RRepoGEO

REPOGEO REPORT · LITE

sail-sg/Adan

Default branch main · commit 2c65beaf · scanned 6/5/2026, 8:46:47 PM

GitHub: 819 stars · 70 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 sail-sg/Adan, 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
    Strengthen README opening to highlight Adan's competitive edge

    Why:

    CURRENT
    This is an official PyTorch implementation of **Adan**. See the paper here. If you find our adan helpful or heuristic to your projects, please cite this paper and also star this repository. Thanks!
    COPY-PASTE FIX
    Adan is a cutting-edge Adaptive Nesterov Momentum optimizer specifically engineered for significantly faster and more efficient training of deep learning models. It delivers superior performance and faster convergence, particularly benefiting large language models (LLMs) and diffusion models, outperforming many traditional optimizers. This repository provides the official PyTorch implementation.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2208.06677
  • lowreadme#3
    Add a 'Key Advantages' or 'Why Adan?' section to README

    Why:

    COPY-PASTE FIX
    ## Key Advantages of Adan
    
    - **Faster Convergence:** Achieves optimal performance in fewer training steps compared to conventional optimizers.
    - **Improved Generalization:** Leads to models with better performance on unseen data.
    - **Optimized for Large Models:** Particularly effective for training large language models (LLMs), vision transformers (ViTs), and diffusion models.
    - **Adaptive Nesterov Momentum:** Combines the benefits of adaptive learning rates with Nesterov momentum for robust and efficient optimization.

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 sail-sg/Adan
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AdamW
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AdamW · recommended 2×
  2. Adam · recommended 2×
  3. SGD with Momentum · recommended 2×
  4. RAdam · recommended 1×
  5. Lookahead · recommended 1×
  • CATEGORY QUERY
    How to accelerate deep learning model training using a more efficient PyTorch optimizer?
    you: not recommended
    AI recommended (in order):
    1. AdamW
    2. Adam
    3. SGD with Momentum
    4. RAdam
    5. Lookahead
    6. Adabelief

    AI recommended 6 alternatives but never named sail-sg/Adan. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best adaptive momentum optimizers for large language models and diffusion models?
    you: not recommended
    AI recommended (in order):
    1. AdamW
    2. Lion (Evolved Sign Momentum)
    3. AdaFactor
    4. Adam
    5. SGD with Momentum
    6. Sophia (Second-order Optimizer for Deep Learning)

    AI recommended 6 alternatives but never named sail-sg/Adan. 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 sail-sg/Adan?
    pass
    AI named sail-sg/Adan explicitly

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

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

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

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sail-sg/Adan — 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