RRepoGEO

REPOGEO REPORT · LITE

LLaVA-VL/LLaVA-NeXT

Default branch main · commit bce12e47 · scanned 6/26/2026, 6:52:58 AM

GitHub: 4,695 stars · 464 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 LLaVA-VL/LLaVA-NeXT, 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 description to the About section

    Why:

    COPY-PASTE FIX
    LLaVA-NeXT provides open large multimodal models for advanced vision-language understanding and reasoning, serving AI researchers and developers.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    large-multimodal-models, llava, vision-language-model, multimodal-ai, deep-learning, computer-vision, natural-language-processing, vlm
  • highreadme#3
    Add a clear introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    Add the following text immediately after the `# LLaVA-NeXT: Open Large Multimodal Models` heading: 'LLaVA-NeXT enhances large language models with advanced multimodal capabilities for vision-language understanding and reasoning, primarily serving AI researchers and developers in the field. This repository provides the foundational models and resources for LLaVA-NeXT, with the latest training pipeline now maintained in `lmms-engine`.'

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 LLaVA-VL/LLaVA-NeXT
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/diffusers · recommended 1×
  3. huggingface/peft · recommended 1×
  4. huggingface/datasets · recommended 1×
  5. huggingface/accelerate · recommended 1×
  • CATEGORY QUERY
    How can I develop an open-source large vision-language model for multimodal AI applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Diffusers (huggingface/diffusers)
    3. PEFT (huggingface/peft)
    4. Datasets (huggingface/datasets)
    5. Accelerate (huggingface/accelerate)
    6. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    7. DeepSpeed (microsoft/DeepSpeed)
    8. Megatron-LM (NVIDIA/Megatron-LM)
    9. MMDetection (open-mmlab/mmdetection)
    10. MMAction2 (open-mmlab/mmaction2)
    11. MMEngine (open-mmlab/mmengine)
    12. TensorFlow (tensorflow/tensorflow)
    13. Keras (keras-team/keras)
    14. JAX (google/jax)
    15. Flax (google/flax)

    AI recommended 15 alternatives but never named LLaVA-VL/LLaVA-NeXT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust open-source frameworks for training advanced multimodal perception models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. Hugging Face Transformers
    3. MMDetection3D
    4. MMEngine
    5. Detectron2
    6. Keras
    7. TensorFlow
    8. JAX
    9. Flax
    10. DeepSpeed

    AI recommended 10 alternatives but never named LLaVA-VL/LLaVA-NeXT. 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 LLaVA-VL/LLaVA-NeXT?
    pass
    AI named LLaVA-VL/LLaVA-NeXT explicitly

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

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

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

Embed your GEO score

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LLaVA-VL/LLaVA-NeXT — 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