Instructions to use zer0int/CLIP-GmP-ViT-L-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zer0int/CLIP-GmP-ViT-L-14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="zer0int/CLIP-GmP-ViT-L-14") 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("zer0int/CLIP-GmP-ViT-L-14") model = AutoModelForZeroShotImageClassification.from_pretrained("zer0int/CLIP-GmP-ViT-L-14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "CLIP-GmP-ViT-L-14/", | |
| "architectures": [ | |
| "CLIPModel" | |
| ], | |
| "initializer_factor": 1.0, | |
| "logit_scale_init_value": 4.6052, | |
| "model_type": "clip", | |
| "projection_dim": 768, | |
| "text_config": { | |
| "dropout": 0.0, | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "model_type": "clip_text_model", | |
| "num_attention_heads": 12, | |
| "projection_dim": 768 | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.2", | |
| "vision_config": { | |
| "dropout": 0.0, | |
| "hidden_size": 1024, | |
| "intermediate_size": 4096, | |
| "model_type": "clip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "patch_size": 14, | |
| "projection_dim": 768 | |
| } | |
| } | |