Instructions to use pat883/swiglu-hip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use pat883/swiglu-hip with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("pat883/swiglu-hip") - Notebooks
- Google Colab
- Kaggle
Fused SwiGLU Shared-Expert (RDNA4/gfx1201)
Fused SwiGLU shared-expert GEMV (small-M dense path) native HIP kernel.
Built with kernel-builder for AMD RDNA4 (gfx1201).
Load with the kernels library:
from kernels import get_kernel
kernel = get_kernel("pat883/swiglu-hip")
Requires a ROCm PyTorch build (torch 2.10 / ROCm 7.x) on an RDNA4 card. Built variants:
torch210-cxx11-rocm70andtorch210-cxx11-rocm71.
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