Model reference · open weights
siglip2-patch16-224 is an open-weight embedding model from google, 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.
About
SigLIP 2 Base SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features. Intended uses You can use the raw model for tasks like zero-shot image classification and image-text retrieval, or as a vision encoder for VLMs (and other vision tasks). Here is how to use this model to perform zero-shot image classification: You can encode an image using the Vision Tower like so: For more code examples, we refer to the siglip documentation. Training procedure SigLIP 2 adds some clever training objectives on top of SigLIP: 1. Decoder loss 2. Global-local and masked prediction loss 3. Aspect ratio and resolution adaptibility Training data SigLIP 2 is pre-trained on the WebLI dataset (Chen et al., 2023). Compute The model was trained on up to 2048 TPU-v5e chips. Evaluation results Evaluation of SigLIP 2 is shown below (taken from the paper). BibTeX entry and citation info
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | |
|---|---|
| Type | Embedding models |
| Parameters (lead) | 375M |
| Variants | 1 |
| Runs with | transformers |
| Released | 2025-02-17 |
| Popularity | 1.4M downloads / month |
| Likes | 131 |
| Licence | Open weights |
How it works
Variants
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.
| Variant | Params | Precision | VRAM | Fits 16 GB | Weights |
|---|---|---|---|---|---|
| siglip2-base-patch16-224 | 375M | BF16 | ~0.9 GB | ✓ | Weights ↗ |
Using it via the API
Once AxForge deploys siglip2-patch16-224 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (siglip2-patch16-224 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":"siglip2-patch16-224","input":"text to embed"}'
Details
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Papers
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗
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