veomni-seed-omni
Skill2.3k starsChanged 3 days agoApache-2.0View on GitHub
SeedOmni development guidance for Open-VeOmni. Use when adding or modifying `veomni/models/seed_omni/`, Omni modules, module mixins, CPU preprocessors, conversation carrier handling, training or generation graph YAML, SeedOmni split-checkpoint scripts, per-module accelerators, distributed/eager Omni inference, or SeedOmni validation. Triggers include: new OmniModule, modify SeedOmni module, wire graph, update `training_graph` or `generation_graph`, split SeedOmni checkpoint, add Preprocessor, change conversation_list flow, configure per-module FSDP2/DDP/emb/ep, or debug SeedOmni train/infer graph behavior.
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 3 are distinctive, and 3 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use this file as a routing entrypoint and load the references matching the task from the routing table below.
- Distinctive: line 18
Treat current code as the source of truth (graph/module source and live configs).
- Very specific: line 21
Use live fake_model, Janus, and Qwen modules as implementation maps.
- Very specific: line 62
Use veomni-new-model first when adding a new HuggingFace model family under veomni/models/transformers/.
- Distinctive: line 64
Use this skill after that model exists, when wrapping it as SeedOmni modules or wiring it into Omni graphs.
- Very specific: line 66
Use veomni-develop for unrelated framework work outside SeedOmni.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
SeedOmni Skill
Use this file as a routing entrypoint and load the references matching the task from the routing table below.
First Checks
- Confirm the task touches SeedOmni:
- Source code under
veomni/models/seed_omni/. - Configs under
configs/seed_omni/. - SeedOmni data collation or
conversation_listpreprocessing. - SeedOmni checkpoint splitting or module registration.
- Source code under
- Treat current code as the source of truth (graph/module source and live configs).
- Choose the smallest applicable reference set from the routing table below.
- Use live
fake_model, Janus, and Qwen modules as implementation maps.
Routing
| Task | Read |
| Understand SeedOmni architecture, runtime hierarchy, and hard constraints | .agents/knowledge/veomni-seed-omni.md, references/source-of-truth.md |
Change data flow, conversation_list, CPU preprocessing, dummy items, or raw request preprocessing | references/data-flow.md |
| Add or modify a module mixin/model/config/processor | references/module-contract.md |
| Edit training graph or generation FSM YAML | references/graph-runtime.md, then the matching templates/*.yaml |
| Add a new model config layout | references/graph-runtime.md, templates/base.template.yaml, templates/modules_train.template.yaml |
| Add distributed/eager inference module config | references/per-module-parallel.md, templates/modules_infer_eager.template.yaml, templates/modules_infer_fsdp.template.yaml |
| Update split-checkpoint conversion | references/checkpoint-splitting.md |
| Validate a SeedOmni change | references/validation.md |
Current Baseline
- Entry-time samples use
{"conversation_list": [...]}.seedomni_transformruns ordered module CPU preprocessors (OmniProcessor) per sample in the Dataset stage, andSeedOmniCollatorgroups preprocessed samples into a micro-batch before GPU forward. ModulePreprocessorBaseis a core data-flow contract, shared by training and inference. Training runs it insidedata_transform(seedomni_transform); inference runs it once before the FSM.- Modules communicate by mutating and returning the shared
conversation_listcarrier. - Training graphs are flat edge lists over
module[.method]endpoints.to: endis the virtual sink. Execution order is derived by topo sort. - Generation graphs are FSMs. Each state body is an ordered inline edge list.
doneis framework-injected; usemodule_signalanddefaulttransitions. - Store tokenizers, processors, and other assets in the owning module's subfolder.
Templates
Templates under templates/ are skeletons, not authoritative examples. Use them
to start a new config file, then compare against the live configs listed in
references/module-contract.md.
Sibling Skills
- Use
veomni-new-modelfirst when adding a new HuggingFace model family underveomni/models/transformers/. - Use this skill after that model exists, when wrapping it as SeedOmni modules or wiring it into Omni graphs.
- Use
veomni-developfor unrelated framework work outside SeedOmni.
Trending
Quiet since September.about the whole repository
Who's talking about ByteDance-Seed/VeOmni
- verl-project/verl“[roadmap] verl 26Q2 roadmap”GitHub · Apr 1, 2026 · 28 reactions
- linkedin/Liger-Kernel“[NPU Roadmap, Updated to 2026-Q2] NPU support for Liger-Kernel”GitHub · Dec 8, 2025 · 17 reactions
- verl-project/verl-omni“[RFC] VeOmni Backend Integration Plan for H2”GitHub · May 19, 2026 · 2 reactions
- verl-project/verl“[veomni, fsdp] feat: enable fused top-K distillation kernel for OPD”GitHub · May 27, 2026 · 1 reactions
- @pythonhub.dev“VeOmni Scaling any Modality Model Training to any Accelerators with PyTorch native Training Framework.…”Bluesky · Apr 18, 2025 · 1 likes
The biggest 5 of 22 mentions linking to the repository that our crawl found. Each line is their own headline.
See all 22
Talked about on GitHub and Bluesky (22 times).
On GitHub · 21
- Apr 1, 2026[roadmap] verl 26Q2 roadmap — verl-project/verl28 reactions · 2 comments
- Dec 8, 2025[NPU Roadmap, Updated to 2026-Q2] NPU support for Liger-Kernel — linkedin/Liger-Kernel17 reactions · 32 comments
- May 19, 2026[RFC] VeOmni Backend Integration Plan for H2 — verl-project/verl-omni2 reactions · 7 comments
- May 27, 2026[veomni, fsdp] feat: enable fused top-K distillation kernel for OPD — verl-project/verl1 reaction
- Sep 30, 2026[veomni, model, doc] feat: support MTP training for Qwen3.5 models — verl-project/verl0 reactions
16 more on GitHub
- Sep 28, 2026[RFC]: HyperParallel 统一补全 .nv-meta 在线/离线数据源能力 — mindspore-ai/hyper-parallel0 reactions
- Sep 24, 2026[model, ops, perf] feat: accelerate Qwen4-Exp QSA and NPU norm/rope — Crystal-jiang/VeOmni0 reactions
- Sep 9, 2026[algo, trainer, rollout, vllm] feat: add reverse KL top-k with fixed-token prefill scoring — verl-project/verl0 reactions · 4 comments
- Sep 3, 2026[veomni] feat: add async_activation_offload config — verl-project/verl0 reactions
- Aug 29, 2026[Feature request]Support Qwen3.5 series MTP training on Veomni Backend — verl-project/verl0 reactions
- Aug 27, 2026[veomni] feat: DeepSeek V4 QAT bf16 fake quant training — verl-project/verl0 reactions
- Aug 21, 2026[veomni] feat:add async activation offloading support — verl-project/verl0 reactions
- Aug 3, 2026[veomni] feat: add DeepSeek V4 support — verl-project/verl0 reactions · 1 comment
- Jun 22, 2026feat(glm): add GLM5 indexer LoRA targets — NVIDIA-NeMo/Megatron-Bridge0 reactions · 4 comments
- Jun 17, 2026[skills] docs: capture VeOmni MiniMax M3 migration workflow — Kirrito-k423/AutoModelMigrate0 reactions
- Jun 5, 2026[Add] VeOmni (ByteDance-Seed/VeOmni) — PyTorchKR/landscape0 reactions
- Feb 14, 2026[veomni, trainer] fix: padding pixel value with padding_scale for vl model — verl-project/verl0 reactions
- Nov 17, 2025Data duplication with `split_dataset_by_node` and `interleaved_dataset` — huggingface/datasets0 reactions · 3 comments
- Sep 12, 2025Target trajectory format investigation — ManifoldRG/software-control0 reactions · 2 comments
- Aug 9, 2025Feat: Add VeOmni-inspired trainer-free training script — smellslikeml/VQASynthX0 reactions
- Jul 23, 2025Set up finetuning scripts for UI-TARS1.5 — ManifoldRG/software-control0 reactions
On Bluesky · 1
- Apr 18, 2025VeOmni Scaling any Modality Model Training to any Accelerators with PyTorch native Training Framework.… — @pythonhub.dev1 likes and repost
In this repository14 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .cursor/rules/no-section-divider-comments.mdcCursor rule
- .cursor/rules/skills-reusable-only.mdcCursor rule
- create-prSkill
- veomni-debugSkill
- veomni-developSkill
- veomni-fix-docsSkill
- veomni-gpu-to-npuSkill
- veomni-new-modelSkill
- veomni-new-opSkill
- veomni-patchgen-modelSkill
- veomni-profileSkill
- veomni-reviewSkill
- veomni-uv-updateSkill
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