All Courses

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

Quiz
Beta

Chapter: Preparing Your Local Environment

Test your understanding and practice the concepts from this chapter

Quiz Instructions

This quiz contains 14 questions to help you practice.
You need to score at least 70% to pass.
Attempts: Unlimited.
Your highest score will be kept.
Please attempt this quiz without assistance; however, feel free to refer to the chapter notes or use a code interpreter if needed.
This assessment is for your learning and understanding, It is not formally accredited (yet).

Question Format

The questions are designed to be engaging, focusing on understanding, application, and interpretation rather than rote memorization. Expect scenario-based problems that test your ability to apply what you've learned.

Attempts

Best scores and quiz attempts will appear.

© 2025 ApX Machine Learning