RRepoGEO

REPOGEO REPORT · LITE

PrunaAI/pruna

Default branch main · commit e54baae1 · scanned 6/19/2026, 12:26:28 PM

GitHub: 1,221 stars · 91 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
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 PrunaAI/pruna, 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
    Add a clear, direct opening sentence to the README

    Why:

    COPY-PASTE FIX
    Pruna is an open-source deep learning model optimization framework designed for developers, enabling you to build and deploy faster, smaller, and more efficient AI models.
  • mediumreadme#2
    Add a 'Why Pruna?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## Why Pruna?
While tools like NVIDIA TensorRT and OpenVINO offer specialized optimization, Pruna provides a unified, developer-centric framework for comprehensive deep learning model optimization across various architectures (LLMs, Diffusion, Vision Transformers, Speech Recognition). It simplifies complex techniques like quantization, pruning, distillation, and compilation into a few lines of code, making advanced model efficiency accessible to all developers.
  • lowabout#3
    Refine the repository description for stronger keyword alignment

    Why:

    CURRENT
    Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.
    COPY-PASTE FIX
    Pruna is an open-source deep learning model optimization framework for developers, providing a comprehensive suite of techniques (quantization, pruning, distillation, compilation) to deliver faster, smaller, and more efficient AI models with minimal overhead.

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 PrunaAI/pruna
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT · recommended 2×
  2. OpenVINO Toolkit · recommended 2×
  3. ONNX Runtime · recommended 2×
  4. TensorFlow Lite · recommended 2×
  5. PyTorch · recommended 2×
  • CATEGORY QUERY
    How to make AI models run faster and more efficiently for deployment?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. Apache TVM
    5. TensorFlow Lite
    6. PyTorch
    7. TensorFlow Model Optimization Toolkit
    8. DeepSpeed
    9. FairScale

    AI recommended 9 alternatives but never named PrunaAI/pruna. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective frameworks for reducing deep learning model size and inference cost?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. TensorFlow Lite
    4. TensorFlow Model Optimization Toolkit
    5. TensorFlow Extended (TFX)
    6. ONNX Runtime
    7. ONNX Optimizer
    8. ONNX Quantization
    9. NVIDIA TensorRT
    10. OpenVINO Toolkit
    11. Model Optimizer
    12. Inference Engine
    13. DeepSpeed
    14. ZeRO (Zero Redundancy Optimizer)
    15. Hugging Face Optimum

    AI recommended 15 alternatives but never named PrunaAI/pruna. 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 PrunaAI/pruna?
    pass
    AI named PrunaAI/pruna explicitly

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

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

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

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PrunaAI/pruna — 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