Instructions to use wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M") 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("wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M") model = AutoModelForZeroShotImageClassification.from_pretrained("wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M: direct link, hf CLI and curl.
- Browser
- Download file 93.9 MB
-
https://huggingface.co/wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M/resolve/main/pytorch_model.bin
93.9 MB
- Xet hash:
- 923851bace5b1e1d2f366829665339f06ffdf1eb75b4dd49df7132386a14ac35
- Size of remote file:
- 93.9 MB
- SHA256:
- 45d832b8c61a631df7b739fecd3fe895a92e57e3a3f318ede459ee0ca6e9c565
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