Instructions to use TencentARC/QA-CLIP-ViT-L-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TencentARC/QA-CLIP-ViT-L-14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="TencentARC/QA-CLIP-ViT-L-14") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("TencentARC/QA-CLIP-ViT-L-14") model = AutoModelForZeroShotImageClassification.from_pretrained("TencentARC/QA-CLIP-ViT-L-14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2b68ba62f54563a3587fa95ef1c67d2bedce383523e8a062c973b5e0161deeb
- Size of remote file:
- 1.63 GB
- SHA256:
- 51bd34605dce589ffd7715ae69b267f1ec8a54923a3dc069251370deab311ffc
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