Instructions to use timm/vit_so400m_patch14_siglip_gap_448.pali2_10b_docci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use timm/vit_so400m_patch14_siglip_gap_448.pali2_10b_docci with timm:
import timm model = timm.create_model("hf_hub:timm/vit_so400m_patch14_siglip_gap_448.pali2_10b_docci", pretrained=True) - Transformers
How to use timm/vit_so400m_patch14_siglip_gap_448.pali2_10b_docci 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_gap_448.pali2_10b_docci")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("timm/vit_so400m_patch14_siglip_gap_448.pali2_10b_docci", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model card for vit_so400m_patch14_siglip_gap_448.pali2_10b_docci
timm PaliGemma2 (SigLIP image encoder only, with global avg pooling) weights from https://huggingface.co/google/paligemma2-10b-ft-docci-448-jax.
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