RRepoGEO

REPOGEO REPORT · LITE

lucidrains/x-transformers

Default branch main · commit 9f5f70e2 · scanned 6/29/2026, 8:31:54 AM

GitHub: 5,918 stars · 514 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
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 lucidrains/x-transformers, 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 README's opening statement to emphasize experimental features

    Why:

    CURRENT
    A concise but fully-featured transformer, complete with a set of promising experimental features from various papers.
    COPY-PASTE FIX
    A concise, fully-featured PyTorch library for rapidly implementing and experimenting with a wide array of novel transformer architectures, attention mechanisms, and components from recent research papers.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    artificial-intelligence, attention-mechanism, deep-learning, transformers
    COPY-PASTE FIX
    artificial-intelligence, attention-mechanism, deep-learning, transformers, pytorch, transformer-architectures, experimental-ai, machine-learning-research
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/x-transformers/

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 lucidrains/x-transformers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch · recommended 1×
  3. TensorFlow · recommended 1×
  4. JAX · recommended 1×
  5. Flax · recommended 1×
  • CATEGORY QUERY
    How can I implement a full-attention transformer model with experimental features in Python?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. JAX
    4. Flax
    5. Haiku
    6. Hugging Face Transformers
    7. MindSpore

    AI recommended 7 alternatives but never named lucidrains/x-transformers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good Python library for building GPT-like or encoder-decoder transformer architectures easily?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. Keras
    4. DeepSpeed
    5. JAX/Flax

    AI recommended 5 alternatives but never named lucidrains/x-transformers. 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 lucidrains/x-transformers?
    pass
    AI named lucidrains/x-transformers explicitly

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

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

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

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