RRepoGEO

REPOGEO REPORT · LITE

mbzuai-oryx/MobiLlama

Default branch main · commit bd69ac5c · scanned 6/12/2026, 5:37:51 PM

GitHub: 667 stars · 51 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 mbzuai-oryx/MobiLlama, 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 H1 to emphasize edge/mobile device suitability

    Why:

    CURRENT
    # 📱🦙 MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT (🔥 ICLR'25 SLLM Workshop - SPOTLIGHT)
    COPY-PASTE FIX
    # 📱🦙 MobiLlama: Accurate & Lightweight Small Language Model (SLLM) for Edge Devices (🔥 ICLR'25 SLLM Workshop - SPOTLIGHT)
  • mediumtopics#2
    Add more specific topics related to on-device and edge AI

    Why:

    CURRENT
    efficient-llm, llm, mobile-llm, slm, tiny-llm
    COPY-PASTE FIX
    efficient-llm, llm, mobile-llm, slm, tiny-llm, on-device-ai, edge-ai, mobile-ai, resource-constrained-llm, sllm
  • mediumreadme#3
    Add a concise "Key Features" section to highlight core benefits

    Why:

    COPY-PASTE FIX
    ## ✨ Key Features

*   **On-Device Processing:** Designed for direct execution on mobile and edge hardware.
*   **Energy Efficiency:** Optimized for minimal power consumption.
*   **Low Memory Footprint:** Requires significantly less memory than larger LLMs.
*   **Response Efficiency:** Delivers fast inference for real-time applications.
*   **High Accuracy:** Achieves performance comparable to larger models with fewer parameters.

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 mbzuai-oryx/MobiLlama
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Gemma 2B
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Gemma 2B · recommended 1×
  2. Llama 3 8B · recommended 1×
  3. Phi-3-mini · recommended 1×
  4. Mistral 7B · recommended 1×
  5. TinyLlama 1.1B · recommended 1×
  • CATEGORY QUERY
    What are good small language models for running directly on mobile devices?
    you: not recommended
    AI recommended (in order):
    1. Gemma 2B
    2. Llama 3 8B
    3. Phi-3-mini
    4. Mistral 7B
    5. TinyLlama 1.1B
    6. MobileLLM

    AI recommended 6 alternatives but never named mbzuai-oryx/MobiLlama. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for efficient and lightweight LLMs suitable for deployment on edge hardware.
    you: not recommended
    AI recommended (in order):
    1. Llama.cpp (ggerganov/llama.cpp)
    2. TinyLlama (PKU-YuanGroup/TinyLlama)
    3. Phi-2 (microsoft/phi-2)
    4. MobileLLM (OpenGVLab/MobileLLM)
    5. NanoGPT (karpathy/nanoGPT)
    6. OpenVINO (openvinotoolkit/openvino)
    7. MLC LLM (mlc-ai/mlc-llm)

    AI recommended 7 alternatives but never named mbzuai-oryx/MobiLlama. 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 mbzuai-oryx/MobiLlama?
    pass
    AI named mbzuai-oryx/MobiLlama explicitly

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

  • If a team adopts mbzuai-oryx/MobiLlama in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mbzuai-oryx/MobiLlama 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 mbzuai-oryx/MobiLlama solve, and who is the primary audience?
    pass
    AI named mbzuai-oryx/MobiLlama 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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MARKDOWN (README)
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mbzuai-oryx/MobiLlama — 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