Kernels
attention
flash-attention
flash-attn-4
sm120
sm121
blackwell
rtx5090
rtx-pro-6000
dgx-spark
cute-dsl
Instructions to use SecondNatureComputing/flash-attn-4-sm120 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use SecondNatureComputing/flash-attn-4-sm120 with Kernels:
# !pip install kernels from kernels import get_kernel # a version (or an explicit revision) is required; see the "Files and versions" tab for the available ones kernel = get_kernel("SecondNatureComputing/flash-attn-4-sm120", version=1) - Notebooks
- Google Colab
- Kaggle
Commit History
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Restructure: build/torch-cuda/ layout with relative imports for self-contained kernels.get_kernel() use
Verified signaturePreviously the kernel had files at torch-ext/flash_attn_4_sm120_sncbl/*.py with absolute 'from flash_attn.cute.X' imports, which required users to also install Dao-AILab/flash-attention separately. Now the package follows the kernels-community/flash-attn4 layout: build/torch-cuda/*.py with relative imports throughout (160 import lines rewritten across 27 files). Self-contained, loadable via kernels.get_kernel() without external flash-attn install.02fc236