Instructions to use sxdave/tester with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sxdave/tester with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sxdave/tester") 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("sxdave/tester") model = AutoModelForImageClassification.from_pretrained("sxdave/tester", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1af8c28317e48f2f92682ee5d9a1aeb120155a3f494f20f10a4027cbf7ac9163
- Size of remote file:
- 687 MB
- SHA256:
- 45d11304ee82531b61885e58b9af5760ad270ee53b3aaf1cc761a200881b421f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.