# Welcome to VeOmni
[](https://github.com/ByteDance-Seed/VeOmni/stargazers)
[](https://arxiv.org/abs/2508.02317)
[](https://veomni.readthedocs.io/en/latest/)
[](https://raw.githubusercontent.com/ByteDance-Seed/VeOmni/refs/heads/main/docs/assets/wechat.png)
VeOmni is a versatile framework for both single- and multi-modal pre-training and post-training. It empowers users to seamlessly scale models of any modality across various accelerators, offering both flexibility and user-friendliness.
---
```{toctree}
:maxdepth: 1
:caption: Get Started
get_started/installation/install.md
get_started/installation/install_ascend_x86.md
get_started/installation/install_ascend_arm.md
```
```{toctree}
:maxdepth: 1
:caption: Usage
usage/arguments.md
usage/basic_modules.md
usage/multimodal_data_processing.md
usage/data_packing_and_dyn_bsz.md
usage/support_new_models/guide_and_checklist.md
usage/support_new_models/qwen3_vl_example.md
usage/support_new_models/qwen3_omni_moe_example.md
usage/support_new_models/dit_model_guide.md
usage/checkpoint_conversion.md
usage/trainer.md
usage/agent_workflow.md
```
```{toctree}
:maxdepth: 1
:caption: Hardware Support
hardware_support/get_started_npu.md
hardware_support/typical_usage.md
hardware_support/npu_variables.md
hardware_support/precision_analysis.md
hardware_support/profiling_analysis.md
hardware_support/AscendDockerUsage/build_a2_docker.md
hardware_support/AscendDockerUsage/build_a3_docker.md
hardware_support/FAQ.md
```
```{toctree}
:maxdepth: 1
:caption: Examples
examples/qwen3.md
examples/qwen3_5.md
examples/qwen3_moe.md
examples/qwen3_vl.md
examples/qwen3_omni_moe.md
examples/wan2.1.md
examples/qwen3_dpo.md
```
```{toctree}
:maxdepth: 1
:caption: Key Features
key_features/model_loader.md
key_features/preprocessor_registry.md
key_features/ep_fsdp2.md
key_features/ulysses.md
key_features/lora.md
```
```{toctree}
:maxdepth: 1
:caption: Design
design/kernel_selection.md
```
```{toctree}
:maxdepth: 1
:caption: Transformers v5 Updates
transformers_v5/index.md
```
---
## Citation
If you find VeOmni useful for your research and applications, feel free to give us a star ⭐ or cite us using:
```bibtex
@article{ma2025veomni,
title={VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo},
author={Ma, Qianli and Zheng, Yaowei and Shi, Zhelun and Zhao, Zhongkai and Jia, Bin and Huang, Ziyue and Lin, Zhiqi and Li, Youjie and Yang, Jiacheng and Peng, Yanghua and others},
journal={arXiv preprint arXiv:2508.02317},
year={2025}
}
```
## About [ByteDance Seed Team](https://team.doubao.com/)
Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society. You can get to know Bytedance Seed better through the following channels👇
