RRepoGEO

REPOGEO REPORT · LITE

linkedin/Liger-Kernel

Default branch main · commit bbbafc91 · scanned 5/27/2026, 12:17:22 PM

GitHub: 6,387 stars · 533 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 linkedin/Liger-Kernel, 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
    Add a concise problem statement to the README's opening

    Why:

    CURRENT
    The current README immediately follows the H1 with a table of badges.
    COPY-PASTE FIX
    Add a sentence like: 'Liger Kernel provides highly optimized Triton kernels specifically designed to accelerate large language model training and finetuning workflows, offering significant performance improvements over standard implementations.' right after the H1 and before any tables or table of contents.
  • mediumabout#2
    Expand the repository description to highlight the problem solved

    Why:

    CURRENT
    Efficient Triton Kernels for LLM Training
    COPY-PASTE FIX
    Accelerate large language model (LLM) training and finetuning with Liger Kernel's highly optimized Triton kernels, designed to significantly boost performance and efficiency for demanding AI workloads.
  • mediumcomparison#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with Alternatives' or 'Why Liger Kernel?' that explicitly outlines how Liger Kernel differs from and improves upon common alternatives like raw Triton, FlashAttention, or other optimized kernel libraries for LLM training.

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 linkedin/Liger-Kernel
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
openai/triton
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. openai/triton · recommended 2×
  2. CUDA C++ · recommended 1×
  3. cuBLAS · recommended 1×
  4. cuDNN · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How to optimize LLM training performance using custom GPU kernels?
    you: not recommended
    AI recommended (in order):
    1. CUDA C++
    2. cuBLAS
    3. cuDNN
    4. Triton (openai/triton)
    5. OpenAI's Triton Examples/Kernels (openai/triton)
    6. PyTorch C++/CUDA Extensions (pytorch/pytorch)
    7. TensorFlow Custom Ops (C++/CUDA) (tensorflow/tensorflow)
    8. NVIDIA CUTLASS (NVIDIA/cutlass)

    AI recommended 8 alternatives but never named linkedin/Liger-Kernel. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best Triton-based libraries for accelerating large language model finetuning?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Triton
    2. PyTorch
    3. FlashAttention
    4. DeepSpeed
    5. bitsandbytes

    AI recommended 5 alternatives but never named linkedin/Liger-Kernel. 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 linkedin/Liger-Kernel?
    pass
    AI named linkedin/Liger-Kernel explicitly

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

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

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

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linkedin/Liger-Kernel — 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