RRepoGEO

REPOGEO REPORT · LITE

EvolvingLMMs-Lab/lmms-engine

Default branch main · commit 2fe89ebd · scanned 6/2/2026, 6:21:54 PM

GitHub: 783 stars · 35 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 EvolvingLMMs-Lab/lmms-engine, 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
  • highlicense#1
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, specifying the chosen open-source license (e.g., MIT, Apache-2.0, or a custom license if applicable) to clearly define usage terms.
  • highreadme#2
    Strengthen README opening to highlight unique value for multimodal models

    Why:

    CURRENT
    <h4>A simple, unified multimodal models training engine. Lean, flexible, and built for hacking at scale.</h4>
    COPY-PASTE FIX
    Update the opening section of the README to explicitly state `lmms-engine`'s core differentiator as 'a unified, end-to-end engine for training and deploying a diverse range of Large Multimodal Model (LMM) architectures, abstracting away architectural specifics for consistent development.' This should be placed immediately after the main title/description.
  • mediumtopics#3
    Expand GitHub topics to include 'framework' and 'training'

    Why:

    CURRENT
    agi, large-language-models, multimodal, unified-multimodal-models, video-generation
    COPY-PASTE FIX
    agi, large-language-models, multimodal, unified-multimodal-models, video-generation, machine-learning-framework, deep-learning-training

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 EvolvingLMMs-Lab/lmms-engine
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. huggingface/accelerate · recommended 2×
  3. google/jax · recommended 2×
  4. google/flax · recommended 2×
  5. tensorflow/tensorflow · recommended 2×
  • CATEGORY QUERY
    What are the best tools for training unified multimodal AI models at scale?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning (PyTorchLightning/pytorch-lightning)
    2. Hugging Face Transformers (huggingface/transformers)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. JAX (google/jax)
    5. Flax (google/flax)
    6. TensorFlow (tensorflow/tensorflow)
    7. Keras (keras-team/keras)
    8. DeepSpeed (microsoft/DeepSpeed)
    9. Ray Train (ray-project/ray)
    10. NVIDIA NeMo (NVIDIA/NeMo)

    AI recommended 10 alternatives but never named EvolvingLMMs-Lab/lmms-engine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a flexible framework for developing and experimenting with multimodal large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Diffusers (huggingface/diffusers)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. PyTorch Lightning (Lightning-AI/lightning)
    5. DeepSpeed (microsoft/DeepSpeed)
    6. OpenAI CLIP (openai/CLIP)
    7. OpenAI DALL-E
    8. TensorFlow (tensorflow/tensorflow)
    9. Keras (keras-team/keras)
    10. JAX (google/jax)
    11. Flax (google/flax)

    AI recommended 11 alternatives but never named EvolvingLMMs-Lab/lmms-engine. 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 EvolvingLMMs-Lab/lmms-engine?
    pass
    AI named EvolvingLMMs-Lab/lmms-engine explicitly

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

  • If a team adopts EvolvingLMMs-Lab/lmms-engine in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EvolvingLMMs-Lab/lmms-engine 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 EvolvingLMMs-Lab/lmms-engine solve, and who is the primary audience?
    pass
    AI named EvolvingLMMs-Lab/lmms-engine 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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