RRepoGEO

REPOGEO REPORT · LITE

IST-DASLab/gptq

Default branch main · commit 2d65066e · scanned 5/16/2026, 1:58:23 PM

GitHub: 2,305 stars · 196 forks

AI VISIBILITY SCORE
65 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
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 IST-DASLab/gptq, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify the problem solved by GPTQ in the README's opening sentence

    Why:

    CURRENT
    This repository contains the code for the ICLR 2023 paper GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers.
    COPY-PASTE FIX
    GPTQ provides accurate post-training quantization for generative pretrained transformers, significantly reducing their memory footprint and accelerating inference. This repository contains the code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers".
  • mediumcomparison#2
    Add a comparison section highlighting GPTQ's differentiators

    Why:

    COPY-PASTE FIX
    Add a new section, perhaps titled "Why GPTQ?" or "Comparison to Alternatives", that explicitly states GPTQ's unique advantages, such as achieving 4-bit PTQ for LLMs with minimal accuracy degradation, compared to other methods.

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
1 / 2
50% of queries surface IST-DASLab/gptq
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
5%
Of all named tools, what % are you?
Top rival
Hugging Face `transformers` library
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face `transformers` library · recommended 2×
  2. ONNX Runtime · recommended 1×
  3. NVIDIA TensorRT · recommended 1×
  4. PyTorch 2.x `torch.quantization` · recommended 1×
  5. PyTorch `torch.nn.utils.prune` · recommended 1×
  • CATEGORY QUERY
    How to reduce the memory footprint and inference latency of large transformer models?
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime
    2. NVIDIA TensorRT
    3. PyTorch 2.x `torch.quantization`
    4. Hugging Face `transformers` library
    5. PyTorch `torch.nn.utils.prune`
    6. NVIDIA Apex `fused_dense_sparse_attention`
    7. Hugging Face `transformers` library
    8. Hugging Face Optimum
    9. Intel Neural Compressor
    10. OpenVINO
    11. NVIDIA Triton Inference Server
    12. Google Cloud TPUs
    13. AWS Inferentia

    AI recommended 13 alternatives but never named IST-DASLab/gptq. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective post-training quantization methods for generative AI models to improve efficiency?
    you: #2
    AI recommended (in order):
    1. AWQ
    2. GPTQ ← you
    3. SmoothQuant
    4. OFT
    5. QAS
    6. PyTorch's `torch.quantization`
    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 IST-DASLab/gptq?
    pass
    AI named IST-DASLab/gptq explicitly

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

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

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

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IST-DASLab/gptq — 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