Instructions to use omkarp/colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omkarp/colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="omkarp/colab") 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("omkarp/colab") model = AutoModelForImageClassification.from_pretrained("omkarp/colab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from omkarp/colab: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/omkarp/colab/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://omkarp/colab/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/omkarp/colab/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 44ec03af64040603578b23482ba4537146c54e830b6dd3a0d6f7ce5577739505
- Size of remote file:
- 343 MB
- SHA256:
- 9c469ae58975f0b56cc414f63ae0a7c907ca48f0a014f644f3433a740ca943b9
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