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 tokenizer.json from mooncakex/img2: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
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https://huggingface.co/mooncakex/img2/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
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hf download hf://mooncakex/img2@refs/pr/1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/mooncakex/img2/resolve/refs%2Fpr%2F1/tokenizer.json
711 kB
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