Instructions to use HuggingFaceM4/tiny-random-siglip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HuggingFaceM4/tiny-random-siglip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="HuggingFaceM4/tiny-random-siglip", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("HuggingFaceM4/tiny-random-siglip", trust_remote_code=True) model = AutoModelForZeroShotImageClassification.from_pretrained("HuggingFaceM4/tiny-random-siglip", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 950 Bytes
ffd5378 e06a98d ffd5378 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
"_name_or_path": "HuggingFaceM4/tiny-random-siglip",
"architectures": [
"SiglipModel"
],
"auto_map": {
"AutoConfig": "HuggingFaceM4/tiny-random-siglip--configuration_siglip.SiglipConfig",
"AutoModel": "HuggingFaceM4/tiny-random-siglip--modeling_siglip.SiglipModel"
},
"initializer_factor": 1.0,
"logit_scale_init_value": 2.6592,
"model_type": "siglip",
"projection_dim": 512,
"text_config": {
"hidden_size": 144,
"intermediate_size": 538,
"model_type": "siglip_text_model",
"num_attention_heads": 2,
"num_hidden_layers": 3,
"projection_dim": 64,
"vocab_size": 32000
},
"torch_dtype": "float32",
"transformers_version": "4.37.0.dev0",
"vision_config": {
"hidden_size": 144,
"image_size": 30,
"intermediate_size": 538,
"model_type": "siglip_vision_model",
"num_attention_heads": 2,
"num_hidden_layers": 3,
"patch_size": 2,
"projection_dim": 64
}
}
|