RRepoGEO

REPOGEO REPORT · LITE

adonis-dym/memory_reduced_optimizer

Default branch main · commit 9456bdce · scanned 6/9/2026, 8:07:58 AM

GitHub: 529 stars · 64 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 adonis-dym/memory_reduced_optimizer, 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
    Add a concise 'About' description for the repository

    Why:

    COPY-PASTE FIX
    Memory-reduced variants of popular deep learning optimizers (Adam, Adan, Lion) that reuse gradient space to significantly reduce GPU memory footprint during training.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    deep-learning, machine-learning, optimizer, memory-reduction, gpu-memory, pytorch, adam, adan, lion, neural-networks
  • mediumreadme#3
    Refine the README's main heading for clarity and impact

    Why:

    CURRENT
    # Reducing Memory Footprint in Deep Network Training by Gradient Space Reutilization
    COPY-PASTE FIX
    # Memory-Reduced Deep Learning Optimizers (Adam_R, Adan_R, Lion_R)

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 adonis-dym/memory_reduced_optimizer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA/apex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA/apex · recommended 2×
  2. microsoft/DeepSpeed · recommended 2×
  3. PyTorch · recommended 1×
  4. TensorFlow/Keras · recommended 1×
  5. facebookresearch/fairscale · recommended 1×
  • CATEGORY QUERY
    How to reduce GPU memory usage when training large deep learning models effectively?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow/Keras
    3. NVIDIA APEX (NVIDIA/apex)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. FairScale (facebookresearch/fairscale)
    6. Megatron-LM (NVIDIA/Megatron-LM)
    7. FlashAttention / FlashAttention-2 (HazyResearch/flash-attention)
    8. Longformer (allenai/longformer)
    9. Reformer

    AI recommended 9 alternatives but never named adonis-dym/memory_reduced_optimizer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some memory-efficient optimizers for deep neural network training?
    you: not recommended
    AI recommended (in order):
    1. AdamW
    2. Gradient Checkpointing
    3. DeepSpeed ZeRO (microsoft/DeepSpeed)
    4. PyTorch (pytorch/pytorch)
    5. SGD with Momentum
    6. AdaFactor
    7. Hugging Face Transformers (huggingface/transformers)
    8. fairseq (facebookresearch/fairseq)
    9. Lion (EvoLved Sign Momentum)
    10. LAMB (Layer-wise Adaptive Moments optimizer for Batching)
    11. NVIDIA's Apex (NVIDIA/apex)
    12. bitsandbytes (TimDettmers/bitsandbytes)

    AI recommended 12 alternatives but never named adonis-dym/memory_reduced_optimizer. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 adonis-dym/memory_reduced_optimizer?
    pass
    AI named adonis-dym/memory_reduced_optimizer explicitly

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

  • If a team adopts adonis-dym/memory_reduced_optimizer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named adonis-dym/memory_reduced_optimizer 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 adonis-dym/memory_reduced_optimizer solve, and who is the primary audience?
    pass
    AI did not name adonis-dym/memory_reduced_optimizer — likely talking about a different project

    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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  • Brand-free category queries5 vs 2 in Lite
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