Instructions to use masterhaniwa/CLIP_ft_sim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use masterhaniwa/CLIP_ft_sim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="masterhaniwa/CLIP_ft_sim") 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_sim") model = AutoModelForZeroShotImageClassification.from_pretrained("masterhaniwa/CLIP_ft_sim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from masterhaniwa/CLIP_ft_sim: direct link, hf CLI and curl.
- Browser
- Download file 599 MB
-
https://huggingface.co/masterhaniwa/CLIP_ft_sim/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://masterhaniwa/CLIP_ft_sim@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/masterhaniwa/CLIP_ft_sim/resolve/refs%2Fpr%2F1/pytorch_model.bin
599 MB
- Xet hash:
- f7e5e7070eac78a4414c39463e2fda3d3656fc59dcd01d23b18f45aeb7370d78
- Size of remote file:
- 599 MB
- SHA256:
- 1422b25ffe5222f62df7058ea0a8274be47380fd5253f568ecd9598f5fc3fe3e
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