RRepoGEO

REPOGEO REPORT · LITE

EvolvingLMMs-Lab/LLaVA-OneVision-2

Default branch main · commit ea90da5b · scanned 6/1/2026, 3:22:16 PM

GitHub: 1,009 stars · 72 forks

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 statement to specify its VLM/MLLM focus

    Why:

    CURRENT
    <p align="center">
      <strong>Fully Open Framework for Democratized Multimodal Training</strong>
    </p>
    COPY-PASTE FIX
    <p align="center">
      <strong>LLaVA-OneVision-2: A Fully Open Framework for Democratized Vision-Language Model (VLM) and Multimodal Large Language Model (MLLM) Training</strong>
    </p>
  • mediumcomparison#2
    Add a 'Why LLaVA-OneVision-2?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why LLaVA-OneVision-2?
    
    Unlike general-purpose machine learning frameworks like Hugging Face Transformers or PyTorch Lightning, LLaVA-OneVision-2 is specifically engineered as a comprehensive, open framework for the democratized training and fine-tuning of Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs). We provide integrated tools, datasets, and models tailored for multimodal understanding, offering a specialized platform that accelerates research and development in this domain.
  • lowtopics#3
    Expand repository topics with more specific training and framework keywords

    Why:

    CURRENT
    llava, llava-onevision, llm, mllm, qwen3, vision-language-model
    COPY-PASTE FIX
    llava, llava-onevision, llm, mllm, qwen3, vision-language-model, vlm-training, mllm-training, multimodal-framework, llm-finetuning, multimodal-ai

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 1×
  3. OpenMMLab · recommended 1×
  4. DeepSpeed · recommended 1×
  5. JAX/Flax · recommended 1×
  • CATEGORY QUERY
    Looking for an open framework to train custom vision-language models from scratch.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. OpenMMLab
    4. DeepSpeed
    5. JAX/Flax

    AI recommended 5 alternatives but never named EvolvingLMMs-Lab/LLaVA-OneVision-2. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a democratized platform for developing and fine-tuning multimodal large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Google Colab
    3. Kaggle Notebooks
    4. transformers
    5. diffusers
    6. peft
    7. RunwayML
    8. Google Cloud Vertex AI
    9. Model Garden
    10. Weights & Biases (W&B)
    11. AWS SageMaker
    12. Azure Machine Learning
    13. Replicate
    14. OpenAI API

    AI recommended 14 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 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?

  • 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 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?

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