RRepoGEO

REPOGEO REPORT · LITE

qwopqwop200/GPTQ-for-LLaMa

Default branch triton · commit e985b700 · scanned 5/22/2026, 12:42:35 AM

GitHub: 3,072 stars · 452 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 qwopqwop200/GPTQ-for-LLaMa, 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 the README's opening statement to clarify this project's role

    Why:

    CURRENT
    I am currently focusing on AutoGPTQ and recommend using AutoGPTQ instead of GPTQ for Llama.
    COPY-PASTE FIX
    This repository provides the original GPTQ implementation for LLaMa, serving as a foundational reference for 4-bit quantization. For active development and broader model support, AutoGPTQ is the recommended successor.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    quantization, llama, gptq, llm, deep-learning, machine-learning, ai, gpu-memory-optimization
  • mediumabout#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/AutoGPTQ/AutoGPTQ

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 qwopqwop200/GPTQ-for-LLaMa
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
bitsandbytes
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. bitsandbytes · recommended 2×
  2. AutoGPTQ · recommended 1×
  3. AWQ · recommended 1×
  4. PyTorch · recommended 1×
  5. Hugging Face Optimum · recommended 1×
  • CATEGORY QUERY
    What are effective techniques for quantizing large language models to save GPU memory?
    you: not recommended
    AI recommended (in order):
    1. AutoGPTQ
    2. AWQ
    3. PyTorch
    4. Hugging Face Optimum
    5. bitsandbytes
    6. SpQR
    7. llama.cpp

    AI recommended 7 alternatives but never named qwopqwop200/GPTQ-for-LLaMa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a one-shot 4-bit weight quantization solution for large models on Linux.
    you: not recommended
    AI recommended (in order):
    1. GPTQ (Generalized Post-training Quantization)
    2. Hugging Face `optimum` library
    3. `AutoGPTQ` library
    4. AWQ (Activation-aware Weight Quantization)
    5. `AutoAWQ` library
    6. bitsandbytes
    7. QLoRA (Quantized LoRA)
    8. NVIDIA TensorRT
    9. ONNX Runtime

    AI recommended 9 alternatives but never named qwopqwop200/GPTQ-for-LLaMa. 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 qwopqwop200/GPTQ-for-LLaMa?
    pass
    AI did not name qwopqwop200/GPTQ-for-LLaMa — 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 qwopqwop200/GPTQ-for-LLaMa in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qwopqwop200/GPTQ-for-LLaMa 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 qwopqwop200/GPTQ-for-LLaMa solve, and who is the primary audience?
    pass
    AI did not name qwopqwop200/GPTQ-for-LLaMa — 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

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qwopqwop200/GPTQ-for-LLaMa — 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
qwopqwop200/GPTQ-for-LLaMa — RepoGEO report