linux-nvidia-cuda-python-cursorrules-prompt-file — Cursor rule
Sets Cursor agent guidelines for the Hugging Face model quantization pipeline on Linux servers with Nvidia CUDA or AMD ROCm GPUs.
.cursor/rules/linux-nvidia-cuda-python-cursorrules-prompt-file.mdcWhat linux-nvidia-cuda-python-cursorrules-prompt-file does
This rule sets instructions for working on the srt-model-quantizing project by SolidRusT Networks. The codebase downloads models from Hugging Face, quantizes them, and uploads the results back to a Hugging Face-compatible repository using Python or Bash.
The prompt instructs the assistant to handle failures gracefully, such as incompatible model structures, and suggest explicit fixes. It also requires keeping dead code out of the repository, maintaining updated instructions in README.md, and tracking task status in a dedicated markdown alignment file.
The project targets Linux servers exclusively. Hardware setups need either Nvidia CUDA or AMD ROCm GPUs to run the quantization scripts.
What it helps with
- Build a model quantization pipeline for Hugging Face
- Handle hardware and quantization failures with clear error outputs
- Clean dead or duplicated code from the codebase
- Keep README.md and project alignment markdown files in sync
Example prompts for the agent
- Implement a model download and quantization script for Nvidia CUDA
- Add error handling for unsupported Hugging Face model architectures
- Update the development alignment markdown file with recent bug fixes
How to install linux-nvidia-cuda-python-cursorrules-prompt-file
Manually: put the files in place
.cursor/rules/linux-nvidia-cuda-python-cursorrules-prompt-file.mdcFrontmatter: description, globs, alwaysApply
Manually: put the files in place
.claude/rules/linux-nvidia-cuda-python-cursorrules-prompt-file.mdThe paths frontmatter field limits the files: move globs from the source into paths
AGENTS.md
Codex reads AGENTS.md: paste the rule text there
Manually: put the files in place
.github/instructions/linux-nvidia-cuda-python-cursorrules-prompt-file.instructions.mdThe applyTo field sets which files it applies to: move globs from the source into applyTo
Manually: put the files in place
.devin/rules/linux-nvidia-cuda-python-cursorrules-prompt-file.mdManually: put the files in place
.clinerules/linux-nvidia-cuda-python-cursorrules-prompt-file.mdStep by step, with commands for each platform: Cursor rules: .mdc files, four modes and what replaced .cursorrules
Questions about linux-nvidia-cuda-python-cursorrules-prompt-file
How to install linux-nvidia-cuda-python-cursorrules-prompt-file in Cursor?
Add the rule file to your project root at .cursor/rules/linux-nvidia-cuda-python-cursorrules-prompt-file.mdc.
What does this rule configure?
It defines instructions for developing the srt-model-quantizing pipeline, covering code cleanliness, error handling, and documentation updates.
What hardware is supported?
It runs exclusively on Linux servers equipped with Nvidia CUDA or AMD ROCm graphics cards.
Source text
linux-nvidia-cuda-python-cursorrules-prompt-file.mdc · CC0-1.0
--- description: "Cursor rules for Linux development with NVIDIA CUDA and Python integration." globs: **/* alwaysApply: false --- 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 README.md 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.
© PatrickJS · CC0-1.0 · PatrickJS/awesome-cursorrules