Instructions to use svnfs/vit4fosscomm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use svnfs/vit4fosscomm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="svnfs/vit4fosscomm") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("svnfs/vit4fosscomm") model = AutoModelForImageClassification.from_pretrained("svnfs/vit4fosscomm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from svnfs/vit4fosscomm: direct link, hf CLI and curl.
- Browser
- Download file 88.3 MB
-
https://huggingface.co/svnfs/vit4fosscomm/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://svnfs/vit4fosscomm/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/svnfs/vit4fosscomm/resolve/main/pytorch_model.bin
88.3 MB
- Xet hash:
- d28873ff9a36820c9bba205989a304731a1c998535e33b77b8851408f76ff601
- Size of remote file:
- 88.3 MB
- SHA256:
- a367ad27a199633d21a32ebd78d9951dc5ab64e35507d2fad336a539b4d0d162
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.