siglip-so-patch14-384

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What siglip-so-patch14-384 is

SigLIP (shape-optimized model) SigLIP model pre-trained on WebLi at resolution 384x384. It was introduced in the paper Sigmoid Loss for Language Image Pre-Training by Zhai et al. and first released in this repository. This model has the SoViT-400m architecture, which is the shape-optimized version as presented in Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design by Alabdulmohsin et al. Disclaimer: The team releasing SigLIP did not write a model card for this model so this model card has been written by the Hugging Face team. Model description SigLIP is CLIP, a multimodal model, with a better loss function. The sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise similarities for normalization. This allows further scaling up the batch size, while also performing better at smaller batch sizes. A TLDR of SigLIP by one of the authors can be found here. Intended uses & limitations You can use the raw model for tasks like zero-shot image classification and image-text retrieval. See the model hub to look for other versions on a task that interests you. How to use Here is how to use this model to perform zero-shot image classification: Alternatively, one can leverage the pipeline API which abstracts away the complexity for the user: For more code examples, we refer to the documentation. Training procedure Training data SigLIP is pre-trained on the WebLI dataset (Chen et al., 2023). Preprocessing Images are resized/rescaled to the same resolution (384x384) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). Texts are tokenized and padded to the same length (64 tokens). Compute The model was trained on 16 TPU-v4 chips for three days. Evaluation results Evaluation of SigLIP compared to CLIP is shown below (taken from the paper). alt="drawing" width="600"/ BibTeX entry and citation info

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

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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.

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Call it like any OpenAI endpoint

Once AxForge deploys siglip-so-patch14-384 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (siglip-so-patch14-384 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":"siglip-so-patch14-384","input":"text to embed"}'

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