Instructions to use ismot/14t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ismot/14t5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="ismot/14t5") 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("ismot/14t5") model = AutoModelForZeroShotImageClassification.from_pretrained("ismot/14t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from ismot/14t5: direct link, hf CLI and curl.
- Browser
- Download file 1.71 GB
-
https://huggingface.co/ismot/14t5/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://ismot/14t5/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/ismot/14t5/resolve/main/flax_model.msgpack
1.71 GB
- Xet hash:
- 8eea36a84e57e6617853d8011ebdbf2df119daaa1c8510e6fbcaf2f2c23591ce
- Size of remote file:
- 1.71 GB
- SHA256:
- 156f677ed4495acd1ec7197249c091b85c240267c82f2f7f2e4eae4177931fed
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