Instructions to use rrivera1849/LUAR-MUD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rrivera1849/LUAR-MUD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="rrivera1849/LUAR-MUD", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rrivera1849/LUAR-MUD", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0ceb3a02a667752761278cafc93f460dd1aff64321748f32c2481f100fda443c
- Size of remote file:
- 330 MB
- SHA256:
- ccd1f426f7cedc28a0572524f9581257597cd710cccb39140177390709cb61bc
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