A small tech firm, tngtech, released an open‑source model fusion called DeepSeek‑R1T‑Chimera that merges R1 inference with V3‑0324 without fine‑tuning, distillation, or prompts, achieving the same intelligence as R1 while reducing token output by 40% and speeding up inference.
DeepSeek leverages AI to automate multi‑source integration, data cleaning, warehouse modeling, real‑time processing, governance, metadata management, reporting, cloud scaling, and decision support, offering twelve distinct use cases that boost efficiency, intelligence, and scalability of modern data warehouses.
The Zhejiang University IT Center report highlights DeepSeek's AI technology across more than 40 real‑world cases in agriculture, manufacturing, finance, healthcare and education, demonstrating data‑driven, intelligent solutions that accelerate industry transformation and modernization.
This article explains the fundamentals of Retrieval‑Augmented Generation, demonstrates how to create and query vector indexes using StarRocks, shows how DeepSeek provides embeddings and answer generation, and walks through a complete end‑to‑end RAG pipeline with code examples and a web UI.
This guide outlines eight concrete methods DeepSeek uses to improve data quality—including automated cleaning, validation, classification, monitoring, governance standards, anomaly detection, integration, and intelligent analysis—providing actionable steps for organizations to enhance data accuracy, completeness, consistency, and usability.
This guide shows how to integrate Zabbix with a locally deployed DeepSeek large language model via Webhook, enabling automatic analysis of alerts, generation of root‑cause explanations and remediation suggestions, and delivering results through WeChat bots, dashboards, or email to reduce MTTR and manual effort.
This article provides an in‑depth analysis of DeepSeek’s Janus and Janus‑Pro models, explaining how decoupling visual encoding resolves the conflict between multimodal understanding and generation, detailing training stages, data scaling, architectural choices, and presenting extensive benchmark results that demonstrate significant performance gains.
The article analyzes DeepSeek’s private‑deployment solutions, detailing selection criteria, deployment forms, service models, hardware‑software cost breakdown, technical innovations that lower model and compute barriers, and their impact on government and enterprise AI adoption.
DeepSeek leverages large‑model AI to automate requirement analysis, intelligent modeling, performance tuning, and value extraction in data warehouses, addressing low development efficiency, high O&M cost, latency, and lack of intelligence while showcasing concrete use‑case results across finance, e‑commerce, and manufacturing.
This guide compares DeepSeek’s local and online versions, outlines hardware and privacy advantages of offline deployment, and provides a detailed step‑by‑step tutorial—including Ollama installation, model selection, command execution, and UI plugin setup—to help users run DeepSeek on their own machines.
This article provides a comprehensive technical guide for privately deploying DeepSeek large language models, covering model and runtime parameter selection, hardware sizing calculations, software stack preparation, inference service setup, performance tuning, and security monitoring considerations.
This guide walks you through the fundamentals of Retrieval‑Augmented Generation, introduces the open‑source RAGFlow framework, details installation steps, shows how to integrate DeepSeek LLMs, and explores practical application scenarios such as intelligent customer service and enterprise document QA.
The article explains GRPO, a reinforcement‑learning algorithm that extends PPO with group sampling, no value network, dual penalties and KL regularisation, showing how it improves efficiency and stability when fine‑tuning large language models such as DeepSeek‑Math and DeepSeek‑R1.
This guide walks through setting up VS Code, installing the Cline plugin, configuring a Gaode Map MCP Server API key, and using the DeepSeek model to generate a personalized park‑recommendation agent and a visual HTML page, while also explaining the stdio‑based communication between Cline and the MCP Server.
This article outlines twelve practical DeepSeek AI applications for data governance, covering automated classification, dynamic privacy masking, compliance checks, quality monitoring, intelligent integration, lineage analysis, metadata management, smart retrieval, strategy formulation, security risk handling, lifecycle control, and performance evaluation.
This report outlines the innovative advantages of the DeepSeek‑R1 large language model, explains its underlying architecture and operation, details practical prompt engineering techniques, and explores concrete use‑cases that empower education and academic research through advanced AI capabilities.
This article aggregates multiple independent evaluations of DeepSeek‑R1 across major cloud providers, comparing accuracy on AIME math problems, token‑per‑second throughput, first‑token latency, stability under high concurrency, and overall service reliability, ultimately highlighting Volcano Engine as the top performer.
After deploying the full‑precision DeepSeek‑R1 model on a 2×8‑GPU ACS cluster, repeated stress tests showed GPU memory usage continuously rising without release; this article details the investigation, reproduces the behavior, examines vLLM logs, Prometheus metrics, and reveals PyTorch’s caching allocator as the root cause, offering mitigation tips.
While most eyes remain on familiar AI giants, China’s Alibaba and DeepSeek are unveiling open‑source video and inference models that run on consumer GPUs, sparking a regulatory scramble and threatening the dominance of closed‑source AI, heralding a rapid, disruptive shift across the industry.
This guide shows how to use DeepSeek‑V3‑0324 and Claude 3.5/3.7 to generate professional SVG graphics for articles and presentations, explains the impact of model capability and prompt quality, provides ready‑to‑use prompt templates, and demonstrates basic and advanced usage scenarios such as prototype drawing, image re‑drawing, and colorful newspaper‑style visuals.
The article analyzes the limitations of current visual language models and large reasoning models, proposes a combined Vision‑Reasoning Model (VRM), details its architecture using LLaVA, describes end‑to‑end fine‑tuning and reinforcement‑learning reward design, and argues that such models will become the next breakthrough in AI.
This guide demonstrates how to create a Spring Boot backend that integrates DeepSeek's large language model via the Spring AI OpenAI starter, covering project setup, dependency configuration, API key management, and a sample controller that provides AI-powered chat responses such as weather forecasts.
The article breaks down the three core stages of large‑language‑model training—pre‑training, supervised fine‑tuning, and RLHF—explaining their purpose, methods, and concrete examples while noting DeepSeek‑R1’s recent breakthrough and its implications for AI development.
This article reviews the latest updates from major LLM providers—DeepSeek V3’s parameter boost and longer context, Qwen2.5‑Omni’s open‑source multimodal 7B model, Google Gemini 2.5 Pro’s 1 M‑token window and multimodal prowess, and OpenAI GPT‑4o’s image generation and reduced pricing—highlighting technical specs, capabilities, and availability.
This step‑by‑step tutorial shows how to set up CUDA 12.4, install required packages, prepare a JSON dataset and custom reward, troubleshoot out‑of‑memory errors, and launch DeepSeek R1 training on an 8‑GPU A100 cluster using Accelerate, Deepspeed zero‑3 and vLLM configurations.
The article analyses two months of community attempts to reproduce DeepSeek R1, highlighting that model scaling, high‑quality data, robust training infrastructure, and careful hyper‑parameter tuning outweigh pure reward‑based tricks, and it outlines common pitfalls and future research directions.
This comprehensive analysis by the Peking University AI Alignment team dissects the technical innovations behind DeepSeek‑R1, DeepSeek‑R1 Zero, and Kimi‑K1.5, covering reinforcement‑learning‑based post‑training, rule‑based rewards, GRPO optimization, scaling laws, multimodal extensions, safety challenges, and future research directions.
This guide presents a comprehensive collection of DeepSeek prompt templates for MySQL, covering SQL generation, optimization, data analysis, database administration, debugging, and advanced features, enabling beginners and seasoned developers alike to craft accurate queries, improve performance, and resolve errors efficiently.
The article provides a comprehensive evaluation of DeepSeek‑V3‑0324, highlighting its superior inference, coding, and long‑text abilities, benchmark rankings that place it near GPT‑4.5, extensive code‑generation tests, and advanced Function Calling features that make it the preferred model for building AI agents.
In a side‑by‑side test on the Trae platform, the author used Claude Sonnet 3.5 to create a functional Twitter‑to‑image‑card browser extension in roughly twenty minutes, while DeepSeek‑R1 required multiple prompt iterations, manual bug fixes, and still produced visual glitches, demonstrating Claude’s superior reliability for frontend plugin generation.
This article explains the fundamentals of AI video technology, details the features of Alibaba Cloud's Tongyi Wanxiang platform, demonstrates how to use DeepSeek for script generation, and provides a complete workflow—including code examples—for producing high‑quality AI‑generated videos.
The report analyzes DeepSeek's latest V3 and R1 models, highlights their scaling‑law‑driven cost reductions, explains how Huawei Ascend optimizes inference by cutting KV‑Cache storage and improving compute efficiency, and surveys the model’s deployments across finance, government, manufacturing, and healthcare sectors.
This article walks through the entire process of deploying the DeepSeek‑R1 large language model on a personal machine, covering hardware requirements, Ollama installation, model download, service startup, remote access configuration, and visual UI integration with Dify, complete with concrete commands and screenshots.
The DeepSeek V3-0324 model, released on March 24, 2025 with 6.85 trillion parameters and a Mixture‑of‑Experts architecture, is fully open‑source on Hugging Face and brings notable upgrades in coding ability, structured responses, stability, generation length, and speed, while offering performance comparable to leading closed‑source models such as Claude 3.7.
The article introduces DeepSeek's new V3-0324 model, highlights its performance gains and new features like function calling and standardized JSON output, demonstrates Chinese and frontend coding tests, provides Java code examples for AI integration, and concludes with a summary of its business impact.
DeepSeek, a Chinese AI startup, offers open‑source large language models—DeepSeek‑V3 for general tasks and DeepSeek‑R1 for intensive reasoning—featuring MoE, MLA, low‑cost training, and competitive performance against OpenAI’s GPT‑4o, while providing detailed usage guidance and cost analysis.
This case study demonstrates how the AI assistant ChatDBA identifies and resolves a tenant data‑synchronization failure in an OceanBase primary‑backup cluster, detailing four interactive troubleshooting rounds, the final SQL fix, and a comparative analysis with the DeepSeek‑R1 model.
The article introduces DeepSeek4j 1.4, a Java‑centric framework that overcomes Spring AI’s limitations by preserving DeepSeek’s chain‑of‑thought, supporting streaming output, and offering a simple Spring Boot starter with reactive, configurable, and ready‑to‑use APIs for AI developers.
This guide shows how programmers can leverage the DeepSeek large language model to automatically generate Mermaid code for flowcharts, sequence diagrams, class diagrams, and other visualizations, dramatically reducing manual diagramming effort and improving documentation efficiency.
The interview explores DeepSeek R1's open‑source weights, its multi‑stage training pipeline—including pre‑training, supervised fine‑tuning, and RLHF—alongside innovations such as self‑consistency, chain‑of‑thought prompting, distillation, MoE architectures, and cost considerations, highlighting its impact on the future of large language models.
This guide walks you through using DeepSeek’s large‑model API via the OpenAI request format, covering API advantages, key parameters, Python setup, code examples for single and multi‑turn conversations, and a full reference table of request options.
This guide explains how effective communication with AI—through clear prompts, step‑by‑step task breakdowns, and critical‑thinking techniques—can unlock deeper reasoning, avoid hallucinations, and boost personal productivity across various work scenarios.
The 2025 DeepSeek Insight report analyzes DeepSeek’s new large‑model releases, compares US and Chinese AI ecosystems, outlines diverse application scenarios such as government, healthcare and aerospace, and provides practical guidance for safely leveraging these models despite their current limitations.
This article analyzes how DeepSeek’s multimodal large model overcomes traditional digital‑twin simulation bottlenecks through dynamic modeling, generative data augmentation, and low‑cost open‑source architecture, enabling smarter city traffic, industrial design, and water‑resource management while reshaping the industry’s AI‑driven simulation landscape.
This guide walks through setting up an Anaconda environment, installing Unsloth, downloading the DeepSeek‑R1‑Distill‑Llama‑8B model, preparing a medical CoT dataset, configuring LoRA parameters, running a short fine‑tuning job, and evaluating the customized model with structured prompts.
This collection provides a step‑by‑step tutorial series of sixteen short videos demonstrating how to access, configure, and use the DeepSeek large language model across various office applications such as Word, Excel, PowerPoint, as well as platforms like WPS and online APIs.
This manual introduces the fundamental concepts of the DeepSeek‑R1 model, explains large‑model and AIGC technologies, and provides practical guidance for selecting and efficiently using AI tools, helping readers grasp the deeper value of DeepSeek and related applications.
Early adopters benchmarked the M3 Ultra‑powered Mac Studio running the 671‑billion‑parameter DeepSeek R1 model, achieving around 11 tokens per second in practice (up to 20 tokens/s theoretically), and compared its performance and cost against GPU‑based solutions and the newer M4 Max hardware.
This article compiles a market overview of 52 DeepSeek integrated machines, detailing GPU chips, price ranges from tens of thousands to millions, major Chinese cloud vendors, and diverse application scenarios such as intelligent customer service, data processing, and smart governance.
This article outlines a comprehensive AI‑Ops solution built on DeepSeek, covering its technical architecture, data collection stack, AI engine deployment, key functional modules, implementation roadmap, model training, security design, cost estimates, and risk mitigation strategies for modern operations teams.
The author walks through installing OpenManus, configuring it to use DeepSeek (and an Ollama‑based vision model), runs a sample financial data query, and reports that the system is slow, sometimes inaccurate, and still requires further optimization.
The article explains the fundamentals of prompt engineering—clarity, output control, and efficiency—then presents a curated collection of 35 validated DeepSeek (or other LLM) prompts organized into seven practical categories, each accompanied by concrete usage examples and task specifications.
The article analyzes how Chinese researchers dominate AI research worldwide, detailing their roles in US tech giants, Chinese model teams, talent‑attraction policies in both countries, and the strategic implications of this "internal" competition for the future of artificial intelligence.
DeepSeek4j 1.4 introduces a Java‑centric, Spring Boot‑compatible framework that fully preserves DeepSeek's chain‑of‑thought capabilities, adds reactive streaming, and provides simple one‑line API integration, addressing previous limitations in mainstream frameworks and offering ready‑to‑use configuration and code examples.
This article provides a concise guide on using SpringAI to connect Java applications with the domestic large‑language model DeepSeek, covering design philosophy, configuration, code examples for chat, streaming, structured output, security hardening, performance tuning, and production best practices.
This tutorial walks through privately deploying the DeepSeek R1 14B model on an AutoDL GPU server using Ollama, configuring systemd and ports, exposing the service, and integrating it with a GitLab CI pipeline to generate AI‑driven code review results sent via a Feishu bot.
This guide walks you through setting up a private AI knowledge base using Webman AI 5.4.0, deploying Redis‑Stack, installing the illuminate/redis component, adding Ollama with DeepSeek and other embedding models, configuring Redis, importing training data, running the training process, and configuring role prompts for accurate AI responses.
This article reviews Alibaba Cloud's Tongyi Lingma AI programmer, highlighting its new model selection feature—including DeepSeek V3, R1, Qwen2.5‑Max and the open‑source QwQ‑32B—its impressive benchmark performance, step‑by‑step code generation for a CMS notice module, cross‑language integration with DeepSeek‑R1, and practical developer experiences comparing version 1.0 and 2.0.
This guide explains how to create a proprietary knowledge base for DeepSeek by leveraging Baidu Cloud's VectorDB, offering both code‑first and no‑code approaches to integrate internal data securely and enable accurate AI‑driven Q&A for business applications.
The open‑source DeepSeek‑Flow‑AI platform combines Spring Boot 3.4 back‑end APIs with a Vue 3 front‑end to deliver an enterprise‑grade knowledge base and intelligent multi‑turn dialogue system, supporting private deployment, role‑based access, analytics, CRM/ERP integration, and easy installation via Maven and Yarn.
This guide from Tsinghua University’s New Media Research Center systematically explores DeepSeek’s entry methods, interaction strategies, subject‑specific tutoring, emotional support, ethical risks, age‑specific parenting solutions, tool integration, and future educational outlook, offering parents actionable AI‑powered techniques for digital home learning.
This article provides a detailed, step‑by‑step guide to installing, configuring, and using DeepSeek—a powerful command‑line data processing tool—covering basic operations, advanced features, scripting tips, and troubleshooting to help users efficiently import, clean, analyze, and visualize data.
The article presents a structured methodology for crafting DeepSeek prompts, covering a four‑element template, scenario‑specific techniques, high‑frequency use cases such as data analysis, document generation, and automation, advanced knowledge‑base customization, tool integration, reliability checks, workflow optimization, and common pitfalls.
An in‑depth analysis reveals how DeepSeek’s V3 and R1 large‑language models achieve superior inference performance and cost efficiency on Huawei’s Ascend AI platform, detailing architectural optimizations, KV‑Cache reductions, multimodal support, real‑world deployments across finance, government, manufacturing, and the projected impact on the AI industry.
This tutorial explains how to set up Ollama for CPU or GPU environments, run the DeepSeek R1:7b large language model, and use the open‑source Dify platform to create and deploy a custom AI application, providing step‑by‑step commands and configuration details.
This article analyzes DeepSeek’s recent breakthroughs—including the Multi‑Head Latent Attention (MLA), Group Relative Policy Optimization (GRPO), and a refined Mixture‑of‑Experts design—along with its three‑stage training pipeline, RL‑only R1‑Zero variant, and benchmark comparisons against GPT‑4o‑Mini and Llama 3.1, highlighting both gains and remaining challenges.
This article analyzes DeepSeek’s latest large‑model breakthroughs, covering the MLA attention compression, GRPO alignment algorithm, MoE load‑balancing redesign, multi‑stage training pipelines, reinforcement‑learning tricks, and performance comparisons with GPT‑4o‑Mini and Llama 3.1, highlighting both strengths and remaining challenges.
The article examines Baidu Cloud’s integration of DeepSeek large‑model hardware, detailing the Baige and Qianfan one‑box systems, their technical specs, deployment speed, and how they enable state‑owned enterprises across energy, manufacturing, and logistics to accelerate AI‑driven digital transformation.
The article analyzes how DeepSeek’s unexpected success has triggered a strategic rethink across Chinese AI firms, prompting shifts from product‑centric growth to foundational model research, reshaping talent structures at Tencent and ByteDance, and questioning where the true barriers to AGI lie.
Learn how to deploy the new QwQ‑32B inference model alongside DeepSeek‑R1 using the Higress AI gateway, covering environment setup, model configuration, routing, token‑level rate limiting, content safety, semantic caching, and advanced features like automatic fallback and internet‑search integration.
This guide demonstrates how to leverage the DeepSeek AI model to automatically create Mermaid diagram code for flowcharts, Gantt charts, and sequence diagrams, walk through the required prompts, show the generated code, and compare Mermaid with traditional mind‑mapping tools.
This article provides a detailed technical analysis of FP8 training, comparing Nvidia’s TransformerEngine approach with DeepSeek V3’s novel scheme, and examines how block‑wise scaling, high‑precision accumulation, and vector length and correlation affect quantization error and signal‑to‑noise ratio in large‑language‑model training.
This article introduces deepseek4j, a Java SDK for DeepSeek AI models, walks through Maven setup, Spring Boot configuration, basic and advanced usage examples—including streaming, synchronous, SSE debugging, and web‑search integration—while detailing key configuration options and best‑practice notes.
The article outlines three practical approaches to get the most out of DeepSeek—using it as a conversational assistant, integrating its API to power AI tools such as the Chrome immersive‑translation plugin, and leveraging it for AI‑assisted programming—while comparing the V3 and R1 models and offering concrete configuration steps.
This tutorial demonstrates how to create an AI-driven Spring Boot application by integrating DeepSeek's large language model, covering project setup, dependency configuration, API key management, and implementing a REST controller that provides weather forecasts via a conversational interface.
DeepSeek’s three‑model LLM lineup—V3, R1‑Zero and R1—delivers high performance while cutting training expenses to under $600 k, a fraction of the $0.6‑1 B typical for comparable models, signaling a major shift in China’s AI compute demand and supply chain dynamics.
DeepSeek R1, an open‑source large language model, leverages rule‑based, large‑scale reinforcement learning and mixed supervised‑fine‑tuning data to achieve deep reasoning comparable to OpenAI o1, illustrating China’s rapid AI progress, the importance of efficiency, and the democratizing impact of open AI research.
This article provides a comprehensive analysis of DeepSeek, covering its foundational AI research, technology stack, product offerings, and the broader upstream, midstream, and downstream AI industry landscape, including hardware, server, cloud, and market trends.
This article walks through installing OpenManus locally using Python 3.12, cloning its GitHub repository, configuring DeepSeek LLM credentials, launching the service, and prompting the agent to generate a full WeChat mini‑program, while sharing observations on performance, token cost, and limitations.
The article analyzes how DeepSeek's rapid rise has triggered a strategic rethink across Chinese AI startups and tech giants, prompting a shift from product‑centric growth to deep‑model research, while examining the real barriers to AGI and the importance of time‑advantage in the large‑model race.
This tutorial explains how to enhance large language models' logical reasoning by using DeepSeek‑R1's deep‑thinking mode, few‑shot prompting, chain‑of‑thought, and zero‑shot chain‑of‑thought techniques, providing concrete examples, comparisons, and a step‑by‑step template for effective prompt design.
The article introduces DeepSeek4j 1.4, a Java Spring Boot library that overcomes existing framework limitations by preserving DeepSeek's chain‑of‑thought capabilities, adding full reactive streaming, and providing a simple one‑line API along with quick‑start instructions and code examples.
This guide explains how to set up Baidu Baige's PD‑separated deployment for the DeepSeek R1 large‑language model, covering resource preparation, data acquisition, Prefill and Decode service configuration, and API invocation to achieve lower latency and higher throughput.
This presentation reviews the development, technical details, and societal impact of DeepSeek's R1 model, explaining its reasoning capabilities, training pipeline, comparisons with other models, and future directions for AI research and product applications.
The 2025 DeepSeek analysis report examines the V3 and R1 models' novel Transformer‑based technologies, their performance gains, and how they are reshaping global AI competition, boosting domestic AI valuations, and ushering in an open‑source AI breakthrough that could spark the next killer applications.
This article explains how to create a high‑performance multi‑model chat agent on the Dify platform by combining DeepSeek‑R1 for reasoning and Gemini for answer generation, covering the underlying principles, configuration steps, API integration, performance benchmarks, and practical deployment guidance.
Amid a global digital surge, 45% of China’s central state‑owned enterprises have deployed the DeepSeek large‑model platform, rapidly integrating AI across energy, power, telecom, construction and other sectors to boost intelligent transformation and operational efficiency.
This article explores how the AI model DeepSeek can assist high‑school students in tackling challenging sequence problems from the 2024 Chinese college entrance exam, detailing its reasoning process, strengths, pitfalls, and practical tips for using AI to train mathematical thinking rather than just obtain answers.
Prompt engineering transforms vague queries into precise, reliable AI responses by structuring prompts with clear instructions, context, input, and output specifications, and by using role‑playing and formatting tricks, enabling models like DeepSeek and OpenAI to deliver accurate, consistent results across tasks.
DeepSeek’s Open‑Source Week unveiled five high‑performance projects—FlashMLA, DeepEP, DeepGEMM, DualPipe/EPLB, and 3FS—each delivering novel GPU optimizations, communication kernels, matrix‑multiplication libraries, parallelism strategies, and a distributed file system that together dramatically accelerate large‑scale AI training and inference workloads.
This tutorial walks beginners through obtaining a DeepSeek API key, setting up an Alibaba Cloud ECS instance, configuring the WeChat public‑account interface, cloning and configuring the open‑source COW project, and finally deploying a Python service that connects the WeChat bot to the DeepSeek large‑language model.
This article demonstrates how to combine DeepSeek's AI‑driven content generation with Kimi's visual design engine to create professional, data‑rich PPTs up to 80% faster, offering reusable prompts, a four‑step workflow, a real‑world case study, and advanced collaboration tips.
DeepSeek’s large model combines a Transformer‑based architecture, Mixture of Experts, multi‑head latent attention, massive multimodal pre‑training, model compression techniques, reinforcement learning, open APIs, and ethical safeguards, delivering high‑performance, scalable AI across language, vision, and decision‑making tasks.
This article explains the concepts, evolution, and core capabilities of API gateways, then provides a detailed, cloud‑native tutorial on configuring Alibaba Cloud's API Gateway to connect with DeepSeek, covering prerequisites, service setup, AI API creation, multi‑model routing, and debugging procedures.
The article analyzes DeepSeek's recent releases—V3 dialogue model and R1 inference model—detailing their launch dates, rapid popularity surge, R1's reinforcement‑learning‑based design for code and math tasks, and provides links to related Beijing University technical reports while stripping promotional sales content.
This guide walks Java developers through preparing the environment, installing the CodeGPT plugin, configuring DeepSeek API keys and models, and using the AI assistant for code generation, completion, explanation, and troubleshooting directly within IntelliJ IDEA, while also showing usage statistics.
Researchers from Nanjing University of Aeronautics, Nanjing University of Technology and Oxford show that high‑instruction prompts dramatically boost large language models' mathematical reasoning, enabling DeepSeek‑R1 and Qwen2.5 to solve complex polynomial tasks and even produce a new counterexample to Hilbert's 17th problem.
The article delivers a PPT‑style deep dive into the DeepSeek series—from the original LLM through DeepSeek‑MoE, Math, V2, V3 and R1—highlighting core innovations such as Multi‑Head Latent Attention, fine‑grained MoE, GRPO reinforcement learning, Multi‑Token Prediction, DualPipe parallelism and FP8 training that together achieve high performance at a fraction of traditional costs, and notes their integration into Tencent’s OlaChat intelligent assistant.
The article analyzes DeepSeek‑R1’s low‑cost inference architecture, Chinese language optimizations, novel prompt‑engineering techniques, and the practical challenges of deploying large domestic models, offering insights into vertical AI applications and the evolving open‑source ecosystem in China.
Compiled by Tsinghua University experts, this guide systematically analyzes the DeepSeek‑R1 inference model and DeepResearch platform, offering multi‑model comparisons, real‑world case studies, and end‑to‑end AI‑driven solutions from data collection to report generation for researchers.
