Getting Started with Local LLMs
Chapter 1: Introduction to Large Language Models
What Is a Large Language Model (LLM)?
A Simple View of How LLMs Work
Understanding Tokens and Text Generation
Why Run LLMs Locally?
Local vs. Cloud-Based LLMs
Quiz for Chapter 1
Chapter 2: Preparing Your Local Environment
Hardware Considerations: CPU
Hardware Considerations: RAM
Hardware Considerations: GPU and VRAM
Checking Your System Specifications
Operating System Compatibility
Installing Python (Optional but Recommended)
Introduction to the Command Line / Terminal
Quiz for Chapter 2
Chapter 3: Finding and Selecting Local LLMs
Where to Find LLM Models: Hugging Face Hub
Understanding Model Sizes and Parameters
Model Formats: GGUF and Others
Quantization: Making Models Smaller
Reading Model Cards for Information
Model Licenses and Usage Restrictions
Choosing Your First Model
Quiz for Chapter 3
Chapter 4: Running Your First Local LLM
Introduction to Local LLM Runners
Setting up Ollama
Downloading a Model with Ollama
Running a Model with Ollama (Command Line)
Setting up LM Studio
Finding and Downloading Models in LM Studio
Loading and Chatting with a Model in LM Studio
Introduction to llama.cpp (Concept)
Hands-on Practical: Running a Model
Quiz for Chapter 4
Chapter 5: Basic Interaction and Prompting
What is a Prompt?
Your First Prompt: Simple Questions
Giving Instructions
Understanding Context Window
Basic Prompt Formatting Tips
Temperature and Creativity
Common Interaction Patterns
Practice: Simple Prompting Techniques
Quiz for Chapter 5
Why Run LLMs Locally?
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- Data Privacy in the Age of AI, Alex Kampmann, Kevin K. K. Lee, Yacine Goudiaby, Alex Pentland, 2021 IEEE Security & Privacy Magazine, Vol. 19 (IEEE) DOI: 10.1109/MSEC.2021.3082980 - Discusses the challenges and implications of data privacy as AI technologies become more pervasive, underscoring the value of local data processing for sensitive information.
- Transformers: State-of-the-Art Natural Language Processing, Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clément Delangue, Anthony Moi, Pierric Phillipe, Christophe Simon, Korbinian Stzimmer, Sylvain Gugger, 2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (Association for Computational Linguistics) DOI: 10.18653/v1/2020.emnlp-demos.6 - Introduces the Hugging Face Transformers library, which is fundamental for accessing, downloading, and running a wide array of open-source LLMs locally, supporting customization and experimentation.
- Accelerating Large Language Model Inference, NVIDIA Developer Documentation, 2024 (NVIDIA) - Provides insights into optimizing performance for running LLMs, including discussions on hardware and software techniques important for efficient local inference on consumer or dedicated hardware.