RRepoGEO

REPOGEO REPORT · LITE

wgwang/awesome-LLMs-In-China

Default branch main · commit f2a1119c · scanned 5/10/2026, 12:37:38 PM

GitHub: 6,446 stars · 558 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 wgwang/awesome-LLMs-In-China, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    awesome-list, llm, large-language-models, china, chinese-llms, ai, artificial-intelligence, awesome
  • highreadme#2
    Clarify the README's opening sentence to emphasize "awesome list" nature

    Why:

    CURRENT
    中国大模型大全，全面收集有明确来源的大模型情况，包括机构、来源信息和分类等，随时更新。
    COPY-PASTE FIX
    这是一个精心策划的中国大模型列表（Awesome List），全面收集并持续更新有明确来源的大模型情况，包括机构、来源信息和分类等。
  • mediumabout#3
    Expand the repository's "About" description

    Why:

    CURRENT
    中国大模型
    COPY-PASTE FIX
    中国大模型列表：一个全面收集并持续更新的中国大语言模型（LLMs）精选资源库。

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 wgwang/awesome-LLMs-In-China
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Hub
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Hub · recommended 1×
  2. Qwen · recommended 1×
  3. Baichuan · recommended 1×
  4. ChatGLM · recommended 1×
  5. Yi · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of large language models developed in China?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Hub
    2. Qwen
    3. Baichuan
    4. ChatGLM
    5. Yi
    6. DeepSeek
    7. Pangu-Σ
    8. SenseChat
    9. MiniCPM
    10. arXiv
    11. ACL Anthology
    12. Google Scholar
    13. TechNode
    14. Caixin Global
    15. SCMP Tech
    16. ERNIE Bot
    17. Hunyuan
    18. Tongyi Qianwen
    19. GitHub Repositories
    20. Wikipedia
    21. LessWrong
    22. The Batch by DeepLearning.AI

    AI recommended 22 alternatives but never named wgwang/awesome-LLMs-In-China. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the leading general-purpose AI models originating from Chinese companies?
    you: not recommended
    AI recommended (in order):
    1. ERNIE Bot (文心一言)
    2. Tongyi Qianwen (通义千问)
    3. SenseChat (商量)
    4. Zhipu AI's GLM (智谱AI)
    5. Pangu-α (盘古大模型)
    6. iFlytek Spark (讯飞星火)

    AI recommended 6 alternatives but never named wgwang/awesome-LLMs-In-China. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 wgwang/awesome-LLMs-In-China?
    pass
    AI did not name wgwang/awesome-LLMs-In-China — 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?

  • If a team adopts wgwang/awesome-LLMs-In-China in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wgwang/awesome-LLMs-In-China 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 wgwang/awesome-LLMs-In-China solve, and who is the primary audience?
    pass
    AI did not name wgwang/awesome-LLMs-In-China — 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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wgwang/awesome-LLMs-In-China — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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