RRepoGEO

REPOGEO REPORT · LITE

BAAI-DCAI/Bunny

Default branch main · commit 08273acb · scanned 6/21/2026, 10:32:55 PM

GitHub: 1,053 stars · 76 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 BAAI-DCAI/Bunny, 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 README opening to explicitly state the problem and solution

    Why:

    CURRENT
    Bunny is a family of lightweight but powerful multimodal models.
    COPY-PASTE FIX
    Bunny addresses the challenge of computational inefficiencies and fragmented ecosystems in Vision-Language Model (VLM) development by offering a family of lightweight yet powerful multimodal models.
  • mediumtopics#2
    Expand topics with more specific keywords

    Why:

    CURRENT
    chatgpt, chinese, english, gpt-4, mllm, multimodal-large-language-models, vlm
    COPY-PASTE FIX
    chatgpt, chinese, english, gpt-4, mllm, multimodal-large-language-models, vlm, lightweight-vlm, efficient-ai, high-resolution-images, compact-models
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add the URL for the Technical Report (e.g., `https://arxiv.org/abs/XXXX.XXXXX`) or the Hugging Face Space to the 'Homepage' field in the repository settings.

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 BAAI-DCAI/Bunny
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MiniGPT-4
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MiniGPT-4 · recommended 2×
  2. BLIP-2 · recommended 2×
  3. PaliGemma · recommended 2×
  4. LLaVA · recommended 1×
  5. OpenFlamingo · recommended 1×
  • CATEGORY QUERY
    Seeking a lightweight multimodal model for efficient visual and textual reasoning tasks.
    you: not recommended
    AI recommended (in order):
    1. MiniGPT-4
    2. LLaVA
    3. BLIP-2
    4. OpenFlamingo
    5. PaliGemma

    AI recommended 5 alternatives but never named BAAI-DCAI/Bunny. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are powerful, compact vision-language models capable of processing high-resolution images?
    you: not recommended
    AI recommended (in order):
    1. LLaVA-1.5
    2. MiniGPT-4
    3. BLIP-2
    4. InstructBLIP
    5. PaliGemma

    AI recommended 5 alternatives but never named BAAI-DCAI/Bunny. 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 BAAI-DCAI/Bunny?
    pass
    AI named BAAI-DCAI/Bunny explicitly

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

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

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

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BAAI-DCAI/Bunny — 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