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所在平台: Udemy |
课程主页: https://www.udemy.com/course/llm-enggineering-course-run-models-locally/
课程评论：没有评论
课程名称：LLM速成课程：本地运行模型，掌握LLM工程 课程概况：厌倦了依赖需订阅、API密钥及持续互联网连接的云端AI工具？为何还要等待你的LLM工程旅程？在这门实用的快速课程中，你将学习如何在自己的机器上完全离线、本地运行强大的开源语言模型——永久免费，无需重复费用。没有第三方服务，只有你、你的笔记本电脑和自己的AI环境。我将逐步指导你在Windows、macOS和Linux上的具体设置过程。此外，你还将探索实际的LLM功能，如工具、增强检索生成（RAG）、流式响应及如何使用Python脚本和提示与LLM进行交互。无论你是学生、开发者还是技术专家，这门课程都能让你完全掌控自己的LLM学习和工作流程——无需供应商锁定或云依赖。 你将学习到： - 设置本地环境以运行前沿LLM。 - 使用简单的命令行工具下载和管理模型。 - 编写Python脚本互动和提示本地模型。 - 通过实际编码示例学习提示工程，理解其对LLM应用的影响。 - 探索关键的LLM概念，如使用LangChain的RAG、工具（可调用函数）、流式处理、嵌入、向量数据库和提示工程。 - 构建隐私优先、可重用的LLM设置，适用于内部工具、研究或个人项目。 - 永久避免API密钥、订阅和互联网依赖。 适合人群： - 熟悉Python基础的工程师和开发者。 - 希望在私密离线环境中学习LLM的学生或专业人士。 - 寻找无需依赖外部API的内部AI工具的组织。 这是一门无废话、不涉及不必要理论的务实速成课程，采用独特的离线优先方法。课程结束时，你不仅能够在电脑上完全运行模型，还将学习到可在现实环境中应用的实用LLM概念。让我们开始吧！
Tired of relying on cloud-based AI tools that require subscriptions, API keys, and constant internet access to run LLMs? Why hold your LLM Enggineering journey?In this hands-on, fast-track course, you'll learn how to run powerful open-source language models locally on your own machine - 100% offline, private, and free forever. No recurring costs. No third-party services. Just you, your laptop, and your own AI environment.I'll guide you through the exact setup process step-by-step on Windows, macOS, and Linux. Then, I will go beyond just setup - you'll also explore real-world LLM features like tools, Retrieval-Augmented Generation (RAG), streaming responses, and how to interact with LLMs using Python scripts and prompts.Whether you're a student, developer, or tech pro, this course empowers you to take full control of your LLM learning and workflows - without vendor lock-in or cloud dependency.What You'll Learn:Set up a local environment to run cutting-edge LLMs.Use simple command-line tools to download and manage models.Write Python scripts to interact with and prompt local models.Learn Prompt Engineering with handson coding examples and understand how it impacts llm applicationsExplore key LLM concepts like RAG using LangChain, tools(callable functions), streaming, embeddings, vector databases and prompt engineering.Build a privacy-first, reusable LLM setup for internal tools, research, or personal projects.Avoid API keys, subscriptions, and internet dependency - forever.Who This Course Is For:Engineers & developers familiar with Python basics.Students or professionals looking to learn LLMs in a private, offline environment.Organizations exploring internal AI tools without relying on external APIs.I skip the fluff and unnecessary theory - this is a practical, no-nonsense crash course for modern LLM engineering with a unique offline-first approach.By the end, you'll not only be running models entirely on your computer, but also have learned practical LLM concepts that you can apply across real-world environments. Let's get started!
