RRepoGEO

REPOGEO REPORT · LITE

cambrian-mllm/cambrian

Default branch main · commit 539ffc32 · scanned 6/26/2026, 4:22:30 PM

GitHub: 2,005 stars · 138 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 cambrian-mllm/cambrian, 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
  • highreadme#1
    Reposition README's opening to highlight unique value and use cases

    Why:

    COPY-PASTE FIX
    Immediately after the main title, add: "Cambrian-1 is a family of fully open, vision-centric multimodal LLMs designed to dynamically query and integrate external knowledge bases *before* generating responses. It offers a modular framework for developing, training, and evaluating MLLMs, making it particularly effective for advanced computer vision tasks and instruction tuning."
  • mediumreadme#2
    Add specific use case examples to the README

    Why:

    COPY-PASTE FIX
    Add a section titled 'Key Use Cases' or 'Applications' with bullet points such as:
    - **Advanced Computer Vision Tasks:** Leverage Cambrian-1's vision-centric design for complex image understanding and analysis.
    - **Multimodal Instruction Tuning:** Fine-tune models with combined text and image inputs for highly specific tasks.
    - **Dynamic Knowledge Integration:** Build MLLMs that can consult external data sources for more informed and accurate responses.
    - **MLLM Development & Evaluation:** Utilize a modular framework to streamline the creation and assessment of new multimodal models.
  • lowtopics#3
    Expand GitHub topics with more specific keywords

    Why:

    CURRENT
    chatbot, clip, computer-vision, dino, instruction-tuning, large-language-models, llms, mllm, multimodal-large-language-models, representation-learning
    COPY-PASTE FIX
    chatbot, clip, computer-vision, dino, instruction-tuning, large-language-models, llms, mllm, multimodal-large-language-models, representation-learning, vision-language-models, multimodal-ai, image-text-understanding, visual-qa, knowledge-augmented-llm

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 cambrian-mllm/cambrian
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LLaVA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LLaVA · recommended 2×
  2. CogVLM · recommended 2×
  3. Fuyu-8B · recommended 2×
  4. MiniGPT-4 · recommended 1×
  5. MiniGPT-v2 · recommended 1×
  • CATEGORY QUERY
    What open-source multimodal large language models are best for computer vision tasks?
    you: not recommended
    AI recommended (in order):
    1. LLaVA
    2. CogVLM
    3. Fuyu-8B
    4. MiniGPT-4
    5. MiniGPT-v2
    6. BakLLaVA

    AI recommended 6 alternatives but never named cambrian-mllm/cambrian. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    I need an LLM that can process both text and images for advanced instruction tuning.
    you: not recommended
    AI recommended (in order):
    1. GPT-4V
    2. Gemini
    3. LLaVA
    4. Fuyu-8B
    5. CogVLM

    AI recommended 5 alternatives but never named cambrian-mllm/cambrian. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 cambrian-mllm/cambrian?
    pass
    AI named cambrian-mllm/cambrian explicitly

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

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

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

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MARKDOWN (README)
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cambrian-mllm/cambrian — 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