GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
array
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
array
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
markdown
% pip install git+https://github.com/huggingface/transformers accelerate% pip install qwen-vl-utils[decord]==0.0.8from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessormodel = 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.
| 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 |
markdown
@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},}
| 30.65 |
| 38.00 |
| 50.87 |