Instructions to use jwna/prmt_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jwna/prmt_embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="jwna/prmt_embedding") 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("jwna/prmt_embedding") model = AutoModelForZeroShotImageClassification.from_pretrained("jwna/prmt_embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from jwna/prmt_embedding: direct link, hf CLI and curl.
- Browser
- Download file 506 Bytes
-
https://huggingface.co/jwna/prmt_embedding/resolve/main/config.json
- Command line
-
hf download hf://jwna/prmt_embedding/config.json
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curl -L -o config.json https://huggingface.co/jwna/prmt_embedding/resolve/main/config.json
506 Bytes
| { | |
| "_name_or_path": "openai/clip-vit-base-patch16", | |
| "architectures": [ | |
| "CLIPModel" | |
| ], | |
| "initializer_factor": 1.0, | |
| "logit_scale_init_value": 2.6592, | |
| "model_type": "clip", | |
| "projection_dim": 512, | |
| "text_config": { | |
| "bos_token_id": 0, | |
| "dropout": 0.0, | |
| "eos_token_id": 2, | |
| "model_type": "clip_text_model" | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.47.1", | |
| "vision_config": { | |
| "dropout": 0.0, | |
| "model_type": "clip_vision_model", | |
| "patch_size": 16 | |
| } | |
| } | |