Instructions to use CollectionStudio/siglip2-large-patch16-256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CollectionStudio/siglip2-large-patch16-256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="CollectionStudio/siglip2-large-patch16-256") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CollectionStudio/siglip2-large-patch16-256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 537 Bytes
e357c77 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"initializer_factor": 1.0,
"model_type": "siglip",
"text_config": {
"hidden_size": 1024,
"intermediate_size": 4096,
"model_type": "siglip_text_model",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"projection_size": 1024,
"vocab_size": 256000
},
"transformers_version": "4.49.0.dev0",
"vision_config": {
"hidden_size": 1024,
"image_size": 256,
"intermediate_size": 4096,
"model_type": "siglip_vision_model",
"num_attention_heads": 16,
"num_hidden_layers": 24
}
}
|