Welcome to the LLM in Containers repository! This project is dedicated to providing comprehensive examples and guidelines for running Large Language Models (LLMs) and related tools in the ecosystem in containers. Our primary focus is on utilizing tools like Docker and Kubernetes to create scalable, reproducible, and easily deployable LLM applications.
The advent of LLMs has revolutionized various sectors from natural language processing to AI-driven content creation. However, deploying these models efficiently and effectively remains a challenge due to their size and complexity. Containerization offers a solution by encapsulating the model, its dependencies, and the runtime environment, ensuring that it runs consistently across different computing environments. This repository aims to lower the barrier to entry for utilizing LLMs in a containerized setup, fostering innovation and experimentation.
- Examples: Ready-to-use examples demonstrating how to containerize different LLMs for various use cases.
- Best Practices: Guidelines on optimizing performance, managing resources, and ensuring security while running LLMs in containers.
- Tutorials: Step-by-step instructions to get you started with containerizing LLMs, tailored for both beginners and experienced users.
- Community Contributions: A collaborative space for users to share their own examples, tips, and tricks related to LLM containerization.
- Clone the Repository: Get the latest examples and documentation.
- Choose an Example: Navigate through various examples to find one that suits your need.
- Follow the Tutorial: Each example comes with a detailed tutorial to guide you through the process.
- Deploy and Experiment: Use these examples as a starting point for your projects or as a learning tool.
Distributed under the MIT License. See LICENSE for more information.
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