RRepoGEO

REPOGEO REPORT · LITE

open-mmlab/Multimodal-GPT

Default branch main · commit 9c73e47a · scanned 5/19/2026, 10:13:10 AM

GitHub: 1,516 stars · 131 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
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 open-mmlab/Multimodal-GPT, 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 emphasize its role as a training framework

    Why:

    CURRENT
    # 🤖 Multi-modal GPT

Train a multi-modal chatbot with visual and language instructions!
    COPY-PASTE FIX
    # 🤖 Multi-modal GPT: A Framework for Visual Instruction Tuning

This repository offers a comprehensive framework to train multi-modal chatbots, specifically designed for visual and language instruction tuning.
  • mediumtopics#2
    Add more specific topics related to training frameworks and visual instruction tuning

    Why:

    CURRENT
    flamingo, gpt, gpt-4, llama, multimodal, transformer, vision-and-language
    COPY-PASTE FIX
    flamingo, gpt, gpt-4, llama, multimodal, transformer, vision-and-language, visual-instruction-tuning, llm-fine-tuning, multimodal-llm, training-framework
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Insert relevant project homepage URL here, e.g., a project page or OpenMMLab's main site if applicable]

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 open-mmlab/Multimodal-GPT
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. OpenAI GPT-4V · recommended 1×
  3. Google Gemini · recommended 1×
  4. Llama-3-V · recommended 1×
  5. LLaVA · recommended 1×
  • CATEGORY QUERY
    How to train a multi-modal chatbot capable of understanding both images and text?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V
    2. Google Gemini
    3. Llama-3-V
    4. LLaVA
    5. Llama 2
    6. Vicuna
    7. BLIP-2
    8. FlanT5
    9. OPT
    10. InstructBLIP
    11. Hugging Face Transformers
    12. Diffusers
    13. ViT
    14. CLIP
    15. Mistral

    AI recommended 15 alternatives but never named open-mmlab/Multimodal-GPT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a framework to fine-tune large language models with visual instruction data.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. LoRA (Low-Rank Adaptation)
    5. peft
    6. MMDetection / MMEngine

    AI recommended 6 alternatives but never named open-mmlab/Multimodal-GPT. 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 open-mmlab/Multimodal-GPT?
    pass
    AI named open-mmlab/Multimodal-GPT explicitly

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

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

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

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open-mmlab/Multimodal-GPT — 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