Instructions to use mmmorks/siglip2-so400m-patch16-384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmmorks/siglip2-so400m-patch16-384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="mmmorks/siglip2-so400m-patch16-384") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mmmorks/siglip2-so400m-patch16-384", dtype="auto") - MLX
How to use mmmorks/siglip2-so400m-patch16-384 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir siglip2-so400m-patch16-384 mmmorks/siglip2-so400m-patch16-384
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<bos>", | |
| "clean_up_tokenization_spaces": false, | |
| "do_lower_case": true, | |
| "eos_token": "<eos>", | |
| "is_local": true, | |
| "local_files_only": false, | |
| "mask_token": "<mask>", | |
| "max_length": 64, | |
| "model_input_names": [ | |
| "input_ids" | |
| ], | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_to_multiple_of": null, | |
| "pad_token": "<pad>", | |
| "pad_token_type_id": 0, | |
| "padding_side": "right", | |
| "processor_class": "SiglipProcessor", | |
| "sp_model_kwargs": {}, | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "GemmaTokenizer", | |
| "unk_token": "<unk>", | |
| "use_default_system_prompt": false | |
| } | |