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xiaomi-research
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Model Details
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xiaomi-research
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Qwen/Qwen2.5-VL-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The model produces a three-part output; only the <function> block is scored:
text
<think>reasoning</think><rec>natural-language recommendation</rec><function>{"model_recommendation": [...]}</function>python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "<your-hf-id>/ProactiveMobile-Qwen2.5-VL-7B", torch_dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained("<your-hf-id>/ProactiveMobile-Qwen2.5-VL-7B")For batched benchmark inference and scoring (Type-Acc / Precision / Recall / F1 /
FTR), see evaluation/inference_and_eval.py in the
code repository.
Evaluated on the full ProactiveMobile benchmark (3,658 instances) with greedy decoding (temperature = 0), which gives deterministic, fully reproducible results.
| Split | Type-Acc | Precision | Recall | F1 | FTR↓ |
|---|---|---|---|---|---|
| Overall | 49.29 | 54.23 | 54.19 | 53.83 | 8.86 |
| L1 | 72.41 | 75.20 | 75.33 | 75.16 | 6.98 |
| L2 | 57.93 | 63.52 | 63.44 | 63.10 | 7.12 |
| L3 | 38.99 | 43.90 | 43.85 | 43.45 | 11.23 |
| TEXT | 64.04 | 65.60 | 65.58 | 65.46 | 1.31 |
| GUI | 34.57 | 42.88 | 42.81 | 42.23 | 24.37 |
All scores are percentages.
This model is a derivative of Qwen2.5-VL-7B-Instruct (Apache 2.0), so the model weights are distributed under Apache 2.0. It was fine-tuned on the ProactiveMobile dataset, which is released under CC BY-NC-SA 4.0; use of this model is therefore subject to that dataset license — non-commercial use only, attribution required, share-alike.
bibtex
@inproceedings{kong2026proactivemobile, title = {ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices}, author = {Kong, Dezhi and Feng, Zhengzhao and Liang, Qiliang and others}, booktitle = {CVPR}, year = {2026}}from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor model = Qwen2_5_VLForConditionalGeneration.from_pretrained( "<your-hf-id>/ProactiveMobile-Qwen2.5-VL-7B", torch_dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained("<your-hf-id>/ProactiveMobile-Qwen2.5-VL-7B")@inproceedings{kong2026proactivemobile, title = {ProactiveMobile: A Comprehensive Benchmark for Boosting Proactive Intelligence on Mobile Devices}, author = {Kong, Dezhi and Feng, Zhengzhao and Liang, Qiliang and others}, booktitle = {CVPR}, year = {2026}}