RRepoGEO

REPOGEO REPORT · LITE

microsoft/VPTQ

Default branch main · commit 942c3151 · scanned 6/16/2026, 5:41:38 AM

GitHub: 680 stars · 52 forks

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 microsoft/VPTQ, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, quantization, post-training-quantization, low-bit-quantization, large-language-models, deep-learning, ai, machine-learning
  • mediumabout#2
    Refine the repository description

    Why:

    CURRENT
    VPTQ, A Flexible and Extreme low-bit quantization algorithm
    COPY-PASTE FIX
    VPTQ: A flexible and extreme low-bit post-training quantization algorithm for Large Language Models.
  • lowreadme#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with State-of-the-Art Quantization Methods

This section will compare VPTQ with other leading quantization techniques for LLMs, such as AutoGPTQ, SpQR, AWQ, and QLoRA, highlighting key differences in methodology, performance, and applicability.

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 microsoft/VPTQ
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
SpQR
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. SpQR · recommended 2×
  2. AutoGPTQ · recommended 1×
  3. Optimum · recommended 1×
  4. bitsandbytes · recommended 1×
  5. PEFT · recommended 1×
  • CATEGORY QUERY
    How to achieve extreme low-bit quantization for large language models to reduce memory footprint?
    you: not recommended
    AI recommended (in order):
    1. AutoGPTQ
    2. Optimum
    3. bitsandbytes
    4. PEFT
    5. SqueezeLLM
    6. Hugging Face Transformers
    7. SpQR

    AI recommended 7 alternatives but never named microsoft/VPTQ. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for post-training quantization to compress LLMs to under 2 bits?
    you: not recommended
    AI recommended (in order):
    1. GPTQ
    2. AWQ
    3. SpQR
    4. QLoRA
    5. SmoothQuant
    6. OWQ
    7. ZeroQuant
    8. Hugging Face Optimum (huggingface/optimum)
    9. AutoGPTQ (PanQiWei/AutoGPTQ)
    10. bitsandbytes (TimDettmers/bitsandbytes)

    AI recommended 10 alternatives but never named microsoft/VPTQ. 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 microsoft/VPTQ?
    pass
    AI named microsoft/VPTQ explicitly

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

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

    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 microsoft/VPTQ. 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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HTML
<a href="https://repogeo.com/en/r/microsoft/VPTQ"><img src="https://repogeo.com/badge/microsoft/VPTQ.svg" alt="RepoGEO" /></a>
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microsoft/VPTQ — 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