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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library_name: kernels
license: mit
---
> [!CAUTION]
> 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](https://github.com/huggingface/kernels). This card was automatically generated.
## How to use
```python
# 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__`
- `FlashMLASchedMeta`
- `get_mla_metadata`
- `flash_mla_with_kvcache`
- `flash_attn_varlen_func`
- `flash_attn_varlen_qkvpacked_func`
- `flash_attn_varlen_kvpacked_func`
- `flash_mla_sparse_fwd`
## Benchmarks
Benchmarking script is available for this kernel. Run `kernels benchmark kernels-community/flash-mla`.
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