Instructions to use cs-giung/clip-vit-gigantic-patch14-fullcc2.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cs-giung/clip-vit-gigantic-patch14-fullcc2.5b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cs-giung/clip-vit-gigantic-patch14-fullcc2.5b") 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 AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("cs-giung/clip-vit-gigantic-patch14-fullcc2.5b") model = AutoModelForZeroShotImageClassification.from_pretrained("cs-giung/clip-vit-gigantic-patch14-fullcc2.5b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 666 Bytes
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"architectures": [
"CLIPModel"
],
"initializer_factor": 1.0,
"logit_scale_init_value": 2.6592,
"model_type": "clip",
"projection_dim": 1280,
"text_config": {
"hidden_act": "quick_gelu",
"hidden_size": 1280,
"intermediate_size": 5120,
"model_type": "clip_text_model",
"num_attention_heads": 20,
"num_hidden_layers": 32
},
"torch_dtype": "float32",
"transformers_version": "4.34.0",
"vision_config": {
"hidden_act": "quick_gelu",
"hidden_size": 1664,
"intermediate_size": 8192,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_hidden_layers": 48,
"patch_size": 14
}
}
|