This repository provides a comprehensive, hands-on tutorial for deploying and utilizing Ollama, an open-source tool for running large language models (LLMs) locally, even on CPU-only hardware. It targets individuals, learners, and developers seeking to experiment with LLMs without requiring expensive GPU resources, enabling local model management and application development.

How It Works

The tutorial guides users through Ollama's features, including installation across various operating systems (macOS, Windows, Linux, Docker), custom model importing (GGUF, PyTorch, Safetensors), and leveraging the Ollama REST API with multiple programming languages (Python, Java, JavaScript, C++). It also covers integration with popular frameworks like LangChain and demonstrates practical applications such as building local RAG systems and AI agents with FastAPI and WebUI.

Quick Start & Requirements

• Installation: Ollama can be installed via official binaries for macOS, Windows, and Linux, or using Docker.
• Prerequisites: Primarily CPU-based, but custom GPU usage is also covered.
• Resources: Designed for consumer-grade hardware.
• Documentation: https://datawhalechina.github.io/handy-ollama/

Highlighted Details

• Focuses on enabling LLM deployment on CPU, addressing resource limitations.
• Covers Ollama's REST API with examples in Python, Java, JavaScript, and C++.
• Demonstrates integration with LangChain for RAG and Agent applications.
• Includes deployment of visualization interfaces like FastAPI and WebUI.

Maintenance & Community

The project welcomes contributions via issues and pull requests. It acknowledges the official Ollama repository.

Licensing & Compatibility

Licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). This license restricts commercial use and requires derivative works to be shared under the same terms.

Limitations & Caveats

The project highlights significant security risks associated with Ollama's default configuration, including unauthorized access and model theft, recommending strict security hardening measures like restricting network access, implementing authentication, and updating Ollama to secure versions.