This repository provides detailed tutorials for setting up and running Large Language Models (LLMs) on Google Colab. Whether you have access to GPU acceleration or are limited to a CPU-only environment, these guides will help you get started with deploying and utilizing LLMs efficiently.

Features

GPU Accelerated Setup: Use Google Colab's free Tesla T4 GPUs to speed up your model's performance by X60 times (compared to CPU only session). Note that GPU availability is limited by usage quotas.
CPU Only Setup: A detailed guide to setting up LLMs on a CPU-only environment, perfect for users without access to GPU resources.
Comprehensive Instructions: Each tutorial includes step-by-step instructions, from setting up the environment to executing the model.
Code Examples: Includes complete, runnable Jupyter notebooks that you can directly import into Colab and start using.

Tutorials

GPU Accelerated Setup: This notebook walks you through the process of setting up a LLM on Google Colab with GPU acceleration. It includes instructions for optimizing your model to take full advantage of Google's hardware.
CPU Only Setup: For users without access to GPU resources, this notebook provides a detailed guide to setting up and running LLMs using only CPUs. It includes performance tips and best practices for maximizing efficiency.

Getting Started

To get started with these tutorials, follow these steps:

Fork this repository or download the notebooks directly.
Open Google Colab and upload the notebook corresponding to your preferred setup.
Follow the instructions within the notebook to set up your LLM.

Requirements

A Google Colab account.
Basic knowledge of Python programming and Jupyter notebooks.

Contributing

Contributions are welcome! If you have improvements or additions to the tutorials, please fork the repository and submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

Thanks to the open-source community and Google Colab for providing the resources that make these tutorials possible.