and inference tool code on open-source platforms under thepermissive MIT License， tables，。
copyright。
where developers create deployable modelsusing designed training methods. The models consist of multi-layered neural networks withparameters ranging from billions to trillions. These parameters are continuously optimizedduring training through gradient descent algorithms. Model training can generally be dividedinto two steps: pre-training and optimization training. Pre-training: The goal of pre-training is to train the model with datasets， conversation。
and control model technology and services. Userscan query basic service information， safety。
spam， we have established a rigorousdata governance process. First， or non-factualcontent， content safety， at this stage。
including but not limited to selecting high-quality training data sources， anddeployment. These measures include， financial。
including concerns aboutprivacy protection。
and employing retrieval-augmented generation (RAG) techniques.However， Ltd. (hereinafter "we" or "DeepSeek") is aresearch team dedicated to exploring AGI， but are not limited to， including text， choose。
protect， for DeepSeek's product services， we have addedprominent warning labels on DeepSeek's welcome page。
AI may generate incorrect， data security， to ensure data quality， and use personal information。
please note that the service does not constitute any advice orcommitment or represent opinions in any professional field. If you require professionalservices，thereby enabling text generation， during theoptimization training phase，DeepSeek hereby publishes Training Data Summary： 1. Pre-training Phase During the pre-training phase， please refer to the DeepSeek Privacy Policy . To ensure model safety。
align with human preferences and needs。
we apply secure encryption， pleasecarefully read theDeepSeek Privacy Policy. I. Basic Principles of DeepSeek Models Currently， delete theirhistorical data， bias， serving as references forthe community and researchers and helping the public gain a deeper understanding of eachmodel's technical principles and details. II. Data Used for DeepSeek Model Training The capabilities of DeepSeek models are built on high-quality， we do not offer services involving user profiling orpersonalized recommendations. Users are also given the right to opt out. For informationon how to opt out of AI training， we donot intentionally collect personal information to associate with any specific account orindividual， legal， a phenomenon known as "hallucination." Hallucination is a challenge faced by theentire AI industry. DeepSeek is committed to reducing hallucination rates through research， or questions regarding theexercise of these rights， with a small portion potentiallybased on user input. If user input is used to construct training data， thereby enhancing fairness. 2. Optimization Training Phase During the optimization training phase， DeepSeek models are open-source， and atthe bottom of the interactive interface， we publicly release allmodel weights， orother professional inquiries， or unique identification information from our trainingdata sources to minimize the risk of collecting any personal information. However。
including butnot limited to the right to know， and other capabilities. It can proficientlyperform a wide range of text-based tasks and be integrated into various downstream systemsor applications. Specifically， when using this service for medical， we respect and safeguard the rights granted to users by law， due tothe vast scale of pre-training data， and the technology is not yet mature. Due to thelimitations of current model principles， we typically need to construct or annotate a set ofquestion-answer pair data manually or automatically to train the model. Thesequestion-answer pairs are produced by our research team，recognizing that large-scale datasets may inherently contain statistical biases， ensuring that all dataacquisition and usage occur within a legal and compliant framework. To enhance transparency， requests。
AI is still in its early stages， and diversity， and security. This document introduces and explains the basicprinciples and training methods of DeepSeek models。
opt out of data usage for model training， thereby mitigating risksassociated with improper use of the model. For specific rules on how we collect，DeepSeek publishes comprehensive technical reports for each model， providing you with a detailedunderstanding of how DeepSeek operates. This will help you use DeepSeek more effectivelywhile ensuring your right to know and control during usage， some publicly available online content or licensed datafrom other providers may incidentally contain personal information. We employ technicalmeasures to screen and remove such information from the training data as much as possibleand conduct tests before using the data for training. Additionally， nor do we proactively use it to train our models. We exclude sensitiveinformation， the model can understand andgenerate coherent text but may not yet answer questions or perform tasks accurately.Further training adjustments are required. Optimization Training: Also known as fine-tuning， allowing users to freely download and deploy them. Additionally， credit card numbers。
pornography， and unlockexpertise in specific domains. After optimization training， enabling it toacquire general language understanding and generation capabilities. During this phase， themodel learns language patterns and knowledge associations from text data throughlarge-scale self-supervised learning. After pre-training， parameters， we combinealgorithmic and manual review methods to identify and mitigate the impact of these biases onthe model's values。
focusing on fundamental model technology researchand adhering to an open-source approach. We aim to promote technological inclusivity throughopenness， conducting model safety assessments， and personal privacy， performing red team testing， please refer to our [Privacy Policy] or contact us at [privacy@deepseek.com]. ， large-scale， so this section willalso introduce our open-source efforts. 1. Model Training The model training phase is the development stage， transparency， the model typically employssupervised fine-tuning (SFT) or reinforcement learning (RL) methods to learn how to answerquestions according to instructions， the foundational models provided by DeepSeek are all large-scale language modelsbased on deep neural networks. These models operate in two main stages: the training phaseand the inference phase. Additionally， establishing internal riskmanagement systems， the model can encode and compute input information to predict the next token， we cannot guarantee that the model will not produce hallucinations.To further mitigate the potential adverse effects caused by hallucinations， optimizingalignment strategies， and diverse datasources. We place great emphasis on and strictly comply with laws and regulations related tointellectual property。
the model computes andinfers based on user input to generate corresponding responses。
Model Mechanism and Training Methods of DeepSeek Hangzhou DeepSeek Artificial Intelligence Co.， training， and more. If you have any claims， we construct specialized safety data to align the model withhuman values， the model better meetspractical requirements and can be deployed. 2. Model Inference The inference phase is when the model is deployed to provide services. Once trained anddeployed， strictde-identification， at the end of generated text。
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， omitted， trade secrets， optimization training further adjuststhe model parameters based on the pre-trained model using task-specific data to adapt itto real-world application scenarios. During this phase。
we use filters to automatically screen and remove raw datacontaining hate speech， andcode. It is important to note that the model uses an autoregressive generation method， specifically reminding users that the content isAI-generated and may be inaccurate. The content generated by the model is for reference only and should not be treated asprofessional advice. Specifically， corpus data is required for training. This phase primarilyuses the following two categories of data: Public Data : We use publiclyavailable information on the internet to build the model's broad understanding of worldknowledge. We employ technical methods to acquire and filter these freely accessible datato enrich the model's knowledge base. Licensed Data : We collaborate withthird-party data providers to obtain proprietary datasets through legally signedagreements. We ensure all collaborations are based on lawful authorization. The pre-training phase does not require personal information for training. Therefore， or potential infringement. Second， consult experts and make decisions under their guidance. The output of thissoftware should not serve as the basis for further actions or inactions. 2. Misuse Risks The risks of AI technology misuse are widely recognized globally， violence， and discrimination. Thetechnology itself is neutral; risks arise from its practical application and must beconsidered in the context of usage scenarios and intended purposes. DeepSeek takes the potential risks of AI technology applications very seriously. We strictlycomply with legal and regulatory requirements and take reasonable measures to continuouslyenhance model safety throughout the entire lifecycle of model development， andimproving model and service transparency. At the same time， and anonymization to make it cannot be linked to any specificindividual. We also deploy measures to make personal information not appear in the model'soutputs to other users and we will not use it for user profiling or personalizedrecommendations. To be clear， predictingthe most likely subsequent sequence of tokens based on the input context throughprobabilistic calculations. This process is not a simple retrieval or "copy-paste" of textfrom the original training data. The model does not store copies of the original trainingdata but dynamically generates contextually appropriate responses based on its deepunderstanding of language structure and semantic relationships. 3. Model Open-Source DeepSeek is committed to open-sourcing its models. To this end。