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:
- 2fa3058ee7e0ad7522361bdc271726b07cabd09a028a5a3189683f397620fbdb
- Size of remote file:
- 2.65 GB
- SHA256:
- 83c3e80deec221b93087a1cbb767234d5f9d13279cc0770cdfcb3573c6034e15
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