Instructions to use Efficient-Large-Model/Sol-Attn-Kernel-Source with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use Efficient-Large-Model/Sol-Attn-Kernel-Source with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("Efficient-Large-Model/Sol-Attn-Kernel-Source") - Notebooks
- Google Colab
- Kaggle
File size: 883 Bytes
8e9f35a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | # Source provenance
- Upstream repository: <https://github.com/NVlabs/Sana>
- Upstream branch: `sol-engine`
- Upstream commit: `8a26fb0ec9e353125ead798cb2e312d5ce48cded`
- Upstream path: `techniques/sparse_backends/sol_attn`
- Kernel Hub target: `Efficient-Large-Model/Sol-Attn`
`torch-ext/sol_attn` contains the complete upstream package at the pinned
commit. Its only packaging-level changes are semantically equivalent
absolute-to-relative internal imports. Kernel Hub loads each kernel version
under an isolated module name, so hard-coded `sol_attn.*` package imports
would otherwise escape that namespace.
Given a checkout of the pinned Sana commit, verify the file inventory, all
non-import source text, and import semantics with:
```bash
python tools/verify_upstream.py /path/to/Sana
```
The publishing process does not add files, workflows, or commits to
`NVlabs/Sana`.
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