Model reference · open weights

vit_large_patch16_siglip_256.v2_webli

vit_large_patch16_siglip_256.v2_webli is an open-weight embedding model from timm, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.

Embeddings timm 1 variants 64k downloads/mo
Request this model on EU hardware All served models Not on the shared API today — deployed on request.

About

What vit_large_patch16_siglip_256.v2_webli is

Model card for vitlargepatch16siglip256.v2webli A SigLIP 2 ViT (image encoder only) for timm. Equivalent to image tower from https://huggingface.co/timm/ViT-L-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

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

Makertimm
TypeEmbedding models
Parameters (lead)316M
Variants1
Runs withtimm
Released2025-02-21
Popularity64k downloads / month
Likes2
LicenceOpen weights

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 — the basis of search and RAG.

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

VariantParamsPrecisionVRAMFits 16 GBWeights
vit_large_patch16_siglip_256.v2_webli316MBF16~0.7 GBWeights ↗

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys vit-large-patch16-siglip-256-v2-webli for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (vit-large-patch16-siglip-256-v2-webli below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"vit-large-patch16-siglip-256-v2-webli","input":"text to embed"}'

Details

Languages, data & research

Trained / evaluated on

webli

Tags

timm pytorch safetensors transformers image-feature-extraction siglip siglip2 dataset:webli

Papers

Licence

Open weights

Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗

Sources

Weights & code

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