RRepoGEO

REPOGEO REPORT · LITE

microsoft/LLaVA-Med

Default branch main · commit 30697ca5 · scanned 5/9/2026, 5:57:29 PM

GitHub: 2,188 stars · 285 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 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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    multimodal-ai, vision-language-model, biomedicine, medical-imaging, llm, vlm, healthcare-ai, gpt-4-level
  • mediumreadme#2
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    The LLaVA-Med project is released under the terms specified in the [LICENSE](LICENSE) file. Please review the file for full details on usage and distribution.
  • mediumreadme#3
    Add a 'Why LLaVA-Med?' or 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ## Why LLaVA-Med?
LLaVA-Med stands out with its specialized focus on the medical domain, providing a large language-and-vision assistant specifically fine-tuned for understanding and generating responses related to medical images and text. Unlike general-purpose Vision-Language Models or broader ML frameworks, LLaVA-Med is optimized for high-stakes healthcare applications.

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 microsoft/LLaVA-Med
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MONAI
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. MONAI · recommended 1×
  2. Hugging Face Transformers · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. Keras · recommended 1×
  5. FastAI · recommended 1×
  • CATEGORY QUERY
    How to build multimodal AI models for medical image analysis and text understanding?
    you: not recommended
    AI recommended (in order):
    1. MONAI
    2. Hugging Face Transformers
    3. PyTorch Lightning
    4. Keras
    5. FastAI
    6. OpenCV
    7. Scikit-learn

    AI recommended 7 alternatives but never named microsoft/LLaVA-Med. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a tool to fine-tune large vision-language models for healthcare applications.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. MONAI (Project-MONAI/MONAI)
    4. OpenAI API
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)
    7. Google Cloud Vertex AI

    AI recommended 7 alternatives but never named microsoft/LLaVA-Med. 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 microsoft/LLaVA-Med?
    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?

  • 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

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

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MARKDOWN (README)
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HTML
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microsoft/LLaVA-Med — 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