Technical Tutorial
2.4k 2026-05-01
datawhalechina/handy-ollama
A comprehensive tutorial guiding users to deploy large language models locally on CPU using Ollama, making LLM inference accessible without dedicated GPU resources.
Core Features
Detailed Ollama installation and configuration across macOS, Windows, Linux, and Docker.
Guidance on custom model import from various formats (GGUF, Pytorch/Safetensors) and prompt customization.
Comprehensive usage of Ollama REST API in Python, Java, JavaScript, and C++.
Integration instructions for Ollama with LangChain in Python and JavaScript.
Deployment of visualization interfaces (FastAPI, WebUI) and local RAG/Agent applications.
Detailed Introduction
This project offers a hands-on tutorial for deploying large language models (LLMs) locally using Ollama, specifically targeting CPU environments. It addresses the common barrier of requiring expensive GPU resources for LLM deployment, making advanced AI accessible to a broader audience. The guide covers everything from basic installation and custom model integration to advanced API usage and application development with LangChain, empowering individuals to run and experiment with LLMs on their personal computers.