ByteDance-Seed/VeOmni

Health v2 · maintenance only

Adoption not part of the score

2246 stars · 294 forks observed · 2026-10-17

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

VeOmni is a PyTorch-native framework for single- and multi-modal model pre-training and post-training, with a modular, trainer-free design exposing full training logic. It scales any-modality models (including omni and MoE models) across accelerators using distributed recipes like FSDP2, sequence parallelism, and experts parallelism.

Use cases

• pretrain a large language model on multiple GPUs
• train a vision-language or omni-modal model at scale
• fine-tune a Qwen3-MoE model with experts parallelism
• run post-training or RL training for LLMs
• scale training across different accelerator types with PyTorch native tools

When to choose

• you need transparent, trainer-free training scripts with full control over training logic
• you are training large MoE or multimodal models requiring FSDP2, sequence parallelism, or experts parallelism
• you want a PyTorch-native framework without heavy trainer abstractions

When to avoid

• you prefer high-level trainer abstractions like HuggingFace Trainer or PyTorch Lightning
• you only need simple single-GPU fine-tuning of small models
• you need a framework with broad non-PyTorch backend support

Facets

framework · maturity active

llm-training machine-learning deep-learning large-language-models machine-learning deep-learning gpu-computing python distributed-training multimodal fsdp2 sequence-parallelism mixture-of-experts pytorch post-training reinforcement-learning gpu linux

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markdown · JSON · MCP: product_card(name="ByteDance-Seed/VeOmni")