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
Uploaded using `kernel-builder`.
Browse files
README.md
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---
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library_name: kernels
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license: other
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---
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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.
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## How to use
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```python
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# make sure `kernels` is installed: `pip install -U kernels`
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from kernels import get_kernel
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kernel_module = get_kernel("kernels-community/msa", version=0)
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sparse_atten_func = kernel_module.sparse_atten_func
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sparse_atten_func(...)
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```
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## Available functions
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- `sparse_atten_func`
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- `sparse_atten_nvfp4_kv_func`
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- `sparse_decode_atten_func`
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- `SparseDecodePagedAttentionWrapper`
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- `fp4_indexer_block_scores`
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- `build_k2q_csr`
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- `SparseK2qCsrBuilderSm100`
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- `Nvfp4QuantizedTensor`
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- `quantize_bf16_to_nvfp4_128x4`
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- `quantize_kv_bf16_to_nvfp4_128x4`
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- `dequantize_nvfp4_128x4_to_bf16`
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- `swizzle_nvfp4_scale_to_128x4`
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- `nvfp4_global_scale_from_amax`
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## Benchmarks
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No benchmark available yet.
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## Source code
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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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