RRepoGEO

REPOGEO REPORT · LITE

SkunkworksAI/BakLLaVA

Default branch main · commit 0ca96415 · scanned 5/30/2026, 6:27:27 PM

GitHub: 719 stars · 49 forks

AI VISIBILITY SCORE
52 /100
Needs work
Category recall
1 / 2
Avg rank #5.0 when recommended
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 SkunkworksAI/BakLLaVA, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    BakLLaVA is a state-of-the-art multimodal large language model (MLLM) that integrates visual understanding with natural language processing, built on a Mistral 7B backbone for advanced visual instruction tuning.
  • mediumreadme#2
    Clarify licensing for commercial use in README

    Why:

    CURRENT
    Usage and License Notices: The data and checkpoint is intended and licensed for research use only. They are also restricted to uses that follow the license agreement of LLaMA, Vicuna and GPT-4. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the
    COPY-PASTE FIX
    Usage and License Notices: The code in this repository is licensed under Apache-2.0. However, the data and trained checkpoints are intended and licensed for research use only under CC BY NC 4.0 (non-commercial). Additionally, their use is restricted by the license agreements of underlying models like LLaMA, Vicuna, and GPT-4. Please review all applicable licenses before use, especially for commercial applications.

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
1 / 2
50% of queries surface SkunkworksAI/BakLLaVA
Avg rank
#5.0
Lower is better. #1 = top recommendation.
Share of voice
6%
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. Lightning-AI/lightning · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    How can I build an AI that understands both visual information and natural language instructions?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. 🤗 Diffusers (huggingface/diffusers)
    3. 🤗 PEFT (huggingface/peft)
    4. PyTorch Lightning (Lightning-AI/lightning)
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)
    7. OpenAI CLIP (openai/CLIP)
    8. MMDetection (open-mmlab/mmdetection)
    9. MMDetection3D (open-mmlab/mmdetection3d)
    10. DeepPavlov (deepmipt/DeepPavlov)
    11. Microsoft's Florence Foundation Models

    AI recommended 11 alternatives but never named SkunkworksAI/BakLLaVA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open source models for advanced visual instruction tuning of large language models?
    you: #5
    AI recommended (in order):
    1. LLaVA
    2. InstructBLIP
    3. MiniGPT-4
    4. Qwen-VL
    5. BakLLaVA ← you
    6. CogVLM
    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 SkunkworksAI/BakLLaVA?
    pass
    AI named SkunkworksAI/BakLLaVA explicitly

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

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

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

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