veomni 0.1.12

VeOmni: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo

🍪 Overview

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.

Our guiding principles when building VeOmni are:

• Flexibility and Modularity: VeOmni is built with a modular design, allowing users to decouple most components and replace them with their own implementations as needed.

• Trainer-free: VeOmni supports linear training scripts that avoid rigid, structured trainer classes (e.g., PyTorch-Lightning or HuggingFace Trainer). These training scripts expose the entire training logic to users for maximum transparency and control. Besides, VeOmni supports a basic trainer for text-only or vlm/omni models training and a rl trainer as a trainer backend in reinforcement learning.

• Omni model native: VeOmni enables users to effortlessly scale any omni-model across devices and accelerators.

• Torch native: VeOmni is designed to leverage PyTorch’s native functions to the fullest extent, ensuring maximum compatibility and performance.

🔥 Latest News

• [2025/11] Our Paper OmniScale: Scaling Any Modality Model Training with Model-Centric Distributed Recipe Zoo was accepted by AAAI 2026
• [2025/09] We release first offical release v0.1.0 of VeOmni.
• [2025/08] We release VeOmni Tech report and open the WeChat group. Feel free to join us!
• [2025/04] We release VeOmni!

📚 Key Features

• FSDP2 backend for training.
• Sequence Parallelism with Deepspeed Ulysess, support with non-async and async mode.
• Experts Parallelism support large MOE model training, like Qwen3-Moe.
• Efficient GroupGemm kernel for Moe model, Liger-Kernel.
• Compatible with HuggingFace Transformers models. Qwen3, Qwen3-VL, Qwen3-Moe, etc
• Dynamic batching strategy, Omnidata processing
• Torch Distributed Checkpoint for checkpoint.
• Support for NVIDIA GPU, AMD ROCm, and Ascend NPU training.
• Experiment tracking with wandb

📝 Upcoming Features and Changes

• VeOmni v0.2 Roadmap https://github.com/ByteDance-Seed/VeOmni/issues/268, https://github.com/ByteDance-Seed/VeOmni/issues/271
• Vit balance tool https://github.com/ByteDance-Seed/VeOmni/issues/280
• Validation dataset during training https://github.com/ByteDance-Seed/VeOmni/issues/247
• RL post training for omni-modality models with VeRL https://github.com/ByteDance-Seed/VeOmni/issues/262

🚀 Getting Started

Documentation

Quick Start

• Installation
• Quick Start with Qwen3

✏️ Supported Models

Support new models to VeOmni see Support New Models

⛰️ Performance

For more details, please refer to our paper.

💡 Awesome work using VeOmni

• dFactory: Easy and Efficient dLLM Fine-Tuning
• LMMs-Engine
• UI-TARS: Pioneering Automated GUI Interaction with Native Agents
• OpenHA: A Series of Open-Source Hierarchical Agentic Models in Minecraft
• UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning
• Open-dLLM: Open Diffusion Large Language Models
• LingBot-VLA: A Pragmatic VLA Foundation Model

🎨 Contributing

Contributions from the community are welcome! Please check out CONTRIBUTING.md our project roadmap(To be updated),

📝 Citation and Acknowledgement

If you find VeOmni useful for your research and applications, feel free to give us a star ⭐ or cite us using:

@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}
}

Thanks to the following projects for their excellent work:

• ByteCheckpoint
• veScale
• Liger-Kernel
• LLaMA-Factory
• torchtitan
• torchtune

Star History

🌱 About ByteDance Seed Team

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👇