Model Card for Model ID
A strong spatial Qwen 2.5 VL baseline.
Post-trained on SAT, and just the answers from Video-R1. The exact mix is 60% SAT, 40% Video-R1.
News:
- Feb 3, 2026: There was a bug with this model where it was producing random outputs for some Transformers packages. It has been fixed, please redownload the model and let us know if you still face this issue.
Get Started
% pip install git+https://github.com/huggingface/transformers accelerate
% pip install qwen-vl-utils[decord]==0.0.8
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
model = Qwen2_5_VLForConditionalGeneration.from_pretrained("array/Qwen2.5-VL-SAT")
processor = AutoProcessor.from_pretrained(
exp_confs["model_path"],
trust_remote_code=model_config.trust_remote_code
)
Please see the paper for details on training and evaluation datasets and metrics.
Results
| Model | MV | RelDep | SpRel | Jig | IQT | BLINK Avg | BLINK Reas | SAT-R | VSI Avg | VSI Reas | ERQA | Avg (All) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Qwen2.5-VL (7B) | 39.00 | 61.29 | 92.38 | 58.66 | 25.33 | 55.33 | 41.00 | 59.00 | 23.96 | 22.96 | 38.91 | 44.30 |
| + SAT | 57.14 | 87.09 | 74.12 | 58.66 | 30.00 | 61.40 | 48.60 | 71.66 | 32.40 | 30.65 | 38.00 | 50.87 |
Citation [optional]
@misc{ray2025satdynamicspatialaptitude,
title={SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models},
author={Arijit Ray and Jiafei Duan and Ellis Brown and Reuben Tan and Dina Bashkirova and Rose Hendrix and Kiana Ehsani and Aniruddha Kembhavi and Bryan A. Plummer and Ranjay Krishna and Kuo-Hao Zeng and Kate Saenko},
year={2025},
eprint={2412.07755},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.07755},
}