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REPOGEO REPORT · LITE

EvolvingLMMs-Lab/lmms-eval

Default branch main · commit 247bebd8 · scanned 5/20/2026, 1:31:28 PM

GitHub: 4,145 stars · 589 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
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 EvolvingLMMs-Lab/lmms-eval, 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
  • mediumlicense#1
    Clarify license details in README

    Why:

    COPY-PASTE FIX
    Add a 'License' section to the README, stating 'This project is licensed under the terms specified in the LICENSE file. Please refer to the LICENSE file for full details.'
  • lowcomparison#2
    Expand 'Why lmms-eval?' section with explicit comparisons

    Why:

    COPY-PASTE FIX
    Expand the 'Why lmms-eval?' section in the README to include explicit comparisons, highlighting its unified multimodal scope across text, image, video, and audio against LLM-only evaluation tools (like LM-Harness) and broader ML frameworks (like OpenMMLab).

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-eval
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenMMLab
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenMMLab · recommended 2×
  2. EleutherAI's LM-Harness · recommended 1×
  3. Hugging Face Evaluate Library · recommended 1×
  4. MMEval · recommended 1×
  5. PyTorch-Lightning · recommended 1×
  • CATEGORY QUERY
    How can I evaluate the performance of large multimodal models across various data types?
    you: not recommended
    AI recommended (in order):
    1. EleutherAI's LM-Harness
    2. Hugging Face Evaluate Library
    3. OpenMMLab
    4. MMEval
    5. PyTorch-Lightning
    6. Keras
    7. DeepSpeed
    8. FairScale
    9. NumPy
    10. SciPy
    11. Scikit-learn

    AI recommended 11 alternatives but never named EvolvingLMMs-Lab/lmms-eval. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a unified toolkit to benchmark LLMs on audio, video, and image tasks.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Evaluate (huggingface/evaluate)
    3. Hugging Face Datasets (huggingface/datasets)
    4. Gradio (gradio-app/gradio)
    5. OpenMMLab
    6. MMDetection (open-mmlab/mmdetection)
    7. MMAction2 (open-mmlab/mmaction2)
    8. MMAudio
    9. MMEval (open-mmlab/mmeval)
    10. PyTorch Lightning (Lightning-AI/lightning)
    11. TorchMetrics (Lightning-AI/torchmetrics)
    12. DeepMind's Acme (deepmind/acme)
    13. Microsoft's DeepSpeed (microsoft/DeepSpeed)
    14. Megatron-LM (NVIDIA/Megatron-LM)

    AI recommended 14 alternatives but never named EvolvingLMMs-Lab/lmms-eval. 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 EvolvingLMMs-Lab/lmms-eval?
    pass
    AI named EvolvingLMMs-Lab/lmms-eval 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-eval in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EvolvingLMMs-Lab/lmms-eval 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-eval solve, and who is the primary audience?
    pass
    AI named EvolvingLMMs-Lab/lmms-eval explicitly

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

Embed your GEO score

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EvolvingLMMs-Lab/lmms-eval — 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