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
Hardware Considerations: RAM
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- Computer Organization and Design RISC-V Edition: The Hardware/Software Interface, David A. Patterson, John L. Hennessy, 2020 (Morgan Kaufmann) - Classic textbook covering fundamental computer architecture, including memory hierarchy, RAM, virtual memory, and swapping, which are core concepts for understanding hardware limitations.
- A Survey of Quantization Methods for Efficient Neural Network Inference, Gyeong-Nam Jung, Jae-Hong Lee, Min-Su Song, Jae-Joon Han, 2020 Sensors, Vol. 20 (MDPI) DOI: 10.3390/s20205953 - Comprehensive survey of various quantization techniques for neural networks, directly relevant to the 'Quantization' section that reduces RAM requirements for LLMs.
- Large Model Inference, Hugging Face Team, 2023 (Hugging Face) - Official documentation describing strategies for running large models efficiently, covering memory considerations and techniques like offloading, which relates to RAM and VRAM usage.