RRepoGEO

REPOGEO REPORT · LITE

Om-Alve/smolGPT

Default branch main · commit 001f9c1a · scanned 6/24/2026, 3:58:06 PM

GitHub: 1,471 stars · 124 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 Om-Alve/smolGPT, 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
  • highabout#1
    Add a concise About description

    Why:

    CURRENT
    Description: (none)
    COPY-PASTE FIX
    A minimal PyTorch implementation for training your own small LLM from scratch, designed for educational purposes and simplicity, featuring efficient training, flash attention, and modern sampling techniques.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    Topics: (none)
    COPY-PASTE FIX
    ["pytorch", "llm", "gpt", "transformer", "flash-attention", "educational", "machine-learning", "deep-learning", "language-model", "from-scratch", "tiny-llm"]
  • mediumreadme#3
    Emphasize the educational and 'from scratch' nature in the README's opening

    Why:

    CURRENT
    # SMOL-GPT 🦾

A minimal PyTorch implementation for training your own small LLM from scratch. Designed for educational purposes and simplicity, featuring efficient training, flash attention, and modern sampling techniques.
    COPY-PASTE FIX
    # SMOL-GPT 🦾

A minimal PyTorch implementation for training your own small LLM from scratch. Designed explicitly for educational purposes and simplicity, this project helps you understand modern LLM architectures without abstraction overhead. It features efficient training, flash attention, and modern sampling techniques.

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 Om-Alve/smolGPT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. karpathy/nanoGPT · recommended 1×
  3. pytorch/examples · recommended 1×
  4. karpathy/minGPT · recommended 1×
  5. The Annotated Transformer · recommended 1×
  • CATEGORY QUERY
    Looking for a minimal PyTorch example to train a small language model from scratch.
    you: not recommended
    AI recommended (in order):
    1. nanoGPT (karpathy/nanoGPT)
    2. PyTorch official examples (pytorch/examples)
    3. Hugging Face `transformers` library (huggingface/transformers)
    4. MinGPT (karpathy/minGPT)
    5. The Annotated Transformer
    6. `pytorch-nlp` (yunjey/pytorch-nlp)

    AI recommended 6 alternatives but never named Om-Alve/smolGPT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to implement a custom LLM with modern features like flash attention and efficient sampling?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. flash-attention (Dao-AILab/flash-attention)
    3. Hugging Face Transformers (huggingface/transformers)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. Megatron-LM (NVIDIA/Megatron-LM)
    6. JAX (google/jax)
    7. Flax (google/flax)
    8. jax-flash-attention
    9. TensorFlow (tensorflow/tensorflow)
    10. Keras (keras-team/keras)
    11. MLX (ml-explore/mlx)

    AI recommended 11 alternatives but never named Om-Alve/smolGPT. 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 Om-Alve/smolGPT?
    pass
    AI named Om-Alve/smolGPT explicitly

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

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

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

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Om-Alve/smolGPT — 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