RRepoGEO

REPOGEO REPORT · LITE

microsoft/LLaVA-Med

Default branch main · commit 30697ca5 · scanned 6/19/2026, 3:21:54 PM

GitHub: 2,214 stars · 291 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
53 /100
Needs work
Category recall
1 / 2
Avg rank #4.0 when recommended
Rule findings
1 pass · 1 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 microsoft/LLaVA-Med, 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
  • highreadme#1
    Strengthen README's opening statement for clarity

    Why:

    CURRENT
    *Visual instruction tuning towards building large language and vision models with GPT-4 level capabilities in the biomedicine space.*
    COPY-PASTE FIX
    *LLaVA-Med is a specialized large language-and-vision AI assistant for biomedicine, enabling advanced medical image analysis and understanding with GPT-4 level capabilities.*
  • mediumhomepage#2
    Add project homepage URL

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2306.00890

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
1 / 2
50% of queries surface microsoft/LLaVA-Med
Avg rank
#4.0
Lower is better. #1 = top recommendation.
Share of voice
4%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 1×
  2. Project-MONAI/MONAI · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. BERT · recommended 1×
  5. RoBERTa · recommended 1×
  • CATEGORY QUERY
    How can I build a multimodal AI assistant for medical image analysis and understanding?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. Monai (Project-MONAI/MONAI)
    3. Hugging Face Transformers (huggingface/transformers)
    4. BERT
    5. RoBERTa
    6. GPT-2
    7. LLaMA
    8. Vision-and-Language Transformers (ViLT)
    9. CLIP
    10. TensorFlow (tensorflow/tensorflow)
    11. Keras (keras-team/keras)
    12. TensorFlow Medical Imaging (TFMI) (tensorflow/medical-imaging)
    13. OpenCV (opencv/opencv)
    14. Fast.ai (fastai/fastai)
    15. NVIDIA Clara Parabricks
    16. NVIDIA Clara Train SDK
    17. Gradio (gradio-app/gradio)
    18. Streamlit (streamlit/streamlit)
    19. SHAP (shap/shap)
    20. LIME (marcotcr/lime)

    AI recommended 20 alternatives but never named microsoft/LLaVA-Med. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a large vision-language model for healthcare applications with GPT-like performance.
    you: #4
    AI recommended (in order):
    1. Med-PaLM 2
    2. Med-PaLM M
    3. GPT-4V
    4. LLaVA-Med ← you
    5. Med-Flamingo
    6. BioMed-BLIP
    7. PathVLM
    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 microsoft/LLaVA-Med?
    pass
    AI did not name microsoft/LLaVA-Med — 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 microsoft/LLaVA-Med in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/LLaVA-Med 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 microsoft/LLaVA-Med solve, and who is the primary audience?
    pass
    AI named microsoft/LLaVA-Med 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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  • Brand-free category queries5 vs 2 in Lite
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