Instructions to use mooncakex/img2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mooncakex/img2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="mooncakex/img2") 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("mooncakex/img2") model = AutoModelForZeroShotImageClassification.from_pretrained("mooncakex/img2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mooncakex/img2: direct link, hf CLI and curl.
- Browser
- Download file 899 MB
-
https://huggingface.co/mooncakex/img2/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://mooncakex/img2@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mooncakex/img2/resolve/refs%2Fpr%2F1/pytorch_model.bin
899 MB
- Xet hash:
- 70d081e3b0e485cd0f54f6a85677d6a2c7f9281e7efce054b9b4f538f6b02526
- Size of remote file:
- 899 MB
- SHA256:
- d1cafd330892f620f969335716a5bf297c512ee300d53dbc06e8ed0859d9c30b
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