This article provides a step‑by‑step guide to creating a breakfast‑shop reception AI agent on Baidu's Wenxin Intelligent Agent platform, highlighting its core features, model selection with DeepSeek R1, and practical tips for configuring personas, knowledge bases, and plugins.
This guide explains how to install Ollama on a Windows machine, configure its environment, and use it to download and run the DeepSeek‑R1 1.5B large language model locally, enabling offline AI interactions without relying on remote servers.
This guide explains how to download, install, and configure Ollama on Windows and Linux, set up environment variables, select a DeepSeek model, and call the Ollama API from a SpringBoot application with example code snippets.
The NSA mechanism introduces a three‑branch hardware‑optimized sparse attention architecture—token compression, token selection, and sliding window—combined with learnable gating to balance global and local context, dramatically improving inference speed and efficiency for long‑context large language models.
This tutorial walks you through configuring a Huawei Cloud host, installing the AutoGen framework, setting up DeepSeek V3 model API keys, and using the model to automatically generate Python code for a graphical two‑player battle game, complete with step‑by‑step instructions and sample commands.
This article explains what Web Agents are, their ReAct‑style reasoning loop, key implementation technologies such as observation parsing, multimodal models, and browser control tools like Selenium and Playwright, and demonstrates building a DeepSeek‑powered Web Agent with the Browser‑use framework, including code samples and performance insights.
Learn how to create a locally hosted chatbot powered by DeepSeek R1 32b, using Ollama and Docker, integrate Dify for model management, and add web‑search capability through SEARXNG, covering environment setup, search logic, content extraction, testing, and optimization tips.
This guide walks you through the complete workflow of preparing instruction data, deploying the DeepSeek‑R1 teacher model, using Alibaba Cloud PAI to generate teacher responses, distilling a smaller Qwen2.5‑7B‑Instruct student model, fine‑tuning it, and deploying the final service, with performance comparisons on several math‑reasoning benchmarks.
This guide walks you through creating a Spring Boot project, configuring DeepSeek's large language model via SiliconFlow, setting up OpenAI‑compatible parameters, and implementing a REST controller that returns weather forecasts using the model, complete with step‑by‑step code snippets, configuration files, and deployment instructions.
After a winter break surge, DeepSeek AI models have been swiftly adopted across Chinese universities, enabling local deployments for teaching, research, and campus services, while facing bans and security concerns abroad, highlighting both rapid domestic integration and international challenges.
This article explores DeepSeek’s cutting‑edge distillation technology, detailing its definition, underlying principles, innovative data‑model fusion, architecture choices, training strategies, performance gains over large language models, and the remaining challenges in knowledge transfer and multimodal data processing.
The article examines DeepSeek’s surge in popularity by analyzing its timely release, cost‑effective performance, open‑source approach, and broader AI ecosystem trends, while also sharing expert predictions and practical coding tool recommendations for developers.
This guide compares DeepSeek‑R1’s 1.5B/7B/8B, 14B/32B, and 70B/671B versions, detailing their characteristics, typical applications, and the specific CPU, memory, and GPU specifications required for local deployment, helping you select the optimal model for your resources.
This guide explains how to locally deploy the DeepSeek large‑language model using Ollama on Windows, macOS, and Linux, configure model storage and CORS, build personal and enterprise RAG knowledge bases with AnythingLLM and Open WebUI, and integrate the model into a Spring AI application via Docker and Docker‑Compose.
This article reviews DeepSeek's recent breakthroughs, including the MLA attention redesign, GRPO alignment algorithm, MoE enhancements, multi‑stage training pipelines (SFT, RL, preference tuning, distillation), and comparative performance against GPT‑4o‑Mini and Llama 3.1, highlighting both strengths and remaining challenges.
This article introduces the “Plain Explanation Expert” AI agent built on DeepSeek‑R1, explains its prompt framework—including role, skills, and output format—demonstrates usage through direct prompt copying and smart‑agent configuration in tools like Cherry Studio, and provides concrete examples, memory tricks, and SVG visualizations to help users quickly master complex concepts.
This article explains how to use Alibaba Cloud ACK Edge to manage on‑premise GPU resources and seamlessly fall back to cloud‑based ACS Serverless GPU via virtual nodes for deploying DeepSeek R1 inference, covering environment preparation, model download, storage setup, custom scheduling, and scaling strategies.
This guide walks you through deploying the DeepSeek‑R1‑Distill model on Volcengine CPU ECS instances, covering use‑case scenarios, recommended server types, Docker setup, environment configuration, and verification steps to achieve cost‑effective, high‑compatibility AI inference.
The article introduces DeepSeek4j 1.4, a Java‑based framework that overcomes Spring AI’s limitations by fully preserving DeepSeek’s chain‑of‑thought and billing features, adding reactive streaming, providing Spring Boot starter integration, and offering quick‑start code samples and configuration guidance.
This article explains how to integrate DeepSeek's large language models into a Spring AI application, covering model selection, API key configuration, URL setup, dependency inclusion, and providing complete Java code examples for both synchronous and streaming chat interactions.
This article provides a comprehensive tutorial on locally deploying the 671‑billion‑parameter DeepSeek R1 model using Ollama, covering model selection, hardware requirements, dynamic quantization, detailed installation steps, performance observations, and practical recommendations for consumer‑grade hardware.
This article examines DeepSeek, a Chinese AGI‑focused AI model, and demonstrates how prompt engineering can enable it to assist database architects, development DBAs, and operations DBAs across various real‑world scenarios, while also discussing its broader impact on individuals, vendors, and enterprises.
This guide explains how to download, quantize, and run the full‑size 671‑billion‑parameter DeepSeek R1 model on local hardware using Ollama, covering model selection, hardware requirements, step‑by‑step deployment commands, optional web UI setup, performance observations, and practical recommendations.
This guide shows how to quickly integrate Alibaba Cloud's AI Search platform and the DeepSeek‑R1 large model with Elasticsearch to create a smart AI Assistant that automates cluster diagnostics, query generation, and visual analytics for operations tasks.
This guide explains how to deploy the DeepSeek‑R1 large language model on a Knative platform using an A10 GPU, covering preparation, service creation with appropriate annotations, YAML configuration, verification via curl, custom domain setup, and optional personal AI assistant deployment.
The article analyzes DeepSeek R1’s release, showing how rising base‑model thresholds, a shift toward infrastructure‑centric skills, and the rise of retrieval‑augmented generation are rapidly diminishing traditional large‑model algorithm engineer positions while reshaping the broader AI industry landscape.
Shenzhen’s Futian district deployed the DeepSeek 2.0 government model across 70 AI‑powered “digital‑intelligence employees”, covering 11 business types and 240 scenarios, cutting document generation from five days to minutes, raising review efficiency by 90%, lowering error rates below 5%, and prompting a rapid nationwide rollout of similar AI‑enabled public‑service systems.
This guide explains what a knowledge base and Retrieval‑Augmented Generation (RAG) are, why personal knowledge bases are valuable, and provides step‑by‑step instructions for using Cloud Studio's DeepSeek‑R1 CPU template to set up and query a private knowledge base with Open‑WebUI or AnythingLLM.
This guide shows how to use the built‑in DeepSeek integration in the latest WPS version to create AI‑generated PowerPoint presentations, covering installation, activation, prompt design, template selection, custom template upload, and final editing, all without extra software or API keys.
This guide walks Java developers through preparing the environment, installing the CodeGPT plugin, configuring DeepSeek API keys, and using the AI-powered code assistant within IntelliJ IDEA, including code completion, explanation, and question‑answer features, with usage statistics and sample code.
This article examines DeepSeek’s various versions, detailing their core modules, underlying principles, architecture diagrams, and performance metrics, while illustrating the internal logic and advantages of each model to guide enthusiasts, professionals, and practitioners toward deeper AI innovation insights.
This article examines DeepSeek's breakthrough AI techniques—including a revamped MoE architecture, aggressive data distillation, ultra‑low‑energy training, novel multi‑stage training strategies, and custom AI chips—that enable a 7B model to rival GPT‑4 while consuming a fraction of the resources.
DeepSeek introduces the NSA sparse attention mechanism, combining dynamic hierarchical sparsity, coarse token compression and fine token selection to achieve up to 11.6× faster inference, lower pre‑training cost, and superior benchmark performance across general, long‑context, and chain‑of‑thought tasks.
The article explores DeepSeek’s innovative self‑correction system—combining a Mixture‑of‑Experts architecture with reinforcement‑learning feedback—to achieve real‑time error detection, dynamic knowledge‑graph updates, and enhanced safety in high‑risk fields like autonomous driving and medical diagnostics.
This article outlines practical cloud‑native deployment options for DeepSeek models, explains common engineering challenges such as traffic spikes, latency, security, quota control, and provides detailed AI‑gateway solutions—including fallback, content safety, API key management, gray‑release routing, caching, and observability—to ensure reliable large‑model applications.
The article explains how to use prompt‑engineering techniques on Claude 3.5 Sonnet to mimic DeepSeek‑R1’s transparent reasoning, detailing background, prompt design, iterative optimization, and the broader impact on AI communication and user expression.
The guide explains how to build AI agents—digital labor forces that follow instructions, plan tasks, and use tools—using ByteDance’s no‑code Coze platform, outlining a 3‑phase, 10‑step framework, emphasizing business‑first design, tool integration, and concise, scenario‑driven development with real‑world case studies.
The article analyzes how massive compute resources drive AI breakthroughs, highlighting Grok 3's top‑tier performance, DeepSeek's efficient engineering under constraints, and the emerging post‑training paradigm that reshapes competition among major AI players.
The surge in demand for private AI deployment has prompted multiple vendors to launch DeepSeek one‑stop machines—integrated hardware solutions that support the full DeepSeek model family, offering higher stability, easier setup, customization, cost savings, and data security across diverse industry scenarios.
This guide walks you through using Alibaba Cloud's PAI‑RAG platform to deploy a DeepSeek large‑language‑model assistant that combines real‑time web search with an enterprise knowledge‑base, covering deployment, network‑search configuration, testing, and advanced enterprise features.
The article analyzes how the shift from data‑driven to knowledge‑driven operations, powered by large language models like DeepSeek, forces companies to build dynamic knowledge‑management platforms that integrate personal and corporate knowledge, improve efficiency, and create sustainable competitive advantage.
The DeepSeek report details rapid model releases from 2024 onward, highlighting innovations such as model distillation, a 671 B MoE architecture, FP8 mixed‑precision, and the Janus‑Pro multimodal framework, while also documenting major cloud and chip providers' integration of these models into their services.
The DeepSeek paper on Native Sparse Attention (NSA) presents a hardware‑aligned, trainable sparse‑attention architecture that slashes O(n²) costs, delivers up to 11.6× speedups and 2.3‑point accuracy gains on long‑context benchmarks, and reduces training expense by 47% while scaling to 64k tokens.
The article explains how DeepSeek‑R1 advances large language model reasoning by releasing a lightweight distilled version, sharing a complete training pipeline—including pre‑training, supervised fine‑tuning, and reinforcement learning—introducing long‑chain reasoning data, a transitional inference model, and a comprehensive RL optimization that together yield strong mathematical and logical capabilities.
This guide shows how to overcome on‑premise compute limits by registering a local Kubernetes cluster to Alibaba Cloud ACK One, provisioning ACS GPU resources, and deploying the DeepSeek‑R1 inference model with the vLLM framework through a series of concrete commands and YAML configurations.
This article explains how to obtain a DeepSeek API key, configure Spring AI with the appropriate base URL and model, and provides Java code examples for both synchronous and streaming chat interactions using the DeepSeek large‑language model.
This guide explains the fundamentals of prompt engineering for large language models such as DeepSeek and ChatGPT‑4o, illustrating clear‑prompt design, giving models time to think, chaining prompts, iterative refinement, and advanced tricks with concrete good and bad examples.
This article explains DeepSeek's knowledge‑distillation approach for compressing large language models into small, efficient student models, details step‑by‑step local deployment requirements, performance optimizations, and highlights the cost, privacy, and application benefits of running the distilled model on‑premise.
DeepSeek V3 cuts training to 2.788 M H800 GPU‑hours with FP8 mixed‑precision and a fully optimized framework, slashes token costs by 96% versus ChatGPT O1, and its efficient inference and model‑compression techniques are reshaping AI‑agent development, spurring demand for low‑latency, high‑bandwidth optical modules and edge‑computing infrastructure.
DeepSeek4j 1.4 brings a Java‑native integration framework that fully preserves DeepSeek's chain‑of‑thought capabilities, adds reactive streaming, and offers a one‑line Spring Boot starter, enabling developers to quickly embed the model with simple configuration and rich debugging tools.
This tutorial walks beginners through obtaining a DeepSeek API key, configuring a WeChat public account, setting up an Alibaba Cloud ECS instance, installing required dependencies, editing configuration files, and running the chatgpt‑on‑wechat project to enable AI‑powered replies via the WeChat platform.
This guide explains how to deploy large‑scale AI models such as Ollama and DeepSeek‑R1 on a Kubernetes 1.30 cluster, covering hardware requirements, PVC and deployment manifests, service exposure, image pulling, verification steps, API access, and monitoring with Prometheus and Grafana.
This article breaks down DeepSeek's LLM training pipeline, explaining the massive pre‑training phase, instruction fine‑tuning, reinforcement‑learning‑from‑human‑feedback, and the distinct roles of its V3 instruction model and R1 reasoning model, while also highlighting performance metrics and current limitations.
The article investigates why DeepSeek frequently returns a “server busy” message, analyzing factors such as sudden traffic spikes, compute and bandwidth limitations, security attacks, and maintenance policies, and then offers actionable solutions including query optimization, off‑peak usage, third‑party cloud platforms, and local deployment.
Microsoft's OmniParser V2.0 transforms large language models such as DeepSeek‑R1, GPT‑4o, and Qwen‑2.5VL into visual AI agents by accurately detecting interactive UI elements, providing semantic descriptions, and generating structured representations that boost inference speed, reduce latency by 60%, and dramatically improve benchmark accuracy.
Discover a comprehensive collection of 24 structured prompting techniques—from basic role‑play formulas to advanced cross‑disciplinary and managerial frameworks—designed to help users of DeepSeek and other large language models craft precise, high‑impact queries that dramatically improve response quality and efficiency.
This article details a step‑by‑step deployment of the DeepSeek R1 model on Huawei Ascend 910B NPUs, covering FP8‑to‑BF16 weight conversion, custom container image preparation, configuration of MindIE services, common pitfalls, and practical troubleshooting tips for large‑scale inference.
The article compares DeepSeek with ChatGPT, highlighting DeepSeek’s superior performance in math and reasoning, lower cost, hardware requirements, and how to locally deploy the R1 model using Ollama and a GUI, illustrating its potential to reshape the AI landscape.
This guide walks you through using DeepSeek R1 via the official website or popular third‑party cloud services, compares free token quotas, explains token accounting, and provides step‑by‑step instructions for configuring API access and AI clients such as Chatbox, Cherry Studio, and Dify.
This guide walks you through preparing the environment, installing Docker, cloning the DeepSeek repository, running the model with Docker or Ollama for quick start, using the enterprise API, and deploying the same model on Alibaba Cloud's free Bailei service within minutes.
This article presents a theoretical and experimental analysis of converting Group Query Attention (GQA) models to Multi‑Head Linear Attention (MLA) using the TransMLA method, demonstrating superior expressiveness and performance on DeepSeek‑based large language models while keeping KV‑Cache costs unchanged.
DeepSeek‑R1 excels at deep reasoning but lacks native structured output; this guide explains why structured output matters, outlines common API‑level techniques, and provides three practical solutions—using an auxiliary model with a LangChain chain, a LangGraph workflow, and a ReAct agent—complete with code snippets and JSON‑mode tips.
DeepSeek, the popular Chinese large‑language model, boosts data‑warehouse engineers' productivity by offering free, open‑source AI assistance across code writing, model design, metadata management, data quality monitoring, and governance, ultimately maximizing enterprise data asset value.
This article walks through installing Ollama, downloading the DeepSeek‑r1:1.5b model, configuring Spring AI in a Spring Boot 3 project, and exposing the model via a REST endpoint with Server‑Sent Events, providing complete code snippets and step‑by‑step screenshots for a functional AI integration.
This guide explains how to locally deploy the open‑source DeepSeek model using Ollama, enhance interaction with Chatbox and Page Assist, and connect a local knowledge base via AnythingLLM's RAG architecture, providing step‑by‑step instructions, hardware requirements, and API examples for a self‑hosted AI system.
This guide walks you through setting up a local DeepSeek large language model, covering environment preparation, model acquisition, dependency installation, FastAPI service creation, Docker containerization, optional front‑end interface, performance tuning, and common troubleshooting steps.
The article examines how WeChat's recent integration of DeepSeek R1 for AI‑driven search not only offers a smooth user experience but also signals a strategic shift in the Chinese AI rivalry, highlighting Tencent's push to boost ARPU while questioning Baidu's slower response.
This article explains why the massive DeepSeek V3/R1 model (671 B parameters) is hard to deploy and introduces three key techniques—model distillation, quantization, and fine‑tuning—that can shrink, accelerate, or specialize large models, while outlining their trade‑offs and practical steps.
This article explains what reasoning language models are, outlines their strengths and weaknesses, details DeepSeek R1's three variants and their training pipelines—including pure reinforcement learning, SFT + RL, and distillation—while also discussing inference‑time scaling techniques and related research such as Sky‑T1 and TinyZero.
The article reviews DeepSeek's open‑source R1 and V3 large language models, highlighting their technical breakthroughs, cost advantages, expert opinions, industry adoption across chips, cloud services, and applications, and discusses future directions for model scaling, distillation, and AI competition.
This article provides an in‑depth technical analysis of DeepSeek’s model distillation technology, covering its core principles, innovative data‑model fusion strategies, architecture design, training optimizations, performance benchmarks, and the remaining challenges of scaling distillation to multimodal tasks.
This guide shows how to use Tencent Cloud's Knowledge Engine to integrate DeepSeek‑R1 and V3 models, configure API keys, set up Webman AI, and deploy a connected AI application with OpenAI‑compatible endpoints, all with step‑by‑step instructions and code examples.
The article provides an in‑depth overview of DeepSeek's transformer‑based foundation, Mixture‑of‑Experts architecture, novel attention mechanisms, multi‑token prediction, FP8 mixed‑precision training, knowledge distillation, reinforcement‑learning approaches, and compares its performance and cost advantages against leading models such as GPT and Gemini.
This article reviews a new risk‑sensitive trading agent that combines reinforcement learning with large language models to extract stock recommendations and news‑based risk scores, describes the extended CVaR‑PPO algorithm, presents extensive experiments on the FNSPID dataset, and discusses the resulting performance gains and future work.
This comprehensive tutorial walks users through preparing their system, downloading the appropriate DeepSeek installer, and performing detailed installation steps for Windows, macOS, and Linux, followed by initial launch, configuration, and troubleshooting tips to ensure a successful setup of the data analysis tool.
This guide shows how developers can quickly integrate DeepSeek's powerful language model into WeChat mini‑programs using Tencent Cloud, covering three integration approaches, a three‑step voice‑interaction setup, and a concise list of related cloud products and resources.
The DeepSeek series introduces a refined Mixture‑of‑Experts architecture with fine‑grained expert partitioning, shared experts, and learnable load‑balancing, alongside innovations such as Group Relative Policy Optimization, Multi‑Head Latent Attention, Multi‑Token Prediction, mixed‑precision FP8 training, and the R1/R1‑Zero models that use Long‑CoT reasoning, reinforcement‑learning pipelines, and distillation to achieve OpenAI‑comparable performance at lower cost.
This article provides an in‑depth overview of DeepSeek’s model distillation technology, covering its definition, core principles, innovative data‑model distillation integration, architecture design, training strategies, performance gains, and the challenges of scaling to multimodal data.
Baidu’s sudden open‑source of ERNIE 4.5, triggered by DeepSeek R1’s rapid adoption and impressive performance gains, signals a market shift from closed AI ecosystems to open‑source dominance, forcing giants to rethink power, control, and user‑centric strategies.
This article explains how to integrate DeepSeek's large language models—both the chat‑oriented deepseek‑chat and the reasoning‑focused deepseek‑reasoner—into a Spring AI application, covering API key setup, base‑URL configuration, model selection, and providing full code examples for dependency, configuration, and a simple chat controller.
This guide walks you through deploying DeepSeek models on Alibaba Cloud PAI, integrating SerpAPI for live web search, building a private knowledge base, and assembling a RAG-enabled chatbot workflow, all within 30 minutes, enabling enterprises to create intelligent applications that combine large‑model capabilities with up‑to‑date information.
Arm CEO Rene Haas dismisses DeepSeek’s claimed $5.6 million training cost as a rumor, while the Chinese startup’s low‑cost, high‑performance models spark debate over AI development economics, geopolitics, and looming government bans worldwide.
The article presents DeepSeek founder Liang Wenfeng’s 60 reflections on artificial general intelligence, large‑model research, open‑source culture, talent strategy, and the broader AI ecosystem, while also highlighting his vision for democratizing AI and upcoming AI‑coding events in Beijing.
Facing frequent “service busy” errors on DeepSeek’s website, this guide shows how to bypass those limits by pairing a local client such as Cherry Studio or Chatbox with cloud‑based API services from providers like Alibaba Cloud, Huawei, ByteDance, Tencent, or Baidu, enabling smooth, cost‑aware access to the top‑tier DeepSeek‑R1‑671B model.
This guide walks you through deploying the DeepSeek‑R1 large‑language‑model inference service on Alibaba Cloud ACK One registered clusters using ACS GPU compute, covering model preparation, OSS storage setup, PersistentVolume configuration, arena‑based service deployment, and verification steps with concrete commands and parameters.
This article explains how to quickly deploy the full‑parameter DeepSeek‑R1 671B model in a multi‑node GPU‑enabled Kubernetes cluster on Alibaba Cloud ACK, covering prerequisites, model parallelism, vLLM‑Ray distributed deployment, service verification, and integration with Dify to build a private AI Q&A assistant.
This tutorial explains how to prepare the environment, install the CodeGPT plugin, configure DeepSeek API keys and model settings in IntelliJ IDEA, and use the AI assistant for code generation, completion, explanation, and troubleshooting, while also showing usage statistics and a complete Java example.
This guide walks through building a Java RAG system with DeepSeek R1, Milvus, and Spring, covering environment setup, vector model integration via OpenAI protocol, Maven dependencies, data embedding, and a chat endpoint that combines semantic retrieval with LLM generation.
This article introduces the DeepSeek family of models, explains the concepts of online search and deep reasoning, details the two‑phase training pipeline with data augmentation and reinforcement learning, and showcases practical experiments and deployment examples for the R1 and distilled variants.
This article demystifies DeepSeek R1 by explaining key concepts such as online search integration and the R1 model, detailing its two‑phase training pipeline, core techniques like iterative data enhancement, and showcases practical reproductions, benchmark tests, and deployment examples for AI developers.
This article explains what reasoning language models are, when they are needed, and reviews four main techniques— inference‑time scaling, pure reinforcement learning, combined SFT + RL, and distillation—illustrated with DeepSeek‑R1’s development, cost analysis, and low‑budget alternatives.
This guide walks you through configuring Tencent Cloud AI Code Assistant to use DeepSeek models—either via the DeepSeek public API or a locally‑deployed Ollama instance—covering prerequisites, step‑by‑step setup, required hardware, and command‑line examples.
This article introduces DeepSeek, a high‑efficiency search engine optimized for large‑scale data, explains how to configure it within the Cursor database tool using code snippets, and demonstrates its applications such as semantic search, content recommendation, intelligent data analysis, and document similarity matching.
The article analyzes DeepSeek’s recent breakthroughs—including its Mixture‑of‑Experts architecture, cost‑effective inference optimizations, high‑accuracy multimodal processing, and open‑source collaboration—while also offering a curated bundle of technical e‑books covering AI chips, networking, storage, and more.
The DeepSeek research framework 2025 reveals that its V3 and R1 models, built on Transformer with MLA and DeepSeek MoE technologies, are accelerating training efficiency, reshaping domestic AI valuation, and positioning open‑source AI as a disruptive force in the global market.
This article provides a comprehensive tutorial on installing Ollama, downloading and running the DeepSeek‑R1 model on a Mac, explains the benefits of local deployment for stability and privacy, and shows how to integrate the model with the Chatbox AI visual interface.
This article compiles a curated reading list of foundational and recent research papers—from the original Transformer to chain‑of‑thought, mixture‑of‑experts, and reinforcement‑learning studies—that together explain the breakthroughs behind DeepSeek‑R1 and guide readers through the technical evolution of modern large language models.
This guide explains how to prepare an Alibaba Cloud GPU instance, install Docker and NVIDIA tools, pull or build a container image, and run the FP8‑quantized DeepSeek‑R1‑Distill‑Qwen‑32B model using vLLM and OpenWebUI for both offline and online inference.
This guide walks you through creating a lightweight compute instance, adding it to Baidu Baige AI heterogeneous computing platform, deploying the vLLM tool, loading and serving small‑scale dense models such as DeepSeek, Llama and Qwen, and provides recommended configuration lists to achieve low‑cost, high‑performance inference.
To use DeepSeek in IntelliJ IDEA, install either the Continue or CodeGPT plugin via the Plugins marketplace, add the DeepSeek model and your API key in the plugin settings, then generate code, tests, or explanations directly in the IDE, noting the public API may occasionally fail.
