Instructions to use replicate/flash-mla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use replicate/flash-mla with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("replicate/flash-mla") - Notebooks
- Google Colab
- Kaggle
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Download README.md from replicate/flash-mla: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/replicate/flash-mla/resolve/main/README.md
- Command line
-
hf download hf://replicate/flash-mla/README.md
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curl -L -o README.md https://huggingface.co/replicate/flash-mla/resolve/main/README.md
1.17 kB
metadata
library_name: kernels
license: mit
Starting from September 13, 2026, we will be removing the "model" type repositories of kernels (e.g., kernels-community/flash-attn3). Make sure you're using a latest version of kernels. If you face any disruption, please report them here: https://github.com/huggingface/kernels/issues/new.
This is the repository card of kernels-community/flash-mla that has been pushed on the Hub. It was built to be used with the kernels library. This card was automatically generated.
How to use
# make sure `kernels` is installed: `pip install -U kernels`
from kernels import get_kernel
kernel_module = get_kernel("kernels-community/flash-mla")
__version__ = kernel_module.__version__
__version__(...)
Available functions
__version__FlashMLASchedMetaget_mla_metadataflash_mla_with_kvcacheflash_attn_varlen_funcflash_attn_varlen_qkvpacked_funcflash_attn_varlen_kvpacked_funcflash_mla_sparse_fwd
Benchmarks
Benchmarking script is available for this kernel. Run kernels benchmark kernels-community/flash-mla.