RRepoGEO

REPOGEO REPORT · LITE

thu-ml/SpargeAttn

Default branch main · commit ae5b629e · scanned 6/29/2026, 12:48:14 PM

GitHub: 1,005 stars · 95 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 thu-ml/SpargeAttn, 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 core value proposition to the top of the README

    Why:

    CURRENT
    The README starts with 'Recommended API' and the core description is further down.
    COPY-PASTE FIX
    Move the sentence 'The official implementation of SpargeAttn, a universal training-free sparse attention accelerating language, image, and video models.' to be the first paragraph immediately after the H1 title.
  • mediumreadme#2
    Add a 'Key Features' section to highlight differentiators

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Key Features' or 'Why SpargeAttention?' after the initial description, listing points like 'Training-free acceleration,' 'Universal applicability (LLM, Vision, Video),' and 'Plug-and-play API.'
  • mediumcomparison#3
    Add a brief comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a section like 'How SpargeAttention Compares' or 'Key Differentiators' that briefly explains how it stands out from other sparse attention methods (e.g., 'Unlike methods requiring specific training or architecture changes, SpargeAttention is training-free and universally applicable across various models and modalities.').

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 thu-ml/SpargeAttn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT · recommended 1×
  2. OpenVINO Toolkit · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. DeepSpeed-MII · recommended 1×
  5. torch.compile · recommended 1×
  • CATEGORY QUERY
    How to accelerate large language model and vision transformer inference without retraining?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. DeepSpeed-MII
    5. torch.compile
    6. FlashAttention

    AI recommended 6 alternatives but never named thu-ml/SpargeAttn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a plug-and-play sparse attention library to speed up deep learning models.
    you: not recommended
    AI recommended (in order):
    1. FlashAttention-2
    2. Longformer
    3. BigBird
    4. Sparse Transformers
    5. Reformer

    AI recommended 5 alternatives but never named thu-ml/SpargeAttn. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 thu-ml/SpargeAttn?
    pass
    AI named thu-ml/SpargeAttn explicitly

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

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

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

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thu-ml/SpargeAttn — 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