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
What is a Prompt?
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- Introduction to prompt engineering, OpenAI, 2023 (OpenAI) - Official guide explaining prompt engineering concepts, starting with the basic definition of a prompt.
- Guide to prompt design, Google for Developers, 2024 - Official documentation from Google providing an overview of prompt design, including the role of prompts in interacting with LLMs.
- A Survey of Large Language Models, Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen, 2023 arXiv preprint DOI: 10.48550/arXiv.2303.18223 - This comprehensive survey provides a broad overview of LLMs, including their interaction mechanisms like prompting and in-context learning. It establishes the context for how prompts are used.