handy-ollama provides a Jupyter Notebook–oriented guide to deploying LLMs locally on CPU using Ollama, with tutorials across installation, API usage, LangChain integration, and visualization. It has CPU-focused tutorials and a Chinese-language emphasis, plus REST API and RAG/Agent examples.
Collecting history — the radar snapshots this repo daily. The trend line appears after 3 days of data (1 so far).
What it is
handy-ollama is a tutorial project that aims to teach local deployment of large language models using Ollama on CPU. It covers installation and configuration across macOS, Windows, Linux, and Docker; custom model import from GGUF and other formats; Ollama REST API usage; LangChain integrations; and visualization interfaces via FastAPI or WebUI, plus local RAG and Agent use cases. The project includes a structured navigation of chapters and docs for practical deployment.
How it works
The project provides documentation and notebooks that guide through Ollama concepts, installation steps, model import methods, and REST API usage in Python, Java, JavaScript, C++, and Golang. It also includes sections on integrating Ollama with LangChain in Python and JavaScript, and deploying visualization UIs with FastAPI or WebUI. It emphasizes local CPU deployment without GPU requirements and demonstrates local RAG and agent-oriented applications.
Getting started
Getting started is through the docs and notebooks in the repository:
- Installation and configuration sections cover macOS, Windows, Linux, and Docker.
- 3.1 自定义导入模型 for importing models, 3.2 自定义模型存储位置 for storage location changes, and 3.3 自定义在 GPU 中运行 for GPU customization.
- 4.x sections detail using Ollama API in multiple languages, and 5.x sections cover LangChain integrations.
- 6.x sections cover deploying visual interfaces with FastAPI and WebUI.
Exact commands to reproduce steps are provided in the README's linked docs, e.g., "Cite exact commands as written in the README".
Recent releases
The latest releases section lists: - none
Traction
Stars: 2486 Forks: 315 Open issues: 8
Behind the repo
This repository is focused on tutorials and notebooks for local LLM deployment via Ollama, with documentation in Chinese and English, and links to online reading. It has a GitHub project page, a LICENSE note, and a Star History chart link.
Caveats
License is listed as CC BY-NC-SA 4.0 in the README badges. The README notes a Beta public testing phase. The repository language is Jupyter Notebook. The project was created 2024-07-30 and lastPush 2026-01-15.