Run TinyLlama Locally: From Setup to Deployment

Course Overview

Learn how run TinyLlama locally

This comprehensive course guides you through installing, configuring, and deploying a production-ready AI model, showcasing both M-series Mac computers running macOS Sonoma or later and windows.

What You’ll Learn

• Understanding the architecture and capabilities of TinyLlama 1.1B
• Setting up a professional development environment for AI applications
• Managing dependencies and virtual environments
• Working with the Hugging Face ecosystem
• Implementing and customising a local chat interface
• Optimising model performance on Apple Silicon (Windows version also available)

Prerequisites

• Basic Python programming knowledge (You can use AI for this if you have zero coding knowledge)
• Familiarity with using terminal/command line
• Mac computer with M1/M2/M3 chip running macOS 14 (Sonoma) or later or Windows computer/laptop with similar specs
• 8GB+ RAM
• 2GB free storage space
• VSCode or similar code editor

Course Duration

• Estimated completion time: 1 hours
• Self-paced learning
• Hands-on project included

Why Run TinyLlama Locally?

While cloud-based AI services dominate the market, running models locally offers unique advantages in privacy, cost, and control. TinyLlama represents a sweet spot between capability and resource requirements, making it perfect for local deployment. This course empowers you to break free from API costs and usage limits while maintaining full control over your AI applications.

Target Audience

This course is ideal for:

• Developers interested in AI deployment
• Data scientists exploring local LLM solutions
• Technical professionals seeking privacy-focused AI options
• Students learning about practical ML applications
• Anyone interested in running AI models locally

What Makes This Course Special

• Step-by-step, practical approach
• Real-world implementation focus
• No cloud dependencies after setup
• Privacy-first methodology
• Optimised for Apple Silicon
• Industry-standard tools and practices

By The End Of This Course

You will have a fully functional, locally running implementation of TinyLlama that you can customise for your specific needs. You’ll understand not just the how, but the why behind each step, enabling you to adapt the knowledge to other models and use cases.