LLM Evaluation Projects Repository

Welcome to the LLM Evaluation repository! This repository contains various projects focused on evaluating Large Language Models (LLMs) using lm-evaluation-harness and other benchmarking tools. These projects aim to measure model performance across multiple datasets and evaluation metrics.

📌 Repository Overview

This repository includes scripts, configurations, and methodologies for systematically assessing LLMs. Each project is documented with setup instructions and usage guidelines.

🛠️ Installation & Setup

To run the evaluation projects, follow these steps:

🖥️ Local Setup (if VS Code or local setup is present)

1. Clone the Repository

   git clone https://github.com/saishshinde15/LLM_Eval.git
   cd LLM_Eval

2. Create and Activate a Virtual Environment (Optional)

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies

   pip install -r requirements.txt

4. Run an Evaluation

   • Navigate to the specific evaluation script and follow the instructions in its README.

☁️ Running Jupyter Notebooks on Google Colab (if no local setup is present)

For evaluation projects containing Jupyter notebooks, you can run them on Google Colab:

1. Open the GitHub repository in your browser.
2. Navigate to the desired Jupyter Notebook (.ipynb file).
3. Click on the "Open in Colab" button (or manually open it by replacing github.com with colab.research.google.com/github/ in the URL).
4. Run the notebook in Colab without needing local setup.

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📖 Documentation & Usage

Each evaluation project includes:

• A description of the evaluation task
• Setup and execution instructions
• Performance metrics and expected outputs

🤝 Contributing

If you’d like to contribute, feel free to:

• Fork this repository
• Create a feature branch
• Submit a pull request with your improvements

📜 License

This repository is licensed under the MIT License.

📧 Contact

For any questions or collaborations, feel free to reach out via GitHub issues or email at [email protected].

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Evaluate your LLMs efficiently! 🚀