Instructions to use hf-internal-testing/tiny-random-SiglipVisionModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SiglipVisionModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-SiglipVisionModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-SiglipVisionModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-SiglipVisionModel") - Notebooks
- Google Colab
- Kaggle
File size: 523 Bytes
00164e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"_name_or_path": "hf-internal-testing/tiny-random-SiglipModel",
"architectures": [
"SiglipVisionModel"
],
"attention_dropout": 0.1,
"dropout": 0.1,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 32,
"image_size": 30,
"initializer_range": 0.02,
"intermediate_size": 37,
"layer_norm_eps": 1e-06,
"model_type": "siglip_vision_model",
"num_attention_heads": 4,
"num_channels": 3,
"num_hidden_layers": 2,
"patch_size": 2,
"torch_dtype": "float32",
"transformers_version": "4.46.2"
}
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