Cambrian-S-7B is a spatially-grounded multimodal large language model that excels at spatial reasoning in video understanding. It achieves state-of-the-art performance on visual-spatial benchmarks while maintaining competitive performance on general video understanding tasks.

Model Details

Architecture: Qwen2.5-7B-Instruct + SigLIP2-SO400M vision encoder + 2-layer MLP adapter
Parameters: 7B
Vision Encoder: SigLIP-384 (SiGLIP)
Training: 4-stage pipeline (image alignment → image IT → video IT → spatial IT)
Training Data: Trained on VSI-590K (spatial reasoning) + general video instruction data

Usage

python

from cambrian.model.builder import load_pretrained_model
from cambrian.mm_utils import process_images, tokenizer_image_token
from cambrian.conversation import conv_templates

model_path = "nyu-visionx/Cambrian-S-7B"
tokenizer, model, image_processor, _ = load_pretrained_model(model_path, None, "cambrian-s-7b", device_map="cuda")

# Process image/video
conv = conv_templates["qwen_2"].copy()
conv.append_message(conv.roles[0], "<image>\nWhat objects are in this scene?")
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()

# Generate
output_ids = model.generate(input_ids, images=image_tensor, image_sizes=image_sizes)

Citation

bibtex

@article{yang2025cambrian,
  title={Cambrian-S: Towards Spatial Supersensing in Video},
  author={Yang, Shusheng and Yang, Jihan and Huang, Pinzhi and Brown, Ellis and others},
  journal={arXiv preprint arXiv:2025},
  year={2025}
}