RRepoGEO

REPOGEO REPORT · LITE

UbiquitousLearning/mllm

Default branch main · commit 729ca4c9 · scanned 5/26/2026, 7:26:52 PM

GitHub: 1,524 stars · 201 forks

AI VISIBILITY SCORE
40 /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
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 UbiquitousLearning/mllm, 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 README opening to highlight MLLM specialization

    Why:

    CURRENT
    **Fast and lightweight multimodal LLM inference engine for mobile and edge devices**
    COPY-PASTE FIX
    **Fast and lightweight multimodal LLM inference engine for mobile and edge devices.** Unlike general mobile ML frameworks, MLLM is purpose-built for efficient, on-device inference of multimodal large language models, enabling advanced AI capabilities directly on edge devices.
  • mediumtopics#2
    Add more specific topics for edge/mobile MLLM deployment

    Why:

    CURRENT
    ai, llama, llm, mobile, multimodal
    COPY-PASTE FIX
    ai, llama, llm, mobile, multimodal, edge-ai, on-device-ai, npu, inference-engine, multimodal-ai
  • lowcomparison#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison

Unlike general mobile machine learning frameworks such as TensorFlow Lite, MediaPipe, or Core ML, MLLM is specifically designed as a dedicated inference engine for multimodal large language models (MLLMs). While those frameworks offer broad support for various ML models on mobile and edge devices, MLLM focuses on optimizing the unique computational demands of MLLMs, providing specialized support for their architecture and enabling advanced multimodal AI capabilities directly on device.

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 UbiquitousLearning/mllm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
google/mediapipe
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. google/mediapipe · recommended 1×
  2. apple/coremltools · recommended 1×
  3. googlesamples/mlkit · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. microsoft/onnxruntime · recommended 1×
  • CATEGORY QUERY
    What are the best options for running multimodal LLM inference on mobile devices?
    you: not recommended
    AI recommended (in order):
    1. MediaPipe (google/mediapipe)
    2. Core ML (apple/coremltools)
    3. ML Kit (googlesamples/mlkit)
    4. TensorFlow Lite (tensorflow/tensorflow)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. PyTorch Mobile (pytorch/pytorch)

    AI recommended 6 alternatives but never named UbiquitousLearning/mllm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I deploy fast multimodal AI models efficiently on edge devices with NPU support?
    you: not recommended
    AI recommended (in order):
    1. Qualcomm AI Engine Direct (QNN)
    2. TensorRT
    3. OpenVINO Toolkit
    4. Arm NN
    5. MediaTek NeuroPilot
    6. TensorFlow Lite

    AI recommended 6 alternatives but never named UbiquitousLearning/mllm. 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 UbiquitousLearning/mllm?
    pass
    AI named UbiquitousLearning/mllm explicitly

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

  • If a team adopts UbiquitousLearning/mllm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named UbiquitousLearning/mllm 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 UbiquitousLearning/mllm solve, and who is the primary audience?
    pass
    AI named UbiquitousLearning/mllm 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 UbiquitousLearning/mllm. 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
<a href="https://repogeo.com/en/r/UbiquitousLearning/mllm"><img src="https://repogeo.com/badge/UbiquitousLearning/mllm.svg" alt="RepoGEO" /></a>
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UbiquitousLearning/mllm — 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