RRepoGEO

REPOGEO REPORT · LITE

ByteDance-Seed/VeOmni

Default branch main · commit af843783 · scanned 5/26/2026, 3:47:00 PM

GitHub: 1,948 stars · 197 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface ByteDance-Seed/VeOmni, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening sentence to highlight multi-modality and distributed scaling

    Why:

    CURRENT
    VeOmni is a versatile framework for both single- and multi-modal pre-training and post-training.
    COPY-PASTE FIX
    VeOmni is a versatile, model-centric framework for *scaling any modality* (single- or multi-modal) deep learning model training across distributed accelerators, offering a flexible, trainer-free approach for pre-training and post-training.
  • mediumhomepage#2
    Add the official documentation link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://veomni.readthedocs.io/en/latest/

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface ByteDance-Seed/VeOmni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Lightning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Lightning · recommended 1×
  2. PyTorch Distributed Data Parallel (DDP) · recommended 1×
  3. PyTorch Fully Sharded Data Parallel (FSDP) · recommended 1×
  4. TorchElastic · recommended 1×
  5. Hugging Face Accelerate · recommended 1×
  • CATEGORY QUERY
    How to scale multi-modal deep learning model training across distributed accelerators?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. PyTorch Distributed Data Parallel (DDP)
    3. PyTorch Fully Sharded Data Parallel (FSDP)
    4. TorchElastic
    5. Hugging Face Accelerate
    6. Hugging Face Transformers library
    7. DeepSpeed
    8. TensorFlow Distributed Strategy API
    9. MirroredStrategy
    10. MultiWorkerMirroredStrategy
    11. ParameterServerStrategy
    12. DTensor
    13. Ray Train
    14. Horovod

    AI recommended 14 alternatives but never named ByteDance-Seed/VeOmni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a flexible framework for distributed model training without rigid trainer classes.
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    2. Accelerate (huggingface/accelerate)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Ray Train (ray-project/ray)
    5. Horovod (horovod/horovod)
    6. PyTorch DDP/FSDP (pytorch/pytorch)

    AI recommended 6 alternatives but never named ByteDance-Seed/VeOmni. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of ByteDance-Seed/VeOmni?
    pass
    AI named ByteDance-Seed/VeOmni explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts ByteDance-Seed/VeOmni in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ByteDance-Seed/VeOmni explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo ByteDance-Seed/VeOmni solve, and who is the primary audience?
    pass
    AI named ByteDance-Seed/VeOmni explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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ByteDance-Seed/VeOmni — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite