Instructions to use p1atdev/siglip2-base-patch16-384-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p1atdev/siglip2-base-patch16-384-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="p1atdev/siglip2-base-patch16-384-vision")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("p1atdev/siglip2-base-patch16-384-vision") model = AutoModel.from_pretrained("p1atdev/siglip2-base-patch16-384-vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 417 Bytes
5a2dfcb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"architectures": [
"SiglipVisionModel"
],
"attention_dropout": 0.0,
"hidden_act": "gelu_pytorch_tanh",
"hidden_size": 768,
"image_size": 384,
"intermediate_size": 3072,
"layer_norm_eps": 1e-06,
"model_type": "siglip_vision_model",
"num_attention_heads": 12,
"num_channels": 3,
"num_hidden_layers": 12,
"patch_size": 16,
"torch_dtype": "float32",
"transformers_version": "4.50.3"
}
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