RRepoGEO

REPOGEO REPORT · LITE

DLYuanGod/MegaTrain

Default branch main · commit 7f5c9597 · scanned 6/11/2026, 11:47:40 PM

GitHub: 606 stars · 59 forks

AI VISIBILITY SCORE
30 /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
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 DLYuanGod/MegaTrain, 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 repository's About section

    Why:

    COPY-PASTE FIX
    Train 100B+ parameter LLMs in full precision on a single GPU by offloading parameters to CPU RAM, treating GPUs as transient compute engines.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2604.05091

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 DLYuanGod/MegaTrain
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch FSDP
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch FSDP · recommended 2×
  2. microsoft/deepspeed · recommended 1×
  3. TimDettmers/bitsandbytes · recommended 1×
  4. huggingface/accelerate · recommended 1×
  5. Gradient Checkpointing · recommended 1×
  • CATEGORY QUERY
    How can I train massive large language models on a single GPU without memory errors?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/deepspeed)
    2. bitsandbytes (TimDettmers/bitsandbytes)
    3. Hugging Face Accelerate (huggingface/accelerate)
    4. PyTorch FSDP
    5. Gradient Checkpointing
    6. LoRA
    7. FlashAttention (Dao-AILab/flash-attention)

    AI recommended 7 alternatives but never named DLYuanGod/MegaTrain. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What methods allow training large AI models by offloading parameters to CPU RAM?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. PyTorch FSDP
    3. Accelerate
    4. Colossal-AI
    5. FairScale

    AI recommended 5 alternatives but never named DLYuanGod/MegaTrain. 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 DLYuanGod/MegaTrain?
    pass
    AI named DLYuanGod/MegaTrain explicitly

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

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

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

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MARKDOWN (README)
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DLYuanGod/MegaTrain — 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