Instructions to use hf-internal-testing/tiny-random-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-vit") 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("hf-internal-testing/tiny-random-vit") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-vit") - Inference
- Notebooks
- Google Colab
- Kaggle
Upload ONNX weights
#2
by Xenova HF Staff - opened
[Automated] Converted using Optimum. Models will be merged manually by @Xenova once they have been checked with Transformers.js.
Xenova changed pull request title from [Awaiting approval] Upload ONNX weights to Upload ONNX weights
Xenova changed pull request status to merged