An introduction to large language models for scientific research - a practical introduction to using models.
Table of Contents
This repository is a hands-on tutorial on how to use large language models.
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.
There are a few ways to run this notebook. The easiest way is via codespaces:
- Fork the repository so that you have you own version of the code.
- Open the repository in codespaces.
That's it! The development environment will be set up for you and you can start running the code.
You can also clone the forked repo and run the notebook locally. We recommend using the included devcontainer to ensure you have the correct dependencies installed.
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of this material is an ongoing process, and given the rapid advancement of LLM libraries may contain bugs or out of date information.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.