REPOGEO REPORT · LITE

fla-org/flash-linear-attention

Default branch main · commit 2decb7ad · scanned 5/12/2026, 7:13:03 PM

GitHub: 5,083 stars · 521 forks

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

highreadme#1

Strengthen README's opening paragraph to highlight core focus

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. Pull requests are welcome!

COPY-PASTE FIX

💥 Flash Linear Attention (FLA) provides hardware-optimized, production-ready implementations for cutting-edge sequence models, with a primary focus on **linear attention** and **state space models (SSMs)**. FLA offers efficient building blocks and layers for modern LLM architectures, verified across NVIDIA, AMD, and Intel hardware.

highabout#2

Update repository description for clarity and specificity

Why:

CURRENT

🚀 Efficient implementations for emerging model architectures

COPY-PASTE FIX

Hardware-optimized implementations for linear attention, state space models, and hybrid LLM architectures.

mediumtopics#3

Add specific topics for linear 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

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

Of all named tools, what % are you?

Top rival

FlashAttention-2

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

FlashAttention-2 · recommended 1×
Mamba · recommended 1×
DeepSpeed · recommended 1×
PyTorch · recommended 1×
JAX · recommended 1×

CATEGORY QUERY
Seeking efficient hardware-accelerated implementations for linear attention and state space models.
you: not recommended
AI recommended (in order):
1. FlashAttention-2
2. Mamba
3. DeepSpeed
4. PyTorch
5. JAX
6. TensorRT
AI recommended 6 alternatives but never named fla-org/flash-linear-attention. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are optimized building blocks for modern sequence models, including hybrid LLM architectures?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library (huggingface/transformers)
2. PyTorch (pytorch/pytorch)
3. FlashAttention (Dao-AILab/flash-attention)
4. xFormers (facebookresearch/xformers)
5. DeepSpeed (microsoft/DeepSpeed)
6. Hugging Face Accelerate (huggingface/accelerate)
7. ONNX Runtime (microsoft/onnxruntime)
8. TensorRT (NVIDIA/TensorRT)
9. LoRA (Low-Rank Adaptation)
10. QLoRA
11. Hugging Face PEFT library (huggingface/peft)
AI recommended 11 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?

Embed your GEO score

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MARKDOWN (README)

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HTML

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite