DeepSeek is a series of AI models developed by

The Transformer architecture serves as the foundation of DeepSeek, functioning similarly to a super information processor capable of handling various sequential data such as text and speech. At its core lies the attention mechanism, which operates much like human focus when reading lengthy articles by automatically concentrating on important sections. The Transformer’s attention mechanism enables the model to zero in on key content while processing large volumes of information, thereby understanding relationships between pieces of information regardless of their proximity or distance[1].

This represents an enhancement over traditional attention mechanisms. When dealing with long texts, such as research papers or novels, the MLA mechanism can more precisely assign weights to sentences and paragraphs, pinpointing the core meaning of the text without getting distracted like traditional mechanisms tend to do. For instance, in machine translation when translating long documents, it accurately grasps the significance of each word within its context, ensuring precise translation into the target language. Furthermore, in DeepSeek – V3, through the use of low-rank joint compression mechanism, MLA condenses Key – Value matrices into low-dimensional potential vectors, significantly reducing memory consumption[2].

In the MoE architecture, different expert modules may experience uneven workload distribution. The weight-free loss-based load balancing strategy effectively addresses this issue by making the workload of each expert module more uniform, avoiding situations where some modules are overloaded while others remain idle, and thus enhances the overall model performance [1].

Traditional models typically predict tokens one by one, but DeepSeek’s Multi-Token Prediction technology can predict multiple tokens at once, much like how people often speak several words consecutively to convey a complete idea. This approach enables the model to reason faster and generate more coherent content [1].

During the model training process, data precision is crucial. FP8 mixed precision training is an innovative training method that allows models to use more suitable data precision during training, reducing computational load while maintaining training accuracy, thereby saving time and costs, and making large-scale model training easier. It also makes training feasible and effective on extremely large models, as demonstrated by DeepSeek – V3 through the FP8 mixed precision training framework [2]

Essentially, it is the process of transferring the knowledge learned by a large model to a smaller model, similar to a teacher imparting knowledge to a student. For example, DeepSeek – R1 uses knowledge distillation to transfer the capabilities of long-chain reasoning models to standard LLMs, thereby enhancing their inference abilities [1]

For training R1 – Zero, pure reinforcement learning is adopted to allow the model to learn through trial and error. For example, in game scenarios, the model attempts different operations and judges its own right or wrong based on rewards or penalties provided by the game, gradually finding the best operating methods. However, this training method leads to some issues with model outputs, such as endless repetition and poor readability. Nevertheless, it opens up new directions for model training[1]

DeepSeek – R1 introduces multi-stage training and cold start data, which helps improve model performance. However, the specific mechanisms remain unclear, as there is limited publicly available information explaining its detailed principles, leaving further investigation needed[1]

The DeepSeek model, with its powerful technical architecture, has demonstrated extensive application scenarios and outstanding performance across various fields.

• Smart Customer Service System Development: A technology company utilized DeepSeek – V3 to develop an intelligent customer service system. Thanks to its outstanding performance in natural language processing, it can accurately analyze and understand users’ intentions, providing high-quality responses. This application significantly improved customer satisfaction, resolved numerous issues in the enterprise’s customer service processes, and contributed to enhancing operational efficiency[7].
• Long Text Analysis and Summarization: A legal technology company used DeepSeek – V3 to analyze massive amounts of legal documents and generate summaries. Leveraging the model’s strong capabilities in handling long texts, such as supporting input up to 128K tokens, it effectively addresses complex and lengthy legal documents, aiding legal professionals in quickly accessing key information. This significantly enhances case analysis speed, legal research efficiency, and information extraction efficiency[7].
• Text Translation: In the professional field of machine translation, DeepSeek’s multi-head potential attention (MLA) mechanism accurately understands the precise meaning of each word in the source language text within its context. This enables more accurate translation into the target language. It is not limited to general short-text translation tasks but also excels in accuracy and efficiency for long document translations.

• A developer uses DeepSeek – V3 to automatically generate Python code, such as creating a simple calculator implementation. This process significantly reduces development time and improves efficiency because DeepSeek – V3 excels in code generation and multi-language programming evaluation. It demonstrates strong code generation capabilities by understanding programming logic requirements and generating usable code segments, surpassing multiple competitors. It is suitable for various scenarios, including beginners writing basic code or experienced developers quickly generating code templates [7]

A research team utilized DeepSeek – V3 to process datasets containing images and text, achieving automatic generation and description of multimedia content. This was made possible by DeepSeek – V3’s hybrid expert architecture, enabling efficient multi-modal data processing, integrating image and text information for in-depth analysis, thereby advancing the development of multi-modal AI applications. This progress holds significant importance for scenarios requiring comprehensive handling of both images and text, with vast potential applications in areas such as digital media content creation and intelligent image annotation[7].

• Accuracy Improvement: DeepSeek – V3 adopted multi-head potential attention (MLA) and DeepSeekMoE technology during training, significantly enhancing model performance and accuracy. In Hungary’s latest high school math exam test, its open-source large model achieved a high score of 65, surpassing the same-level LLaMA – 2 model and approaching the level of GPT – 4, demonstrating outstanding understanding and computing capabilities, with exceptional performance in mathematical reasoning and other areas such as inference and programming. It has excelled in multiple Chinese-English public evaluation benchmarks [14]
• Effective Handling of Long Texts: Supports long context expansion, capable of processing input texts up to 128K in length, which is highly beneficial for scenarios such as long document handling and long conversations. Tasks like long text translation and content extraction analysis can be effectively addressed using this model.