Instructions to use Jumpr/HF_model_ci_test-AutoModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jumpr/HF_model_ci_test-AutoModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jumpr/HF_model_ci_test-AutoModel", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jumpr/HF_model_ci_test-AutoModel", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 55a5b55d28c27a52cf404f9559999c72db721472a9cc8952263ee1295b5cc54e
- Size of remote file:
- 1.77 MB
- SHA256:
- 921a60e369dea1704b30a156fd93b2d8b3b6b0f9c7a7aedc30461ebfe1ef2672
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