RRepoGEO

REPOGEO REPORT · LITE

google-deepmind/gemma

Default branch main · commit 5621d2db · scanned 5/25/2026, 4:52:05 PM

GitHub: 5,266 stars · 934 forks

AI VISIBILITY SCORE
28 /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
2 / 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-deepmind/gemma, 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-model, jax, deep-learning, machine-learning, ai, generative-ai, google-deepmind, fine-tuning, open-source-llm
  • highreadme#2
    Strengthen the README's opening to emphasize JAX and fine-tuning

    Why:

    CURRENT
    Gemma is a family of open-weights Large Language Model (LLM) by Google DeepMind, based on Gemini research and technology. This repository contains the implementation of the `gemma` PyPI package. A JAX library to use and fine-tune Gemma.
    COPY-PASTE FIX
    Gemma is a family of open-weights Large Language Models (LLMs) by Google DeepMind, built on Gemini research and technology. This repository provides the official `gemma` PyPI package: a JAX-native library designed for efficient use and fine-tuning of Gemma models, enabling developers and researchers to integrate and customize state-of-the-art LLMs.
  • mediumreadme#3
    Add a sentence to the README clarifying Gemma's niche or differentiator

    Why:

    COPY-PASTE FIX
    Unlike more general-purpose LLM frameworks, Gemma offers a highly optimized, JAX-native experience specifically for Google DeepMind's open-weight models, making it ideal for researchers and developers focused on high-performance JAX environments.

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-deepmind/gemma
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. abetlen/llama-cpp-python · recommended 1×
  4. ollama/ollama · recommended 1×
  5. vllm-project/vllm · recommended 1×
  • CATEGORY QUERY
    How can I integrate an open-source large language model into my Python application?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. LangChain (langchain-ai/langchain)
    3. llama-cpp-python (abetlen/llama-cpp-python)
    4. Ollama (ollama/ollama)
    5. vLLM (vllm-project/vllm)
    6. LiteLLM (BerriAI/litellm)

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

    Show full AI answer
  • CATEGORY QUERY
    Looking for a JAX-based library to fine-tune open-source large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. JAX/Flax
    3. Trax
    4. EleutherAI's GPT-NeoX

    AI recommended 4 alternatives but never named google-deepmind/gemma. 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-deepmind/gemma?
    pass
    AI did not name google-deepmind/gemma — likely talking about a different project

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

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