Instructions to use zxcej/AICE_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zxcej/AICE_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="zxcej/AICE_model") 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("zxcej/AICE_model") model = AutoModelForImageClassification.from_pretrained("zxcej/AICE_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from zxcej/AICE_model: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/zxcej/AICE_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://zxcej/AICE_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/zxcej/AICE_model/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 2001ae69f198ad0c3c61ffb778223f85448e78e6faf77fb7d112bb8c19dae639
- Size of remote file:
- 343 MB
- SHA256:
- 9b45ffd0664ea6d51033a2c13ca237f6b66f705a6373acbcb46ca71dd699e123
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.