Instructions to use ongp/70btclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ongp/70btclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ongp/70btclassification") 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("ongp/70btclassification") model = AutoModelForImageClassification.from_pretrained("ongp/70btclassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ongp/70btclassification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/ongp/70btclassification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ongp/70btclassification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ongp/70btclassification/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- aa222f75892deacbbaf527a0387ea4ca783c0316e2d4b08ece079bf24047ad29
- Size of remote file:
- 343 MB
- SHA256:
- 9da34de4ffabb8477433470ca3ca0ff8dacefb9e3d5b4595bd43bb251fc56565
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