Instructions to use hf-internal-testing/tiny-random-RegNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RegNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-RegNetModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-RegNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-RegNetModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-RegNetModel: direct link, hf CLI and curl.
- Browser
- Download file 240 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-RegNetModel/resolve/311e7b52fa512f9c34d4ff81129b371ee7b9e2d4/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-RegNetModel@311e7b52fa512f9c34d4ff81129b371ee7b9e2d4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-RegNetModel/resolve/311e7b52fa512f9c34d4ff81129b371ee7b9e2d4/pytorch_model.bin
240 kB
- Xet hash:
- a4b839bd16e08ac848bdea2d69163b90ce735ef094a16e7caea1eb9dc6113a1f
- Size of remote file:
- 240 kB
- SHA256:
- 826eddc617c87f2ed12b506bfb6549fea417773cb032742b51e067e698af2683
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