RRepoGEO

REPOGEO REPORT · LITE

OpenPPL/ppq

Default branch master · commit e39eecb9 · scanned 6/24/2026, 8:06:52 PM

GitHub: 1,803 stars · 282 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 OpenPPL/ppq, 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 the README's opening sentence to clarify PPQ's role as an offline tool

    Why:

    CURRENT
    PPQ 是一个可扩展的、高性能的、面向工业应用的神经网络量化工具。
    COPY-PASTE FIX
    Add this sentence immediately after the main title: "PPL Quantization Tool (PPQ) is a powerful, extensible, and high-performance *offline neural network quantization tool* designed to prepare models for efficient deployment across various inference frameworks like TensorRT, OpenVINO, and ONNX Runtime."
  • hightopics#2
    Expand repository topics with more specific optimization and edge AI terms

    Why:

    CURRENT
    caffe, cuda, deep-learning, neural-network, onnx, open-source, pytorch, quantization
    COPY-PASTE FIX
    caffe, cuda, deep-learning, neural-network, onnx, open-source, pytorch, quantization, model-optimization, edge-ai, inference-acceleration, deep-learning-optimization
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the official project homepage URL, for example: `https://openppl.github.io/ppq/`

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 OpenPPL/ppq
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. OpenVINO Toolkit · recommended 2×
  3. NVIDIA TensorRT · recommended 2×
  4. TensorFlow Lite · recommended 1×
  5. PyTorch Mobile · recommended 1×
  • CATEGORY QUERY
    How can I effectively quantize deep learning models to accelerate inference on edge devices?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. ONNX Runtime
    3. PyTorch Mobile
    4. OpenVINO Toolkit
    5. NVIDIA TensorRT
    6. Apache TVM
    7. Edge TPU Compiler

    AI recommended 7 alternatives but never named OpenPPL/ppq. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an offline neural network quantization tool compatible with PyTorch and ONNX models.
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. ONNX Runtime
    3. NVIDIA TensorRT
    4. PyTorch Quantization
    5. TFLite Converter

    AI recommended 5 alternatives but never named OpenPPL/ppq. 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 OpenPPL/ppq?
    pass
    AI named OpenPPL/ppq explicitly

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

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

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

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OpenPPL/ppq — 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