Instructions to use lewtun/tiny-clip-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lewtun/tiny-clip-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="lewtun/tiny-clip-test") 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("lewtun/tiny-clip-test") model = AutoModelForZeroShotImageClassification.from_pretrained("lewtun/tiny-clip-test") - Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "bos_token": { | |
| "__type": "AddedToken", | |
| "content": "<|startoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "crop_size": 30, | |
| "do_lower_case": true, | |
| "eos_token": { | |
| "__type": "AddedToken", | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "errors": "replace", | |
| "model_max_length": 77, | |
| "name_or_path": "openai/clip-vit-base-patch32", | |
| "pad_token": "<|endoftext|>", | |
| "processor_class": "CLIPProcessor", | |
| "size": 30, | |
| "special_tokens_map_file": "/Users/lewtun/.cache/huggingface/hub/models--openai--clip-vit-base-patch32/snapshots/f4881ba48ee4d21b7ed5602603b9e3e92eb1b346/special_tokens_map.json", | |
| "tokenizer_class": "CLIPTokenizer", | |
| "unk_token": { | |
| "__type": "AddedToken", | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": true, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |