Instructions to use masterhaniwa/CLIP_ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use masterhaniwa/CLIP_ft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="masterhaniwa/CLIP_ft") 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("masterhaniwa/CLIP_ft") model = AutoModelForZeroShotImageClassification.from_pretrained("masterhaniwa/CLIP_ft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from masterhaniwa/CLIP_ft: direct link, hf CLI and curl.
- Browser
- Download file 599 MB
-
https://huggingface.co/masterhaniwa/CLIP_ft/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://masterhaniwa/CLIP_ft@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/masterhaniwa/CLIP_ft/resolve/refs%2Fpr%2F1/pytorch_model.bin
599 MB
- Xet hash:
- 0a8bf95d5572507f603adf0d2ffe427475f1a164b1870e6406d0672953c1257f
- Size of remote file:
- 599 MB
- SHA256:
- 44e4f7025b2c19c677c8037a34cdbf0a65f63fe79907dafe2e60d397f390169f
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