SigLino-MoE-0.3-0.6B

Accepted at CVPR 2026

This work stems from the CVPR 2026 AMoE paper, which designs and applies distillation into a Mixture-of-Experts (MoE) vision architecture. We have chosen the name SigLino for better clarity (SigLIP2 + DINOv3).

Sparse MoE variant of SigLino with top-6 out of 28 experts routing. 0.3B active parameters, 0.6B total.

Part of the SigLino model family.

Usage

import torch
from PIL import Image
from transformers import AutoModel, AutoImageProcessor

# Load model and processor
model_id = "tiiuae/siglino-moe-0.3-0.6B"
model = AutoModel.from_pretrained(model_id, trust_remote_code=True).to("cuda", dtype=torch.bfloat16)
processor = AutoImageProcessor.from_pretrained(model_id, trust_remote_code=True)

# Preprocess image
image = Image.open("image.jpg").convert("RGB")
inputs = processor(image, return_tensors="pt").to("cuda")
inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)

# Inference
with torch.no_grad():
    outputs = model(**inputs)

# Access specialized features
# Options: 'siglino' (768d), 'siglip2' (1152d), 'dinov3' (1024d)
patch_features = outputs["patch_features"]["siglino"]    # (Batch, Tokens, 768)
summary_features = outputs["summary_features"]["siglip2"] # (Batch, 1152)

Model Details

Results (512x512, ensemble features)

Citation

If you use this work in your research, please cite:

@article{chaybouti2025amoe,
  title={AMoE: Agglomerative Mixture-of-Experts Vision Foundation Models},
  author={Chaybouti, Sofian and Narayan, Sanath and Dahou, Yasser and Le Khac, Phuc H. and Singh, Ankit and Huynh, Ngoc Dung and Para, Wamiq Reyaz and Kuehne, Hilde and Hacid, Hakim},
  journal={arXiv preprint arXiv:2512.20157},
  year={2025}
}