Instructions to use hf-internal-testing/tiny-random-levit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-levit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-levit") 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-levit") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-levit") - Notebooks
- Google Colab
- Kaggle
Fix inference
Browse files- config.json +6 -6
- pytorch_model.bin +2 -2
config.json
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"model_type": "levit",
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"padding": 1,
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"drop_path_rate": 0,
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"hidden_sizes": [
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"id2label": {
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"model_type": "levit",
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"num_channels": 3,
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"padding": 1,
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pytorch_model.bin
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