llm_engineering AI技能包
llm_engineering AI技能包 是 AI Skill Hub 本期精选AI工具之一。已获得 6.0k 颗 GitHub Star，综合评分 8.2 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
📚 深度解析
**为什么要使用开源工具而非商业 SaaS？**
对于个人开发者和有隐私需求的用户，本地部署的开源工具意味着数据不离本机，不受第三方服务商的数据政策约束。同时，开源工具通常没有使用次数限制和月度费用，一次安装即可长期使用，对于高频使用场景的总拥有成本（TCO）远低于订阅制商业工具。
**安装与环境准备**
llm_engineering AI技能包 依赖 Jupyter 运行环境。建议通过 pyenv（Python）或 nvm（Node.js）管理 Jupyter 版本，避免全局环境污染。对于新手用户，推荐先创建虚拟环境（python -m venv venv && source venv/bin/activate），再安装依赖，这样即使出现问题也可以随时删除虚拟环境重新开始，不影响系统稳定性。
**社区与维护**
GitHub Issue 和 Discussion 是获取帮助的最快渠道。在提问前建议先检查 Closed Issues（已关闭的问题），大多数常见问题都已有解答。遇到 Bug 时，提供 pip list 的输出、完整错误堆栈和最小可复现示例，能显著提高开发者响应速度。AI Skill Hub 将持续追踪 llm_engineering AI技能包 的版本更新，及时通知重要功能变化。
📋 工具概览
llm_engineering AI技能包 是一款基于 Jupyter Notebook 开发的开源工具，专注于 LLM工程、教学资源、Jupyter实战 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
📖 中文文档
llm_engineering AI技能包 是一款基于 Jupyter Notebook 开发的开源工具，专注于 LLM工程、教学资源、Jupyter实战 等核心功能。作为 GitHub 开源项目，它拥有活跃的社区支持和持续的版本迭代，代码完全透明可审计，支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流，都能提供稳定可靠的解决方案。
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
# 克隆仓库 git clone https://github.com/ed-donner/llm_engineering cd llm_engineering # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
- 访问 GitHub 仓库页面
- 按照 README 文档完成依赖安装
- 根据系统环境完成初始化配置
- 参考官方示例或文档开始使用
- 遇到问题可在 GitHub Issues 中查找解答
# 查看帮助 llm_engineering --help # 基本运行 llm_engineering [options] <input> # 详细使用说明请查阅文档 # https://github.com/ed-donner/llm_engineering
# llm_engineering 配置说明 # 查看配置选项 llm_engineering --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export LLM_ENGINEERING_CONFIG="/path/to/config.yml"
LLM Engineering - Master AI and LLMs
Important note: see my warning about Llama3.3 below - it's too large for home computers! Stick with llama3.2 - several students have missed this warning...
We will start the course by installing Ollama so you can see results immediately! 1. Download and install Ollama from https://ollama.com noting that on a PC you might need to have administrator permissions for the install to work properly 2. On a PC, start a Command prompt / Powershell (Press Win + R, type cmd, and press Enter). On a Mac, start a Terminal (Applications > Utilities > Terminal). 3. Run ollama run llama3.2 or for smaller machines try ollama run llama3.2:1b - please note steer clear of Meta's latest model llama3.3 because at 70B parameters that's way too large for most home computers! 4. If this doesn't work: you may need to run ollama serve in another Powershell (Windows) or Terminal (Mac), and try step 3 again. On a PC, you may need to be running in an Admin instance of Powershell. 5. And if that doesn't work on your box, I've set up this on the cloud. This is on Google Colab, which will need you to have a Google account to sign in, but is free: https://colab.research.google.com/drive/1-_f5XZPsChvfU1sJ0QqCePtIuc55LSdu?usp=sharing
Any problems, please contact me!
Before the Setup instructions - a special note
Early on in the course (on Day 2), I give a demo of a very cool, popular product called Claude Code. It's an AI coding tool, similar to Cursor that we use on the course. I'm only showing this as an example of Agentic AI in action; it's not a tool that's covered explicitly on this course, particularly as we're in Cursor. But if you want to use Claude Code yourself, the Quick Start guide from Anthropic is here.
OK - now on to Setup instructions
After we do the Ollama quick project, and after I introduce myself and the course, we get to work with the full environment setup.
Hopefully I've done a decent job of making these guides bulletproof - but please contact me right away if you hit roadblocks:
Setup instructions: Setup Instructions All Platforms
An important point on API costs (which are optional! No need to spend if you don't wish)
During the course, I'll suggest you try out the leading models at the forefront of progress, known as the Frontier models. I'll also suggest you run open-source models using Google Colab. These services have some charges, but I'll keep cost minimal - like, a few cents at a time. And I'll provide alternatives if you'd prefer not to use them.
Please do monitor your API usage to ensure you're comfortable with spend; I've included links below. There's no need to spend anything more than a couple of dollars for the entire course. Some AI providers such as OpenAI require a minimum credit like \$5 or local equivalent; we should only spend a fraction of it, and you'll have plenty of opportunity to put it to good use in your own projects. During Week 7 you have an option to spend a bit more if you're enjoying the process - I spend about \$10 myself and the results make me very happy indeed! But it's not necessary in the least; the important part is that you focus on learning.
Monitoring API charges
You can keep your API spend very low throughout this course; you can monitor spend at the dashboards: here for OpenAI, here for Anthropic.
The charges for the exercsies in this course should always be quite low, but if you'd prefer to keep them minimal, then be sure to always choose the cheapest versions of models: 1. For OpenAI: Always use model gpt-4.1-nano in the code 2. For Anthropic: Always use model claude-3-haiku-20240307 in the code instead of the other Claude models 3. During week 7, look out for my instructions for using the cheaper dataset
Please do message me or email me at ed@edwarddonner.com if this doesn't work or if I can help with anything. I can't wait to hear how you get on.
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Other resourcesI've put together this webpage with useful resources for the course. This includes links to all the slides.https://edwarddonner.com/2024/11/13/llm-engineering-resources/ Please keep this bookmarked, and I'll continue to add more useful links there over time. |
Free alternative to Paid APIs
See Guide 9 in the guides directory for the detailed approach with exact code for Ollama, Gemini, OpenRouter and more!
Answers to the most common questions
My Cursor looks different to yours (new splash screen) Can I use Gemini or free models instead of OpenAI Yes! Where are the course resources How does this course fit in with your others? Can I take this course with no programming background? What job can I get after taking this course?
高质量教学资源，6k+星标验证其价值。代码示例��用性强，适合系统学习LLM工程实践。定期维护更新，社区活跃度高。
- 构建多智能体协作系统的 Agent 开发者
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- CLI：直接 npm install -g / pip install，命令行调用
- 本地部署：CPU 8GB 起，GPU 推荐 16GB+ 显存
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 开源免费，支持本地部署，数据完全自主可控
- 活跃的 GitHub 开源社区，持续迭代更新
- 提供详细文档和使用示例，新手友好
- 支持自定义配置，灵活适配不同使用环境
- 可作为基础组件集成进现有技术栈或进行二次开发
- 构建多智能体协作系统的 Agent 开发者
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
👥 适合人群
🎯 使用场景
- 本地部署运行，保护数据隐私，满足合规要求
- 自定义集成到现有系统，扩展技术栈能力
- 作为开源基础组件进行商业化二次开发
⚖️ 优点与不足
- +GitHub 6.0k Star，社区高度认可
- +MIT 协议，可免费商用
- +完全开源免费，无授权费用
- +本地部署，数据完全自主可控
- +开发者社区支持，遇问题可查可问
- −安装和初始配置可能需要一定技术基础
- −功能完整性通常不如成熟商业产品
- −技术支持主要依赖开源社区，响应速度不稳定
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一，可自由商用、修改、分发，仅需保留版权声明。
🔗 相关工具推荐
❓ 常见问题 FAQ
经综合评估，llm_engineering AI技能包 在AI工具赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | llm_engineering |
| 原始描述 | 开源AI工具：Repo to accompany my mastering LLM engineering course。⭐6.0k · Jupyter Notebook |
| Topics | LLM工程教学资源Jupyter实战提示工程模型应用 |
| GitHub | https://github.com/ed-donner/llm_engineering |
| License | MIT |
| 语言 | Jupyter Notebook |
收录时间：2026-05-16 · 更新时间：2026-05-19 · License：MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。