RRepoGEO

REPOGEO REPORT · LITE

LeanModels/DFloat11

Default branch master · commit 45773388 · scanned 6/4/2026, 9:23:27 PM

GitHub: 636 stars · 37 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 LeanModels/DFloat11, 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
  • highabout#1
    Clarify "About" description to counter miscategorization

    Why:

    CURRENT
    DFloat11 [NeurIPS '25]: Lossless Compression of LLMs and DiTs for Efficient GPU Inference
    COPY-PASTE FIX
    DFloat11 (NeurIPS '25): Lossless compression framework for Large Language Models (LLMs) and Diffusion Transformers (DiTs), enabling efficient GPU inference with bit-for-bit identical outputs. This project is focused on AI model compression, not formal verification or the Lean proof assistant.
  • mediumhomepage#2
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://huggingface.co/DFloat11
  • lowreadme#3
    Add a clarifying sentence to the README's introduction

    Why:

    COPY-PASTE FIX
    This project is dedicated to AI model compression and is distinct from formal verification or floating-point arithmetic within proof assistants.

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 LeanModels/DFloat11
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AutoGPTQ
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AutoGPTQ · recommended 1×
  2. optimum · recommended 1×
  3. AutoAWQ · recommended 1×
  4. bitsandbytes · recommended 1×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    How to losslessly compress large language models for more efficient GPU inference?
    you: not recommended
    AI recommended (in order):
    1. AutoGPTQ
    2. optimum
    3. AutoAWQ
    4. bitsandbytes
    5. ONNX Runtime
    6. DeepSpeed-MII
    7. TensorRT

    AI recommended 7 alternatives but never named LeanModels/DFloat11. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to reduce GPU memory footprint for large AI model inference without accuracy loss.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. PyTorch
    4. TensorFlow
    5. NVIDIA Apex (NVIDIA/apex)
    6. TensorFlow Model Optimization Toolkit
    7. Hugging Face Transformers (huggingface/transformers)
    8. ONNX Runtime (microsoft/onnxruntime)
    9. DeepSpeed (microsoft/DeepSpeed)
    10. FairScale (facebookresearch/fairscale)

    AI recommended 10 alternatives but never named LeanModels/DFloat11. 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 LeanModels/DFloat11?
    pass
    AI named LeanModels/DFloat11 explicitly

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

  • If a team adopts LeanModels/DFloat11 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LeanModels/DFloat11 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 LeanModels/DFloat11 solve, and who is the primary audience?
    pass
    AI named LeanModels/DFloat11 explicitly

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

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LeanModels/DFloat11 — 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