Instructions to use J-RUM/professions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use J-RUM/professions with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="J-RUM/professions") 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("J-RUM/professions") model = AutoModelForImageClassification.from_pretrained("J-RUM/professions", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from J-RUM/professions: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/J-RUM/professions/resolve/a6dda39f1be980f98d1f1938dec8445f76bb8e67/pytorch_model.bin
- Command line
-
hf download hf://J-RUM/professions@a6dda39f1be980f98d1f1938dec8445f76bb8e67/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/J-RUM/professions/resolve/a6dda39f1be980f98d1f1938dec8445f76bb8e67/pytorch_model.bin
343 MB
- Xet hash:
- 42f8e7fe8cfc74488dc0f2563d6f7e62125c56772a514e438f1ba77e5ec64a0e
- Size of remote file:
- 343 MB
- SHA256:
- 0b0ed0038bffd682fac0d8542854fe1c256b3b04d15e6b460b67b1165cad740c
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