RRepoGEO

REPOGEO REPORT · LITE

allenai/OLMoE

Default branch main · commit 357454f4 · scanned 5/9/2026, 2:03:25 PM

GitHub: 1,022 stars · 112 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 allenai/OLMoE, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • mediumreadme#1
    Rephrase README opening to emphasize model's utility for users

    Why:

    CURRENT
    This repository provides an overview of all resources for the paper "OLMoE: Open Mixture-of-Experts Language Models".
    COPY-PASTE FIX
    This repository provides the official implementation, pre-trained models, and resources for OLMoE, a fully open, state-of-the-art Mixture-of-Experts (MoE) language model designed for efficient inference, pretraining, and fine-tuning experiments.
  • lowcomparison#2
    Add a comparison section to differentiate OLMoE

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with other MoE Models' that highlights OLMoE's unique strengths, such as its full transparency, reproducibility, and specific architectural choices, compared to models like Mixtral, Qwen1.5-MoE, and DeepSeek-MoE.

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 allenai/OLMoE
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mixtral 8x7B
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Mixtral 8x7B · recommended 1×
  2. Mixtral 8x22B · recommended 1×
  3. Qwen1.5-MoE · recommended 1×
  4. DeepSeek-MoE · recommended 1×
  5. Llama 3 · recommended 1×
  • CATEGORY QUERY
    Looking for an open-source mixture-of-experts language model for efficient inference.
    you: not recommended
    AI recommended (in order):
    1. Mixtral 8x7B
    2. Mixtral 8x22B
    3. Qwen1.5-MoE
    4. DeepSeek-MoE

    AI recommended 4 alternatives but never named allenai/OLMoE. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source large language models for pretraining and fine-tuning experiments?
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. Mistral 7B / Mixtral 8x7B
    3. Gemma
    4. Llama 2
    5. Falcon
    6. MPT

    AI recommended 6 alternatives but never named allenai/OLMoE. 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 allenai/OLMoE?
    pass
    AI named allenai/OLMoE explicitly

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

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

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

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allenai/OLMoE — 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