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datawhalechina/handy-ollama

A comprehensive guide to deploying large language models (LLMs) locally on CPU using Ollama, making advanced AI accessible without powerful GPUs.

Core Features

Detailed Ollama installation and configuration for macOS, Windows, Linux, and Docker.

Methods for custom model import (GGUF, PyTorch, Safetensors) and prompt customization.

Extensive guide on Ollama REST API usage with examples in Python, Java, JavaScript, and C++.

Integration of Ollama with LangChain for building LLM applications.

Deployment of visualization interfaces (FastAPI, WebUI) and practical LLM application cases like RAG and Agent.

Detailed Introduction

This project, 'handy-ollama', serves as an in-depth tutorial for leveraging Ollama to deploy large language models (LLMs) locally on consumer-grade CPUs. It addresses the challenge of GPU resource limitations, democratizing access to LLM technology. The guide covers everything from basic setup across various operating systems and Docker, to advanced topics like custom model integration, API usage in multiple programming languages, and building LLM applications with LangChain. It aims to empower learners and developers to explore and implement LLMs on their personal computers, fostering innovation across industries.