RRepoGEO

REPOGEO REPORT · LITE

google/paxml

Default branch main · commit 8d63e9a2 · scanned 6/15/2026, 9:56:53 AM

GitHub: 555 stars · 72 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 google/paxml, 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 README's opening to highlight unique value

    Why:

    CURRENT
    # Paxml (aka Pax)

Pax is a framework to configure and run machine learning experiments on top of Jax.
    COPY-PASTE FIX
    # Paxml (aka Pax): A JAX-based Framework for Extreme-Scale ML Training and Experimentation

Pax is a JAX-based machine learning framework for training large scale models, enabling advanced and fully configurable experimentation and parallelization with industry-leading model flop utilization rates.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/paxml/
  • lowreadme#3
    Elaborate on Paxml's key benefits in the README introduction

    Why:

    CURRENT
    Pax is a framework to configure and run machine learning experiments on top of Jax.
    COPY-PASTE FIX
    Pax is a JAX-based machine learning framework for training large scale models. It allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates, making it ideal for complex deep learning research and production.

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 google/paxml
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Lightning
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Lightning · recommended 2×
  2. DeepSpeed · recommended 2×
  3. Hugging Face Accelerate · recommended 1×
  4. JAX/Flax · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How to efficiently train large language models using a scalable machine learning framework?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. DeepSpeed
    3. Hugging Face Accelerate
    4. JAX/Flax
    5. TensorFlow
    6. Megatron-LM

    AI recommended 6 alternatives but never named google/paxml. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework provides advanced experimentation and parallelization for deep learning models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. Ray Tune
    3. DeepSpeed
    4. TensorFlow Extended (TFX)
    5. Horovod

    AI recommended 5 alternatives but never named google/paxml. 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 google/paxml?
    pass
    AI named google/paxml explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of google/paxml. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/google/paxml.svg)](https://repogeo.com/en/r/google/paxml)
HTML
<a href="https://repogeo.com/en/r/google/paxml"><img src="https://repogeo.com/badge/google/paxml.svg" alt="RepoGEO" /></a>
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google/paxml — 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