Instructions to use lysandre/tiny-vit-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lysandre/tiny-vit-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="lysandre/tiny-vit-random") 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("lysandre/tiny-vit-random") model = AutoModelForImageClassification.from_pretrained("lysandre/tiny-vit-random", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lysandre/tiny-vit-random: direct link, hf CLI and curl.
- Browser
- Download file 324 kB
-
https://huggingface.co/lysandre/tiny-vit-random/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lysandre/tiny-vit-random/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lysandre/tiny-vit-random/resolve/main/pytorch_model.bin
324 kB
- Xet hash:
- ff91413fd981a820cd121389138b3b62ab86b950b7298e8098ec86c465d8a36a
- Size of remote file:
- 324 kB
- SHA256:
- 6defef5e9665497e716a435067016c2167733e84c2f790150abf4d135f7d368e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.