RRepoGEO

REPOGEO REPORT · LITE

allenai/OLMo

Default branch main · commit 090253da · scanned 6/30/2026, 8:12:12 AM

GitHub: 6,564 stars · 776 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 allenai/OLMo, 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
  • highabout#1
    Update repository description to reflect archived status

    Why:

    CURRENT
    Modeling, training, eval, and inference code for OLMo
    COPY-PASTE FIX
    ARCHIVED: This repository contains the original OLMo code, now inactive. For the latest OLMo releases and updates, please visit allenai/OLMo-core.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, llm, deep-learning, machine-learning, nlp, generative-ai, pretraining, fine-tuning, pytorch, research
  • mediumreadme#3
    Refine README H1 and introductory paragraph for clarity on archived status

    Why:

    CURRENT
    <h1>OLMo: Open Language Model</h1>
...
OLMo is a repository for training and using AI2's state-of-the-art open language models. It is designed by scientists, for scientists.
    COPY-PASTE FIX
    <h1>OLMo: Open Language Model (Archived)</h1><p>This repository serves as an archive for the original OLMo project, providing historical code for training and using AI2's state-of-the-art open language models. For active development and the latest versions, please refer to the OLMo-core repository.</p>

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/OLMo
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. Lightning-AI/lightning · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. NVIDIA/Megatron-LM · recommended 1×
  5. google/jax · recommended 1×
  • CATEGORY QUERY
    What frameworks exist for pretraining and fine-tuning large language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Megatron-LM (NVIDIA/Megatron-LM)
    5. JAX (google/jax)
    6. Flax (google/flax)
    7. TensorFlow (tensorflow/tensorflow)

    AI recommended 7 alternatives but never named allenai/OLMo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking open-source tools to build, evaluate, and deploy custom generative AI models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Accelerate
    3. Hugging Face Inference Endpoints
    4. FastAPI
    5. Triton
    6. PyTorch
    7. TensorFlow
    8. DeepSpeed
    9. MLflow
    10. ONNX Runtime
    11. Kubeflow

    AI recommended 11 alternatives but never named allenai/OLMo. 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/OLMo?
    pass
    AI named allenai/OLMo 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/OLMo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named allenai/OLMo 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/OLMo solve, and who is the primary audience?
    pass
    AI named allenai/OLMo 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 allenai/OLMo. 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/allenai/OLMo.svg)](https://repogeo.com/en/r/allenai/OLMo)
HTML
<a href="https://repogeo.com/en/r/allenai/OLMo"><img src="https://repogeo.com/badge/allenai/OLMo.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

allenai/OLMo — 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