Instructions to use pat883/gdn-hip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use pat883/gdn-hip with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("pat883/gdn-hip") - Notebooks
- Google Colab
- Kaggle
| library_name: kernels | |
| tags: | |
| - kernels | |
| - rocm | |
| # GDN-HIP (Gated Delta Net, RDNA4/gfx1201) | |
| Native HIP + rocWMMA kernels for the Gated Delta Net linear-attention path: gdn_prefill/decode (+wmma/chunked), causal_conv1d, gated RMSNorm, with native backward + differentiable training wrappers. | |
| Built with [kernel-builder](https://github.com/huggingface/kernel-builder) for AMD RDNA4 (gfx1201). | |
| Load with the [`kernels`](https://github.com/huggingface/kernels) library: | |
| ```python | |
| from kernels import get_kernel | |
| kernel = get_kernel("pat883/gdn-hip") | |
| ``` | |
| > Requires a ROCm PyTorch build (torch 2.10 / ROCm 7.x) on an RDNA4 card. Built variants: | |
| > `torch210-cxx11-rocm70` and `torch210-cxx11-rocm71`. | |