tori29umai/PrecompiledWheels

Precompiled Wheels

Prebuilt Python wheels for CUDA and PyTorch combinations that are awkward to build locally.

Filename convention:

<package>-<version>+cu<cuda>.torch<torch_ver>-<py_tag>-<abi_tag>-<platform>.whl

Example:

sageattention-2.2.0+cu130.torch2.11-cp313-cp313-win_amd64.whl

This means:

• CUDA 13.0
• PyTorch 2.11
• Python 3.13
• Windows x86_64

Files

Windows x86_64

File	Notes
deep_gemm-2.3.0+cu128.torch2.10-cp310-cp310-win_amd64.whl	DeepGEMM, Python 3.10, CUDA 12.8, Torch 2.10
flash_attn-2.8.3-cp311-cp311-win_amd64.whl	FlashAttention 2, Python 3.11
sageattention-2.2.0+cu128.torch2.10-cp310-cp310-win_amd64.whl	SageAttention 2.2.0, Python 3.10, CUDA 12.8, Torch 2.10
sageattention-2.2.0+cu130.torch2.11-cp312-cp312-win_amd64.whl	SageAttention 2.2.0, Python 3.12, CUDA 13.0, Torch 2.11
sageattention-2.2.0+cu130.torch2.11-cp313-cp313-win_amd64.whl	SageAttention 2.2.0, Python 3.13, CUDA 13.0, Torch 2.11
xformers-0.0.35+cu130.torch2.11-py39-none-win_amd64.whl	Existing stable-ABI xformers wheel
xformers-0.0.35+cu130.torch2.11-cp312-cp312-win_amd64.whl	Repacked xformers wheel for Python 3.12
xformers-0.0.35+cu130.torch2.11-cp313-cp313-win_amd64.whl	Repacked xformers wheel for Python 3.13

Linux aarch64

File	Notes
flash_attn-2.8.3-cp310-cp310-linux_aarch64.whl	FlashAttention 2, Python 3.10
flash_attn-2.8.4+cu130.torch2.11-cp312-cp312-linux_aarch64.whl	FlashAttention 2, Python 3.12, CUDA 13.0, Torch 2.11
flash_attn-2.8.4+cu130.torch2.11-cp313-cp313-linux_aarch64.whl	FlashAttention 2, Python 3.13, CUDA 13.0, Torch 2.11
sageattention-2.2.0+cu130.torch2.11-cp312-cp312-linux_aarch64.whl	SageAttention 2.2.0, Python 3.12, CUDA 13.0, Torch 2.11
sageattention-2.2.0+cu130.torch2.11-cp313-cp313-linux_aarch64.whl	SageAttention 2.2.0, Python 3.13, CUDA 13.0, Torch 2.11
xformers-0.0.35+cu130.torch2.11-py39-none-linux_aarch64.whl	Existing stable-ABI xformers wheel
xformers-0.0.35+cu130.torch2.11-cp312-cp312-linux_aarch64.whl	Repacked xformers wheel for Python 3.12
xformers-0.0.35+cu130.torch2.11-cp313-cp313-linux_aarch64.whl	Repacked xformers wheel for Python 3.13

Pure Python / Any platform

File	Notes
angelslim-0.0.0.dev0+cu128.torch2.10-py3-none-any.whl	Pure Python wheel

Install examples

Windows, Python 3.12 / Torch 2.11 / CUDA 13.0

pip install --index-url https://download.pytorch.org/whl/cu130 torch==2.11.0+cu130
$base = "https://huggingface.co/datasets/tori29umai/PrecompiledWheels/resolve/main"
pip install "$base/sageattention-2.2.0+cu130.torch2.11-cp312-cp312-win_amd64.whl"
pip install "$base/xformers-0.0.35+cu130.torch2.11-cp312-cp312-win_amd64.whl"

Windows, Python 3.13 / Torch 2.11 / CUDA 13.0

pip install --index-url https://download.pytorch.org/whl/cu130 torch==2.11.0+cu130
$base = "https://huggingface.co/datasets/tori29umai/PrecompiledWheels/resolve/main"
pip install "$base/sageattention-2.2.0+cu130.torch2.11-cp313-cp313-win_amd64.whl"
pip install "$base/xformers-0.0.35+cu130.torch2.11-cp313-cp313-win_amd64.whl"

Linux aarch64, Python 3.12 / Torch 2.11 / CUDA 13.0

pip install --index-url https://download.pytorch.org/whl/cu130 torch==2.11.0+cu130
BASE=https://huggingface.co/datasets/tori29umai/PrecompiledWheels/resolve/main
pip install "$BASE/flash_attn-2.8.4+cu130.torch2.11-cp312-cp312-linux_aarch64.whl"
pip install "$BASE/sageattention-2.2.0+cu130.torch2.11-cp312-cp312-linux_aarch64.whl"
pip install "$BASE/xformers-0.0.35+cu130.torch2.11-cp312-cp312-linux_aarch64.whl"

Linux aarch64, Python 3.13 / Torch 2.11 / CUDA 13.0

pip install --index-url https://download.pytorch.org/whl/cu130 torch==2.11.0+cu130
BASE=https://huggingface.co/datasets/tori29umai/PrecompiledWheels/resolve/main
pip install "$BASE/flash_attn-2.8.4+cu130.torch2.11-cp313-cp313-linux_aarch64.whl"
pip install "$BASE/sageattention-2.2.0+cu130.torch2.11-cp313-cp313-linux_aarch64.whl"
pip install "$BASE/xformers-0.0.35+cu130.torch2.11-cp313-cp313-linux_aarch64.whl"

Notes

• These wheels are redistributed for convenience.
• Please check each upstream project's license before reuse.

Upstream projects

Package	Repository
flash_attn	https://github.com/Dao-AILab/flash-attention
sageattention	https://github.com/thu-ml/SageAttention
xformers	https://github.com/facebookresearch/xformers
deep_gemm	https://github.com/deepseek-ai/DeepGEMM
angelslim	https://github.com/Tencent/AngelSlim