RRepoGEO

REPOGEO REPORT · LITE

Vahe1994/SpQR

Default branch main · commit 543d10e5 · scanned 6/12/2026, 7:23:14 PM

GitHub: 552 stars · 45 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Vahe1994/SpQR, 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
    Reposition the README H1 and opening sentence for clarity

    Why:

    CURRENT
    # SpQR model compression

It accompanies the research paper "SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression" .
    COPY-PASTE FIX
    # SpQR: Near-Lossless LLM Weight Compression

This repository provides the implementation for SpQR, a novel method for near-lossless weight compression of Large Language Models (LLMs) using sparse-quantized representations. It accompanies the research paper "SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression".
  • highabout#2
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Near-lossless weight compression for Large Language Models (LLMs) using sparse-quantized representations.

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 Vahe1994/SpQR
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 4×
  2. huggingface/optimum · recommended 2×
  3. TimDettmers/bitsandbytes · recommended 2×
  4. huggingface/transformers · recommended 2×
  5. openvinotoolkit/openvino · recommended 2×
  • CATEGORY QUERY
    How can I reduce the memory footprint of large language models for deployment?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum (huggingface/optimum)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. Intel Neural Compressor (intel/neural-compressor)
    4. bitsandbytes (TimDettmers/bitsandbytes)
    5. NVIDIA NNCF (NVIDIA/NNCF)
    6. PyTorch (pytorch/pytorch)
    7. Hugging Face Transformers (huggingface/transformers)
    8. DeepSpeed (microsoft/DeepSpeed)
    9. vLLM (vllm-project/vllm)
    10. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    11. OpenVINO (openvinotoolkit/openvino)

    AI recommended 11 alternatives but never named Vahe1994/SpQR. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods for near-lossless weight compression in large language models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Quantization (pytorch/pytorch)
    2. TensorFlow Model Optimization Toolkit (TFMOT) (tensorflow/model-optimization)
    3. NVIDIA TensorRT
    4. OpenVINO (openvinotoolkit/openvino)
    5. AWQ (Activation-aware Weight Quantization)
    6. AutoGPTQ (PanQiWei/AutoGPTQ)
    7. Optimum (Hugging Face) (huggingface/optimum)
    8. GPTQ (Generative Pre-trained Transformer Quantization)
    9. bitsandbytes (TimDettmers/bitsandbytes)
    10. SmoothQuant
    11. NVIDIA's N:M Sparsity
    12. NVIDIA Apex (NVIDIA/apex)
    13. PyTorch Pruning (pytorch/pytorch)
    14. PEFT (Parameter-Efficient Fine-tuning) library by Hugging Face (huggingface/peft)
    15. Hugging Face Transformers (huggingface/transformers)
    16. PyTorch (pytorch/pytorch)
    17. TensorFlow (tensorflow/tensorflow)

    AI recommended 17 alternatives but never named Vahe1994/SpQR. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 Vahe1994/SpQR?
    pass
    AI named Vahe1994/SpQR explicitly

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

  • If a team adopts Vahe1994/SpQR in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Vahe1994/SpQR 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 Vahe1994/SpQR solve, and who is the primary audience?
    pass
    AI named Vahe1994/SpQR explicitly

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

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Vahe1994/SpQR — 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