Instructions to use kernels-staging/msa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use kernels-staging/msa with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("kernels-staging/msa") - Notebooks
- Google Colab
- Kaggle
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library_name: kernels
license: other
---
This is the repository card of kernels-community/msa 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/msa", version=0)
sparse_atten_func = kernel_module.sparse_atten_func
sparse_atten_func(...)
```
## Available functions
- `sparse_atten_func`
- `sparse_atten_nvfp4_kv_func`
- `sparse_decode_atten_func`
- `SparseDecodePagedAttentionWrapper`
- `fp4_indexer_block_scores`
- `build_k2q_csr`
- `SparseK2qCsrBuilderSm100`
- `Nvfp4QuantizedTensor`
- `quantize_bf16_to_nvfp4_128x4`
- `quantize_kv_bf16_to_nvfp4_128x4`
- `dequantize_nvfp4_128x4_to_bf16`
- `swizzle_nvfp4_scale_to_128x4`
- `nvfp4_global_scale_from_amax`
## Benchmarks
No benchmark available yet.
## Source code
Source code of this kernel originally comes from https://github.com/MiniMax-AI/MSA and it was repurposed for compatibility with `kernels`.
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