptional performance in natural language understanding and generation. Utilizing a Mixture-of-Experts (MoE) architecture, this model boasts an impressive 671 billion parameters, with only 37 billion activated per token, allowing for efficient processing and high-quality output across a range of tasks.

Key Features:

• Mixture-of-Experts Architecture: Employs a dynamic activation mechanism that activates only the necessary parameters for each task, optimizing resource utilization.
• Multi-Head Latent Attention (MLA): Enhances context understanding by extracting key details multiple times, improving accuracy and efficiency.
• Multi-Token Prediction (MTP): Generates several tokens simultaneously, significantly speeding up inference and enhancing performance on complex benchmarks.
• Exceptional Performance Metrics: Achieves high scores across various benchmarks, including MMLU (87.1%), BBH (87.5%), and mathematical reasoning tasks.
• Efficient Training: Requires only 2.788 million GPU hours for full training, demonstrating remarkable cost-effectiveness.

Intended Use:

DeepSeek-V3 is designed for developers and researchers looking to implement advanced natural language processing capabilities in applications such as chatbots, educational tools, content generation, and coding assistance.

Language Support:

The model supports multiple languages, enhancing its applicability in diverse linguistic contexts.

Technical Details

Architecture:

DeepSeek-V3 utilizes a Mixture-of-Experts (MoE) architecture that allows for efficient processing by activating only a subset of its parameters based on the task at hand. This architecture is complemented by Multi-Head Latent Attention (MLA) to improve context understanding.

Training Data:

The model was trained on a comprehensive dataset consisting of 14.8 trillion tokens sourced from diverse and high-quality texts.

• Data Source and Size: The training data encompasses a wide range of topics and genres to ensure robustness and versatility in responses.
• Diversity and Bias: The training data was curated to minimize biases while maximizing diversity in topics and styles, enhancing the model's effectiveness in generating varied outputs.

Performance Metrics and Comparison to Other Models:

Usage

Code Samples:

The model is available on the AI/ML API platform as "DeepSeek V3" .

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-V3 is available under an open-source license that allows both research and commercial usage rights while ensuring compliance with ethical standards regarding creator rights.

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Get DeepSeek V3 API here.