Instructions to use TJKlein/CLIP-ViT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TJKlein/CLIP-ViT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="TJKlein/CLIP-ViT") 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("TJKlein/CLIP-ViT") model = AutoModelForZeroShotImageClassification.from_pretrained("TJKlein/CLIP-ViT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from TJKlein/CLIP-ViT: direct link, hf CLI and curl.
- Browser
- Download file 1.71 GB
-
https://huggingface.co/TJKlein/CLIP-ViT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://TJKlein/CLIP-ViT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/TJKlein/CLIP-ViT/resolve/main/pytorch_model.bin
1.71 GB
- Xet hash:
- c79273d42d2fca376f15256f2feb3077ee6be54aa85a89e679487c1d47d39e17
- Size of remote file:
- 1.71 GB
- SHA256:
- 14a6f32ef2d4c9266f782f5c9292715bed92842ef73b69d59a93bac4127eec19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.