Uploaded using `kernel-builder`.

README.md

@@ -1,71 +1,18 @@
 ---
+library_name: kernels
 license: bsd-3-clause
-tags:
-  - kernels
 ---
 
-# sglang-flash-attn3
+This is the repository card of kernels-community/sgl-flash-attn3 that has been pushed on the Hub. It was built to be used with the [`kernels` library](https://github.com/huggingface/kernels). This card was automatically generated.
 
-Pre-built Flash Attention 3 (forward-only) CUDA kernels from [sgl-flash-attn](https://github.com/sgl-project/sgl-flash-attn), packaged for the [HuggingFace kernels library](https://github.com/huggingface/kernels). Requires Hopper (sm_90+).
+## How to use
 
-Kernel source: [kernels-community/sgl-flash-attn3](https://github.com/huggingface/kernels-community/tree/main/sgl-flash-attn3)
+Usage example not available.
 
-## Usage
-
-```bash
-pip install kernels
-```
-
-```python
-from kernels import get_kernel
-
-fa3 = get_kernel("kernels-community/sgl-flash-attn3", revision="v1")
-
-fa3.flash_attn_varlen_func(q, k, v, cu_seqlens_q, cu_seqlens_k, causal=True)
-fa3.flash_attn_with_kvcache(q, k_cache, v_cache, cache_seqlens=cache_seqlens, causal=True)
-fa3.is_fa3_supported()  # True on H100/H200
-```
-
-## SGLang Integration
-
-Entry point: [`python/sglang/srt/layers/attention/flashattention_backend.py`](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/layers/attention/flashattention_backend.py)
-
-Original:
-```python
-from sgl_kernel.flash_attn import flash_attn_varlen_func as flash_attn_varlen_func_fa3
-from sgl_kernel.flash_attn import flash_attn_with_kvcache as flash_attn_with_kvcache_fa3
-```
-
-Replace with:
-```python
-from kernels import get_kernel
-_fa3_mod = get_kernel("kernels-community/sgl-flash-attn3", revision="v1")
-flash_attn_varlen_func_fa3 = _fa3_mod.flash_attn_varlen_func
-flash_attn_with_kvcache_fa3 = _fa3_mod.flash_attn_with_kvcache
-```
-
-Same pattern in 5 other files:
-- `dual_chunk_flashattention_backend.py`
-- `nsa_backend.py`
-- `xpu_backend.py`
-- `vision.py`
-- `multimodal_gen/runtime/layers/attention/backends/flash_attn.py`
+## Available functions
 
+Function list not available.
 
 ## Benchmarks
 
-H100 NVL, Qwen2.5-3B-Instruct, FA3. All deltas within noise - **zero performance regression**.
-
-| Scenario | Native `sgl_kernel` FA3 tok/s | HF Hub FA3 tok/s | Δ |
-|:--|--:|--:|:--|
-| Short Gen (128→32) | 40,934 | 39,878 | -2.6% |
-| Long Gen (256→1024) | 25,054 | 26,239 | +4.7% |
-| Long Prefill (2048→128) | 53,833 | 54,283 | +0.8% |
-| Bursty (512→256, 16rps) | 6,518 | 6,527 | +0.1% |
-| High Concurrency (256→256) | 40,666 | 40,522 | -0.4% |
-
-## Credits
-
-- [Tri Dao](https://tridao.me/) - [Flash Attention 3](https://tridao.me/blog/2024/flash3/)
-- [SGLang](https://github.com/sgl-project/sglang) - `sgl_kernel` FA3 implementation
-- [HuggingFace](https://huggingface.co/kernels-community) - [kernel-builder](https://huggingface.co/blog/kernel-builder) infrastructure
+No benchmark available yet.