RRepoGEO

REPOGEO REPORT · LITE

jingyaogong/minimind-v

Default branch master · commit ee18831d · scanned 5/8/2026, 11:07:24 AM

GitHub: 7,829 stars · 849 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 jingyaogong/minimind-v, 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
  • highreadme#1
    Reposition the README's core value proposition to the very top

    Why:

    CURRENT
    The README excerpt shows several <div align="center"> blocks and empty lines/badges before the actual descriptive text.
    COPY-PASTE FIX
    Move the project's core value proposition to the absolute top of the README, before any badges or decorative elements. For example: 'This project, MiniMind-V, enables training a 65M-parameter vision-language model (VLM) from scratch in just 2 hours on a single GPU, costing as little as $3. It provides a minimal, open-source implementation and a concise tutorial for anyone looking to learn VLM training and inference.'
  • mediumtopics#2
    Add more specific topics to improve category visibility

    Why:

    CURRENT
    artificial-intelligence, chatgpt, vision-language-model
    COPY-PASTE FIX
    vision-language-model, vlm-training, from-scratch, minimal-implementation, single-gpu, ai-tutorial
  • lowreadme#3
    Streamline the README's initial content by removing visual clutter

    Why:

    CURRENT
    The README excerpt shows several <div align="center"> blocks and empty lines/badges before the actual descriptive text.
    COPY-PASTE FIX
    Remove the initial empty <div align="center"> blocks and badge placeholders from the top of the README, ensuring the core introductory text is the very first content after the title.

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 jingyaogong/minimind-v
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. peft · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. DeepSpeed · recommended 1×
  5. FSDP · recommended 1×
  • CATEGORY QUERY
    How can I train a small vision language model quickly and affordably on a single GPU?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. peft
    3. PyTorch Lightning
    4. DeepSpeed
    5. FSDP
    6. OpenCLIP
    7. Lit-GPT
    8. Fastai
    9. AWS EC2 Spot Instances
    10. Google Cloud Preemptible VMs
    11. Azure Spot VMs
    12. Google Colab Pro
    13. Kaggle Notebooks

    AI recommended 13 alternatives but never named jingyaogong/minimind-v. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a minimal open-source implementation to learn vision language model training from scratch.
    you: not recommended
    AI recommended (in order):
    1. minGPT (karpathy/minGPT)
    2. minDALL-E (borisdayma/dalle-mini)
    3. nanoGPT (karpathy/nanoGPT)
    4. makemore (karpathy/makemore)
    5. Hugging Face transformers library (huggingface/transformers)
    6. PyTorch Examples (pytorch/examples)

    AI recommended 6 alternatives but never named jingyaogong/minimind-v. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 jingyaogong/minimind-v?
    pass
    AI named jingyaogong/minimind-v explicitly

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

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

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

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jingyaogong/minimind-v — 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