⚡ qifa – Quick Installer for Flash Attention

One command to detect, select, and install the correct flash-attn wheel for your environment.

✨ Features

• Auto-Detection – Identifies Python version, PyTorch version, CUDA version, and C++11 ABI
• Smart Matching – Finds the correct prebuilt wheel from official GitHub Releases
• Zero Config – Just run qifa install and you're done
• Dry Run Mode – Preview what will be installed before committing

Note: Currently optimized for Linux x86_64 official wheels. Windows/ROCm users will receive guidance but may need manual installation.

📦 Installation

pip install qifa

🚀 Quick Start

# Check your environment and see which wheel will be installed
qifa plan

# Install the matching flash-attn wheel
qifa install

📖 Commands

Options

# Install a specific version
qifa install --version 2.5.8

# Force a specific C++11 ABI setting
qifa install --abi FALSE

# Preview without installing
qifa install --dry-run

🔍 Example Output

$ qifa plan

{
  "python_tag": "cp310",
  "torch_mm": "2.1",
  "torch_cuda": "cu121",
  "chosen_cu_tag": "cu121",
  "abi_FALSE": true,
  "platform": "linux_x86_64",
  "version": "2.5.8",
  "found_asset": "flash_attn-2.5.8+cu121torch2.1cxx11abiFALSE-cp310-cp310-linux_x86_64.whl",
  "download_url": "https://github.com/Dao-AILab/flash-attention/releases/download/..."
}

🛠️ Troubleshooting

Run qifa doctor for detailed guidance on your specific environment.

📄 License

BSD-3-Clause © Voidful