Instructions to use hf-tiny-model-private/tiny-random-ViTModel 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-ViTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-tiny-model-private/tiny-random-ViTModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-tiny-model-private/tiny-random-ViTModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-ViTModel") - Notebooks
- Google Colab
- Kaggle
File size: 537 Bytes
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"_name_or_path": "tiny_models/vit/ViTModel",
"architectures": [
"ViTModel"
],
"attention_probs_dropout_prob": 0.1,
"encoder_stride": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 32,
"image_size": 30,
"initializer_range": 0.02,
"intermediate_size": 37,
"layer_norm_eps": 1e-12,
"model_type": "vit",
"num_attention_heads": 4,
"num_channels": 3,
"num_hidden_layers": 5,
"patch_size": 2,
"qkv_bias": true,
"torch_dtype": "float32",
"transformers_version": "4.28.0.dev0"
}
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