GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
Y-Research-Group
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
Y-Research-Group
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen2.5-VL-7B-InstructThe model is trained in a tool-calling chat format: it wraps reasoning in <think>...</think>,
optionally emits a single image_zoom_in_tool call with a ratio-based bbox_2d
([x1,y1,x2,y2] in [0,1]) to crop the current view, and outputs the final answer in
<answer>...</answer>. Load with transformers (Qwen2_5_VLForConditionalGeneration) or
serve with vLLM, using the standard Qwen2.5-VL processor.
bibtex
@inproceedings{visreason2026,title = {VisReason: A Large-Scale Dataset for Visual Chain-of-Thought Reasoning},author = {Lingxiao Li and Yifan Wang and Xinyan Gao and Chen Tang and Xiangyu Yue and Chenyu You},booktitle = {European Conference on Computer Vision (ECCV)},year = {2026}}