Instructions to use hf-internal-testing/tiny-random-mobilevit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-mobilevit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-mobilevit") 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("hf-internal-testing/tiny-random-mobilevit") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-mobilevit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
[Awaiting approval] Upload ONNX weights
Verified signature[Automated] Converted using [Optimum](https://github.com/huggingface/optimum). Models will be merged manually by @Xenova once they have been checked with [Transformers.js](https://github.com/xenova/transformers.js).1c5b403
-
Upload feature extractor
91d8211 -
Upload MobileViTForImageClassification
077954d -
initial commit
da518d6