Instructions to use SparseLLM/swiglu-80B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/swiglu-80B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SparseLLM/swiglu-80B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c42c914821885fa3012a8214151a1721216cb2350828199736001effd9aaa0da
- Size of remote file:
- 1.8 MB
- SHA256:
- fc4f0bd70b3709312d9d1d9e5ba674794b6bc5abc17429897a540f93882f25fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.