Instructions to use Isaacks/test_push with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Isaacks/test_push with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="Isaacks/test_push")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("Isaacks/test_push") model = SegformerForSemanticSegmentation.from_pretrained("Isaacks/test_push", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Isaacks/test_push: direct link, hf CLI and curl.
- Browser
- Download file 14.9 MB
-
https://huggingface.co/Isaacks/test_push/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Isaacks/test_push@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Isaacks/test_push/resolve/refs%2Fpr%2F1/pytorch_model.bin
14.9 MB
- Xet hash:
- 4802401837cd8b1fd2d2fd199397dd902724542129636433174d21d3f9cd2b79
- Size of remote file:
- 14.9 MB
- SHA256:
- 6af0b23a6b2d67bec1369e82d7c33bc009e8f3b034ee80907a577e1a09a25de5
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