GLM-5.2 is live. #1 throughput on OpenRouter, pay-per-token on FriendliAI. Try it today ➜
cpsu04
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
cpsu04
Model Tree
Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The adapter is intended for four-choice video questions about a robot-held object's direction, distance, or speed during manipulation. Direction labels may be relative to the robot base or the camera, depending on the prompt.
Load it with the exact base model and a PEFT-compatible Qwen2.5-VL inference path. The training answer format is a single choice letter.
Qwen/Qwen2.5-VL-7B-Instruct.On the held-out Robo4D v5 eval set (2,300 questions), this adapter scored 56.52% overall. This is an in-domain, episode-disjoint result.
Robo4D training clips are deliberately filtered for grounded, sustained held-object motion. This adapter should not be assumed to improve broad video reasoning or premise checking, including cases with no visible target or no meaningful motion. Early VLM4D transfer checks showed degradation relative to the base model; treat it as a task-specific research adapter.
python
from transformers import Qwen2_5_VLForConditionalGenerationfrom peft import PeftModelbase = Qwen2_5_VLForConditionalGeneration.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", device_map="auto")model = PeftModel.from_pretrained(base, "cpsu04/Robo4D-v5-Qwen2.5-VL-7B-LoRA")
The adapter contains no training videos or question data. Use of the underlying model and any downstream dataset remains subject to their respective licenses and terms.