Instructions to use hf-tiny-model-private/tiny-random-BitForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-BitForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-BitForImageClassification") 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("hf-tiny-model-private/tiny-random-BitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-BitForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd1891011364d8600c0a8111a146c6266e27d03d5a677c64d46ee177382d6146
- Size of remote file:
- 90.5 kB
- SHA256:
- e8a09d8219329313b4f0d0a83729ef41a043ac4117847ef2edccc369caeeb48a
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