RRepoGEO

REPOGEO REPORT · LITE

zv1131860787/nano-deepspeed

Default branch master · commit 144b6b84 · scanned 6/5/2026, 2:28:28 PM

GitHub: 520 stars · 89 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 zv1131860787/nano-deepspeed, 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
  • highabout#1
    Add a concise description to the 'About' section

    Why:

    COPY-PASTE FIX
    A teaching-oriented re-implementation of DeepSpeed ZeRO to help understand data flow and communication behavior in distributed model training.
  • highreadme#2
    Add a clear license statement to the README

    Why:

    COPY-PASTE FIX
    This project is released under the [Your Chosen License, e.g., MIT] License. Please refer to the `LICENSE` file for full terms.

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 zv1131860787/nano-deepspeed
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 1×
  2. Hugging Face Accelerate · recommended 1×
  3. Microsoft Research · recommended 1×
  4. PyTorch FSDP · recommended 1×
  5. Papers with Code · recommended 1×
  • CATEGORY QUERY
    How to understand distributed model training memory optimization techniques like ZeRO?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. Hugging Face Accelerate
    3. Microsoft Research
    4. PyTorch FSDP
    5. Papers with Code
    6. NVIDIA

    AI recommended 6 alternatives but never named zv1131860787/nano-deepspeed. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a simplified implementation to learn distributed training memory partitioning strategies.
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP (pytorch/pytorch)
    2. DeepSpeed (microsoft/DeepSpeed)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. Colossal-AI (hpcaitech/ColossalAI)
    5. FairScale (facebookresearch/fairscale)

    AI recommended 5 alternatives but never named zv1131860787/nano-deepspeed. 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 zv1131860787/nano-deepspeed?
    pass
    AI did not name zv1131860787/nano-deepspeed — 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?

  • If a team adopts zv1131860787/nano-deepspeed in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zv1131860787/nano-deepspeed 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 zv1131860787/nano-deepspeed solve, and who is the primary audience?
    pass
    AI named zv1131860787/nano-deepspeed explicitly

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

Embed your GEO score

Drop this badge into the README of zv1131860787/nano-deepspeed. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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zv1131860787/nano-deepspeed — 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