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 training_args.bin from Isaacks/test_push: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/Isaacks/test_push/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://Isaacks/test_push@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Isaacks/test_push/resolve/refs%2Fpr%2F1/training_args.bin
3.96 kB
- Xet hash:
- 85520def4b52035cbb88b9fff78c5e51dfc9c0e69b13aa1206429f1e64159328
- Size of remote file:
- 3.96 kB
- SHA256:
- 00f47a636802dcbd4a622c0852d5c952d23d51ecbb788835569f7a8cda9c147b
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