RRepoGEO

REPOGEO REPORT · LITE

princeton-nlp/MeZO

Default branch main · commit 552cb1b7 · scanned 5/16/2026, 9:22:33 PM

GitHub: 1,165 stars · 89 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
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 princeton-nlp/MeZO, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, fine-tuning, memory-efficient, zeroth-order-optimization, deep-learning, nlp, language-models, pytorch, machine-learning
  • highreadme#2
    Rephrase the README's first sentence to immediately state its core problem-solving value

    Why:

    CURRENT
    This is the implementation for the paper Fine-Tuning Language Models with Just Forward Passes.
    COPY-PASTE FIX
    MeZO provides a memory-efficient zeroth-order optimizer for fine-tuning large language models (LLMs) with the same memory footprint as inference.
  • lowhomepage#3
    Add the paper's arXiv link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2305.17333

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 princeton-nlp/MeZO
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/peft
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/peft · recommended 2×
  2. LoRA · recommended 1×
  3. QLoRA · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    Struggling with GPU memory when fine-tuning large language models; need efficient solutions.
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. PEFT (huggingface/peft)
    3. QLoRA
    4. DeepSpeed (microsoft/DeepSpeed)
    5. PyTorch FSDP (pytorch/pytorch)
    6. Gradient Accumulation
    7. Activation Checkpointing
    8. FlashAttention (Dao-AILab/flash-attention)
    9. xFormers (facebookresearch/xformers)

    AI recommended 9 alternatives but never named princeton-nlp/MeZO. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking ways to fine-tune large models without backpropagation, especially for non-differentiable metrics.
    you: not recommended
    AI recommended (in order):
    1. trl library (huggingface/trl)
    2. PyTorch-GA (ritchieng/PyTorch-GA)
    3. DEAP (deap/deap)
    4. Optuna (optuna/optuna)
    5. GPyOpt (SheffieldML/GPyOpt)
    6. baselines (openai/baselines)
    7. Diffusers library (huggingface/diffusers)
    8. PEFT library (huggingface/peft)

    AI recommended 8 alternatives but never named princeton-nlp/MeZO. 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 princeton-nlp/MeZO?
    pass
    AI named princeton-nlp/MeZO explicitly

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

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

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

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princeton-nlp/MeZO — 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