Dev Tools
AnyCrawl
A Node.js crawler that turns websites into LLM-ready data and extracts structured SERP results.
★ 3.5k starsMIT
View guide →A free course to get into large language models, with roadmaps and runnable Colab notebooks
llm-course is an open-source dev tools project by mlabonne (mlabonne/llm-course on GitHub). It has 83,336 GitHub stars and 9,697 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 5 February 2026.
Decide what you want from llm-course and check the README and requirements to confirm it fits your setup and multiple languages environment.
Clone the repository from GitHub (github.com/mlabonne/llm-course) or use the documented install channel if you do not need the source.
Follow the README quickstart to install dependencies and set any required configuration or credentials.
Work through the README's first example end to end, then adapt it to your own project or data.
Structured roadmaps that sequence LLM topics from fundamentals to advanced work.
Hands-on Colab notebooks accompany the lessons so you can run code in the browser.
Covers mathematics, Python, neural networks and NLP foundations first.
Architecture, pre-training and post-training datasets and methods.
Includes fine-tuning and quantization sections with tooling guidance.
The whole course is published openly on GitHub under Apache-2.0.
Start by getting the source and entering the project directory.
git clone https://github.com/mlabonne/llm-course.git
cd llm-courseGet the notebooks and materials locally.
git clone https://github.com/mlabonne/llm-course.git
cd llm-courseMost lessons run directly in Google Colab from the links in the README.
# open the Colab link for a lesson from the READMEIt is maintained by mlabonne and contributors on GitHub, where the repository github.com/mlabonne/llm-course is the canonical home for code, issues and releases.
Yes — it is open source under the Apache-2.0 license. Read the license in the repository for what is permitted, including any conditions on redistribution or commercial use.
No. The course is free; Colab notebooks run on Google Colab, which has a free tier.
Compared with paid LLM courses: This course is free and notebook-driven, covering similar ground to commercial deep-learning specializations.
Clone github.com/mlabonne/llm-course, follow the README quickstart to install it, and run the documented first example before adapting it to your own work.
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