Local LLMs via Ollama & LM Studio - The Practical Guide

Overview

Unlock the Power of Private, Powerful AI on Your Own PC!

Syllabus

• Introduction to Local LLMs
Overview of Large Language Models
Benefits of Running LLMs Locally vs. in the Cloud
Introduction to Ollama and LM Studio
• Setting Up Your Environment
System Requirements for Running Local LLMs
Installing and Configuring Ollama
Installing and Configuring LM Studio
• Understanding LLM Architecture
Core Components of LLMs
Training vs. Inference
Fine-Tuning vs. Pre-trained Models
• Getting Started with Ollama
Overview of the Ollama Interface
Loading and Managing Models
Performing Basic Inference Tasks
• Exploring LM Studio
Navigating the LM Studio Interface
Utilizing Built-in Tools and Features
Integrating LM Studio with Other Applications
• Practical Applications of Local LLMs
Natural Language Processing Tasks
Automating Repetitive Tasks with LLMs
Creating Custom Applications
• Best Practices for Optimizing Performance
Resource Management and Optimization Techniques
Troubleshooting Common Issues
Security and Privacy Considerations
• Advanced Techniques in Local LLM Usage
Customizing Models for Specific Use-Cases
Combining Multiple LLMs for Enhanced Performance
Exploring Cutting-Edge Developments in LLMs
• Case Studies and Real-World Examples
Successful Implementation of Local LLMs in Different Industries
Lessons Learned and Best Practices from Case Studies
• Conclusion
Recap of Key Learning Points
Future Trends in Local LLMs
Final Q&A and Wrap-up Session
• Optional: Capstone Project
Designing and Implementing a Local LLM-based Application
Peer Review and Feedback Session

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Taught by

Maximilian Schwarzmüller

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Subjects

Computer Science