Instructions to use UF-NLPC-Lab/test_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UF-NLPC-Lab/test_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="UF-NLPC-Lab/test_model", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("UF-NLPC-Lab/test_model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d75bd7d33c170a3a8020e407da6ca8d9f53e80de7708cb0bc2182858b3229539
- Size of remote file:
- 440 MB
- SHA256:
- 751afeaf42a2a20bda4be0a547e53e18af05d9ec53407f2d283c3abb7fd5d885
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