Instructions to use carsonpoole/binary-siglip-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use carsonpoole/binary-siglip-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="carsonpoole/binary-siglip-vision")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("carsonpoole/binary-siglip-vision") model = AutoModel.from_pretrained("carsonpoole/binary-siglip-vision") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "./image_model2", | |
| "architectures": [ | |
| "SiglipVisionModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "hidden_act": "gelu", | |
| "hidden_size": 1152, | |
| "image_size": 384, | |
| "intermediate_size": 4304, | |
| "layer_norm_eps": 1e-06, | |
| "model_type": "siglip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 27, | |
| "patch_size": 14, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.40.1" | |
| } | |