RRepoGEO

REPOGEO REPORT · LITE

NVlabs/DoRA

Default branch main · commit 7e2f10ab · scanned 6/9/2026, 8:37:31 AM

GitHub: 975 stars · 65 forks

AI VISIBILITY SCORE
68 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
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 NVlabs/DoRA, 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
    Rephrase README's opening to highlight benefits for stability and capacity

    Why:

    CURRENT
    The Official PyTorch implementation of **DoRA: Weight-Decomposed Low-Rank Adaptation** [ICML2024 (Oral, acceptance rate: 1.5%)].
    COPY-PASTE FIX
    The Official PyTorch implementation of **DoRA: Weight-Decomposed Low-Rank Adaptation** [ICML2024 (Oral, acceptance rate: 1.5%)]. DoRA significantly enhances the learning capacity and training stability of LoRA for fine-tuning large pre-trained models, avoiding any additional inference overhead.
  • mediumlicense#2
    Clarify the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under [describe the license(s) here, e.g., 'a custom research license' or 'a combination of X and Y licenses']. Please refer to the `LICENSE` file for full details.
  • lowabout#3
    Update repository description to highlight key benefits

    Why:

    CURRENT
    [ICML2024 (Oral)] Official PyTorch implementation of DoRA: Weight-Decomposed Low-Rank Adaptation
    COPY-PASTE FIX
    Official PyTorch implementation of DoRA: Weight-Decomposed Low-Rank Adaptation (ICML2024 Oral). DoRA enhances learning capacity and training stability for PEFT of large models.

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
1 / 2
50% of queries surface NVlabs/DoRA
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
7%
Of all named tools, what % are you?
Top rival
QLoRA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. QLoRA · recommended 1×
  2. LongLoRA · recommended 1×
  3. LoRA+ · recommended 1×
  4. LoRA-FA · recommended 1×
  5. AdaLoRA · recommended 1×
  • CATEGORY QUERY
    What are advanced parameter-efficient fine-tuning techniques offering better performance than standard LoRA?
    you: #3
    AI recommended (in order):
    1. QLoRA
    2. LongLoRA
    3. DoRA ← you
    4. LoRA+
    5. LoRA-FA
    6. AdaLoRA
    Show full AI answer
  • CATEGORY QUERY
    How can I improve fine-tuning stability and learning capacity for large language models in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. PyTorch FSDP
    3. bitsandbytes
    4. Hugging Face Accelerate
    5. Hugging Face PEFT library
    6. FlashAttention
    7. torch.optim.AdamW
    8. torch.optim.lr_scheduler.CosineAnnealingLR

    AI recommended 8 alternatives but never named NVlabs/DoRA. 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 NVlabs/DoRA?
    pass
    AI named NVlabs/DoRA explicitly

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

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

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

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NVlabs/DoRA — 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