GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
CewEhao
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
CewEhao
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
This repository contains the OPD-TV Qwen3-VL-8B checkpoint used for the paper's main temporal-grounding evaluation.
OPD-TV studies a supervision transfer gap: enriching the teacher's temporal evidence improves its grounding accuracy, yet the resulting student gains are non-monotonic. The method compares the same teacher across the student's original visual input and an enriched teacher view, isolating the view-induced correction from the standard same-view policy correction. Token-level cross-view compatibility then regulates how strongly this correction enters a target anchored to the same-view teacher distribution.
The student generates trajectories from its original visual input. Privileged evidence is used for teacher supervision during training; inference uses the student alone.
| Artifact | Link |
|---|---|
| Model checkpoint | CewEhao/OPD-TV-Qwen3-VL-8B |
| Training data | CewEhao/OPD-TV-Training-Data |
| Training and evaluation code | Echochef/OPD-TV |
| Evaluation suite | TencentARC/TimeLens-Bench |
Scores are percentages.
| Dataset | mIoU | R@0.3 | R@0.5 | R@0.7 |
|---|---|---|---|---|
| Charades-STA | 52.0 | 72.7 | 57.2 | 32.5 |
| ActivityNet Captions | 54.6 | 69.5 | 60.3 | 43.2 |
| QVHighlights | 67.6 | 82.1 | 73.5 | 58.3 |
The evaluation protocol uses uniform sampling at 2 FPS, a 768-frame cap, a 14,336 visual-token cap, greedy JSON decoding, and up to 64 generated tokens. See the repository instructions for the complete TimeLens-Bench setup.
bash
hf download CewEhao/OPD-TV-Qwen3-VL-8B \--local-dir models/OPD-TV-Qwen3-VL-8B
Load the checkpoint with Transformers or serve it with vLLM using --trust-remote-code.
After installing the code repository and preparing TimeLens-Bench, run its evaluation launcher:
bash
export MODEL_PATH=CewEhao/OPD-TV-Qwen3-VL-8Bexport BENCH_ROOT=/path/to/TimeLens-Benchexport OUTPUT_DIR=/path/to/opd-tv-evalexport CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7bash scripts/eval_opd_tv_timelens.sh
The checkpoint is released under the Apache License 2.0.