Instructions to use hf-internal-testing/tiny-random-CLIPModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-CLIPModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="hf-internal-testing/tiny-random-CLIPModel") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-CLIPModel") model = AutoModelForZeroShotImageClassification.from_pretrained("hf-internal-testing/tiny-random-CLIPModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-CLIPModel: direct link, hf CLI and curl.
- Browser
- Download file 579 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-CLIPModel/resolve/784ae73a3fe7bfe6474e9c5d062f0fff109e09a8/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-CLIPModel@784ae73a3fe7bfe6474e9c5d062f0fff109e09a8/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-CLIPModel/resolve/784ae73a3fe7bfe6474e9c5d062f0fff109e09a8/pytorch_model.bin
579 kB
- Xet hash:
- 652952bd66b3abe41bf5ec0a3065c49c46b5f2560ccffe25bab90f64b3f7bae7
- Size of remote file:
- 579 kB
- SHA256:
- 4d0ce4dd8f7b4bd5fd35cec79eb5bfdd153357d0dea5a7b109a63294468e64d8
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