RRepoGEO

REPOGEO REPORT · LITE

NVIDIA/bionemo-framework

Default branch main · commit d52b0b93 · scanned 6/17/2026, 11:58:33 AM

GitHub: 768 stars · 162 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 NVIDIA/bionemo-framework, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the project's chosen open-source license (e.g., Apache-2.0, MIT).
  • highreadme#2
    Reposition README H1 and opening paragraph to emphasize 'Framework' and 'Biological Foundation Models'

    Why:

    CURRENT
    # BioNeMo Recipes

BioNeMo Recipes provides an easy path for the biological foundation model training community to scale up transformer-based models efficiently.
    COPY-PASTE FIX
    # BioNeMo Framework: Recipes for Biological Foundation Models

BioNeMo Framework provides a comprehensive toolkit and optimized recipes for the biological foundation model training community to efficiently build, adapt, and scale transformer-based models for drug discovery.
  • mediumtopics#3
    Expand repository topics to include more specific biological AI terms

    Why:

    CURRENT
    drug-discovery, gpu, machine-learning, pytorch
    COPY-PASTE FIX
    drug-discovery, gpu, machine-learning, pytorch, transformer-models, foundation-models, biological-ai, protein-engineering

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 NVIDIA/bionemo-framework
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
microsoft/DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. microsoft/DeepSpeed · recommended 1×
  2. pytorch/pytorch · recommended 1×
  3. NVIDIA/Megatron-LM · recommended 1×
  4. huggingface/accelerate · recommended 1×
  5. NVIDIA/apex · recommended 1×
  • CATEGORY QUERY
    How to efficiently scale transformer models for drug discovery research across multiple GPUs?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. PyTorch FSDP (pytorch/pytorch)
    3. Megatron-LM (NVIDIA/Megatron-LM)
    4. Hugging Face Accelerate (huggingface/accelerate)
    5. NVIDIA Apex (NVIDIA/apex)
    6. Ray Train (ray-project/ray)

    AI recommended 6 alternatives but never named NVIDIA/bionemo-framework. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help optimize large PyTorch model training with FSDP for biological research?
    you: not recommended
    AI recommended (in order):
    1. PyTorch FSDP
    2. PyTorch Lightning
    3. Hugging Face Accelerate
    4. DeepSpeed
    5. NVIDIA Apex
    6. Weights & Biases (W&B)
    7. TensorBoard

    AI recommended 7 alternatives but never named NVIDIA/bionemo-framework. 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 NVIDIA/bionemo-framework?
    pass
    AI did not name NVIDIA/bionemo-framework — 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 NVIDIA/bionemo-framework in production, what risks or prerequisites should they evaluate first?
    pass
    AI named NVIDIA/bionemo-framework 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 NVIDIA/bionemo-framework solve, and who is the primary audience?
    pass
    AI named NVIDIA/bionemo-framework explicitly

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

Embed your GEO score

Drop this badge into the README of NVIDIA/bionemo-framework. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/NVIDIA/bionemo-framework"><img src="https://repogeo.com/badge/NVIDIA/bionemo-framework.svg" alt="RepoGEO" /></a>
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NVIDIA/bionemo-framework — 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