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 at revision 79548fcc5b954598799b9317f8d3ec5e347d5c0e. Copyright (c) 2026 SenseTime Group Inc. and/or its affiliates.

Reference implementation: OpenSenseNova/SenseNova-Vision (commit 12ccd96e32b32967a11cacb6c5bd5fe3a555fc0c, Apache-2.0). Paper: Vision as Unified Multimodal Generation.

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

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 (upstream license statement, verbatim) and NOTICE for attribution details.