RRepoGEO

REPOGEO REPORT · LITE

LinkSoul-AI/Chinese-Llama-2-7b

Default branch main · commit a2749381 · scanned 6/18/2026, 10:43:08 PM

GitHub: 2,205 stars · 197 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 LinkSoul-AI/Chinese-Llama-2-7b, 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
    Add a concise English value proposition to the README's opening

    Why:

    COPY-PASTE FIX
    This repository provides the first fully open-source and commercially usable Chinese Llama 2 7B model, along with comprehensive Chinese and English SFT datasets. It strictly follows the `llama-2-chat` input format, ensuring compatibility with all optimizations for the original `llama-2-chat` models.
  • highhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/LinkSoul/Chinese-Llama-2-7b
  • mediumtopics#3
    Add more specific topics for Chinese LLMs and commercial use

    Why:

    CURRENT
    deep-learning, llama2, llama2-docker, llm, pytorch
    COPY-PASTE FIX
    deep-learning, llama2, llama2-docker, llm, pytorch, chinese-llm, chinese-nlp, large-language-model, commercial-use

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 LinkSoul-AI/Chinese-Llama-2-7b
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Yi-34B-Chat
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Yi-34B-Chat · recommended 2×
  2. Baichuan 2 · recommended 1×
  3. Qwen · recommended 1×
  4. ChatGLM3 · recommended 1×
  5. InternLM2 · recommended 1×
  • CATEGORY QUERY
    What are the best open-source large language models for Chinese text generation?
    you: not recommended
    AI recommended (in order):
    1. Baichuan 2
    2. Qwen
    3. ChatGLM3
    4. Yi-34B-Chat
    5. InternLM2

    AI recommended 5 alternatives but never named LinkSoul-AI/Chinese-Llama-2-7b. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a commercially usable LLaMA-compatible model for Chinese language applications with easy deployment.
    you: not recommended
    AI recommended (in order):
    1. Yi-34B-Chat
    2. Qwen-14B-Chat
    3. Baichuan2-13B-Chat
    4. DeepSeek-V2
    5. InternLM2-7B-Chat
    6. Hugging Face
    7. transformers
    8. vLLM
    9. TGI
    10. llama.cpp

    AI recommended 10 alternatives but never named LinkSoul-AI/Chinese-Llama-2-7b. 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 LinkSoul-AI/Chinese-Llama-2-7b?
    pass
    AI did not name LinkSoul-AI/Chinese-Llama-2-7b — 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 LinkSoul-AI/Chinese-Llama-2-7b in production, what risks or prerequisites should they evaluate first?
    pass
    AI named LinkSoul-AI/Chinese-Llama-2-7b 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 LinkSoul-AI/Chinese-Llama-2-7b solve, and who is the primary audience?
    pass
    AI did not name LinkSoul-AI/Chinese-Llama-2-7b — 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

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LinkSoul-AI/Chinese-Llama-2-7b — 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