# InternVL3
InternVL3 is a native multimodal large language model released by OpenGVLab that jointly learns vision and language in a single pre-training stage. LibreYOLO wraps it as an open-vocabulary object detector: any list of text labels becomes the class set, with no fixed head and no fine-tuning required.
Tasks: Detection. Install: pip install "libreyolo[vlm]".
Verified against LibreYOLO v1.5.0.

## Install

InternVL3 needs the `vlm` extra, which pulls in `transformers` for the
chat-template backbone.

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

## Predict

`LibreInternVL3` is a Python class, not a `.pt` checkpoint: it is not loaded
through the `LibreYOLO()` factory, and the `libreyolo` CLI does not resolve it.
The `LibreVLM(...)` factory (`from libreyolo import LibreVLM`) also reaches
this family by alias, e.g. `LibreVLM("internvl3-2b")`; the class used below is
what it constructs. Weights come from OpenGVLab's own `-hf` Hugging Face
repositories, not a LibreYOLO mirror; the first call downloads and caches
them locally, and logs a one-time license notice for the gated Qwen weights
before it does.

**Python**

```python
from libreyolo import LibreInternVL3, SAMPLE_IMAGE

model = LibreInternVL3(size="2b")

# Open vocabulary: any words work, not a fixed class head. Sticky
# across every later predict()/track() call until set again.
model.set_classes(["person", "bicycle", "dog"])
result = model(SAMPLE_IMAGE, save=True)

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

**Raw chat**

```python
from libreyolo import LibreInternVL3, SAMPLE_IMAGE

model = LibreInternVL3(size="2b")

# The escape hatch beneath the detection convenience: free-form
# questions, counting, or any prompt the boxes wrapper doesn't cover.
text = model.chat(SAMPLE_IMAGE, "Describe the scene in one sentence.")
print(text)
```

`result.boxes` carries the parsed detections like any other family.
Confidence is a placeholder: InternVL3 emits no per-box score, so every
detection gets the same constant confidence, and `conf=` only drops rows
below that constant, it does not rank them. `iou` discards near-duplicate
boxes of the same class above the given overlap, a side effect of greedy
decoding repeating an object; it is not a class-wise NMS pass. Skip
`set_classes()` and the vocabulary defaults to the COCO-80 names. See
[prediction](/docs/predict) for sources, streaming and result handling.

## Variants

Three sizes: 1b, 2b and 8b, all OpenGVLab's native `-hf` checkpoints (a Qwen
LLM backbone, not the two-tower architecture the original InternVL paper
describes). LibreYOLO's benchmark harness has not measured this family, so
there are no published accuracy numbers to compare them by; pick a size
against your own compute budget.

LibreYOLO exposes this family for prediction only. `train()`, `val()` and
`export()` all raise `NotImplementedError`: fine-tune upstream and load the
result instead, dataset validation is skipped because a placeholder
confidence would make COCO mAP misleading, and export is out of scope for a
generative model with no state dict to trace.

## 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: InternVL3, Shanghai AI Laboratory (OpenGVLab)
- Upstream license: MIT (code); Qwen License (weights)
- Upstream source: https://github.com/OpenGVLab/InternVL
- LibreYOLO code: MIT
- Weights: MIT (code); Qwen License (weights), distributed by their authors. LibreYOLO does not host or mirror them.
- Interpretation: InternVL3's own code is MIT, permissive and usable in commercial and closed-source products. The `-hf` checkpoints this family loads carry a Qwen LLM backbone and are licensed separately, under Alibaba Cloud's Qwen License: free to use, modify and redistribute with a "Built with Qwen" or "Improved using Qwen" attribution requirement, and a 100 million monthly-active-user ceiling on commercial use above which Alibaba's own authorization is required. LibreYOLO does not host or redistribute these weights: LibreInternVL3 downloads the matching size directly from OpenGVLab/InternVL3-<size>-hf on Hugging Face the first time it runs, and logs a one-time notice for the Qwen License before that download.

InternVL3's own code is MIT, permissive and usable in commercial and
closed-source products. The `-hf` checkpoints this family loads carry a Qwen
LLM backbone and are licensed separately, under Alibaba Cloud's Qwen License:
free to use, modify and redistribute with a "Built with Qwen" or "Improved
using Qwen" attribution requirement, and a 100 million monthly-active-user
ceiling on commercial use above which Alibaba's own authorization is
required. LibreYOLO does not host or redistribute these weights:
`LibreInternVL3` downloads the matching size directly from
`OpenGVLab/InternVL3-<size>-hf` on Hugging Face the first time it runs, and
logs a one-time notice for the Qwen License before that download.

## Citation

```bibtex
@article{zhu2025internvl3,
  title={Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models},
  author={Zhu, Jinguo and Wang, Weiyun and Chen, Zhe and Liu, Zhaoyang and Ye, Shenglong and Gu, Lixin and Tian, Hao and Duan, Yuchen and Su, Weijie and Shao, Jie and others},
  journal={arXiv preprint arXiv:2504.10479},
  year={2025}
}
```

Copied from https://github.com/OpenGVLab/InternVL#citation


## InternVL3 architecture

- InternVL3 1B: [SVG](/diagrams/models/internvl3/1b-detect.svg), [interactive diagram](/diagrams/models/internvl3/1b-detect.html)
- InternVL3 2B: [SVG](/diagrams/models/internvl3/2b-detect.svg), [interactive diagram](/diagrams/models/internvl3/2b-detect.html)
- InternVL3 8B: [SVG](/diagrams/models/internvl3/8b-detect.svg), [interactive diagram](/diagrams/models/internvl3/8b-detect.html)
- InternVL3 family: [SVG](/diagrams/models/internvl3/family.svg), [interactive diagram](/diagrams/models/internvl3/family.html)