Instructions to use zer0int/LongCLIP-GmP-ViT-L-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zer0int/LongCLIP-GmP-ViT-L-14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="zer0int/LongCLIP-GmP-ViT-L-14") 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("zer0int/LongCLIP-GmP-ViT-L-14") model = AutoModelForZeroShotImageClassification.from_pretrained("zer0int/LongCLIP-GmP-ViT-L-14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from zer0int/LongCLIP-GmP-ViT-L-14: direct link, hf CLI and curl.
- Browser
- Download file 1.71 GB
-
https://huggingface.co/zer0int/LongCLIP-GmP-ViT-L-14/resolve/main/model.safetensors
- Command line
-
hf download hf://zer0int/LongCLIP-GmP-ViT-L-14/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/zer0int/LongCLIP-GmP-ViT-L-14/resolve/main/model.safetensors
1.71 GB
- Xet hash:
- 91714dd85ea2c7d671211bb3ee2cd79984bcc36df3e906a9388b1d7b027b1792
- Size of remote file:
- 1.71 GB
- SHA256:
- 8e259dc8f7cef4b289d0c4a84667b310b92060addc590e4df2d227ff7029beaf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.