RRepoGEO

REPOGEO REPORT · LITE

huggingface/nanotron

Default branch main · commit 2411b022 · scanned 5/22/2026, 11:41:51 AM

GitHub: 2,698 stars · 308 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 huggingface/nanotron, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm, large-language-models, distributed-training, deep-learning, pytorch, transformer, parallelism, 3d-parallelism, model-training, hpc
  • highreadme#2
    Clarify the core purpose and differentiator in the README's opening paragraph

    Why:

    CURRENT
    Nanotron is a library for pretraining transformer models. It provides a simple and flexible API to pretrain models on custom datasets. Nanotron is designed to be easy to use, fast, and scalable. It is built with the following principles in mind:
    COPY-PASTE FIX
    Nanotron is a library for **efficiently pretraining large language models (LLMs) using advanced 3D parallelism techniques** (data, tensor, and pipeline parallelism). It provides a simple and flexible API to scale transformer model training on custom datasets. Nanotron is designed to be easy to use, fast, and scalable, built with the following principles in mind:
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://huggingface.co/nanotron

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 huggingface/nanotron
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. Megatron-LM · recommended 2×
  3. Accelerate · recommended 2×
  4. PyTorch FSDP · recommended 2×
  5. FairScale · recommended 1×
  • CATEGORY QUERY
    How can I efficiently pretrain large transformer models using 3D parallelism techniques?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. Megatron-LM
    3. FairScale
    4. Colossal-AI
    5. Accelerate
    6. PyTorch FSDP

    AI recommended 6 alternatives but never named huggingface/nanotron. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools simplify distributed training of large language models on custom datasets?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Accelerate
    3. PyTorch FSDP
    4. DeepSpeed
    5. Megatron-LM
    6. Ray Train
    7. Ray Core
    8. Lightning Fabric
    9. PyTorch Lightning

    AI recommended 9 alternatives but never named huggingface/nanotron. 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 huggingface/nanotron?
    pass
    AI named huggingface/nanotron explicitly

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

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

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

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huggingface/nanotron — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite