RRepoGEO

REPOGEO REPORT · LITE

EvolvingLMMs-Lab/LLaVA-OneVision-2

Default branch main · commit 91bd1cbf · scanned 7/1/2026, 3:41:49 PM

GitHub: 1,119 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)

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

AI VISIBILITY SCORE
33 /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
2 / 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 EvolvingLMMs-Lab/LLaVA-OneVision-2, 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 opening to clarify its specific niche

    Why:

    CURRENT
    The current README opens with a tagline and links, without immediately clarifying its specific scope.
    COPY-PASTE FIX
    Add the following sentence immediately after the main title/logo in the README: "LLaVA-OneVision-2 is a specialized, fully open framework designed for democratized training and evaluation of state-of-the-art Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs), distinct from general-purpose machine learning libraries."
  • mediumabout#2
    Refine the repository's 'About' description for greater specificity

    Why:

    CURRENT
    Fully Open Framework for Democratized Multimodal Training
    COPY-PASTE FIX
    A fully open and unified framework for democratized training and evaluation of state-of-the-art Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs).
  • lowreadme#3
    Add a section to the README highlighting key differentiators

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, titled 'Why LLaVA-OneVision-2? Key Differentiators', starting with a sentence like: 'LLaVA-OneVision-2 stands out from other multimodal frameworks and general ML libraries due to its unique focus on [mention 1-2 key unique features, e.g., unified architecture, specific encoder strengths, democratized access].'

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 EvolvingLMMs-Lab/LLaVA-OneVision-2
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch Lightning · recommended 2×
  3. DeepSpeed · recommended 2×
  4. diffusers · recommended 1×
  5. accelerate · recommended 1×
  • CATEGORY QUERY
    Seeking an open framework for training custom vision-language models from scratch.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. diffusers
    3. accelerate
    4. PyTorch Lightning
    5. OpenMMLab
    6. DeepSpeed
    7. JAX
    8. Flax

    AI recommended 8 alternatives but never named EvolvingLMMs-Lab/LLaVA-OneVision-2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need tools for democratized development of multimodal large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Diffusers
    3. PyTorch Lightning
    4. Keras
    5. OpenAI API
    6. LangChain
    7. DeepSpeed
    8. TensorFlow

    AI recommended 8 alternatives but never named EvolvingLMMs-Lab/LLaVA-OneVision-2. 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 EvolvingLMMs-Lab/LLaVA-OneVision-2?
    pass
    AI did not name EvolvingLMMs-Lab/LLaVA-OneVision-2 — likely talking about a different project

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

  • If a team adopts EvolvingLMMs-Lab/LLaVA-OneVision-2 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EvolvingLMMs-Lab/LLaVA-OneVision-2 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 EvolvingLMMs-Lab/LLaVA-OneVision-2 solve, and who is the primary audience?
    pass
    AI named EvolvingLMMs-Lab/LLaVA-OneVision-2 explicitly

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

Embed your GEO score

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EvolvingLMMs-Lab/LLaVA-OneVision-2 — 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