Linux Nvidia Cuda Python (.cursorrules)

{
    "prompt": "1. **Project Overview**: - **App Name**: 'srt-model-quantizing' - **Developer**: SolidRusT Networks - **Functionality**: A pipeline for downloading models from Hugging Face, quantizing them, and uploading them to a Hugging Face-compatible repository. - **Design Philosophy**: Focused on simplicity—users should be able to clone the repository, install dependencies, and run the app using Python or Bash with minimal effort. - **Hardware Compatibility**: Supports both Nvidia CUDA and AMD ROCm GPUs, with potential adjustments needed based on specific hardware and drivers. - **Platform**: Intended to run on Linux servers only. 2. **Development Principles**: - **Efficiency**: Ensure the quantization process is streamlined, efficient, and free of errors. - **Robustness**: Handle edge cases, such as incompatible models or quantization failures, with clear and informative error messages, along with suggested resolutions. - **Documentation**: Keep all documentation up to date, including the and any necessary instructions or examples. 3. **AI Agent Alignment**: - **Simplicity and Usability**: All development and enhancements should prioritize maintaining the app's simplicity and ease of use. - **Code Quality**: Regularly review the repository structure, remove dead or duplicate code, address incomplete sections, and ensure the documentation is current. - **Development-Alignment File**: Use a markdown file to track progress, priorities, and ensure alignment with project goals throughout the development cycle. 4. **Continuous Improvement**: - **Feedback**: Actively seek feedback on the app's functionality and user experience. - **Enhancements**: Suggest improvements that could make the app more efficient or user-friendly, ensuring any changes maintain the app's core principles. - **Documentation of Changes**: Clearly document any enhancements, bug fixes, or changes made during development to ensure transparency and maintainability.",
    "model": "Claude 3.7 Sonnet / Cursor",
    "aspect_ratio": "1:1",
    "category": "Coding Agents",
    "content_type": "coding_prompt",
    "surface": "agents_hub",
    "quality_score": 80,
    "provenance": "SYSTEM_RULE"
}