Instructions to use stochastic/102722run with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stochastic/102722run with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="stochastic/102722run") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("stochastic/102722run") model = AutoModelForImageClassification.from_pretrained("stochastic/102722run") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edd0356dee7f179e91f457eede92bd8cce250b583e49ce737fa0c8b1c47463b3
- Size of remote file:
- 344 MB
- SHA256:
- 80f25110657f184b52b2cd15b5f773a48fd5b947154f26546d5ecab436f05cbd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.