RRepoGEO

REPOGEO REPORT · LITE

NVlabs/VILA

Default branch main · commit 0f1426e8 · scanned 5/14/2026, 4:52:46 AM

GitHub: 3,793 stars · 320 forks

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 NVlabs/VILA, 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
  • highreadme#1
    Clarify README introduction to highlight specific VLM capabilities

    Why:

    CURRENT
    VILA is a family of open VLMs designed to optimize both efficiency and accuracy for efficient video understanding and multi-image understanding.
    COPY-PASTE FIX
    VILA is a family of state-of-the-art open vision language models (VLMs) designed to optimize both efficiency and accuracy for diverse multimodal AI tasks, including efficient video understanding and processing high-resolution, detail-rich images.
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://nvlabs.github.io/VILA/

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 NVlabs/VILA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
BLIP-2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. BLIP-2 · recommended 1×
  2. LLaVA · recommended 1×
  3. MiniGPT-4 · recommended 1×
  4. InstructBLIP · recommended 1×
  5. Flamingo · recommended 1×
  • CATEGORY QUERY
    What are efficient vision language models for diverse multimodal AI tasks?
    you: not recommended
    AI recommended (in order):
    1. BLIP-2
    2. LLaVA
    3. MiniGPT-4
    4. InstructBLIP
    5. Flamingo
    6. OpenCLIP
    7. Llama 2
    8. Mistral 7B

    AI recommended 8 alternatives but never named NVlabs/VILA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to process high-resolution images and videos with efficient multimodal AI models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. Hugging Face Transformers (huggingface/transformers)
    4. TensorFlow (tensorflow/tensorflow)
    5. Keras (keras-team/keras)
    6. TensorFlow Hub (tensorflow/hub)
    7. MMDetection (open-mmlab/mmdetection)
    8. MMTracking (open-mmlab/mmtracking)
    9. MMAction2 (open-mmlab/mmaction2)
    10. DeepSpeed (microsoft/DeepSpeed)
    11. JAX (google/jax)
    12. Flax (google/flax)
    13. Haiku (deepmind/dm-haiku)
    14. ONNX Runtime (microsoft/onnxruntime)
    15. NVIDIA Triton Inference Server (triton-inference-server/server)

    AI recommended 15 alternatives but never named NVlabs/VILA. 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 NVlabs/VILA?
    pass
    AI named NVlabs/VILA explicitly

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

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

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

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NVlabs/VILA — 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