RRepoGEO

REPOGEO REPORT · LITE

KellerJordan/modded-nanogpt

Default branch master · commit 3546294c · scanned 5/20/2026, 12:37:58 PM

GitHub: 5,272 stars · 773 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 KellerJordan/modded-nanogpt, 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
    Clarify the project's core identity in the README's opening

    Why:

    CURRENT
    # Modded-NanoGPT

This repository hosts the *NanoGPT speedrun*, in which we (collaboratively|competitively) search for the fastest algorithm to use 8 NVIDIA H100 GPUs to train a language model that attains 3.28 cross-entropy loss on the FineWeb validation set.
    COPY-PASTE FIX
    # Modded-NanoGPT

This repository showcases an aggressively optimized implementation of a small language model (derived from NanoGPT) focused on achieving unprecedented training speed. It hosts the *NanoGPT speedrun*, a collaborative effort to find the fastest algorithm to train a language model to 3.28 cross-entropy loss on the FineWeb validation set using 8 NVIDIA H100 GPUs.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, large-language-models, deep-learning, machine-learning, pytorch, training-optimization, gpt, nanogpt, h100, speedrun
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    [Insert relevant URL here, e.g., a project page, paper, or blog post about the speedrun]

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 KellerJordan/modded-nanogpt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. TensorFlow/Keras · recommended 1×
  3. NVIDIA APEX · recommended 1×
  4. PyTorch's `torch.cuda.amp` · recommended 1×
  5. TensorFlow's `tf.keras.mixed_precision` · recommended 1×
  • CATEGORY QUERY
    Looking for methods to drastically reduce training time for small generative AI models.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow/Keras
    3. NVIDIA APEX
    4. PyTorch's `torch.cuda.amp`
    5. TensorFlow's `tf.keras.mixed_precision`
    6. Hugging Face Transformers
    7. `xformers` library
    8. Hugging Face `transformers` library
    9. `DistilBERT`
    10. `webdataset`
    11. `DALI` (NVIDIA Data Loading Library)
    12. `PyTorch DataLoader`
    13. PyTorch's `torch.utils.checkpoint.checkpoint`
    14. TensorFlow's `tf.recompute_grad`
    15. AdamW
    16. Adam
    17. Lion (EvoLved Sign Momentum)

    AI recommended 17 alternatives but never named KellerJordan/modded-nanogpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking highly optimized training approaches for achieving fast convergence in large language models.
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. PyTorch FSDP
    3. NVIDIA Apex
    4. Megatron-LM
    5. FlashAttention
    6. bitsandbytes
    7. Hugging Face Accelerate

    AI recommended 7 alternatives but never named KellerJordan/modded-nanogpt. 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 KellerJordan/modded-nanogpt?
    pass
    AI named KellerJordan/modded-nanogpt explicitly

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

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

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

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KellerJordan/modded-nanogpt — 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