RRepoGEO

REPOGEO REPORT · LITE

AI-Efficiency/Awesome-Model-Quantization

Default branch master · commit c24490bf · scanned 7/10/2026, 7:46:54 PM

GitHub: 2,403 stars · 240 forks

AI VISIBILITY SCORE
30 /100
Critical
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 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 AI-Efficiency/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.

CATEGORY VISIBILITY UNAVAILABLE

The category visibility step did not produce results for this scan (the LLM may have been unreachable). Re-run the diagnosis to get the full GEO picture.

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 AI-Efficiency/Awesome-Model-Quantization?
    skipped
    AI did not name AI-Efficiency/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 AI-Efficiency/Awesome-Model-Quantization in production, what risks or prerequisites should they evaluate first?
    skipped
    AI did not name AI-Efficiency/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?

  • In one sentence, what problem does the repo AI-Efficiency/Awesome-Model-Quantization solve, and who is the primary audience?
    skipped
    AI did not name AI-Efficiency/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?

Embed your GEO score

Drop this badge into the README of AI-Efficiency/Awesome-Model-Quantization. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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AI-Efficiency/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