This repo contains notebooks which walk you through some simple LLM implementations. It uses free software and can all be run inside Google Collab, for maximum accessibility.
You can open the tutorial here:
This repo is currently maintained by @samhollings and @adamhollings - raise and issue or a pull request if you have any suggestions (or just contact us directly!)
This repo contains a tutorial showcasing how simple it is to begin using open-source LLMs in Python.
It shows the use of three models:
- Phi3
- Grammarly CoEdit
- Cappy
The tutorial is designed to be run in Google Colab - to allow it to be freely and easily used by anyone who wants to learn.
- knowledge-wise, some ability with python. Knowledge of data science would be useful, but not crucial (at least at the start). Interest in LLMs will help!
- system-wise: the requirements can be found in the
requirements.txtfile, and can be loadedpip.
Open one of the Google Colab links above! This repo is meant to be used in Google Colab for demo and teaching purposes.
Clone the repository. To learn about what this means, and how to use Git, see the Git guide.
git clone <insert URL>
Make a virtual environment and install the dependencies:
pip install -r requirements.txt
Just take whatever code you need, and reimplement it. This was made mostly in the form of Jupyter notebooks for ease of use and demos, however I wouldn't recommend that for most applications - but it can be very useful when getting started and playing with the code.
The LICENCE file will need to be updated with the correct year and owner
Unless stated otherwise, the codebase is released under the MIT License. This covers both the codebase and any sample code in the documentation.