Model reference · open weights

vit_so400m_patch16_siglip_256.v2_webli

Embeddings timm Image embed 1 build Open weights 62k dl/mo

vit_so400m_patch16_siglip_256.v2_webli is an open-weight embedding model from timm. vit_so400m_patch16_siglip_256.v2_webli (FP32) weighs 856 MB; the smallest configuration that runs it is RTX 3060 12 GB.

  • This model is a SigLIP 2 ViT image encoder developed by timm for image feature extraction.
  • It contains 428M parameters and was trained on the webli dataset.
  • The model is released under the apache-2.0 licence.

Summary of the timm/vit_so400m_patch16_siglip_256.v2_webli model card, 2026-10-01

What it is

Released bytimm
Released2025-02-21
Parameters428M
VRAM856 MB for the weights

What it runs on

Memory and cards for vit_so400m_patch16_siglip_256.v2_webli (FP32)

856 MBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What timm says about vit_so400m_patch16_siglip_256.v2_webli

Read the model card

A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from https://huggingface.co/timm/ViT-SO400M-16-SigLIP2-256.

Model Details

  • Dataset: webli
  • Papers:
    • SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features: https://arxiv.org/abs/2502.14786
    • Sigmoid Loss for Language Image Pre-Training: https://arxiv.org/abs/2303.15343

Citation

@article{tschannen2025siglip,
          title={SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features},
          author={Tschannen, Michael and Gritsenko, Alexey and Wang, Xiao and Naeem, Muhammad Ferjad and Alabdulmohsin, Ibrahim and Parthasarathy, Nikhil and Evans, Talfan and Beyer, Lucas and Xia, Ye and Mustafa, Basil and H'enaff, Olivier and Harmsen, Jeremiah and Steiner, Andreas and Zhai, Xiaohua},
          year={2025},
          journal={arXiv preprint arXiv:2502.14786}
        }

@inproceedings{zhai2023sigmoid,
          title={Sigmoid loss for language image pre-training},
          author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
          booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
          pages={11975--11986},
          year={2023}
        }

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together, which is the basis of search and RAG.
© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms