Instructions to use hf-tiny-model-private/tiny-random-ImageGPTForImageClassification 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-ImageGPTForImageClassification 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-ImageGPTForImageClassification") 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-ImageGPTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ImageGPTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 52837600906b8d112584d51777a6c89cd3331e08f07fa9b8bdfd1b1ea8b733da
- Size of remote file:
- 5.58 MB
- SHA256:
- 16b0e44cdf33daec15ec72c31e12ccdeba4225de3a8ddf763909dbb97ef7b311
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