RRepoGEO

REPOGEO REPORT · LITE

huggingface/optimum-quanto

Default branch main · commit ef3aafb3 · scanned 5/22/2026, 4:31:44 PM

GitHub: 1,041 stars · 86 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
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/optimum-quanto, 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 introduction to clarify its niche as a PyTorch backend

    Why:

    CURRENT
    🤗 Optimum Quanto is a pytorch quantization backend for optimum.
    COPY-PASTE FIX
    🤗 Optimum Quanto is a specialized PyTorch quantization backend for Hugging Face Optimum, offering a programmable, low-bit solution directly within PyTorch. Unlike broader quantization toolkits or hardware-specific frameworks, Quanto focuses on seamless integration with PyTorch models to achieve significant performance and memory footprint reduction.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://huggingface.co/docs/optimum/quanto/index
  • mediumtopics#3
    Expand repository topics for better categorization

    Why:

    CURRENT
    optimum, pytorch, quantization
    COPY-PASTE FIX
    optimum, pytorch, quantization, llm, deep-learning, low-bit-quantization, model-optimization

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/optimum-quanto
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. TensorFlow Lite · recommended 1×
  3. PyTorch Mobile / PyTorch Quantization Toolkit · recommended 1×
  4. OpenVINO Toolkit · recommended 1×
  5. ONNX Runtime with ONNX Quantizer · recommended 1×
  • CATEGORY QUERY
    How can I quantize my deep learning models to improve inference speed and memory?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile / PyTorch Quantization Toolkit
    3. OpenVINO Toolkit
    4. ONNX Runtime with ONNX Quantizer
    5. NVIDIA TensorRT
    6. Apache TVM
    7. Core ML Tools

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

    Show full AI answer
  • CATEGORY QUERY
    What are robust solutions for quantizing neural network weights to int8 or int4?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO (openvinotoolkit/openvino)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. PyTorch Quantization (pytorch/pytorch)
    5. TensorFlow Lite (tensorflow/tensorflow)
    6. QNN
    7. Apache TVM (apache/tvm)

    AI recommended 7 alternatives but never named huggingface/optimum-quanto. 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/optimum-quanto?
    pass
    AI named huggingface/optimum-quanto 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/optimum-quanto in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/optimum-quanto 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/optimum-quanto solve, and who is the primary audience?
    pass
    AI named huggingface/optimum-quanto explicitly

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

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huggingface/optimum-quanto — 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