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Hangzhou DeepSeek Artificial Intelligence Co., Ltd. (hereinafter "we" or "DeepSeek") is a research team dedicated to exploring AGI, focusing on fundamental model technology research and adhering to an open-source approach. We aim to promote technological inclusivity through openness, transparency, and security. This document introduces and explains the basic principles and training methods of DeepSeek models, providing you with a detailed understanding of how DeepSeek operates. This will help you use DeepSeek more effectively while ensuring your right to know and control during usage, thereby mitigating risks associated with improper use of the model.

For specific rules on how we collect, protect, and use personal information, please carefully read the DeepSeek Privacy Policy.

I. Basic Principles of DeepSeek Models

Currently, the foundational models provided by DeepSeek are all large-scale language models based on deep neural networks. These models operate in two main stages: the training phase and the inference phase. Additionally, DeepSeek models are open-source, so this section will also introduce our open-source efforts.

1. Model Training

The model training phase is the development stage, where developers create deployable models using designed training methods. The models consist of multi-layered neural networks with parameters ranging from billions to trillions. These parameters are continuously optimized during training through gradient descent algorithms. Model training can generally be divided into two steps: pre-training and optimization training.

• Pre-training: The goal of pre-training is to train the model with datasets, enabling it to acquire general language understanding and generation capabilities. During this phase, the model learns language patterns and knowledge associations from text data through large-scale self-supervised learning. After pre-training, the model can understand and generate coherent text but may not yet answer questions or perform tasks accurately. Further training adjustments are required.
• Optimization Training: Also known as fine-tuning, optimization training further adjusts the model parameters based on the pre-trained model using task-specific data to adapt it to real-world application scenarios. During this phase, the model typically employs supervised fine-tuning (SFT) or reinforcement learning (RL) methods to learn how to answer questions according to instructions, align with human preferences and needs, and unlock expertise in specific domains. After optimization training, the model better meets practical requirements and can be deployed.

2. Model Inference

The inference phase is when the model is deployed to provide services. Once trained and deployed, the model can encode and compute input information to predict the next token, thereby enabling text generation, conversation, and other capabilities. It can proficiently perform a wide range of text-based tasks and be integrated into various downstream systems or applications. Specifically, for DeepSeek's product services, the model computes and infers based on user input to generate corresponding responses, including text, tables, and code.

It is important to note that the model uses an autoregressive generation method, predicting the most likely subsequent sequence of tokens based on the input context through probabilistic calculations. This process is not a simple retrieval or "copy-paste" of text from the original training data. The model does not store copies of the original training data but dynamically generates contextually appropriate responses based on its deep understanding of language structure and semantic relationships.

3. Model Open-Source

DeepSeek is committed to open-sourcing its models. To this end, we publicly release all model weights, parameters, and inference tool code on open-source platforms under the permissive MIT License, allowing users to freely download and deploy them. Additionally, DeepSeek publishes comprehensive technical reports for each model, serving as references for the community and researchers and helping the public gain a deeper understanding of each model's technical principles and details.

II. Data Used for DeepSeek Model Training

The capabilities of DeepSeek models are built on high-quality, large-scale, and diverse data sources. We place great emphasis on and strictly comply with laws and regulations related to intellectual property, trade secrets, and personal privacy, ensuring that all data acquisition and usage occur within a legal and compliant framework.

1. Pre-training Phase

During the pre-training phase, corpus data is required for training. This phase primarily uses the following two categories of data:

• Public Data: We use publicly available information on the internet to build the model's broad understanding of world knowledge. We employ technical methods to acquire and filter these freely accessible data to enrich the model's knowledge base.
• Licensed Data: We collaborate with third-party data providers to obtain proprietary datasets through legally signed agreements. We ensure all collaborations are based on lawful authorization.

The pre-training phase does not require personal information for training. Therefore, we do not intentionally collect personal information to associate with any specific account or individual, nor do we proactively use it to train our models. We exclude sensitive information, credit card numbers, or unique identification information from our training data sources to minimize the risk of collecting any personal information. However, due to the vast scale of pre-training data, some publicly available online content or licensed data from other providers may incidentally contain personal information. We employ technical measures to screen and remove such information from the training data as much as possible and conduct tests before using the data for training.

Additionally, to ensure data quality, safety, and diversity, we have established a rigorous data governance process. First, we use filters to automatically screen and remove raw data containing hate speech, pornography, violence, spam, or potential infringement. Second, recognizing that large-scale datasets may inherently contain statistical biases, we combine algorithmic and manual review methods to identify and mitigate the impact of these biases on the model's values, thereby enhancing fairness.

2. Optimization Training Phase

During the optimization training phase, we typically need to construct or annotate a set of question-answer pair data manually or automatically to train the model. These question-answer pairs are produced by our research team, with a small portion potentially based on user input. If user input is used to construct training data, we apply secure encryption, strict de-identification, and anonymization to make it cannot be linked to any specific individual. We also deploy measures to make personal information not appear in the model's outputs to other users and we will not use it for user profiling or personalized recommendations. To be clear, we do not offer services involving user profiling or personalized recommendations. Users are also given the right to opt out. For information on how to opt out of AI training, please refer to the DeepSeek Privacy Policy. To ensure model safety, during the optimization training phase, we construct specialized safety data to align the model with human values, enhancing its inherent safety capabilities.

III. Model Limitations and Risks

Risks associated with AI models may arise from two causes:

1. Limitations due to the immaturity of AI technology.

2. Risks due to the misuse of AI technology.

Specifically:

1. Limitations

Currently, AI is still in its early stages, and the technology is not yet mature. Due to the limitations of current model principles, AI may generate incorrect, omitted, or non-factual content, a phenomenon known as "hallucination." Hallucination is a challenge faced by the entire AI industry. DeepSeek is committed to reducing hallucination rates through research, including but not limited to selecting high-quality training data sources, optimizing alignment strategies, and employing retrieval-augmented generation (RAG) techniques. However, at this stage, we cannot guarantee that the model will not produce hallucinations. To further mitigate the potential adverse effects caused by hallucinations, we have added prominent warning labels on DeepSeek's welcome page, at the end of generated text, and at the bottom of the interactive interface, specifically reminding users that the content is AI-generated and may be inaccurate.

The content generated by the model is for reference only and should not be treated as professional advice. Specifically, when using this service for medical, legal, financial, or other professional inquiries, please note that the service does not constitute any advice or commitment or represent opinions in any professional field. If you require professional services, consult experts and make decisions under their guidance. The output of this software should not serve as the basis for further actions or inactions.

2. Misuse Risks

The risks of AI technology misuse are widely recognized globally, including concerns about privacy protection, copyright, data security, content safety, bias, and discrimination. The technology itself is neutral; risks arise from its practical application and must be considered in the context of usage scenarios and intended purposes.

DeepSeek takes the potential risks of AI technology applications very seriously. We strictly comply with legal and regulatory requirements and take reasonable measures to continuously enhance model safety throughout the entire lifecycle of model development, training, and deployment. These measures include, but are not limited to, establishing internal risk management systems, conducting model safety assessments, performing red team testing, and improving model and service transparency.

At the same time, we respect and safeguard the rights granted to users by law, including but not limited to the right to know, choose, and control model technology and services. Users can query basic service information, opt out of data usage for model training, delete their historical data, and more. If you have any claims, requests, or questions regarding the exercise of these rights, please refer to our [Privacy Policy] or contact us at [privacy@deepseek.com].