mirror of
https://github.com/zenlm/zen-blog.git
synced 2026-11-03 14:11:45 +00:00
zeekay
a30ff37468
b3b6eb0 ran a find-and-replace over the posts forked from the QwenLM
blog that turned "Qwen2" and "Qwen2.5" into "zen": Qwen2-57B-A14B read
as zen7B-A14B, Qwen2-72B as zen-72B, Qwen2AudioForConditionalGeneration
as a class that does not exist, and links such as
github.com/QwenLM/Qwen2 and the qianwen-res image paths pointed at
nothing. Every token is put back from the original post, aligned
against the rewritten one: 536 lines across 29 posts, and 116 of the
example files under public/cases.
These 44 posts are about another lab's releases and now say so with
"upstream: true" in their frontmatter. Two Zen posts lose the
base-model names they carried, and the Zen 3 post stops calling itself
the next generation.
66 lines
4.1 KiB
Plaintext
66 lines
4.1 KiB
Plaintext
---
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title: "Qwen2.5-VL-32B: Smarter and Lighter"
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date: "2025-03-24"
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description: "At the end of January this year, we launched the Qwen2.5-VL series of models, which received widespread attention and positive feedback from the community. Building on the Qwen2.5-VL series, we continued to optimize the model using reinforcement learning and open-sourced the new VL model with the beloved 32"
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upstream: true
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---
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<LinkButton href="https://chat.qwenlm.ai" label="QWEN CHAT" external />
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<LinkButton href="https://github.com/QwenLM/Qwen2.5-VL" label="GITHUB" external />
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<LinkButton href="https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5" label="HUGGING FACE" external />
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<LinkButton href="https://modelscope.cn/collections/Qwen25-VL-58fbb5d31f1d47" label="MODELSCOPE" external />
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<LinkButton href="https://discord.gg/yPEP2vHTu4" label="DISCORD" external />
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## Introduction
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At the end of January this year, we launched the Qwen2.5-VL series of models, which received widespread attention and positive feedback from the community. Building on the Qwen2.5-VL series, we continued to optimize the model using reinforcement learning and open-sourced the new VL model with the beloved 32B parameter scale under the Apache 2.0 license — **Qwen2.5-VL-32B-Instruct**. Compared to the previously released Qwen2.5-VL series models, the features of this 32B VL model are as follows:
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- **Responses More Aligned with Human Preferences**: Adjusted the output style to provide more detailed, better-formatted answers that align more closely with human preferences.
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- **Mathematical Reasoning**: Significant improvement in the accuracy of solving complex mathematical problems.
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- **Fine-grained Image Understanding and Reasoning**: Enhanced accuracy and detailed analysis in tasks such as image parsing, content recognition, and visual logic deduction.
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## Performance
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Extensive benchmarking against state-of-the-art (SoTA) models of comparable scale, **Qwen2.5-VL-32B-Instruct** has demonstrated superiority over baselines, e.g., Mistral-Small-3.1-24B and Gemma-3-27B-IT, even surpassing the larger **Qwen2-VL-72B-Instruct**. Notably, it achieves significant advantages in multimodal tasks such as **MMMU**, **MMMU-Pro**, and **MathVista**, which focus on complex, multi-step reasoning. On **MM-MT-Bench**, a benchmark emphasizing subjective user experience evaluation, **Qwen2.5-VL-32B-Instruct** outperforms its predecessor **Qwen2-VL-72B-Instruct** by a substantial margin.
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<Figure src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL-32B/qwen2.5vl-32b-vision.jpg" width="100%" />
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In addition to excelling in visual capabilities, Qwen2.5-VL-32B-Instruct has also achieved top-tier performance in pure text capabilities at the same scale.
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<Figure src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL-32B/qwen2.5vl-32b-text.jpg" width="100%" />
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## Demo Cases
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<Fullwidth>
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{/* Interactive example: cases/reasoning.json */}
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{/* Interactive example: cases/math3.json */}
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{/* Interactive example: cases/math1.json */}
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{/* Interactive example: cases/math2.json */}
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{/* Interactive example: cases/image_understanding.json */}
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</Fullwidth>
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## Next Step
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While **Qwen2.5-VL-32B** has focused on optimizing subjective experience and mathematical reasoning through reinforcement learning—operating within the paradigm of **"fast thinking"**. Our next research direction will prioritize **long and effective reasoning processes** to push the boundaries of visual models in tackling highly complex, multi-step visual reasoning tasks.
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## Citation
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If you find our model helpful, feel free to cite it:
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```
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@article{Qwen2.5-VL,
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title={Qwen2.5-VL Technical Report},
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author={Bai, Shuai and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Song, Sibo and Dang, Kai and Wang, Peng and Wang, Shijie and Tang, Jun and Zhong, Humen and Zhu, Yuanzhi and Yang, Mingkun and Li, Zhaohai and Wan, Jianqiang and Wang, Pengfei and Ding, Wei and Fu, Zheren and Xu, Yiheng and Ye, Jiabo and Zhang, Xi and Xie, Tianbao and Cheng, Zesen and Zhang, Hang and Yang, Zhibo and Xu, Haiyang and Lin, Junyang},
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journal={arXiv preprint arXiv:2502.13923},
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year={2025}
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}
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``` |