使用Ollama和OpenWebUI，轻松探索Meta Llama3–8B

Summary of Ollama Tool for Local Deployment and Application of Llama 3 Model with Open WebUI

In April 2024, Meta released the Llama 3 AI model, sparking excitement in the AI community. Following this, the Ollama tool announced support for Llama 3, simplifying the local deployment of large models. This article outlines the process of deploying and interacting with the Llama 3–8B model locally using Ollama and Open WebUI.

1. Installation of Ollama

The article starts by guiding through the installation of Ollama using a one-line command with curl. After installation, Ollama runs a system service that hosts the core API, accessible at 127.0.0.1:11434. To ensure the service is operational, its status can be confirmed using systemctl. Additionally, modifications to the systemd unit file are made to allow OpenWebUI running in a container to access the Ollama API service.

2. Downloading and Running Large Models

Ollama supports the simple download and execution of large models. The article describes using the tool to download and run a 4-bit quantized version of Llama3-8B on a cloud VM without a GPU. An example is given where a question about the Go programming language is posed, and the model responds with various facts about Go. The command line and RESTful API methods for interacting with Ollama API service are explained, demonstrating the process with a sample request about the color of the sky.

3. Interaction with Large Models Using Open WebUI

Installing and interacting with large models through Open WebUI is made easy with container installation. The article provides a command to install Open WebUI locally using a personal mirror image from Docker Hub. Once the container is up, Open WebUI can be accessed on the host machine's port 13000. After registration and login, users can interact with the Llama3 model deployed by Ollama.

Recommended Reading

The article concludes by recommending the book "Llama Large Model Practical Guide," which covers the theoretical basis to practical applications of large models, such as deployment, fine-tuning, and industry customization using Llama 2, and includes challenges like multi-turn dialogue and building document Q&A models with LangChain.

Recap of Past Highlights

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