Instructions to use hf-internal-testing/tiny-random-ViTMSNForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-ViTMSNForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-ViTMSNForImageClassification") 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-ViTMSNForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-ViTMSNForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-ViTMSNForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 195 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-ViTMSNForImageClassification/resolve/feda819aa7dbb55d850130f4cf1d210858d7eb89/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-ViTMSNForImageClassification@feda819aa7dbb55d850130f4cf1d210858d7eb89/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-ViTMSNForImageClassification/resolve/feda819aa7dbb55d850130f4cf1d210858d7eb89/pytorch_model.bin
195 kB
- Xet hash:
- 075064ef1f35ebddb771311eba2ceac1e8db03c11f3d19d26d9887ab4fe99bbd
- Size of remote file:
- 195 kB
- SHA256:
- b94dfad4e2ec26efbe735375708bbda440c77beb1e2d4c5ff936389a8d055414
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