Instructions to use Jumpr/HF_compatibility_testv3-AutoModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jumpr/HF_compatibility_testv3-AutoModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jumpr/HF_compatibility_testv3-AutoModel", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jumpr/HF_compatibility_testv3-AutoModel", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45055ba4aa18553183f3ba6ccd1ae08bda3195a12d56426ae2c02d6321852bde
- Size of remote file:
- 1.77 MB
- SHA256:
- 6bcae7160f2859ea27bcb6b84f86f3682a7261997459dcca7301a1770fc72e37
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