RRepoGEO

REPOGEO REPORT · LITE

OptimalScale/LMFlow

Default branch main · commit 767e04cf · scanned 5/29/2026, 5:06:52 PM

GitHub: 8,484 stars · 829 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 OptimalScale/LMFlow, 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
  • highreadme#1
    Reposition README's opening to explicitly mention 'Large Language Models' and 'Large Foundation Models' as a toolkit

    Why:

    CURRENT
    An extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community.
    COPY-PASTE FIX
    An extensible, convenient, and efficient toolkit for finetuning and inference of Large Language Models (LLMs) and Large Foundation Models, designed to be user-friendly, speedy and reliable, and accessible to the entire community.
  • mediumtopics#2
    Add 'llm' and 'finetuning' to repository topics

    Why:

    CURRENT
    chatgpt, deep-learning, instruction-following, language-model, pretrained-models, pytorch, transformer
    COPY-PASTE FIX
    chatgpt, deep-learning, finetuning, instruction-following, language-model, llm, pretrained-models, pytorch, transformer
  • mediumreadme#3
    Add a 'Why LMFlow?' or 'Key Features' section highlighting its unique value proposition

    Why:

    COPY-PASTE FIX
    Add a section (e.g., 'Why LMFlow?') that explicitly states: 'LMFlow is designed as a unified, extensible, and comprehensive toolkit for the entire lifecycle of instruction-tuned Large Language Models (LLMs), offering a complete solution from finetuning to inference.'

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 OptimalScale/LMFlow
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. Axolotl · recommended 1×
  5. Ludwig · recommended 1×
  • CATEGORY QUERY
    What's a good framework for finetuning large language models on custom datasets?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. Axolotl
    5. Ludwig
    6. OpenAI Fine-tuning API

    AI recommended 6 alternatives but never named OptimalScale/LMFlow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an extensible deep learning toolkit for efficient large model finetuning and inference.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. JAX/Flax
    5. TensorFlow/Keras
    6. ONNX Runtime
    7. OpenVINO

    AI recommended 7 alternatives but never named OptimalScale/LMFlow. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 OptimalScale/LMFlow?
    pass
    AI named OptimalScale/LMFlow explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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