RRepoGEO

REPOGEO REPORT · LITE

inclusionAI/LLaDA2.0-Uni

Default branch main · commit 3457030a · scanned 6/7/2026, 2:13:00 PM

GitHub: 761 stars · 48 forks

AI VISIBILITY SCORE
23 /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
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 inclusionAI/LLaDA2.0-Uni, 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
  • highlicense#1
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    MIT License
  • mediumabout#2
    Refine the repository description for clarity and keywords

    Why:

    CURRENT
    LLaDA2.0-Uni: Understanding and Generation the World.
    COPY-PASTE FIX
    LLaDA2.0-Uni is a unified dLLM-based Mixture-of-Experts (MoE) model for multimodal understanding, text-to-image generation, and image editing, with support for quantized models.

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 inclusionAI/LLaDA2.0-Uni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Gemini
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Gemini · recommended 1×
  2. DALL-E 3 · recommended 1×
  3. GPT-4V · recommended 1×
  4. Kosmos-2 · recommended 1×
  5. Florence-2 · recommended 1×
  • CATEGORY QUERY
    How to achieve unified multimodal understanding and generation using a diffusion large language model?
    you: not recommended
    AI recommended (in order):
    1. Gemini
    2. DALL-E 3
    3. GPT-4V
    4. Kosmos-2
    5. Florence-2
    6. ImageBind
    7. SeamlessM4T
    8. Hugging Face Transformers
    9. Llama 3
    10. Mistral
    11. Falcon
    12. Stable Diffusion XL
    13. Kandinsky
    14. BLIP-2
    15. LLaVA

    AI recommended 15 alternatives but never named inclusionAI/LLaDA2.0-Uni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient multimodal model for text-to-image generation and image editing, with quantization support.
    you: not recommended
    AI recommended (in order):
    1. Stable Diffusion
    2. Diffusers (huggingface/diffusers)
    3. DeepFloyd IF
    4. Kandinsky 2.x
    5. SDXL
    6. Mini-DALL-E (Craiyon)

    AI recommended 6 alternatives but never named inclusionAI/LLaDA2.0-Uni. 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 inclusionAI/LLaDA2.0-Uni?
    pass
    AI named inclusionAI/LLaDA2.0-Uni explicitly

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

  • If a team adopts inclusionAI/LLaDA2.0-Uni in production, what risks or prerequisites should they evaluate first?
    pass
    AI named inclusionAI/LLaDA2.0-Uni 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 inclusionAI/LLaDA2.0-Uni solve, and who is the primary audience?
    pass
    AI did not name inclusionAI/LLaDA2.0-Uni — 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?

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inclusionAI/LLaDA2.0-Uni — 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