Instructions to use LibreYOLO/LibreDINOv3vith16plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LibreYOLO/LibreDINOv3vith16plus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="LibreYOLO/LibreDINOv3vith16plus")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("LibreYOLO/LibreDINOv3vith16plus") model = AutoModel.from_pretrained("LibreYOLO/LibreDINOv3vith16plus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
LibreDINOv3vith16plus
A mirror of Meta's DINOv3 ViT-H+/16 image encoder,
facebook/dinov3-vith16plus-pretrain-lvd1689m
(pretrained on LVD-1689M), stored in bfloat16 for LibreYOLO.
LibreYOLO's Speridlabs Idol
image-to-3D model reads its input through this encoder. The mirror lets
LibreGen("speridlabs-idol") run without a Hugging Face login or an access
request.
This is a mirror, not a new model
The weights are Meta's. Nothing was retrained, distilled or fine-tuned. The
only change is precision: every float32 tensor of the upstream checkpoint was
cast to bfloat16 (round to nearest even), the precision Speridlabs Idol runs
the encoder in. config.json records torch_dtype: bfloat16; no other field
changed.
- Upstream repository:
facebook/dinov3-vith16plus-pretrain-lvd1689m - Upstream revision:
c807c9eeea853df70aec4069e6f56b28ddc82acc - Upstream
model.safetensorsSHA-256:3e1d4d18b9bfa9f28fad8e9de6a783f1313532d3460efa4cd0b12521d81d1a4d - This
model.safetensorsSHA-256:34252ee11eeebf00d50c528d53d725672f03ad88ff30cb724ddf3920bf67dc4c
Loaded with transformers in bfloat16, this checkpoint and the upstream one
hold identical tensors (all 615) and give bit-identical outputs.
License: read this before use
These weights are not covered by LibreYOLO's MIT license. They are
redistributed under Meta's DINOv3 License, included here as
LICENSE. The license permits redistribution on the condition
that it is passed through: the weights may only be distributed under the same
agreement, with a copy of it. Using or distributing them binds you to it.
Restrictions that pass through, summarized but not replacing the text:
- Use must comply with applicable laws, including Trade Controls and privacy and data protection law, and you must not be the target of Trade Controls.
- No activities subject to ITAR or end uses prohibited by Trade Controls, including military or warfare purposes, nuclear industries or applications, espionage, or the development or use of guns or illegal weapons.
- No reverse engineering or decompiling of the materials.
- Research publications that use these weights must acknowledge them.
- Meta may modify the agreement, with changes effective immediately.
Meta gates its own repository behind an access form. This mirror is not gated; users who need Meta's own access process should obtain the weights from the upstream repository.
Usage
With LibreYOLO, which downloads it automatically:
from libreyolo import LibreGen
LibreGen("speridlabs-idol")("chair.jpg").save("chair.ply")
With transformers:
import torch
from transformers import AutoModel
model = AutoModel.from_pretrained("LibreYOLO/LibreDINOv3vith16plus", dtype=torch.bfloat16)
Citation
@misc{simeoni2025dinov3,
title={{DINOv3}},
author={Sim{\'e}oni, Oriane and Vo, Huy V. and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{\"e}l and Massa, Francisco and Haziza, Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet, Timoth{\'e}e and Moutakanni, Th{\'e}o and Sentana, Leonel and Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie and Brandt, John and Couprie, Camille and Mairal, Julien and J{\'e}gou, Herv{\'e} and Labatut, Patrick and Bojanowski, Piotr},
year={2025},
eprint={2508.10104},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.10104},
}
- Downloads last month
- 36