RRepoGEO

REPOGEO REPORT · LITE

inclusionAI/Ming

Default branch main · commit 2a0c02ae · scanned 6/5/2026, 7:48:27 AM

GitHub: 656 stars · 58 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 inclusionAI/Ming, 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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    multimodal, llm, mllm, generative-ai, speech-synthesis, image-generation, sota, deep-learning, foundation-model
  • highabout#2
    Strengthen the About section description to be more specific and impactful

    Why:

    CURRENT
    Ming - facilitating advanced multimodal understanding and generation capabilities built upon the Ling LLM.
    COPY-PASTE FIX
    Ming-flash-omni 2.0: State-of-the-Art open-source omni-MLLM for advanced multimodal understanding, immersive speech synthesis, and high-dynamic image generation, built on Ling-2.0.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://huggingface.co/inclusionAI/Ming-flash-omni-2.0

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 inclusionAI/Ming
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LLaVA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 2×
  2. CogVLM · recommended 1×
  3. BakLLaVA · recommended 1×
  4. Fuyu-8B · recommended 1×
  5. InternVL · recommended 1×
  • CATEGORY QUERY
    What open-source models offer state-of-the-art multimodal understanding and content generation?
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. CogVLM
    3. BakLLaVA
    4. Fuyu-8B
    5. InternVL
    6. Qwen-VL

    AI recommended 6 alternatives but never named inclusionAI/Ming. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an open-source omni-multimodal model for advanced image generation and immersive speech synthesis.
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion XL
    2. VALL-E X
    3. LLaVA
    4. Bark
    5. BLIP-2
    6. Coqui TTS
    7. DeepFloyd IF
    8. Tortoise-TTS

    AI recommended 8 alternatives but never named inclusionAI/Ming. 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 inclusionAI/Ming?
    pass
    AI named inclusionAI/Ming explicitly

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

  • If a team adopts inclusionAI/Ming in production, what risks or prerequisites should they evaluate first?
    pass
    AI named inclusionAI/Ming 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 inclusionAI/Ming solve, and who is the primary audience?
    pass
    AI named inclusionAI/Ming explicitly

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

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inclusionAI/Ming — 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