Instructions to use hf-tiny-model-private/tiny-random-OneFormerModel 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-OneFormerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-OneFormerModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-OneFormerModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-OneFormerModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-OneFormerModel: direct link, hf CLI and curl.
- Browser
- Download file 47.9 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-OneFormerModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-OneFormerModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-OneFormerModel/resolve/main/pytorch_model.bin
47.9 MB
- Xet hash:
- 43b9db6df2118dedcd1c180fd09ebb91c04d5baa9532b0beadb9a31b4e3e730a
- Size of remote file:
- 47.9 MB
- SHA256:
- e2bd7d298689fe5d1050d610564739eb082d1aca7066ae5dfa49503f5cbbb821
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