> This is the documentation for LibreYOLO 1.5.0. Current version: https://www.libreyolo.com/docs/models/qwen3-vl

# Qwen3-VL
Qwen3-VL is Alibaba's vision-language model with native 2D grounding. LibreYOLO wraps it as an open-vocabulary object detector and exposes its free-form chat directly: supply a class list to detect, or ask it a question.
Tasks: Detection. Install: pip install libreyolo.
Verified against LibreYOLO v1.5.0.

## Install

Qwen3-VL belongs to LibreYOLO's VLM-as-detector tier, a separate product
surface from the checkpoint-based families with its own factory. It needs the
`vlm` extra.

```bash
pip install "libreyolo[vlm]"
```

## Predict

Weights download from Hugging Face on first use and are cached locally.
`LibreVLM()` called with no argument defaults to Qwen3-VL-4B.

**Python**

```python
from libreyolo import LibreVLM, SAMPLE_IMAGE

model = LibreVLM("qwen3-vl-4b")
model.set_classes(["forklift", "pallet", "safety vest"])
result = model.predict(SAMPLE_IMAGE, save=True)

for box in result.boxes:
    print(box.cls, box.conf, box.xyxy)
```

**Chat**

```python
from libreyolo import LibreVLM, SAMPLE_IMAGE

model = LibreVLM("qwen3-vl-4b")

# The escape hatch beneath the detection convenience: any question,
# not just a bounding-box query.
answer = model.chat(SAMPLE_IMAGE, "How many people are wearing a safety vest?")
print(answer)
```

This family loads through the `LibreVLM()` factory, not `LibreYOLO()`: VLM
families declare no checkpoint loader, so the file-suffix routing described on
other model pages does not apply here. `set_classes()` sets the vocabulary
Qwen3-VL is asked to find; it is sticky, so it stays in effect across every
later `predict()`/`track()` call until you set it again. Every detection
carries the same placeholder confidence, so `conf` filtering is all-or-nothing
rather than a ranking; `iou` does have an effect for this family, dropping a
later same-class box once it overlaps an already-kept one past the threshold,
since a repeating generator can otherwise emit near-duplicate boxes for one
object. Unlike Florence-2 and Kosmos-2, Qwen3-VL also answers free-form
questions through `chat()`, the same escape hatch documented on the `LibreVLM`
factory. LibreYOLO's CLI does not cover this tier: there is no
`libreyolo predict model=...` form for it. See [prediction](/docs/v1.5.0/predict) for
sources, streaming and result handling.

## Variants

Three sizes: Qwen3-VL-2B-Instruct, Qwen3-VL-4B-Instruct and Qwen3-VL-8B-Instruct,
loaded as `LibreVLM("qwen3-vl-2b")`, `LibreVLM("qwen3-vl-4b")` and
`LibreVLM("qwen3-vl-8b")`. All three declare a nominal 1024 px input, but the
Qwen processor's own smart-resize decides the actual canvas passed to the
network, so that figure is not a fixed operating resolution the way it is for
the other families on this site. LibreYOLO has not published a benchmark
comparing accuracy across the three sizes.

LibreYOLO does not train, validate or export Qwen3-VL: `train()`, `val()` and
`export()` all raise `NotImplementedError` for every family in this tier (see
the support tier above). Fine-tune Qwen3-VL upstream and load the resulting
weights if you need a custom vocabulary baked in; check `predict()` output by
eye instead of a COCO-style validation pass, since every detection carries the
same placeholder confidence.

## Licensing

Check the license on the Hugging Face repository of the specific weights you download. That repository is authoritative and licenses are not always uniform across a family. This is a description of the licenses involved, not legal advice.

- Original work: Qwen3-VL, Alibaba (Qwen Team)
- Upstream license: Apache-2.0
- Upstream source: https://github.com/QwenLM/Qwen3-VL
- LibreYOLO code: MIT
- Weights: Apache-2.0, distributed by their authors. LibreYOLO does not host or mirror them.
- Interpretation: Apache-2.0 is a permissive license, so these weights can be used in commercial and closed-source products. It asks you to keep its license text and attribution notices with any copy of the weights you redistribute, and it grants a patent license. All three sizes LibreYOLO downloads, Qwen3-VL-2B-Instruct, Qwen3-VL-4B-Instruct and Qwen3-VL-8B-Instruct, carry this license on their Hugging Face repository.

## Citation

```bibtex
@article{Qwen3-VL,
      title={Qwen3-VL Technical Report}, 
      author={Shuai Bai and Yuxuan Cai and Ruizhe Chen and Keqin Chen and Xionghui Chen and Zesen Cheng and Lianghao Deng and Wei Ding and Chang Gao and Chunjiang Ge and Wenbin Ge and Zhifang Guo and Qidong Huang and Jie Huang and Fei Huang and Binyuan Hui and Shutong Jiang and Zhaohai Li and Mingsheng Li and Mei Li and Kaixin Li and Zicheng Lin and Junyang Lin and Xuejing Liu and Jiawei Liu and Chenglong Liu and Yang Liu and Dayiheng Liu and Shixuan Liu and Dunjie Lu and Ruilin Luo and Chenxu Lv and Rui Men and Lingchen Meng and Xuancheng Ren and Xingzhang Ren and Sibo Song and Yuchong Sun and Jun Tang and Jianhong Tu and Jianqiang Wan and Peng Wang and Pengfei Wang and Qiuyue Wang and Yuxuan Wang and Tianbao Xie and Yiheng Xu and Haiyang Xu and Jin Xu and Zhibo Yang and Mingkun Yang and Jianxin Yang and An Yang and Bowen Yu and Fei Zhang and Hang Zhang and Xi Zhang and Bo Zheng and Humen Zhong and Jingren Zhou and Fan Zhou and Jing Zhou and Yuanzhi Zhu and Ke Zhu},
	  journal={arXiv preprint arXiv:2511.21631},
      year={2025}
}
```

Copied from https://github.com/QwenLM/Qwen3-VL#citation


## Qwen3-VL architecture

- Qwen3-VL 2B: [SVG](/diagrams/models/qwen3-vl/2b-detect.svg), [interactive diagram](/diagrams/models/qwen3-vl/2b-detect.html)
- Qwen3-VL 4B: [SVG](/diagrams/models/qwen3-vl/4b-detect.svg), [interactive diagram](/diagrams/models/qwen3-vl/4b-detect.html)
- Qwen3-VL 8B: [SVG](/diagrams/models/qwen3-vl/8b-detect.svg), [interactive diagram](/diagrams/models/qwen3-vl/8b-detect.html)
- Qwen3-VL family: [SVG](/diagrams/models/qwen3-vl/family.svg), [interactive diagram](/diagrams/models/qwen3-vl/family.html)