Instructions to use jayanta/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayanta/test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jayanta/test") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("jayanta/test") model = AutoModelForImageClassification.from_pretrained("jayanta/test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a00066f44b948cb3a674477231a9f50dd0ec83e0c4a8127a94d1cf1a02635cd0
- Size of remote file:
- 343 MB
- SHA256:
- 539fe700d8f663c89b1b921b69c45dd3752b0c91e5e2980bd82feddd0c246276
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.