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_batch10.bin from masterhaniwa/CLIP_ft: direct link, hf CLI and curl.
- Browser
- Download file 599 MB
-
https://huggingface.co/masterhaniwa/CLIP_ft/resolve/main/pytorch_model_batch10.bin
- Command line
-
hf download hf://masterhaniwa/CLIP_ft/pytorch_model_batch10.bin
-
curl -L -o pytorch_model_batch10.bin https://huggingface.co/masterhaniwa/CLIP_ft/resolve/main/pytorch_model_batch10.bin
599 MB
- Xet hash:
- d252db2a3053b023c60a1703bca38d9a846da1e6b3699069aab18795bfddf088
- Size of remote file:
- 599 MB
- SHA256:
- 4ab20456c1a3185fc465a2c9948aeadaa4de813ed620fcad6c5ddef77340d8ea
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