Instructions to use SparseLLM/swiglu-85B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseLLM/swiglu-85B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SparseLLM/swiglu-85B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d4d01c34d2b300937cee509be4c4943a50240d18674b09c1bd36374d96f8fd9e
- Size of remote file:
- 2.65 GB
- SHA256:
- 05c3303f9281c7a88e2014b6992f17e38a44c7e4fd610cfd336717f158cfa31b
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