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.