RRepoGEO

REPOGEO REPORT · LITE

mlabonne/llm-autoeval

Default branch master · commit eca29921 · scanned 6/16/2026, 8:18:04 AM

GitHub: 686 stars · 109 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 mlabonne/llm-autoeval, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-evaluation, google-colab, large-language-models, benchmarking, automation, python
  • highreadme#2
    Reposition the README's opening to highlight Colab and automation

    Why:

    CURRENT
    Simplify LLM evaluation using a convenient Colab notebook.
    COPY-PASTE FIX
    LLM AutoEval is a convenient Google Colab notebook designed to simplify and automate the evaluation of Large Language Models. Just specify your model, benchmark, and GPU, then run it directly in Colab to get shareable performance summaries.
  • mediumhomepage#3
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://colab.research.google.com/drive/1Igs3WZuXAIv9X0vwqiE90QlEPys8e8Oa?usp=sharing

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 mlabonne/llm-autoeval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face `evaluate` library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face `evaluate` library · recommended 1×
  2. `transformers` · recommended 1×
  3. `datasets` · recommended 1×
  4. EleutherAI/lm-evaluation-harness · recommended 1×
  5. `scikit-learn` · recommended 1×
  • CATEGORY QUERY
    How can I quickly benchmark my large language models using a Colab notebook?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face `evaluate` library
    2. `transformers`
    3. `datasets`
    4. `lm-eval-harness` (EleutherAI/lm-evaluation-harness)
    5. `scikit-learn`
    6. `nltk`
    7. `LangChain`
    8. `OpenAI Evals`

    AI recommended 8 alternatives but never named mlabonne/llm-autoeval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an automated solution to evaluate LLMs and generate shareable performance summaries.
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B)
    2. MLflow
    3. Arize AI
    4. LangChain
    5. DeepEval
    6. Galileo

    AI recommended 6 alternatives but never named mlabonne/llm-autoeval. 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 mlabonne/llm-autoeval?
    pass
    AI named mlabonne/llm-autoeval explicitly

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

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

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

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mlabonne/llm-autoeval — 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