Comp SigLIP So400M

🚀 Model Card for CoMP-MM-1B

This is an VFM that supports native image resolution inputs, continually pre-trained from SigLIP.

🚀 Quick Start

Install the github repo, and use the following code to get started with the model.

Basic Usage

import torch
from slimm.model.processor import SliMMQwen2VLProcessor
from slimm.model.utils_vl import process_vision_info
from slimm.model.vision_encoder import CoMPSiglipVisionModel
from PIL import Image

model_path = "SliMM-X/CoMP-SigLIP-So400M"

model = CoMPSiglipVisionModel.from_pretrained(
    model_path, torch_dtype="auto", device_map="cuda", w_merger=False
).to(torch.bfloat16)


processor = SliMMQwen2VLProcessor.from_pretrained(model_path)

image_input = Image.open("https://slimm-x.github.io/comp/figs/teaser.png")
inputs = processor(
    images=image_input,
    return_tensors="pt",
)

inputs = inputs.to("cuda")
output_feat = model(inputs.pixel_values.to(torch.bfloat16), inputs.image_grid_thw)
print(output_feat)

📚 Documentation

Model Sources

• Repository: https://github.com/SliMM-X/CoMP-MM
• Paper: https://arxiv.org/abs/2503.18931
• Project Page: https://slimm-x.github.io/comp

📄 License

The model is licensed under apache-2.0.

📚 Citation

BibTeX:

@article{comp2025,
      title={CoMP: Continual Multimodal Pre-training for Vision Foundation Models}, 
      author={Chen, Yitong and Meng, Lingchen and Peng, Wujian and Wu, Zuxuan and Jiang, Yu-Gang},
      year={2025},
      journal={arXiv preprint arXiv:2503.18931}, 
}