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# llm_google_colab
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A tutorial on how to set up a LLM on Google Colab for both GPU-accelerated and CPU-only session.
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# Setting Up Large Language Models on Google Colab
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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.
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## Features
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- **GPU Accelerated Setup**: Leverage the power of Google Colab's GPUs to significantly speed up your model's performance.
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- **CPU Only Setup**: A detailed guide to setting up LLMs on a CPU-only environment, perfect for users without access to GPU resources.
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- **Comprehensive Instructions**: Each tutorial includes step-by-step instructions, from setting up the environment to executing the model.
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- **Code Examples**: Includes complete, runnable Jupyter notebooks that you can directly import into Colab and start using.
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## Tutorials
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1. **[GPU Accelerated Setup](Setup%20LLM%20on%20Google%20Colab:%20GPU%20Accelerated.ipynb)**: 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.
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2. **[CPU Only Setup](Setup%20LLM%20on%20Google%20Colab:%20CPU%20Only.ipynb)**: 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.
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## Getting Started
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To get started with these tutorials, follow these steps:
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- Fork this repository or download the notebooks directly.
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- Open Google Colab and upload the notebook corresponding to your preferred setup.
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- Follow the instructions within the notebook to set up your LLM.
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## Requirements
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- A Google Colab account.
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- Basic knowledge of Python programming and Jupyter notebooks.
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## Contributing
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Contributions are welcome! If you have improvements or additions to the tutorials, please fork the repository and submit a pull request.
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## License
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This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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## Acknowledgements
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Thanks to the open-source community and Google Colab for providing the resources that make these tutorials possible.
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