Initialize legacy kernels compatibility mirror

README.md

@@ -1,48 +1,9 @@
----
-library_name: kernels
-license: apache-2.0
-tags:
-  - cuda
-  - native-cuda
-  - flashrt
-  - moe
-  - nvfp4
-  - blackwell
----
+# flashrt/grouped-moe-gemv
 
-# grouped-moe-gemv
+This repository is a compatibility mirror for older `kernels` clients
+that resolve repositories through the default Hugging Face model repo API.
 
-Native CUDA FlashRT grouped MoE GEMV kernels for dynamic device-side routing.
-Version 2 covers BF16-activation/NVFP4-weight W4A16 and fully NVFP4 W4A4.
+Canonical Kernel Hub repo: https://huggingface.co/kernels/flashrt/grouped-moe-gemv
 
-Available functions:
-
-- `w4a16_decode_gemv_bf16`
-- `grouped_w4a16_gemv_bf16`
-- `quantize_activations_nvfp4_bf16`
-- `quantize_weights_nvfp4_bf16`
-- `grouped_w4a4_gemv_bf16`
-- `grouped_w4a4_gemv_from_bf16`
-
-W4A4 routing is token-major: packed activations `[M,K/2]`, device indices
-`[M,top_k]`, packed expert library `[E,N,K/2]`, output `[M,top_k,N]`.
-Use `M=routed_pairs, top_k=1` when each expert receives a distinct activation.
-See the repository README for static-buffer and CUDA Graph usage.
-
-Load version 2 with both current and legacy `kernels` clients:
-
-```python
-from kernels import get_kernel
-
-try:
-    moe = get_kernel(
-        "flashrt/grouped-moe-gemv", version=2, trust_remote_code=True
-    )
-except TypeError:  # kernels==0.12.x
-    moe = get_kernel("flashrt/grouped-moe-gemv", version=2)
-```
-
-Use `quantize_activations_nvfp4_bf16` once for `[M,K]`, followed by
-`grouped_w4a4_gemv_bf16` for all `[M,top_k]` routes. The convenience function
-`grouped_w4a4_gemv_from_bf16` performs both calls and accepts preallocated
-`packed`, `sfa`, and `out` buffers for CUDA Graph capture.
+Do not edit this mirror by hand. It is generated from the Kernel Hub
+`vN` branches and contains the same `build/**` artifacts.