RRepoGEO

REPOGEO REPORT · LITE

zhuzilin/ring-flash-attention

Default branch main · commit 78667793 · scanned 5/24/2026, 4:32:09 PM

GitHub: 1,020 stars · 98 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 zhuzilin/ring-flash-attention, 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
  • mediumreadme#1
    Reposition the README's opening sentence to highlight core value

    Why:

    CURRENT
    ## Ring Flash Attention

This repo implements RingAttention using FlashAttention.
    COPY-PASTE FIX
    ## Ring Flash Attention

This repository implements RingAttention using FlashAttention to solve the memory bottleneck of Flash Attention, enabling more efficient training of large language models with long contexts on distributed systems.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/zhuzilin/ring-flash-attention

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 zhuzilin/ring-flash-attention
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
deepmind/perceiver
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. deepmind/perceiver · recommended 2×
  2. Dao-AILab/flash-attention · recommended 1×
  3. vllm-project/vllm · recommended 1×
  4. facebookresearch/xformers · recommended 1×
  5. allenai/longformer · recommended 1×
  • CATEGORY QUERY
    How to implement efficient attention for large language models with long context windows?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention / FlashAttention-2 (Dao-AILab/flash-attention)
    2. PagedAttention (vllm-project/vllm)
    3. xFormers (facebookresearch/xformers)
    4. LongFormer (allenai/longformer)
    5. BigBird (google-research/bigbird)
    6. Perceiver IO (deepmind/perceiver)
    7. Perceiver AR (deepmind/perceiver)
    8. Ring Attention (Deci-AI/ring-attention)
    9. Hugging Face Transformers (huggingface/transformers)

    AI recommended 9 alternatives but never named zhuzilin/ring-flash-attention. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for optimized attention mechanisms to handle variable length sequences in deep learning models.
    you: not recommended
    AI recommended (in order):
    1. Transformer
    2. PyTorch
    3. TensorFlow
    4. Longformer
    5. Hugging Face Transformers library
    6. Reformer
    7. Performer
    8. Lattice AI's Performer
    9. Linformer
    10. BigBird

    AI recommended 10 alternatives but never named zhuzilin/ring-flash-attention. 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 zhuzilin/ring-flash-attention?
    pass
    AI named zhuzilin/ring-flash-attention explicitly

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

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

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

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zhuzilin/ring-flash-attention — 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