Instructions to use robosapiens/class-room with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robosapiens/class-room with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="robosapiens/class-room") 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("robosapiens/class-room") model = AutoModelForImageClassification.from_pretrained("robosapiens/class-room", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from robosapiens/class-room: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/robosapiens/class-room/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://robosapiens/class-room/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/robosapiens/class-room/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- fba04fd58fb0eb4c3ce7dc0aab4ee4e624a75924582c114327508d6af41418c0
- Size of remote file:
- 343 MB
- SHA256:
- 2f11b08046455cbb0e6b1026bb6eaddbb14b3cf0da3dc3347e40ae1893dbf2bc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.