Instructions to use optimum-internal-testing/tiny-random-metaclip_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum-internal-testing/tiny-random-metaclip_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="optimum-internal-testing/tiny-random-metaclip_2") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("optimum-internal-testing/tiny-random-metaclip_2") model = AutoModelForZeroShotImageClassification.from_pretrained("optimum-internal-testing/tiny-random-metaclip_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d329a747e3a9bc5723d3e8be3bf192e0f3cf8d184a32d79e955a0f0584b11b2
- Size of remote file:
- 904 kB
- SHA256:
- ae93576ab7c428ee4c7bf1256637ffdd20f16162e4f07fe38e5c1c079fc16161
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