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.
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.
The article analyzes DeepSeek's open‑source model launch, its rapid user growth, and presents a comprehensive list of 100 practical AI use cases across sectors—grouped by frequency and adoption stage—to illustrate the model's market impact and future potential.
This tutorial guides you through creating an AI‑powered, customizable knowledge space by integrating DeepSeek R1 via Alibaba Cloud Bailei's Model‑as‑a‑Service with the low‑code Mobinext platform, covering setup, configuration, deployment, and future expansion for multi‑tenant use.
The article analyses DeepSeek‑R1’s low‑cost open‑source strategy, superior inference performance (including GPQA benchmark gains over GPT‑4o), its focus on complex reasoning, math and programming, and how these traits reshape AIGC across industries while highlighting remaining privacy and ethical challenges.
This guide walks through setting up a local personal knowledge base using Ollama, DeepSeek‑R1, and AnythingLLM, and demonstrates how to integrate the Continue AI code assistant into VSCode, covering installation, configuration, and usage tips for efficient, secure development.
This guide walks you through setting up a powerful local AI knowledge base using Ollama, DeepSeek‑R1, and AnythingLLM, and shows how to integrate the Continue extension into VSCode for seamless, secure, and efficient development workflows.
This tutorial explains how to deploy the DeepSeek‑r1:1.5b model on a cloud server using Ollama, create a retrieval‑augmented generation (RAG) vector database with the mxbai‑embed‑large embedding model, and build an interactive AI application that answers questions from uploaded PDFs.
This step‑by‑step guide shows Java developers how to prepare their environment, install the CodeGPT plugin, configure DeepSeek with an API key and model settings, and then use the assistant for code generation, completion, explanation, question answering, and usage monitoring within IntelliJ IDEA.
This guide walks you through building a Retrieval‑Augmented Generation (RAG) system by integrating Alibaba Cloud's Hologres vector store, the Proxima high‑performance vector engine, and DeepSeek large language models via PAI‑EAS, covering prerequisites, deployment steps, configuration, and inference verification.
This article examines how to build and improve reasoning‑capable large language models, explains the definition and use‑cases of reasoning models, details DeepSeek‑R1’s training pipeline, compares four key enhancement methods—including inference‑time scaling, pure RL, SFT + RL, and distillation—and offers budget‑friendly advice.
This article evaluates DeepSeek‑R1’s 32B and 70B distilled models alongside the original R1 on a range of reasoning and coding tasks, detailing hardware setup, test methodology, per‑task results, and a comparative analysis of their strengths and weaknesses.
The DeepSeek open‑source week introduced seven breakthrough technologies—FlashMLA, DeepGEMM, DeepEP, DualPipe, EPLB, 3FS, and Smallpond—that together overhaul data flow, algorithmic complexity, hardware utilization, MoE communication, and resource balancing, dramatically improving large‑model training efficiency and lowering entry barriers for the AI industry.
This article demonstrates how a non‑programmer can use the DeepSeek‑powered “AI Programmer” mode in Tongyi Lingma to generate, modify, and deploy a functional WeChat mini‑program entirely through natural language prompts, complete with screenshots of each step.
This tutorial walks you through integrating DeepSeek via Spring AI into a Spring Boot project, covering Spring AI basics, obtaining an API key, adding dependencies and configuration, implementing controller endpoints, testing with Postman, and accessing the full source code.
This article examines how swapping in DeepSeek‑R1 enhances Retrieval‑Augmented Generation with deeper reasoning, outlines its benefits and pitfalls—including slower inference, higher compute costs, and hallucinations—provides a simple hallucination test, and proposes an Agentic RAG research assistant to balance accuracy and creativity.
This guide explains why DeepSeek is a compelling GPT‑4‑level alternative, provides hardware recommendations for various model sizes, and walks through a three‑step Windows deployment using Ollama, including installation, environment configuration, model download, performance tuning, and common troubleshooting tips.
The article outlines DeepSeek's four‑level capability hierarchy—basic multimodal data fusion and dynamic governance, intermediate domain modeling with causal reasoning and multi‑objective optimization, advanced complex system modeling with digital twins and multi‑agent coordination, and ultimate autonomous evolution features such as concept‑space exploration and self‑programming.
This article demonstrates how to combine Playwright web‑scraping of multiple Chinese movie platforms with the DeepSeek AI model to automatically collect data and generate a scientific prediction of the box‑office revenue for the film "Ne Zha 2".
This article examines DeepSeek, a Chinese AGI‑focused AI model, explains prompt‑engineering techniques, and evaluates its performance across database architecture, development, and operations tasks through concrete Q&A examples, SQL plan analysis, and shell‑script generation, while also discussing its broader impact on professionals, vendors and enterprises.
The article analyzes DeepSeek’s latest V3 conversational model and R1 inference model, detailing their MoE architecture, training on H800 GPUs costing about $558 k, comparing compute expenses to Meta’s Llama 3.1, and showing that their API pricing is roughly one‑tenth of GPT‑4o for dialogue and one‑twentieth of OpenAI o1 for inference.
This guide walks you through deploying a private DeepSeek R1 inference service on Alibaba Cloud using CAP and AgentCraft, covering architecture, one‑click deployment, database and vector model configuration, UI customization, cleanup tips, and FAQs for seamless, unlimited AI inference.
DeepSeek’s rapid rise is examined through its RL‑based Time Scaling paradigm, cost‑effective architecture, innovative training pipeline, open‑source strategy, and security challenges, highlighting how these breakthroughs disrupt traditional AI model development, lower resource demands, and influence industry dynamics.
This step‑by‑step guide shows how to set up VS Code with the Cline and Continue extensions, configure the free DeepSeek API, generate website code using AI, and deploy the site via GitHub Pages, enabling anyone to create a personal website without paying for AI tools.
This guide walks you through quickly deploying Baidu's DeepSeek‑R1 model, accessing its WebUI, and enabling key enterprise extensions such as web search, file upload, OCR, and content moderation to integrate AI capabilities into production workflows.
This guide walks you through creating a custom Cloud Native Flow step that calls DeepSeek to automatically review code in Alibaba Cloud Codeup, covering token creation, API key setup, step publishing, pipeline configuration, and viewing AI‑generated review comments.
DeepSeek’s OpenSourceWeek introduced FlashMLA, a GPU‑optimized MLA decoding kernel for Hopper GPUs that leverages FlashAttention and CUTLASS to dramatically improve large‑model inference performance, with early adoption showing up to 30% higher compute utilization and doubled speed in some scenarios.
This guide walks through deploying the full‑feature DeepSeek V3+R1 model on Tencent Cloud, configuring a smart knowledge‑base application, importing documentation, enabling internet search, tuning retrieval parameters, and publishing the app for public use, all without writing code.
The article explains how DeepSeek’s low‑cost large‑language‑model training reduces GPU price pressure, yet paradoxically fuels greater demand for Nvidia hardware by lowering entry barriers, illustrating the modern Jevons paradox and its broader economic and societal implications.
The article analyzes DeepSeek’s three open‑source projects—FlashMLA, DeepEP, and DeepGEMM—showing how they optimize for the China‑only NVIDIA H800 GPU, contrast this with the abundant hardware resources of Western AI firms, and highlight the growing demand for talent that masters both AI models and GPU hardware.
The DeepSeek technical overview details the evolution from the dense 67 B V1 model through the 236 B MoE‑based V2 and 671 B V3 with FP8 training, to the RL‑only R1 series that learns reasoning without supervision, highlighting innovations such as Grouped‑Query Attention, Multi‑Head Latent Attention, load‑balancing‑free MoE, Multi‑Token Prediction, and knowledge distillation, and reporting state‑of‑the‑art benchmark results and open‑source reproduction projects.
The Inspur Metabrain R1 inference server, equipped with FP8 acceleration and a 1128 GB HBM3e memory pool, has been tightly integrated with SGLang 0.4.3 to run the 671‑billion‑parameter DeepSeek R1 model, delivering over 1,000 concurrent user sessions and up to 3,976 tokens/s throughput.
This step‑by‑step guide shows you how to obtain a DeepSeek API key, install VSCode and the open‑source Cline extension, configure the plugin to connect to DeepSeek, and use AI‑powered code assistance while covering common pitfalls and alternative models.
This step‑by‑step guide explains how to use Alibaba Cloud's MaxCompute, DataWorks, and the AI platform PAI to build a custom dataset, fine‑tune the DeepSeek‑R1 distilled model, and deploy the resulting model for practical applications.
The article provides an in‑depth overview of DeepSeek’s large language model, detailing its mixture‑of‑experts and Transformer foundations, novel attention mechanisms, load‑balancing, multi‑token prediction, FP8 mixed‑precision training, and various training regimes such as knowledge distillation and reinforcement learning.
Thirteen engineers praise DeepSeek’s open‑source, reinforcement‑learning‑driven architecture—using FP8 storage and SFT‑free training—to deliver GPT‑4‑level reasoning at one‑twentieth the cost, enabling single‑GPU deployment, lowering barriers for academia and startups, and prompting notable market reactions that could democratize advanced AI.
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 within IntelliJ IDEA, complete with screenshots and sample code.
This article shows designers how to combine AI services such as Deepseek, JiMeng, Tripo, Tongyi and Jianying to accelerate 3D modeling, PPT creation and short‑video production, turning lengthy manual tasks into fast, creative processes.
DeepSeek4j 1.4 introduces a Java‑native framework that fully preserves DeepSeek's chain‑of‑thought and billing features, adds reactive streaming support, and provides a Spring Boot starter for effortless integration, accompanied by quick‑start code, configuration examples, and a built‑in debugging UI.
This tutorial shows how to harness Redis's dual role as a high‑performance cache and a vector database, guiding you through Docker setup, vector storage methods, and Java Lettuce integration to build a private large‑language‑model knowledge base with DeepSeek4J.
This article provides an in‑depth overview of DeepSeek, covering its core multimodal and multilingual features, long‑context capabilities, domain optimizations, security, main functions, diverse application scenarios, and practical usage via web interface or API integration.
DeepSeek R1 builds on the DeepSeek V3 base model using a multi‑stage reinforcement learning pipeline—including GRPO optimization, rule‑based reward modeling, supervised fine‑tuning, language‑consistency rewards, rejection sampling, and distillation—to produce a high‑performing, aligned LLM capable of accurate reasoning.
This article summarizes a Peking University lecture on DeepSeek‑R1, detailing its core concepts, advantages, and historical significance, then explains the underlying mechanisms of large‑model AI and AIGC tools, and finally offers practical guidance for selecting and efficiently applying AI solutions.
This tutorial walks users through installing FlashMLA, integrating it with the vLLM framework, downloading the DeepSeek‑V2‑Lite‑Chat model, benchmarking various MLA implementations, and running a local inference demo that shows FlashMLA’s speed advantage on long‑sequence generation.
This step‑by‑step guide shows how to assemble a Retrieval‑Augmented Generation (RAG) system using Alibaba Cloud Milvus vector search, the DeepSeek large language model, and PAI LangStudio, covering instance creation, data upload, model deployment, connection setup, flow design, and service invocation.
DeepSeek’s distillation technology combines data and model distillation to transfer knowledge from large teacher models to compact student models, detailing its definitions, principles, key innovations, architecture, training methods, performance gains, and challenges, especially in multimodal contexts.
This tutorial explains what Spring AI is, walks through creating a Spring Boot project with Maven, adding the necessary dependencies, writing a simple controller that forwards user messages to a local DeepSeek model, configuring the application, and testing the AI‑powered endpoint.
The article explains why existing Java AI frameworks struggle with DeepSeek R1’s chain‑of‑thought and streaming features, introduces DeepSeek4j 1.4 as a targeted solution, details its core capabilities, and provides a step‑by‑step guide to integrate it with Spring Boot and Project Reactor.
This guide explains how to obtain free API keys for DeepSeek‑R1:7B, 14B, and 32B models from the National Supercomputing Center, walks through the purchase steps, and provides a Python example for calling the models via the provided endpoint.
The author benchmarks DeepSeek's FlashMLA against FlashInfer and several Triton-based implementations, detailing setup challenges, decode‑only bandwidth results, and observations that the official DeepSeek version leads while Triton optimizations show mixed performance across different head sizes.
This guide explains what DeepSeek R1 is, compares its full and distilled versions, details hardware requirements for Linux, Windows, and macOS, and provides step‑by‑step instructions for local deployment using Ollama, LM Studio, Docker, and visual interfaces like Open‑WebUI and Dify.
This guide walks developers through the full lifecycle of using DeepSeek—choosing the right deployment method (API, local machine, or private cloud), selecting model sizes based on hardware, configuring Tencent Cloud services, building AI applications, and integrating the model into development tools and mini‑programs.
This guide shows how to obtain free DeepSeek API access through GitHub Models and SiliconFlow, detailing the required API base URL, key, and model name, how to register, create keys, verify usage with a web chat tool, and compare model choices and platform limits.
During the Chinese New Year break the author used DeepSeek and AISE ClinePRO to build a complete full‑stack product in only 20 hours, demonstrating a twelve‑fold productivity boost over traditional development while showcasing AI‑driven code generation, multilingual support, automated documentation, and DevOps integration.
Flash-MLA is an open‑source GPU kernel optimized for Nvidia Hopper GPUs that compresses the KV cache of multi‑head attention, cutting memory usage by up to 93.3% and delivering 580 TFLOPS compute, thereby dramatically accelerating large‑language‑model inference while lowering cost.
Since the 2017 introduction of the Transformer architecture, this article chronicles the rapid development of large language models—including BERT, GPT series, multimodal systems, and the cost‑effective DeepSeek‑R1—highlighting key innovations, scaling trends, alignment techniques, and their transformative impact across AI research and industry.
This guide shows how open‑source LLMs like DeepSeek can power cost‑effective intelligent Q&A services, and how the cloud‑native Higress API gateway adds real‑time web search, routing, security, and observability to create a production‑grade solution in just a few steps.
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.
