Open-Source Models
Build private AI systems using open-source models like LLaMA. Learn how to balance cost, privacy, and performance in your solutions.
6 Modules • 22 Lessons
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What You Will Learn
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✓ Start with OpenAI to learn the basics of Chat Completion API calls, roles and tooling
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✓ Implement AI systems that balance performance, privacy, and cost using both cloud and local solutions
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✓ Deploy Llama models across the full hardware spectrum — from H100 GPUs to desktop computers
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✓Create Python clients that integrate with Llama models on RunPod, Groq, and local deployments
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✓Build advanced AI companions and practical applications using open-source LLMs
Course Content
6 Modules • 22 Lessons
Introduction
- Unlocking the Power of Local LLMs: Privacy, Cost & Freedom
- State of Open-Source LLMs in 2025: Models, Capabilities & Trends
Optional: Getting Started with Python & OpenAI
- Introduction
- Install Python
- Python Development Essentials: Getting Started with Visual Studio Code
- OpenAI API Setup: Configuring Your API Key
- Integrating OpenAI: Your First API Implementation
Deploying Llama 4 on High-Performance Hardware
- Llama 4 Architecture Deep Dive: Understanding the Game-Changer
- Hitting the Context Limit with Cloud APIs
- Llama 4 Economics: Hardware Requirements & Cost Analysis
- H100 Deployment Masterclass: Setting Up Llama 4 in the Cloud
- Custom Python Clients: Integrating Applications with Llama 4
- Securing Your Llama 4 Deployment: Authentication & Access Control
Desktop Solutions
- From Cloud Power to Desktop Accessibility
- LM Studio: Optimizing Local LLM Performance
- JAN Framework: Simple Desktop Deployment for Beginners
- GPT4All Ecosystem: Versatile Tools for Any LLM
CLI Solutions
- Integrating Local LLMs with Python: The Ollama & LiteLLM API
- Python Development with LM Studio: Command-Line Integration
Building Advanced AI Companions
- From Models to Meaningful Applications
- LM Studio Companion Project: Building Personalized AI Assistants
- DeepSeek R1: Creating Conversational AI with Personality
Example Videos
Requirements
- Beginner programming knowledge - If you can write a simple function and understand variables, you're ready
- Basic understanding of web requests - We'll guide you through all the API connections step by step
- A modern computer - Any laptop from the last few years will work perfectly fine
- Curiosity about AI - No prior machine learning or AI experience needed
Don't worry if you're new to coding! We explain everything from the ground up and provide all the source code you need.
Description
Want to break free from the limitations of proprietary AI? Concerned about censorship, data privacy, or high API costs? Discover how to harness the full power of cutting-edge open-source LLMs with a focus on Meta's groundbreaking Llama 4 family!
This comprehensive course takes you from theory to implementation, showing you exactly how to:
Deploy and Optimize Llama 4
Learn the architecture of Llama 4 Scout and Maverick, understand their transformative capabilities, and deploy them on high-performance hardware. Compare cloud options like Groq with self-hosting on H100 GPUs, analyzing real costs and performance tradeoffs.
Master Cloud and Desktop Deployment
Step-by-step guidance for setting up Llama 4 on RunPod's H100 platform, configuring optimal settings, and creating secure Python clients that integrate with your applications. Not ready for cloud costs? We also cover desktop deployment options like LM Studio, JAN, and GPT4All.
Build Practical Applications
Transform theory into practice by building advanced AI companions with personality and memory using open-source LLMs. Learn to create personalized assistants that deliver uncensored, private interactions while maintaining complete control over your data.
Security and Optimization
Implement proper authentication, secure your deployments, and optimize performance across different hardware configurations. Understand how to balance capabilities, costs, and privacy requirements for your specific use case.
This course bridges the gap between theoretical knowledge and practical implementation, giving you the skills to deploy and leverage Llama 4's impressive capabilities without dependence on proprietary services.
Join now and take control of your AI future with open-source LLMs.
Who this course is for:
- Developers seeking freedom from proprietary AI APIs and their limitations
- AI enthusiasts wanting to explore the full capabilities of uncensored open-source models
- Professionals concerned with data privacy who need local AI processing
- Anyone looking to build practical AI applications with different deployment options based on their resources
Learn from an AI Development Expert
Hi, I'm Sebastian Schlaak, founder of AI-for-Devs.
With a Master’s degree in Informatics and over 15 years of experience in software and AI development, I specialize in helping developers build practical, production-ready AI systems.
My courses are designed to bridge the gap between theory and implementation. Whether you're working with OpenAI, LangChain, or open-source models like Llama, I focus on giving you the tools, code, and confidence to ship real-world applications.
Join 1,000+ developers who’ve used these methods to launch AI tools, streamline workflows, or kick off entirely new product ideas.
