Instructions to use hf-internal-testing/tiny-random-SegformerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SegformerModel 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-SegformerModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-SegformerModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SegformerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-SegformerModel: direct link, hf CLI and curl.
- Browser
- Download file 3.05 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-SegformerModel/resolve/3d4ba8ed2bdf801e6afa855b9d77893f2b7f9e10/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-SegformerModel@3d4ba8ed2bdf801e6afa855b9d77893f2b7f9e10/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-SegformerModel/resolve/3d4ba8ed2bdf801e6afa855b9d77893f2b7f9e10/pytorch_model.bin
3.05 MB
- Xet hash:
- 7cd46ce7e1ee582e9ef4584d55b4bb01cd3b856c955f96f7d626ef4af238be56
- Size of remote file:
- 3.05 MB
- SHA256:
- dac07f7a5ae1d8b079c88b0701afeb25b25c6eb07fbcd715cb11344ab0851dab
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