RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/MobileLLM

Default branch main · commit 6cb80c40 · scanned 5/25/2026, 8:58:34 AM

GitHub: 1,439 stars · 88 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 facebookresearch/MobileLLM, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, mobile-llm, on-device-ai, edge-ai, sub-billion-llm, language-models, machine-learning, deep-learning, icml-2024, model-optimization
  • mediumhomepage#2
    Add a project homepage link

    Why:

    COPY-PASTE FIX
    https://huggingface.co/facebook/MobileLLM
  • mediumreadme#3
    Add a clear statement about the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to that file for full details on the applicable license(s).

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 facebookresearch/MobileLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Lite
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Lite · recommended 1×
  2. PyTorch Mobile · recommended 1×
  3. ONNX Runtime Mobile · recommended 1×
  4. Core ML · recommended 1×
  5. ML Kit · recommended 1×
  • CATEGORY QUERY
    How to deploy high-quality language models efficiently on resource-constrained mobile devices?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime Mobile
    4. Core ML
    5. ML Kit
    6. MediaPipe
    7. DeepSpeed

    AI recommended 7 alternatives but never named facebookresearch/MobileLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best techniques for creating performant, accurate sub-billion parameter LLMs for edge inference?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Quantization
    2. TensorFlow Lite (TFLite)
    3. ONNX Runtime
    4. Hugging Face Transformers Library
    5. DistilBERT
    6. TinyBERT
    7. MobileBERT
    8. Longformer
    9. Reformer
    10. Performer
    11. FlashAttention
    12. MobileViT
    13. EfficientFormer
    14. MobileNetV3
    15. PyTorch Pruning Utilities
    16. TensorFlow Model Optimization Toolkit
    17. NVIDIA's Automatic Mixed Precision (AMP)
    18. NVIDIA's Sparsity features
    19. Transformer-XL
    20. OpenVINO (Intel)
    21. TVM (Apache TVM)

    AI recommended 21 alternatives but never named facebookresearch/MobileLLM. 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 facebookresearch/MobileLLM?
    pass
    AI named facebookresearch/MobileLLM explicitly

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

  • If a team adopts facebookresearch/MobileLLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/MobileLLM 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 facebookresearch/MobileLLM solve, and who is the primary audience?
    pass
    AI named facebookresearch/MobileLLM 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 facebookresearch/MobileLLM. 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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facebookresearch/MobileLLM — 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