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_1213.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_1213.bin
- Command line
-
hf download hf://masterhaniwa/CLIP_ft/pytorch_model_1213.bin
-
curl -L -o pytorch_model_1213.bin https://huggingface.co/masterhaniwa/CLIP_ft/resolve/main/pytorch_model_1213.bin
599 MB
- Xet hash:
- 9edb0e41b376cc224a548a0cc7226499e1c6294dbda0e523fec6672b824af9a2
- Size of remote file:
- 599 MB
- SHA256:
- 481efdbc5b453fb16d2c25c64126cabdb2bdf81038da88ff085ec0c79bf5e5bb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.