Instructions to use hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, OneFormerForUniversalSegmentation processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation") model = OneFormerForUniversalSegmentation.from_pretrained("hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation: direct link, hf CLI and curl.
- Browser
- Download file 47.9 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-OneFormerForUniversalSegmentation/resolve/main/pytorch_model.bin
47.9 MB
- Xet hash:
- 25dfb06a22301414004747fc1cbba59124ae58f7472641a483a338b74bd10397
- Size of remote file:
- 47.9 MB
- SHA256:
- 4fadda1f1d94a9e55734ddf2efabe12844bfddd5d9abcad1c38566cc5769af88
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.