Instructions to use katuzar/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use katuzar/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="katuzar/output") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("katuzar/output") model = AutoModelForImageClassification.from_pretrained("katuzar/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from katuzar/output: direct link, hf CLI and curl.
- Browser
- Download file 111 MB
-
https://huggingface.co/katuzar/output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://katuzar/output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/katuzar/output/resolve/main/pytorch_model.bin
111 MB
- Xet hash:
- 7fe2a2250805ce2b6c071b9165c2e10db3bc05efba34047275927dfab281ee99
- Size of remote file:
- 111 MB
- SHA256:
- 8a23eaed1040707363887644a321aefaacce7cf1a28338d1af157ff3a9e5b194
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