DeepSeek has emerged as a beacon of innovation, offering groundbreaking capabilities that are reshaping the future of AI applications. This article delves into the core strengths of DeepSeek V3 and R1, providing insights into their performance, applications, and future potential.
1. DeepSeek V3 and R1 : Intelligent Gemini
1. DeepSeek V3 : Powerful MoE Language Model
DeepSeek V3 is a powerful MoE (mixture of experts) language model. Its performance in math, code and other tasks is impressive, and it can even compete with closed-source giants such as GPT-4o and Claude-3.5-sonnet , while its training cost is less than $ 6 million. Behind this high efficiency is the unique architectural design of DeepSeek V3 .DeepSeek V3 adopts a multi- token prediction ( MTP ) strategy, which can predict multiple words at a time, greatly improving data utilization efficiency. It also introduces multi-head potential attention ( MLA ), which reduces memory usage by compressing key-value pairs, making the model more efficient when processing long texts. In addition, DeepSeekMoE has made many innovations based on traditional MoE , such as more sophisticated expert division of labor and shared expert mechanism, so that the model can better cope with complex tasks.2. DeepSeek R1 : The pinnacle of reasoning capabilities
If DeepSeek V3 is a powerful basic model, then DeepSeek R1 is the pinnacle of reasoning capabilities based on it. R1 focuses on processing complex tasks. Its performance in mathematics, code, natural language reasoning and other fields is comparable to the official version of OpenAI o1 , and even surpasses o1-mini in some aspects . R1 's reasoning ability comes from its unique training method. It uses reinforcement learning ( RL ) and multi-stage training to allow the model to continuously optimize its reasoning ability during the learning process.2. DeepSeek training tips
1. Data and training strategy
The training data of DeepSeek V3 has been significantly improved compared to V2 , especially in the proportion of mathematics and programming samples. It also expands its multilingual coverage beyond English and Chinese. In terms of inference data, V3 uses high-quality inference data generated based on R1 , while non-inference data is generated by V2.5 and manually labeled and verified.2. Innovation of reward model
DeepSeek uses two reward models in training. For tasks that can be distinguished by clear rules, such as math and programming problems, it uses a rule-based reward model; for tasks that are difficult to distinguish by rules, such as open-ended question answering and creative writing, it uses a model-based reward model. This flexible reward mechanism allows DeepSeek to perform well in different types of tasks.3. DeepSeek R1 : Performance and Limitations
1. Performance
DeepSeek R1 demonstrates excellent performance in multiple benchmarks. On knowledge benchmarks, it significantly outperforms the V3 model on tasks such as MMLU (Comprehensive Knowledge Luminary) and GPQA (Doctoral-level Questions) . In terms of contextual understanding, R1 also demonstrated strong document analysis capabilities in FRAMES ( long context understanding). On mathematics and code tasks, R1 achieved results comparable to or even surpassing OpenAI o1-1217 on tasks such as AIME 2024 , MATH-500 , and Codeforces .2. Limitations
Although DeepSeek R1 has impressive performance, it also has some limitations. For example, R1 's general capabilities (such as function calls, multi-round dialogues, complex role-playing, and JSON output) still lag behind V3 . In addition, R1 may experience language mixing when processing non-Chinese and English questions, and is also sensitive to prompt words. Few sample prompts will continue to degrade its performance.4. Tips: The art of dialogue with DeepSeek
1. Clarity and specificity
Whether you are talking to DeepSeek V3 or R1 , it is key to express your requirements clearly and specifically. For example, don’t just say “ write an article about time management ” , but specify the requirements in detail, such as “ please write an article about how to improve personal time management skills, including three specific methods and explaining in detail the implementation steps of each method ” .2. Background and Rules
Providing context or rules can help DeepSeek better understand the task. For example, when writing a tweet about a new pair of binoculars, detailing the product features, target audience, and word count can help the generated content better meet your expectations.3. Examples and roles
In R1 , the effectiveness of using examples depends on the type of task. For some creative writing tasks, examples may limit the performance of R1 ; for formatting tasks, examples can provide clear guidance. The same is true for setting roles. If the task requires a specific style or background, setting roles can effectively guide DeepSeek 's output.4. Placeholders and Decomposition
Placeholder tags can help DeepSeek better understand the output format. For example, when asked to generate JSON format content, using placeholders allows the model to clearly know the content of each field. Decomposing tasks is to split complex problems into multiple small problems and solve them step by step. This is particularly effective when dealing with complex reasoning tasks.5. Tips for using DeepSeek
1. Treat R1 as a human being
R1 is trained to speak human language , which means we can talk to it in the natural language we usually use to communicate with people. This way of conversation is not only simple, but also can better play the reasoning ability of R1 .2. Learning Thinking Chain
R1 's thinking and reasoning process (thinking chain) is one of its most underestimated abilities. By observing R1 's reasoning process, we can learn how it thinks about problems from different angles and how to break down problems, which can improve our cognitive abilities.3.Ask for advice on methodology
If you want to learn more about how R1 thinks, you can ask it to analyze its own thinking process and even ask it which mental models it uses. This self-analysis not only helps us understand how R1 works, but also allows us to learn new ways of thinking.4. V3 + R1 Combo
In practical applications, we can flexibly choose DeepSeek V3 and R1 according to the task type . For non-strong reasoning tasks, we can use V3 to ask questions and then use R1 to verify; for strong reasoning tasks, or when the answer of R1 is too divergent, we can consider using V3 to converge and summarize.6. Application scenarios of DeepSeek : from text to code, from creativity to practicality
1. Text Generation
DeepSeek 's ability to generate text is unique. Whether it is imitating the writing style of celebrities or continuing classic masterpieces, DeepSeek can handle it easily. For example, you can ask it to write an article about workplace involution in the style of Lu Xun's essays, or write a seven-character regulated verse for the movie "Nezha" in the style of Li Bai. In addition, DeepSeek can also batch generate high-quality Chinese content, such as AI painting prompts, marketing copy, etc., combining AI painting tools and video tools to achieve batch generation of content.2. Code Generation and Debugging
DeepSeek also performs well in code generation. You can ask it to develop a Python game or generate the basic code for an iOS app. For users with zero programming knowledge, DeepSeek can also interpret the code and even add comments to the code to help you better understand and use the code.3. Drawing and Design
Although DeepSeek itself cannot generate drawings directly, it can generate text in Markdown , Mermaid or SVG format and combine it with drawing tools to generate mind maps, flow charts, and SVG vector graphics. This capability is very practical when designing and planning projects.4. API access and local deployment
DeepSeek 's API access function allows it to easily access various software and hardware. Whether it is document software such as Word and WPS , or hardware devices such as iPhone , Android phones, and smart speakers, you can call DeepSeek 's capabilities through the API . In addition, for users who value privacy and information security, DeepSeek also supports local deployment, allowing you to use its powerful functions on your own devices.7. DeepSeek alternatives: choose the path that suits you
As DeepSeek becomes more popular, more and more platforms are beginning to integrate this powerful model. If you are looking for alternatives to DeepSeek , here are some recommendations:1. Online Platform
- MiTa AI Search : supports online search and provides the Long Thinking R1 model, which is suitable for users who need online search.
- Nano AI Search : fast response speed, supports mobile phone number registration and login, provides DeepSeek-R1 ( 360 high-speed dedicated line) and DeepSeek-R1 -full blood version ( 671B model).
- Silicon Mobility : A large-scale cloud service platform that provides online DeepSeek and DeepSeek API applications, suitable for users with local deployment needs.
2. Local deployment
If you prefer local deployment, you can choose the appropriate DeepSeek version according to your device conditions. From low-resource devices to enterprise-level servers, DeepSeek provides a variety of versions for you to choose from. For example, DeepSeek-R1-1.5B is suitable for low-resource device deployment, while DeepSeek-R1-671B (full blood version) is suitable for national or ultra-large-scale AI research.8. The Future of DeepSeek
The emergence of DeepSeek has not only changed our understanding of artificial intelligence, but also demonstrated great potential in practical applications. From text generation to code development, from creative design to practical tools, DeepSeek can handle it easily. Its powerful capabilities allow us to see the future development direction of artificial intelligence - smarter, more efficient, and closer to human needs.With the continuous advancement of technology, DeepSeek will surely play a greater role in more fields. Whether it is education, medical care, finance or creative industries, DeepSeek will become our indispensable assistant. Let us look forward to the future of DeepSeek and expect it to bring us more surprises.
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