Instructions to use cs-giung/clip-vit-gigantic-patch14-fullcc2.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-giung/clip-vit-gigantic-patch14-fullcc2.5b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cs-giung/clip-vit-gigantic-patch14-fullcc2.5b") 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("cs-giung/clip-vit-gigantic-patch14-fullcc2.5b") model = AutoModelForZeroShotImageClassification.from_pretrained("cs-giung/clip-vit-gigantic-patch14-fullcc2.5b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download added_tokens.json from cs-giung/clip-vit-gigantic-patch14-fullcc2.5b: direct link, hf CLI and curl.
- Browser
- Download file 57 Bytes
-
https://huggingface.co/cs-giung/clip-vit-gigantic-patch14-fullcc2.5b/resolve/main/added_tokens.json
- Command line
-
hf download hf://cs-giung/clip-vit-gigantic-patch14-fullcc2.5b/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/cs-giung/clip-vit-gigantic-patch14-fullcc2.5b/resolve/main/added_tokens.json
57 Bytes
| { | |
| "<|endoftext|>": 49407, | |
| "<|startoftext|>": 49406 | |
| } | |