InternVL3

Install

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

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.

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)

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 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

Citation

@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}
}