Instructions to use hf-internal-testing/tiny-random-BeitForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BeitForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-BeitForImageClassification") 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-BeitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-BeitForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-BeitForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 135 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-BeitForImageClassification/resolve/529c741f40147856b0179b9bdf7a5e82bc938531/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-BeitForImageClassification@529c741f40147856b0179b9bdf7a5e82bc938531/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-BeitForImageClassification/resolve/529c741f40147856b0179b9bdf7a5e82bc938531/pytorch_model.bin
135 kB
- Xet hash:
- 62c38b79d7e2492d69c7a26787084d687ab96214fa280feef77a8061834b523b
- Size of remote file:
- 135 kB
- SHA256:
- cd5ddc97ea2c9f993d7391ef52b8c3704d8fd1f07b4517166520b6f9078a6498
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.