RRepoGEO

REPOGEO REPORT · LITE

huggingface/exporters

Default branch main · commit 7a545974 · scanned 6/7/2026, 6:02:44 AM

GitHub: 697 stars · 52 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 huggingface/exporters, 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
    Align README introduction with repo description on TFLite support

    Why:

    CURRENT
    This package lets you export 🤗 Transformers models to Core ML.

> For converting models to TFLite, we recommend using Optimum.
    COPY-PASTE FIX
    This package lets you export 🤗 Transformers models to Core ML and TensorFlow Lite, facilitating their deployment on various platforms, including mobile devices.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/huggingface/exporters
  • lowtopics#3
    Expand topics to include mobile deployment and on-device inference

    Why:

    CURRENT
    coreml, coremltools, deep-learning, machine-learning, model-converter, pytorch, tensorflow, tflite, transformer
    COPY-PASTE FIX
    coreml, coremltools, deep-learning, machine-learning, mobile-deployment, model-converter, on-device-inference, pytorch, tensorflow, tflite, transformer

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 huggingface/exporters
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Mobile
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Mobile · recommended 2×
  2. Core ML · recommended 2×
  3. ONNX Runtime · recommended 2×
  4. MediaPipe · recommended 2×
  5. TensorFlow Lite (TFLite) · recommended 1×
  • CATEGORY QUERY
    How to convert advanced neural network models for on-device mobile inference?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite (TFLite)
    2. PyTorch Mobile
    3. Core ML
    4. ONNX Runtime
    5. MediaPipe
    6. NCNN
    7. MNN

    AI recommended 7 alternatives but never named huggingface/exporters. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's the easiest way to convert deep learning models for efficient mobile deployment?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime
    4. ONNX
    5. Core ML
    6. coremltools
    7. ML Kit
    8. MediaPipe
    9. TFLite Micro

    AI recommended 9 alternatives but never named huggingface/exporters. 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 huggingface/exporters?
    pass
    AI named huggingface/exporters explicitly

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

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

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

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MARKDOWN (README)
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Pro

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huggingface/exporters — 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