RRepoGEO

REPOGEO REPORT · LITE

CVI-SZU/Linly

Default branch main · commit ad223a75 · scanned 5/10/2026, 12:41:49 PM

GitHub: 3,052 stars · 228 forks

AI VISIBILITY SCORE
35 /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
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 CVI-SZU/Linly, 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
    Clarify the overall project license in the README

    Why:

    COPY-PASTE FIX
    Add a clear statement at the top of the README, or in a dedicated 'License' section, specifying the license(s) that apply to the entire Linly project and its components. For example: 'The Linly project, including Chinese-LLaMA and Chinese-Falcon models, is released under [Specify License Here]. The Linly-OpenLLaMA models are released under the Apache 2.0 License.'
  • highhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a project website, a dedicated documentation page, or a prominent blog post) to the 'Website' field in the repository's 'About' section.
  • mediumreadme#3
    Strengthen the README's opening statement to emphasize its role as a leading Chinese LLM project

    Why:

    CURRENT
    本项目向社区提供**中文对话模型 Linly-ChatFlow 、中文基础模型 Chinese-LLaMA (1-2)、Chinese-Falcon 及其训练数据**。
    COPY-PASTE FIX
    Linly is a comprehensive open-source project dedicated to advancing Chinese Large Language Models (LLMs), providing state-of-the-art **Chinese conversational models (Linly-ChatFlow), foundational models (Chinese-LLaMA 1&2, Chinese-Falcon), and high-quality training datasets** to the community.

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 CVI-SZU/Linly
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. ChatGLM · recommended 1×
  4. InternLM · recommended 1×
  5. Pangu-Σ · recommended 1×
  • CATEGORY QUERY
    Need robust open-source large language models for advanced Chinese natural language processing applications.
    you: not recommended
    AI recommended (in order):
    1. Baichuan 2
    2. Qwen
    3. ChatGLM
    4. InternLM
    5. Pangu-Σ
    6. MOSS

    AI recommended 6 alternatives but never named CVI-SZU/Linly. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking resources to develop a custom Chinese conversational AI with efficient deployment options.
    you: not recommended
    AI recommended (in order):
    1. Rasa Open Source
    2. Hugging Face Transformers
    3. PaddleNLP
    4. DeepPavlov
    5. OpenAI API
    6. Google Cloud Dialogflow CX
    7. Microsoft Azure Bot Service
    8. Azure Cognitive Services

    AI recommended 8 alternatives but never named CVI-SZU/Linly. 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 CVI-SZU/Linly?
    pass
    AI named CVI-SZU/Linly explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of CVI-SZU/Linly. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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CVI-SZU/Linly — 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