RRepoGEO

REPOGEO REPORT · LITE

mlfoundations/open_lm

Default branch main · commit 9bb92ef1 · scanned 6/6/2026, 2:38:11 PM

GitHub: 537 stars · 79 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 mlfoundations/open_lm, 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 improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    language-models, llm, pytorch, deep-learning, research, scaling-laws, transformers, distributed-training, machine-learning-framework
  • highreadme#2
    Strengthen the README's opening sentence to clarify its role as a framework

    Why:

    CURRENT
    OpenLM is a minimal but performative language modeling (LM) repository, aimed to facilitate research on medium sized LMs.
    COPY-PASTE FIX
    OpenLM is a minimal yet performant PyTorch-based framework and research toolkit for training medium-sized language models (LMs).
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    [Add the primary project URL, e.g., a dedicated project page or the LAION blog post where it was featured]

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 mlfoundations/open_lm
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. PyTorch Lightning · recommended 2×
  3. DeepSpeed · recommended 2×
  4. FairSeq · recommended 1×
  5. Accelerate · recommended 1×
  • CATEGORY QUERY
    What are the best PyTorch-based frameworks for training medium-sized language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. FairSeq
    5. Accelerate

    AI recommended 5 alternatives but never named mlfoundations/open_lm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a simple yet performant language model training toolkit for research on models up to 7B parameters.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. Megatron-LM
    5. JAX
    6. Flax

    AI recommended 6 alternatives but never named mlfoundations/open_lm. 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 mlfoundations/open_lm?
    pass
    AI named mlfoundations/open_lm explicitly

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

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

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

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mlfoundations/open_lm — 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