Instructions to use nqvii/fold_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nqvii/fold_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nqvii/fold_4") 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("nqvii/fold_4") model = AutoModelForImageClassification.from_pretrained("nqvii/fold_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6751e10a62ff6ab19e08e94abac37c8fa37f012506aa47b0566969519ab15055
- Size of remote file:
- 5.2 kB
- SHA256:
- 633bf80e36a492d896040df035aee1627d8dbed7838732efc5b2e808d91f70a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.