An introduction to large language models for scientific research - a practical introduction to using models.
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Table of Contents
This repository is a hands-on tutorial on how to use large language models for scientific research. It is designed as a reference source for our hands-on LLM workshop, but may be useful for others to get started with LLMs. To get started, follow the link and head to the website.
This project requires some prerequisites in terms of skill level: you should be proficient with Python and PyTorch, and some understanding of git would be helpful. A good indication of skill level would be: can you open VS Code (or some other editor) and create some kind of class with attributes and methods? If so, then you'll probably be fine with this workshop.
We also strongly recommend our introductory course on LLMs
Head to the hands-on course Website »
In order to use this course material, we recommend that you fork the repo and open it in GitHub Codespaces. Please read the Codespaces section in the Setting Up page of the website, and carefully follow those instructions. Failure to do so may result in some sections of the course to not work as intended.
Development of this material is an ongoing process, and given the rapid advancement of LLM libraries may contain bugs or out of date information.
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under an MIT License. See LICENSE for more information.