Instructions to use sgraham/met_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgraham/met_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="sgraham/met_model") 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("sgraham/met_model") model = AutoModelForZeroShotImageClassification.from_pretrained("sgraham/met_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 0_CLIPModel/tokenizer_config.json from sgraham/met_model: direct link, hf CLI and curl.
- Browser
- Download file 604 Bytes
-
https://huggingface.co/sgraham/met_model/resolve/main/0_CLIPModel/tokenizer_config.json
- Command line
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hf download hf://sgraham/met_model/0_CLIPModel/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/sgraham/met_model/resolve/main/0_CLIPModel/tokenizer_config.json
604 Bytes
| {"errors": "replace", "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|startoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "pad_token": "<|endoftext|>", "add_prefix_space": false, "do_lower_case": true, "name_or_path": "openai/clip-vit-base-patch32", "model_max_length": 77} |