RRepoGEO

REPOGEO REPORT · LITE

fla-org/flash-linear-attention

Default branch main · commit bd67d117 · scanned 6/23/2026, 3:57:00 AM

GitHub: 5,247 stars · 562 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
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 fla-org/flash-linear-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

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Update the 'About' description to be more specific

    Why:

    CURRENT
    🚀 Efficient implementations for emerging model architectures
    COPY-PASTE FIX
    🚀 Hardware-optimized library for linear attention, sparse attention, and state space models in modern LLM architectures.
  • mediumtopics#2
    Add more specific topics for attention and state space models

    Why:

    CURRENT
    large-language-models, machine-learning-systems, natural-language-processing, sequence-modeling
    COPY-PASTE FIX
    large-language-models, machine-learning-systems, natural-language-processing, sequence-modeling, linear-attention, state-space-models, hardware-acceleration
  • lowreadme#3
    Explicitly state 'library' in the README's opening sentence

    Why:

    CURRENT
    💥 Flash Linear Attention brings together hardware-efficient building blocks, training-ready layers, and components for modern sequence models, spanning linear attention, sparse attention, state space models, and hybrid LLM architectures. All implementations are platform-agnostic and verified on NVIDIA, AMD, and Intel hardware.
    COPY-PASTE FIX
    💥 Flash Linear Attention is a hardware-optimized library providing efficient building blocks, training-ready layers, and components for modern sequence models, including linear attention, sparse attention, state space models, and hybrid LLM architectures. All implementations are platform-agnostic and verified on NVIDIA, AMD, and Intel hardware.

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 fla-org/flash-linear-attention
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA/FasterTransformer
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA/FasterTransformer · recommended 1×
  2. huggingface/transformers · recommended 1×
  3. huggingface/optimum · recommended 1×
  4. huggingface/accelerate · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How to build efficient large language models with hardware-optimized attention mechanisms?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA FasterTransformer (NVIDIA/FasterTransformer)
    2. Hugging Face Transformers (huggingface/transformers)
    3. Hugging Face Optimum (huggingface/optimum)
    4. Hugging Face Accelerate (huggingface/accelerate)
    5. DeepSpeed (microsoft/DeepSpeed)
    6. PyTorch (pytorch/pytorch)
    7. `torch.compile`
    8. `torch.nn.functional.scaled_dot_product_attention` (SDPA)
    9. TensorFlow (tensorflow/tensorflow)
    10. XLA
    11. TensorFlow-TensorRT
    12. FlashAttention (Dao-AILab/flash-attention)
    13. ONNX Runtime (microsoft/onnxruntime)

    AI recommended 13 alternatives but never named fla-org/flash-linear-attention. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library for accelerated linear attention and state space models across different hardware platforms.
    you: not recommended
    AI recommended (in order):
    1. Mamba
    2. FlashAttention
    3. HazyResearch/mamba (HazyResearch/mamba)
    4. xRNN
    5. PyTorch/TensorFlow
    6. JAX
    7. Optimum

    AI recommended 7 alternatives but never named fla-org/flash-linear-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
    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 fla-org/flash-linear-attention?
    pass
    AI named fla-org/flash-linear-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 fla-org/flash-linear-attention in production, what risks or prerequisites should they evaluate first?
    pass
    AI named fla-org/flash-linear-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 fla-org/flash-linear-attention solve, and who is the primary audience?
    pass
    AI named fla-org/flash-linear-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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  • Brand-free category queries5 vs 2 in Lite
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