RRepoGEO

REPOGEO REPORT · LITE

mbzuai-oryx/LLaVA-pp

Default branch main · commit 5ccb07b4 · scanned 6/16/2026, 6:22:53 AM

GitHub: 842 stars · 59 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 mbzuai-oryx/LLaVA-pp, 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
    Move core value proposition to the top of the README

    Why:

    CURRENT
    The README's first prose explaining the project is under the 'Introduction' section, after 'Latest Updates'.
    COPY-PASTE FIX
    Insert the following sentence directly after the H1: 'This repository enhances the capabilities of the LLaVA 1.5 model by incorporating the latest LLMs, Phi-3 Mini Instruct 3.8B and LLaMA-3 Instruct 8B, to extend its visual understanding.'
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file to the repository root, specifying the terms under which the project can be used, modified, and distributed.
  • lowhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/MBZUAI/LLaMA-3-V

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 mbzuai-oryx/LLaVA-pp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
haotian-liu/LLaVA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. haotian-liu/LLaVA · recommended 2×
  2. salesforce/LAVIS · recommended 2×
  3. Vision-CAIR/MiniGPT-4 · recommended 2×
  4. GPT-4V · recommended 1×
  5. Claude 3 · recommended 1×
  • CATEGORY QUERY
    How can I enhance my existing vision-language model with more advanced conversational AI?
    you: not recommended
    AI recommended (in order):
    1. GPT-4V
    2. Claude 3
    3. Gemini
    4. LLaVA (haotian-liu/LLaVA)
    5. BLIP-2 (salesforce/BLIP2)
    6. InstructBLIP (salesforce/LAVIS)
    7. MiniGPT-4 (Vision-CAIR/MiniGPT-4)

    AI recommended 7 alternatives but never named mbzuai-oryx/LLaVA-pp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source tools integrate powerful large language models for vision-language understanding tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers with 🤗 Transformers Agents (huggingface/transformers)
    2. LLaVA (haotian-liu/LLaVA)
    3. InstructBLIP (salesforce/LAVIS)
    4. MiniGPT-4 (Vision-CAIR/MiniGPT-4)
    5. OpenCLIP (mlfoundations/open_clip)
    6. Kosmos-2 (microsoft/kosmos-2)

    AI recommended 6 alternatives but never named mbzuai-oryx/LLaVA-pp. 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 mbzuai-oryx/LLaVA-pp?
    pass
    AI named mbzuai-oryx/LLaVA-pp explicitly

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

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

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

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mbzuai-oryx/LLaVA-pp — 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