This repository provides detailed tutorials for setting up and running Large Language Models (LLMs) on Google Colab and Kaggle. 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.

• 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.

1. 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.

2. 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.

3. Setting Up LLM on Kaggle GPU: This notebook guides you through the process of setting up a LLM on Kaggle using GPU acceleration. It includes steps to install necessary packages and optimize your model for Kaggle's hardware.

4. Running Ollama on Colab: This notebook guides you through the process of running and serving LLaMA3 through Ollama. It allows learners to interact with LLM easily and experimenting different open-source LLM's.

To get started with these tutorials, follow these steps:

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

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

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

This project is licensed under the GPL-3.0 license - see the LICENSE file for details.

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