Instructions to use simlaharma/vit-base-cifar10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simlaharma/vit-base-cifar10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="simlaharma/vit-base-cifar10") 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("simlaharma/vit-base-cifar10") model = AutoModelForImageClassification.from_pretrained("simlaharma/vit-base-cifar10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from simlaharma/vit-base-cifar10: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/simlaharma/vit-base-cifar10/resolve/main/training_args.bin
- Command line
-
hf download hf://simlaharma/vit-base-cifar10/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/simlaharma/vit-base-cifar10/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 32d4dc1c26911f3807d6a8b07b432e0a4096cad19a2cd82506d86b34e5fece2c
- Size of remote file:
- 3.64 kB
- SHA256:
- ae951fb4537724da66e0d113a792cd47b1295af5e03d24226b4cd551c7380de8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.