RRepoGEO

REPOGEO REPORT · LITE

ymcui/Chinese-Mixtral

Default branch main · commit 263da0ec · scanned 6/7/2026, 1:56:52 AM

GitHub: 613 stars · 43 forks

AI VISIBILITY SCORE
27 /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
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 ymcui/Chinese-Mixtral, 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 project description to the top of the README

    Why:

    CURRENT
    The README currently starts with navigation links and a large blank space before the core project description.
    COPY-PASTE FIX
    Move the paragraph starting with '本项目基于Mistral.ai发布的Mixtral模型进行开发...' to immediately follow any title or badges, making it the first substantive text in the README.
  • mediumreadme#2
    Add a dedicated comparison section in the README

    Why:

    COPY-PASTE FIX
    Add a new section titled '## 与其他中文MoE/长文本LLM对比 (Comparison with other Chinese MoE/Long-Context LLMs)' and briefly highlight how Chinese-Mixtral stands out in terms of training, context window, or efficiency.
  • mediumreadme#3
    Move the list of other projects to a separate section

    Why:

    CURRENT
    中文LLaMA-2&Alpaca-2大模型 | 中文LLaMA&Alpaca大模型 | 多模态中文LLaMA&Alpaca大模型 | 多模态VLE | 中文MiniRBT | 中文LERT | 中英文PERT | 中文MacBERT | 中文ELECTRA | 中文XLNet | 中文BERT | 知识蒸馏工具TextBrewer | 模型裁剪工具TextPruner | 蒸馏裁剪一体化GRAIN
    COPY-PASTE FIX
    Create a new section '## 其他项目 (Other Projects)' at the end of the README and place this list there, ensuring the main body of the README focuses solely on `Chinese-Mixtral`.

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 ymcui/Chinese-Mixtral
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSeek-MoE
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSeek-MoE · recommended 1×
  2. Mixtral 8x7B · recommended 1×
  3. Qwen-MoE · recommended 1×
  4. Yi-MoE · recommended 1×
  5. GLM-MoE · recommended 1×
  • CATEGORY QUERY
    Seeking a large language model with Mixture-of-Experts architecture for Chinese text processing.
    you: not recommended
    AI recommended (in order):
    1. DeepSeek-MoE
    2. Mixtral 8x7B
    3. Qwen-MoE
    4. Yi-MoE
    5. GLM-MoE

    AI recommended 5 alternatives but never named ymcui/Chinese-Mixtral. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Which LLMs offer long context windows for Chinese and can be deployed efficiently?
    you: not recommended
    AI recommended (in order):
    1. Kimi Chat
    2. DeepSeek-V2
    3. Yi-Large
    4. GLM-4
    5. Qwen2
    6. GPT-4o/GPT-4 Turbo

    AI recommended 6 alternatives but never named ymcui/Chinese-Mixtral. 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 ymcui/Chinese-Mixtral?
    pass
    AI did not name ymcui/Chinese-Mixtral — 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 ymcui/Chinese-Mixtral in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ymcui/Chinese-Mixtral 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 ymcui/Chinese-Mixtral solve, and who is the primary audience?
    pass
    AI did not name ymcui/Chinese-Mixtral — 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?

Embed your GEO score

Drop this badge into the README of ymcui/Chinese-Mixtral. 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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MARKDOWN (README)
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ymcui/Chinese-Mixtral — 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