Instructions to use jasmine009/materials with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jasmine009/materials with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jasmine009/materials") 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("jasmine009/materials") model = AutoModelForImageClassification.from_pretrained("jasmine009/materials", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jasmine009/materials: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/jasmine009/materials/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jasmine009/materials/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jasmine009/materials/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 283a83098a8ad9a86bbb3622c1b97861c539da5122b64759c15a84ffeb7593ac
- Size of remote file:
- 343 MB
- SHA256:
- e65880f7920af6a0828a8fee3c6c5c27c536ac4d33a1a8381c91dcdb05434a98
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