rd-net is an application designed to help users explore inference-time drift. It showcases how to reduce repetition collapse in frozen Large Language Models (LLMs). This software is useful for researchers and enthusiasts interested in AI and memory concepts.

To get started with rd-net, follow these simple steps:

1. Ensure your computer meets the following system requirements:

   • Operating System: Windows 10 or later, macOS 10.12 or later, or a recent Linux distribution.
   • Memory: At least 4GB of RAM.
   • Storage: Minimum of 500 MB free space.

2. Go to the Releases page to find the latest version of rd-net.

3. Click on the version link to see the available files for download.

Visit this page to download: rd-net Releases

1. On the Releases page, look for the version you want to download.

2. Choose the appropriate file for your operating system. Here are your options:

   • For Windows, download rd-net-windows.exe.
   • For macOS, download rd-net-macos.dmg.
   • For Linux, download rd-net-linux.tar.gz.

3. Click to download the file and wait for it to finish.

4. Once downloaded, open the file:

   • For Windows: Double-click rd-net-windows.exe to run the installer.
   • For macOS: Open rd-net-macos.dmg, drag the app to your Applications folder, then open it.
   • For Linux: Extract the rd-net-linux.tar.gz file. Open a terminal and navigate to the extracted folder. Run ./rd-net to launch the application.

• Intuitive User Interface: Navigate easily through the application.
• Drift Detection: Discover how LLMs handle representational drift.
• Feedback Mechanism: Provide insights on your experience to help improve future versions.
• Performance Log: Analyze performance metrics and experiment outcomes for better understanding.

To uninstall:

• Windows: Go to Settings > Apps, find rd-net, and select Uninstall.
• macOS: Drag the rd-net app from the Applications folder to the Trash.
• Linux: Delete the extracted folder or use your package manager.

1. Ensure your system meets the requirements.
2. Check for any software updates.
3. Consult the logs for error messages that could point to the issue.

For support, you can open an issue on this GitHub repository or search the existing issues for solutions.

This application covers a range of topics:

• AI
• Context Collapse
• Fast Weights
• Inference Hacking
• Llama
• Llama 3
• LLM
• Memory
• ML
• Novelty
• Repetition Collapse
• Representational Drift
• Research
• Token Collapse
• Transformers

• GitHub Repository
• Documentation
• Issues Tracker

For any inquiries or feedback, you can reach the development team through the issues section on GitHub.

Now that you have everything you need, enjoy exploring the capabilities of rd-net and dive into the world of AI inference!