Instructions to use timm/vit_so400m_patch14_siglip_384.webli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_so400m_patch14_siglip_384.webli with timm:
import timm model = timm.create_model("hf-hub:timm/vit_so400m_patch14_siglip_384.webli", pretrained=True) - Transformers
How to use timm/vit_so400m_patch14_siglip_384.webli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="timm/vit_so400m_patch14_siglip_384.webli")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("timm/vit_so400m_patch14_siglip_384.webli") model = AutoModel.from_pretrained("timm/vit_so400m_patch14_siglip_384.webli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card for vit_so400m_patch14_siglip_384.webli
timm SigLIP (image encoder only, with original attention pooling) weights from https://huggingface.co/timm/ViT-SO400M-14-SigLIP-384
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