Instructions to use hf-internal-testing/tiny-random-BitForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BitForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-BitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-BitForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b379e7925f6f197a6f11bbe120a4d158bce6ca6a2ca8d2d11a7b41060aaca8b8
- Size of remote file:
- 101 kB
- SHA256:
- d1ba8872d3e10ef2b53cf699e49ff3a6c45958fa64c59614c191c77ed7c9fe5c
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