44 lines
2.8 KiB
Markdown
44 lines
2.8 KiB
Markdown
# Setting Up Large Language Models on Google Colab and Kaggle
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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.
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## Features
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- **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.
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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](https://github.com/casualcomputer/llm_google_colab/blob/main/setup_llm_on_google_colab_gpu_accelerated.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](https://github.com/casualcomputer/llm_google_colab/blob/main/setup_llm_on_google_colab_cpu_only.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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3. **[Setting Up LLM on Kaggle GPU](https://github.com/casualcomputer/llm_google_colab/blob/main/setup-llm-on-kaggle-gpu.ipynb)**: 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.
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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 or Kaggle 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 or Kaggle 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 GPL-3.0 license - see the [LICENSE](https://github.com/casualcomputer/llm_google_colab/blob/main/LICENSE) file for details.
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## Acknowledgements
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Thanks to the open-source community, Google Colab, and Kaggle for providing the resources that make these tutorials possible.
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