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 training_args.bin from katuzar/output: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/katuzar/output/resolve/main/training_args.bin
- Command line
-
hf download hf://katuzar/output/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/katuzar/output/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- dabed0b643eb4f33ba45fa771b740354cdc5ff302624254365414fe837faa35c
- Size of remote file:
- 3.44 kB
- SHA256:
- 4749ff594d78b601c5e3308ee207bff3012cf1bad76b70b5c6b87de99f787fa6
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