RRepoGEO

REPOGEO REPORT · LITE

google/tunix

Default branch main · commit e598e223 · scanned 6/18/2026, 8:07:09 AM

GitHub: 2,347 stars · 310 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 google/tunix, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, jax, fine-tuning, reinforcement-learning, machine-learning, deep-learning, tpu, ai, ml
  • highhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://tunix.readthedocs.io/en/latest/index.html
  • mediumreadme#3
    Strengthen the README's opening sentence to emphasize the AI/ML domain

    Why:

    CURRENT
    **Tunix (Tune-in-JAX)** is a JAX based library designed to streamline the post-training of Large Language Models.
    COPY-PASTE FIX
    **Tunix (Tune-in-JAX)** is a cutting-edge JAX-based library for AI/ML practitioners, specifically designed to streamline the post-training of Large Language Models.

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/tunix
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. EleutherAI/GPT-NeoX · recommended 1×
  3. DeepMind/AlphaFold · recommended 1×
  4. JAX/Flax · recommended 1×
  5. Google's official JAX examples/tutorials · recommended 1×
  • CATEGORY QUERY
    How can I efficiently fine-tune large language models using JAX for optimal performance?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. GPT-NeoX (EleutherAI/GPT-NeoX)
    3. AlphaFold (DeepMind/AlphaFold)
    4. JAX/Flax
    5. Google's official JAX examples/tutorials

    AI recommended 5 alternatives but never named google/tunix. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What libraries support reinforcement learning for LLMs, especially with TPU acceleration?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. TRL
    3. accelerate
    4. PyTorch
    5. JAX
    6. Flax
    7. RLax
    8. Acme
    9. DeepSpeed
    10. Stable Baselines3
    11. Ray RLlib
    12. TensorFlow
    13. TF-Agents

    AI recommended 13 alternatives but never named google/tunix. 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/tunix?
    pass
    AI named google/tunix 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/tunix in production, what risks or prerequisites should they evaluate first?
    pass
    AI named google/tunix 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/tunix solve, and who is the primary audience?
    pass
    AI named google/tunix 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/tunix. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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Pro

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google/tunix — 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