RRepoGEO

REPOGEO REPORT · LITE

Meituan-AutoML/MobileVLM

Default branch main · commit 688fdec9 · scanned 5/25/2026, 6:57:23 AM

GitHub: 1,354 stars · 88 forks

AI VISIBILITY SCORE
68 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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 Meituan-AutoML/MobileVLM, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    vision-language-model, mobile-ai, edge-computing, multimodal-ai, efficient-ai, deep-learning, pytorch
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the official project homepage URL (e.g., a dedicated project page, paper link, or the GitHub repo itself) to the repository settings.
  • lowreadme#3
    Add a concise value proposition statement to the README's opening

    Why:

    COPY-PASTE FIX
    Insert a concise sentence or two immediately after the main H1, e.g., 'MobileVLM provides a family of lightweight yet powerful vision-language models specifically designed for efficient deployment on mobile and edge devices, enabling advanced multimodal AI capabilities in resource-constrained environments.'

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
1 / 2
50% of queries surface Meituan-AutoML/MobileVLM
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
5%
Of all named tools, what % are you?
Top rival
MobileNetV3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MobileNetV3 · recommended 2×
  2. EfficientNet-Lite · recommended 2×
  3. MobileVLM V2 · recommended 1×
  4. TinyLlama-V · recommended 1×
  5. EfficientNetV2 · recommended 1×
  • CATEGORY QUERY
    What are the best open vision language models optimized for mobile device deployment?
    you: #1
    AI recommended (in order):
    1. MobileVLM ← you
    2. MobileVLM V2
    3. TinyLlama-V
    4. MobileNetV3
    5. EfficientNetV2
    6. LLaVA-Lite
    7. EfficientNet-Lite
    8. ResNet
    9. MiniGPT-4
    10. BLIP-2
    11. Qwen-VL-Chat
    12. OpenVINO
    13. ONNX Runtime
    14. TFLite
    15. Core ML
    Show full AI answer
  • CATEGORY QUERY
    Seeking efficient multimodal AI models that perform well on resource-constrained mobile hardware.
    you: not recommended
    AI recommended (in order):
    1. MobileViT
    2. MobileNetV3
    3. EfficientNet-Lite
    4. DistilBERT
    5. MobileNetV2
    6. TinyBERT
    7. YOLO-NAS

    AI recommended 7 alternatives but never named Meituan-AutoML/MobileVLM. 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 Meituan-AutoML/MobileVLM?
    pass
    AI named Meituan-AutoML/MobileVLM explicitly

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

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

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

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Meituan-AutoML/MobileVLM — 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