RRepoGEO

REPOGEO REPORT · LITE

qlabs-eng/slowrun

Default branch main · commit 98557a17 · scanned 6/19/2026, 9:28:21 AM

GitHub: 500 stars · 75 forks

AI VISIBILITY SCORE
28 /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
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 qlabs-eng/slowrun, 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
    Update the About description to explicitly state its purpose

    Why:

    CURRENT
    100M tokens. Infinite compute. Lowest val loss wins.
    COPY-PASTE FIX
    A benchmark for language modeling algorithms, focusing on achieving the lowest validation loss on 100M FineWeb tokens with ample compute, contrasting with speed-optimized training.
  • mediumreadme#2
    Reinforce the core purpose in the README's opening statement

    Why:

    CURRENT
    # NanoGPT Slowrun

NanoGPT Slowrun is a new benchmark for language modeling algorithms in the infinite compute, fixed data regime: 100M tokens from FineWeb, no compute/time limit, lowest validation loss wins.[^1]
    COPY-PASTE FIX
    # NanoGPT Slowrun

NanoGPT Slowrun is a novel benchmark specifically designed for **language modeling algorithms**. It focuses on the infinite compute, fixed data regime: 100M tokens from FineWeb, no compute/time limit, where the goal is the lowest validation loss.[^1]

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 qlabs-eng/slowrun
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 1×
  2. GPT-4 · recommended 1×
  3. Mistral Large · recommended 1×
  4. LoRA · recommended 1×
  5. QLoRA · recommended 1×
  • CATEGORY QUERY
    How to achieve lowest validation loss on language models with ample compute?
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. GPT-4
    3. Mistral Large
    4. LoRA
    5. QLoRA
    6. T5
    7. GPT-3.5
    8. Llama 2
    9. Hugging Face Transformers (huggingface/transformers)
    10. AdamW
    11. AdaFactor
    12. Lion
    13. PyTorch (pytorch/pytorch)
    14. TensorFlow (tensorflow/tensorflow)
    15. NVIDIA's Apex (NVIDIA/apex)
    16. DeepSpeed (microsoft/DeepSpeed)
    17. FSDP
    18. Megatron-LM (NVIDIA/Megatron-LM)
    19. Optuna (optuna/optuna)
    20. Weights & Biases Sweeps (wandb/wandb)
    21. Ray Tune (ray-project/ray)

    AI recommended 21 alternatives but never named qlabs-eng/slowrun. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Benchmarking language model training for maximum learning on a fixed dataset?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases
    2. MLflow
    3. Comet ML
    4. TensorBoard
    5. PyTorch Lightning
    6. Hugging Face Transformers
    7. Optuna
    8. Ray Tune

    AI recommended 8 alternatives but never named qlabs-eng/slowrun. 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 qlabs-eng/slowrun?
    pass
    AI did not name qlabs-eng/slowrun — 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 qlabs-eng/slowrun in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qlabs-eng/slowrun 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 qlabs-eng/slowrun solve, and who is the primary audience?
    pass
    AI named qlabs-eng/slowrun explicitly

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

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qlabs-eng/slowrun — RepoGEO report