Instructions to use prithivMLmods/MetaCLIP-2-Cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/MetaCLIP-2-Cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/MetaCLIP-2-Cifar10") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/MetaCLIP-2-Cifar10") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/MetaCLIP-2-Cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-3752/optimizer.pt from prithivMLmods/MetaCLIP-2-Cifar10: direct link, hf CLI and curl.
- Browser
- Download file 174 MB
-
https://huggingface.co/prithivMLmods/MetaCLIP-2-Cifar10/resolve/main/checkpoint-3752/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/MetaCLIP-2-Cifar10/checkpoint-3752/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/MetaCLIP-2-Cifar10/resolve/main/checkpoint-3752/optimizer.pt
174 MB
- Xet hash:
- e3aa306035d519bac25ce1445675f077e54fc947a2efc021b1301bdae94ed073
- Size of remote file:
- 174 MB
- SHA256:
- af42edc2144b4e24fca72977978dd6e390ea064412f9ecef8125eb94d23c9955
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