Chapter 27
🚀 Introduction
简体中文 | English
🎉 Officially Featured
Nov. 6, 2025, This project is now featured in the official Ollama repository and is currently the only Tutorial : https://github.com/ollama/ollama#tutorial
🚀 Introduction
A hands-on tutorial series for Ollama, designed to simplify the process of deploying large language models locally. With this guide, you'll be able to manage and run powerful LLMs right on your CPU, making advanced AI accessible without GPU dependencies!
Our comprehensive tutorial covers everything from beginner basics to advanced usage, complete with real-world examples that demonstrate practical applications of LLM deployment. Whether you're a complete novice or an experienced developer, our step-by-step instructions and practical tips will help you master Ollama and start building your own LLM-powered applications.
Key Topics Covered:
- Introduction, Installation & Configuration:
- macOS, Windows, Linux, and Docker deployment guides.
- Custom Model Integration:
- Import from GGUF, PyTorch, Safetensors, and custom prompt engineering.
- Ollama REST API:
- Comprehensive usage guide with examples in Python, Java, JavaScript, and C++.
- LangChain Integration:
- Seamless integration with Python and JavaScript workflows.
- Visual Interfaces & Use Cases:
- Build interactive chat UIs with FastAPI and WebUI, plus practical RAG and Agent applications.
We warmly welcome contributions! Submit issues or pull requests to help improve this project. Let's make advanced AI accessible to everyone!
Our Mission: Empower every LLM enthusiast to explore and experiment, regardless of programming background or computational resources. We're breaking down technical barriers to make LLM deployment achievable on personal computers. Join us on this exciting journey!
Directory Structure:
docs ---------------------- Markdown documentation files
notebook ------------------ Notebook source code and Python/Java/JavaScript examples
images -------------------- Image assets📖 Read Online: https://datawhalechina.github.io/handy-ollama/
💡 Motivation
The rapid growth of open-source LLMs has democratized AI, but many require GPU resources for deployment. Our goal is to make LLM technology accessible to everyone—regardless of hardware limitations—using Ollama, an open-source tool that enables CPU-based deployment of powerful language models.
This tutorial series empowers learners, hobbyists, and developers to deploy and experiment with LLMs locally, fostering innovation across industries.
🎯 Target Audience
- Resource-Constrained Users: Run LLMs locally without GPU dependencies.
- Developers: Build and test LLM-powered applications on consumer hardware.
- AI Enthusiasts: Experiment with state-of-the-art models without cloud costs.
- Privacy-Conscious Users: Keep sensitive data and model operations local.
✨ Highlights
While many LLM tutorials rely on GPU acceleration, this project focuses exclusively on CPU-based deployment, making advanced AI accessible to anyone with a modern computer. Through hands-on examples and clear explanations, we bridge the gap between theory and practice.
📖 Roadmap
Table of Contents (Work in Progress)
- 1 Ollama Introduction @Youdon
- 2 Ollama Installation & Configuration
- 3 Custom Ollama Usage
- 4 Ollama REST API
- API Guide @林通 @春阳
- Python Integration @春阳
- Java Integration @林通
- JavaScript Integration @春阳
- C++ Integration @林通
- Golang Integration @tomowang
- C# Integration (To be updated)
- Rust Integration (To be updated)
- Ruby Integration (To be updated)
- R Integration (To be updated)
- 5 Ollama with LangChain
- 6 Ollama Visual Interfaces
- 7 Use Cases
Note: We welcome contributions to complete the pending sections! Submit issues or pull requests to help expand this project.
Interested in becoming a maintainer? Contact us to join our core team!
🙏 Acknowledgments
Official Ollama Repository: https://github.com/ollama/ollama
Special thanks to our contributors!
Star History
LICENSE
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.