Instructions to use hf-internal-testing/tiny-random-SamModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SamModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="hf-internal-testing/tiny-random-SamModel")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-SamModel") model = AutoModelForMaskGeneration.from_pretrained("hf-internal-testing/tiny-random-SamModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80d508ba2d1e5ff33b3ca507c1d32f9f5b323f2a46e0364e7a4094d411b59777
- Size of remote file:
- 558 kB
- SHA256:
- a90e23d57fc2cdb4e7a18d9eb9abebca7f21eb3e76b4ec1a6253a3f7d29b3314
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