Instructions to use hf-internal-testing/tiny-random-MobileViTV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileViTV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-MobileViTV2ForImageClassification") 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-MobileViTV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-MobileViTV2ForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.26 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-MobileViTV2ForImageClassification/resolve/39175d0681ee428d6495a789a58c48d4eb10675e/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MobileViTV2ForImageClassification@39175d0681ee428d6495a789a58c48d4eb10675e/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-MobileViTV2ForImageClassification/resolve/39175d0681ee428d6495a789a58c48d4eb10675e/pytorch_model.bin
1.26 MB
- Xet hash:
- 7aba5697fcd89919c279c146c1e751c705d90d8ad05efa7445af9b1b50222728
- Size of remote file:
- 1.26 MB
- SHA256:
- 3073a25578fd7c6c4674f924d31d2973b92c03d87c42a0a173756af3617ceb20
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