Instructions to use falba/google-vit-base-ASL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use falba/google-vit-base-ASL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="falba/google-vit-base-ASL") 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("falba/google-vit-base-ASL") model = AutoModelForImageClassification.from_pretrained("falba/google-vit-base-ASL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from falba/google-vit-base-ASL: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/falba/google-vit-base-ASL/resolve/main/training_args.bin
- Command line
-
hf download hf://falba/google-vit-base-ASL/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/falba/google-vit-base-ASL/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- da56892be9d3f48d8d64fbb46e94833f0974ac0a877c736462d6019bfb2fe9d5
- Size of remote file:
- 4.92 kB
- SHA256:
- 310689f34d419fb0e8121737c9237dca002ed528af27d3eb50811a93f4de533d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.