File size: 3,594 Bytes
c4698c4 b89c01d 90b8908 82354d8 90b8908 eaeaf35 90b8908 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
---
license: apache-2.0
language:
- zh
- en
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
- Qwen/Qwen2-Audio-7B-Instruct
---
<p align="center">
<h1 align="center">NEXUS-O: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision</h1>
<p align="center">
<strong>Che Liu</strong>
,
<strong>Yingji Zhang</strong>
,
<strong>Dong Zhang</strong>
,
<strong>Weijie Zhang</strong>
,
<strong>Chenggong Gong</strong>
,
<strong>Yu Lu</strong>
,
<strong>Shilin Zhou</strong>
,
<strong>Ziliang Gan</strong>
,
<br>
<strong>Ziao Wang</strong>,
<strong>Haipang Wu</strong>,
<strong>Ji Liu</strong>,
<strong>Andre Freitas</strong>,
<strong>Qifan Wang</strong>,
<strong>Zenglin Xu</strong>,
<br>
<strong>Rongjunchen Zhang</strong><sup>♠</sup>,
<strong>Yong Dai</strong><sup>♠</sup>
</p>
<div class="is-size-5 publication-authors" align="center">
<span class="author-block">
<sup>♠</sup>Corresponding author, daiyongya@outlook.com, zhangrongjunchen@myhexin.com
</span>
</div>
<br>
📖<a href="https://arxiv.org/pdf/2503.01879">Paper</a> |🤗<a href="https://huggingface.co/HiThink-Research/NEXUS-O">Model</a></h3> | 🤗<a href="https://huggingface.co/HiThink-Research/NEXUS-O">Training Data (Coming Soon)</a></h3>
<div align="center"></div>
<p align="center">
<p>
NEXUS-O is an industry-scale omni-modal large language model (LLM) that unifies audio, vision, and language understanding into a single modular framework.
Human perception integrates sight, sound, and language — NEXUS-O aims to replicate this ability for intelligent agents across real-world scenarios such as ASR, Speech-to-Speech Chat, and Multimodal Reasoning.
</p>
<img src="static/omni.png">
<p>Architecture of NEXUS-O</p>
<img src="static/train_stage.png">
<p>Training Stages</p>
## 📢 News
- 🚀 [08/01/2025] Our paper has been accepted for ACM MM 2025.
## 💡 Highlights
- 🧩 Modular End-to-End Framework. A highly configurable encoder–LLM–decoder architecture supporting flexible modality combinations and rapid iteration for industry applications.
- 💡 Lightweight Alignment Strategy. Efficient audio–language pre-training built upon the state-of-the-art Qwen2.5-VL model — eliminating the need for costly vision pre-training while retaining strong tri-modal performance.
- 🎧 Synthetic Audio Data Pipeline. A scalable audio synthesis system that generates diverse, high-fidelity audio-text pairs from real-world scenes, enabling robust downstream ASR and S2S tasks.
## TODO
* [x] Rlease NEXUS-O full model weight on HuggingFace
* [ ] Rlease Audio Encoder Training Data
* [ ] Rlease Audio Decoder Training Data
## ✒️Citation
```
@article{liu2025nexus,
title={Nexus: An Omni-Perceptive And-Interactive Model for Language, Audio, And Vision},
author={Liu, Che and Zhang, Yingji and Zhang, Dong and Zhang, Weijie and Gong, Chenggong and Li, Haohan and Lu, Yu and Zhou, Shilin and Lu, Yue and Gan, Ziliang and others},
journal={arXiv preprint arXiv:2503.01879},
year={2025}
}
```
## 📄 License
  **Usage and License Notices**: The data and code are intended and licensed for research use only.
License: Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use
## 💖 Acknowledgement
|