RRepoGEO

REPOGEO REPORT · LITE

Efficient-ML/Awesome-Model-Quantization

Default branch master · commit 69e7fd1d · scanned 6/21/2026, 12:38:07 PM

GitHub: 2,394 stars · 240 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
22 /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
1 / 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 Efficient-ML/Awesome-Model-Quantization, 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 opening to clarify its 'awesome list' nature

    Why:

    CURRENT
    This repo collects papers, documents, and codes about model quantization for anyone who wants to research it.
    COPY-PASTE FIX
    This is an **awesome list** and curated collection of papers, documents, and code about model quantization, designed for researchers and practitioners seeking to understand and implement efficient deep learning techniques.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, for example, using the MIT License text, to clearly state the terms of use for the project.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL in the GitHub 'About' section to `https://github.com/Efficient-ML/Awesome-Model-Quantization`.

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 Efficient-ML/Awesome-Model-Quantization
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Lite · recommended 2×
  2. PyTorch Mobile · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. TensorFlow Model Optimization Toolkit · recommended 1×
  5. NVIDIA's Apex · recommended 1×
  • CATEGORY QUERY
    How to make deep learning models more efficient for deployment on resource-constrained devices?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime
    4. TensorFlow Model Optimization Toolkit
    5. NVIDIA's Apex
    6. Hugging Face Transformers
    7. DistilBERT
    8. TinyBERT
    9. MobileNetV2
    10. MobileNetV3
    11. EfficientNet
    12. SqueezeNet
    13. OpenVINO Toolkit
    14. Core ML

    AI recommended 14 alternatives but never named Efficient-ML/Awesome-Model-Quantization. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best techniques for compressing neural networks to accelerate inference speed?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch (pytorch/pytorch)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    5. NVIDIA's cuSPARSE
    6. DeepSparse (neuralmagic/deepsparse)
    7. Distiller (by Intel) (IntelAI/distiller)
    8. Hugging Face Transformers (huggingface/transformers)
    9. TensorFlow (tensorflow/tensorflow)
    10. OpenVINO Training Extensions (openvinotoolkit/training_extensions)
    11. AutoML (Google Cloud)
    12. MMDetection (open-mmlab/mmdetection)
    13. NumPy (numpy/numpy)
    14. NVIDIA TensorRT
    15. Intel OpenVINO Toolkit (openvinotoolkit/openvino)

    AI recommended 15 alternatives but never named Efficient-ML/Awesome-Model-Quantization. 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 Efficient-ML/Awesome-Model-Quantization?
    pass
    AI did not name Efficient-ML/Awesome-Model-Quantization — likely talking about a different project

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

  • If a team adopts Efficient-ML/Awesome-Model-Quantization in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Efficient-ML/Awesome-Model-Quantization 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 Efficient-ML/Awesome-Model-Quantization solve, and who is the primary audience?
    pass
    AI did not name Efficient-ML/Awesome-Model-Quantization — likely talking about a different project

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

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Efficient-ML/Awesome-Model-Quantization — 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