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llm-course

Free

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

FreeFree tier
Inputs: textOutputs: text
Type
Open Source
Visit Website GitHub (38 stars)

About llm-course

The LLM Course is a comprehensive and free curriculum designed to help learners master Large Language Models (LLMs). It is structured into three main parts: LLM Fundamentals (optional, covering mathematics, Python, and neural networks), LLM Scientist (focusing on building cutting-edge LLMs using techniques like fine-tuning, quantization, and merging), and LLM Engineer (emphasizing the creation and deployment of LLM-based applications). The course includes a rich collection of hands-on Colab notebooks and articles covering topics such as fine-tuning Llama 3, Mistral, and CodeLlama, quantization methods (GGUF, GPTQ, EXL2), model merging with MergeKit, abliteration, and more. Created by mlabonne, the course is freely available on GitHub and is continuously updated with the latest research and tools.

Key Features

Three-part structured curriculum: Fundamentals, Scientist, Engineer
Extensive collection of Colab notebooks for hands-on learning
Covers fine-tuning techniques: QLoRA, ORPO, DPO, Unsloth
Quantization tutorials: GPTQ, GGUF/llama.cpp, ExLlamaV2, AutoQuant
Model merging with MergeKit and MoE creation
LLM evaluation with LLM AutoEval on RunPod
Deployment tools: ZeroSpace for Gradio chat interfaces
Abliteration and dataset deduplication utilities
Roadmaps to guide learning progression
Free and open-source with no cost

Pros & Cons

Pros
  • Completely free and open-source curriculum
  • Hands-on Colab notebooks with step-by-step instructions
  • Covers both foundational theory and practical application
  • Regularly updated with latest techniques and tools
  • Includes notebooks for quantization, fine-tuning, and deployment
  • Active community and support via GitHub
Cons
  • Assumes prerequisite knowledge in Python and machine learning
  • Not a formal course with video lectures or interactive assessments
  • Some notebooks require external cloud services (e.g., RunPod) for GPU access
  • Content is text and notebook-based, may not suit all learning styles

Best For

Learning the fundamentals of large language modelsBuilding and fine-tuning state-of-the-art LLMsQuantizing models for deployment on consumer hardwareMerging multiple models into more capable onesDeveloping and deploying LLM-powered applicationsAutomating LLM evaluation and benchmarkingExploring advanced techniques like abliteration and knowledge graphs

FAQ

Is the LLM Course free?
Yes, the LLM Course is completely free and open-source. The author also offers a paid companion book called 'LLM Engineer's Handbook' but the course itself remains free.
What topics does the course cover?
The course is divided into three parts: LLM Fundamentals (math, Python, neural networks), LLM Scientist (fine-tuning, quantization, merging), and LLM Engineer (building and deploying LLM applications).
Are there practical notebooks included?
Yes, the repository contains numerous Colab notebooks for hands-on tasks such as fine-tuning Llama 3, Mistral, and CodeLlama, quantization in multiple formats, model merging, abliteration, and more.
Do I need a powerful computer to use the course?
Many notebooks are designed to work in free-tier Google Colab, but some advanced tasks (like large-scale fine-tuning or evaluation) may require cloud GPU services such as RunPod.
Who created the LLM Course?
The course was created by mlabonne (Maxime Labonne), a machine learning engineer and author of the 'LLM Engineer's Handbook'.