Instructions to use p1atdev/siglip2-base-patch16-384-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p1atdev/siglip2-base-patch16-384-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="p1atdev/siglip2-base-patch16-384-vision")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("p1atdev/siglip2-base-patch16-384-vision") model = AutoModel.from_pretrained("p1atdev/siglip2-base-patch16-384-vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| base_model: | |
| - google/siglip2-base-patch16-384 | |
| Only vision tower of [google/siglip2-base-patch16-384](https://huggingface.co/google/siglip2-base-patch16-384). |