RRepoGEO

REPOGEO REPORT · LITE

LLaVA-VL/LLaVA-NeXT

Default branch main · commit df179663 · scanned 5/15/2026, 2:38:18 PM

GitHub: 4,663 stars · 460 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 LLaVA-VL/LLaVA-NeXT, 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    LLaVA-NeXT is an open-source large multimodal model (LMM) that significantly improves vision-language understanding, reasoning, and OCR, approaching GPT-4V's capabilities for AI researchers and developers, including advanced generative critic models.
  • mediumreadme#2
    Add a concise introductory sentence to the README

    Why:

    COPY-PASTE FIX
    LLaVA-NeXT is an open-source large multimodal model (LMM) that pushes the state-of-the-art in vision-language understanding, reasoning, and OCR, including advanced generative critic capabilities.

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 LLaVA-VL/LLaVA-NeXT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LLaVA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 1×
  2. CogVLM · recommended 1×
  3. BLIP-2 · recommended 1×
  4. MiniGPT-4 · recommended 1×
  5. BakLLaVA · recommended 1×
  • CATEGORY QUERY
    Need a performant open-source vision-language model for generative AI applications.
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. CogVLM
    3. BLIP-2
    4. MiniGPT-4
    5. BakLLaVA
    6. Fuyu-8B

    AI recommended 6 alternatives but never named LLaVA-VL/LLaVA-NeXT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a vision-language model that performs generative criticism and policy tasks.
    you: not recommended
    AI recommended (in order):
    1. GPT-4o
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. LLaVA (Large Language and Vision Assistant) (llava-vl/llava)
    5. CogVLM (THUDM/CogVLM)
    6. Fuyu-8B (adept-ai/fuyu-8b)

    AI recommended 6 alternatives but never named LLaVA-VL/LLaVA-NeXT. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 LLaVA-VL/LLaVA-NeXT?
    pass
    AI named LLaVA-VL/LLaVA-NeXT explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of LLaVA-VL/LLaVA-NeXT. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/LLaVA-VL/LLaVA-NeXT.svg)](https://repogeo.com/en/r/LLaVA-VL/LLaVA-NeXT)
HTML
<a href="https://repogeo.com/en/r/LLaVA-VL/LLaVA-NeXT"><img src="https://repogeo.com/badge/LLaVA-VL/LLaVA-NeXT.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

LLaVA-VL/LLaVA-NeXT — 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