Instructions to use universalml/gfdfg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalml/gfdfg with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="universalml/gfdfg") 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("universalml/gfdfg") model = AutoModelForImageClassification.from_pretrained("universalml/gfdfg", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a64344aee2e7fac6055269270141bc0490a7d5b03fc8033ef66eecf2c25391fd
- Size of remote file:
- 4.98 kB
- SHA256:
- 47afc6546631ac09caf26cd258df88f0413ee677529841178fd0c1357fd33027
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.