RRepoGEO

REPOGEO REPORT · LITE

wenge-research/YAYI

Default branch main · commit 538ffa54 · scanned 5/25/2026, 11:38:12 AM

GitHub: 2,530 stars · 46 forks

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 wenge-research/YAYI, 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 the core value proposition in the README's opening

    Why:

    CURRENT
    The current README's '介绍' (Introduction) describes the model's training and capabilities, but the explicit 'Chinese optimized' aspect is in a 'News' section.
    COPY-PASTE FIX
    Integrate a clear, concise statement about YAYI being a leading open-source Chinese LLM optimized for enterprise and multi-domain applications directly under the main title or in the first paragraph of the '介绍' section. For example: '雅意大模型是中科闻歌算法团队研发的、为客户打造安全可靠的专属大模型，基于大规模中英文多领域指令数据训练的 LlaMA 2 & BLOOM 系列模型，尤其针对中文企业级应用场景进行了深度优化。'
  • mediumreadme#2
    Add a 'Key Features' or 'Why YAYI?' section

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## 核心优势 (Key Advantages)' or '## 为什么选择雅意 (Why Choose YAYI?)', detailing its specific strengths like '企业级多领域数据优化 (Enterprise-grade multi-domain data optimization)', '高安全性与可靠性 (High security and reliability)', '中文基础能力与领域分析能力 (Strong Chinese foundational and domain analysis capabilities)'.
  • lowtopics#3
    Add more specific domain/application topics

    Why:

    CURRENT
    bloom, chat, chinese, llama, llama2, llm, lora, yayi
    COPY-PASTE FIX
    bloom, chat, chinese, llama, llama2, llm, lora, yayi, enterprise-llm, domain-specific-llm, nlp-for-business

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 wenge-research/YAYI
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Baichuan 2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Baichuan 2 · recommended 1×
  2. Qwen · recommended 1×
  3. ChatGLM3 · recommended 1×
  4. Yi · recommended 1×
  5. InternLM2 · recommended 1×
  • CATEGORY QUERY
    What are good open-source large language models specifically optimized for Chinese text generation?
    you: not recommended
    AI recommended (in order):
    1. Baichuan 2
    2. Qwen
    3. ChatGLM3
    4. Yi
    5. InternLM2

    AI recommended 5 alternatives but never named wenge-research/YAYI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a foundation model suitable for building conversational AI in Chinese enterprise scenarios.
    you: not recommended
    AI recommended (in order):
    1. ERNIE 4.0
    2. SenseChat
    3. Tongyi Qianwen
    4. GLM-4
    5. MiniMax
    6. GPT-4

    AI recommended 6 alternatives but never named wenge-research/YAYI. 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 wenge-research/YAYI?
    pass
    AI named wenge-research/YAYI explicitly

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

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

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

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wenge-research/YAYI — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite