Instructions to use hf-internal-testing/tiny-random-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-vit") 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-vit") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-vit") - Inference
- Notebooks
- Google Colab
- Kaggle
Add model and configuration.
Browse files- config.json +16 -0
- pytorch_model.bin +3 -0
config.json
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{
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"attention_probs_dropout_prob": 0.1,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 32,
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"image_size": 30,
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"initializer_range": 0.02,
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"intermediate_size": 37,
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"layer_norm_eps": 1e-12,
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"model_type": "vit",
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"num_attention_heads": 4,
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"num_channels": 3,
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"num_hidden_layers": 5,
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"patch_size": 2,
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"transformers_version": "4.10.0.dev0"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7563ed482635fb927ffe3cb96471e434c70a02e04ba996ad71b7622bf07400ac
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size 198359
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