Instructions to use V4ldeLund/clip-vit-base-patch16-da-lora-both-equal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use V4ldeLund/clip-vit-base-patch16-da-lora-both-equal with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="V4ldeLund/clip-vit-base-patch16-da-lora-both-equal") 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("V4ldeLund/clip-vit-base-patch16-da-lora-both-equal") model = AutoModelForZeroShotImageClassification.from_pretrained("V4ldeLund/clip-vit-base-patch16-da-lora-both-equal") - Notebooks
- Google Colab
- Kaggle
File size: 389 Bytes
ad18e3d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<|startoftext|>",
"do_lower_case": true,
"eos_token": "<|endoftext|>",
"errors": "replace",
"is_local": false,
"local_files_only": false,
"model_max_length": 77,
"pad_token": "<|endoftext|>",
"processor_class": "CLIPProcessor",
"tokenizer_class": "CLIPTokenizer",
"unk_token": "<|endoftext|>"
}
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