Instructions to use JLB-JLB/Model_folder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JLB-JLB/Model_folder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JLB-JLB/Model_folder") 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("JLB-JLB/Model_folder") model = AutoModelForImageClassification.from_pretrained("JLB-JLB/Model_folder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 01a951cfed444cb8652829c8f06f8e3af13f3e7bf0f4cd56f59b56cd032c9070
- Size of remote file:
- 343 MB
- SHA256:
- 1867fda89a34a973781e6becf77399ee8e483db25d7279dd77a12482f300bc4e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.