Instructions to use pat883/attn-hip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use pat883/attn-hip with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("pat883/attn-hip") - Notebooks
- Google Colab
- Kaggle
Flash-Attention Prefill (RDNA4/gfx1201)
Dense flash-attention prefill (causal + sliding-window + additive mask) as a native HIP + rocWMMA kernel.
Built with kernel-builder for AMD RDNA4 (gfx1201).
Load with the kernels library:
from kernels import get_kernel
kernel = get_kernel("pat883/attn-hip")
Requires a ROCm PyTorch build (torch 2.10 / ROCm 7.x) on an RDNA4 card. Built variants:
torch210-cxx11-rocm70andtorch210-cxx11-rocm71.
- Downloads last month
- 28
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support