Instructions to use ostris/CLIP-ViT-H-14-448 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ostris/CLIP-ViT-H-14-448 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="ostris/CLIP-ViT-H-14-448") 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 AutoTokenizer, CLIPVisionModelWithProjection tokenizer = AutoTokenizer.from_pretrained("ostris/CLIP-ViT-H-14-448") model = CLIPVisionModelWithProjection.from_pretrained("ostris/CLIP-ViT-H-14-448", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 531 Bytes
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"_name_or_path": "",
"architectures": ["CLIPVisionModelWithProjection"],
"attention_dropout": 0.0,
"dropout": 0.0,
"hidden_act": "gelu",
"hidden_size": 1280,
"image_size": 448,
"initializer_factor": 1.0,
"initializer_range": 0.02,
"intermediate_size": 5120,
"layer_norm_eps": 1e-5,
"model_type": "clip_vision_model",
"num_attention_heads": 16,
"num_channels": 3,
"num_hidden_layers": 32,
"patch_size": 14,
"projection_dim": 1024,
"torch_dtype": "float16",
"transformers_version": "4.36.1"
}
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