| --- |
| license: cc-by-nc-4.0 |
| library_name: libreyolo |
| pipeline_tag: any-to-any |
| base_model: sensenova/SenseNova-Vision-7B-MoT |
| tags: |
| - libreyolo |
| - unified-multimodal |
| - object-detection |
| - keypoint-detection |
| - depth-estimation |
| - image-segmentation |
| - ocr |
| - arxiv:2607.06560 |
| --- |
| |
| # SenseNovaVision7b |
|
|
| > **Non-commercial weights.** These model weights are released by SenseTime |
| > under **CC BY-NC 4.0** and may be used for non-commercial purposes only. |
| > The LibreYOLO integration code is MIT; the vendored architecture code is |
| > Apache-2.0. The license of the weights does not change by being mirrored |
| > here. |
|
|
| SenseNova-Vision-7B-MoT, mirrored for LibreYOLO's `LibreVLM` tier. A unified |
| multimodal model (Bagel-MoT architecture: Qwen2.5-7B MoT decoder, SigLIP |
| vision tower, FLUX autoencoder) that serves many vision tasks from one |
| checkpoint: symbolic outputs (boxes, points, keypoints, OCR words) are |
| generated as tagged text, dense outputs (depth maps, segmentation masks, |
| panoptic maps) are generated as images decoded by the VAE. |
|
|
| ## Source |
|
|
| Byte-identical mirror of |
| [sensenova/SenseNova-Vision-7B-MoT](https://huggingface.co/sensenova/SenseNova-Vision-7B-MoT) |
| at revision `79548fcc5b954598799b9317f8d3ec5e347d5c0e`. |
| Copyright (c) 2026 SenseTime Group Inc. and/or its affiliates. |
|
|
| Reference implementation: |
| [OpenSenseNova/SenseNova-Vision](https://github.com/OpenSenseNova/SenseNova-Vision) |
| (commit `12ccd96e32b32967a11cacb6c5bd5fe3a555fc0c`, Apache-2.0). |
| Paper: [Vision as Unified Multimodal Generation](https://arxiv.org/abs/2607.06560). |
|
|
| Weight checksums (SHA-256): |
|
|
| ``` |
| ema.safetensors 96f29abd98791288c5a24087322e964bb9bcabfc2f185ece71543f827bc2b11e |
| ae.safetensors afc8e28272cd15db3919bacdb6918ce9c1ed22e96cb12c4d5ed0fba823529e38 |
| ``` |
|
|
| ## Modifications |
|
|
| None. The weights and configuration files are unmodified upstream bytes; |
| only this card and the NOTICE file are added. |
|
|
| One upstream quirk to be aware of: `tokenizer.json` assigns the chat/vision |
| special tokens ids beyond the checkpoint's embedding table, while |
| `tokenizer_config.json`'s `added_tokens_decoder` records the layout the model |
| was trained with (structured tokens overriding ids 149632-151664). LibreYOLO |
| reconstructs the trained layout at load time; other consumers should do the |
| same or use a legacy slow tokenizer built from `vocab.json`/`merges.txt`. |
|
|
| ## Usage |
|
|
| ```python |
| from libreyolo import LibreVLM |
| |
| model = LibreVLM("sensenova-vision", task="detect") |
| model.set_classes(["person", "bicycle"]) |
| results = model.predict("image.jpg") # Results.boxes |
| |
| model.set_task("depth") |
| results = model.predict("image.jpg") # Results.depth_map |
| |
| model.set_task("segment").set_classes(["the person on the left"]) |
| results = model.predict("image.jpg") # Results.masks |
| ``` |
|
|
| Supported LibreYOLO tasks: `detect`, `point`, `pose`, `ocr`, `depth`, |
| `segment` (referring), `panoptic`. Free-form access via `model.chat(...)` |
| and `model.generate(...)`. |
|
|
| ## License Composition |
|
|
| - Model weights: **CC BY-NC 4.0** (non-commercial), SenseTime |
| - Reference implementation: Apache-2.0 (SenseTime, building on ByteDance's |
| Bagel, Hugging Face transformers, and the Black Forest Labs FLUX |
| autoencoder, all Apache-2.0) |
| - LibreYOLO integration: MIT |
|
|
| See [`LICENSE`](./LICENSE) (upstream license statement, verbatim) and |
| [`NOTICE`](./NOTICE) for attribution details. |
|
|
