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
Model Formats: GGUF and Others
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- GGUF File Format, Georgi Gerganov and llama.cpp contributors, 2024 - Official specification and explanation of the GGUF file format, developed for efficient local LLM inference within the llama.cpp project.
- safetensors Documentation, Hugging Face and safetensors contributors, 2024 - Official documentation for the
safetensorslibrary, which provides a secure and efficient way to save and load model weights, offering an alternative to traditional serialization methods. - Saving and Loading Models, Matthew Inkawhich, 2024 (PyTorch Foundation) - The official PyTorch tutorial on saving and loading models, demonstrating how models trained with PyTorch are typically stored and retrieved using native framework formats.
- Save and load Keras models, TensorFlow Team and contributors, 2024 - The official TensorFlow guide for saving and loading models using the Keras API, covering formats such as SavedModel, which are native to the TensorFlow ecosystem.