RRepoGEO

REPOGEO REPORT · LITE

charent/ChatLM-mini-Chinese

Default branch main · commit 2696eb8d · scanned 5/21/2026, 10:18:08 PM

GitHub: 1,713 stars · 196 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
28 /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
2 / 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 charent/ChatLM-mini-Chinese, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add specific and accurate topics, removing misleading ones

    Why:

    CURRENT
    chatbot, language-model, t5-model, text-generation
    COPY-PASTE FIX
    chatbot, language-model, text-generation, chinese-llm, small-llm, llama, finetuning, information-extraction, rlhf, dpo
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/charent/ChatLM-mini-Chinese

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 charent/ChatLM-mini-Chinese
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Qwen1.5-0.5B-Chat / Qwen1.5-1.8B-Chat
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Qwen1.5-0.5B-Chat / Qwen1.5-1.8B-Chat · recommended 1×
  2. Baichuan2-7B-Chat · recommended 1×
  3. ChatGLM3-6B · recommended 1×
  4. Phi-2 · recommended 1×
  5. TinyLlama-1.1B · recommended 1×
  • CATEGORY QUERY
    Looking for a small, efficient Chinese language model trainable on consumer GPUs.
    you: not recommended
    AI recommended (in order):
    1. Qwen1.5-0.5B-Chat / Qwen1.5-1.8B-Chat
    2. Baichuan2-7B-Chat
    3. ChatGLM3-6B
    4. Phi-2
    5. TinyLlama-1.1B

    AI recommended 5 alternatives but never named charent/ChatLM-mini-Chinese. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I fine-tune a compact generative model for Chinese information extraction tasks?
    you: not recommended
    AI recommended (in order):
    1. BART-base-Chinese
    2. mT5-base
    3. CPM-Generate
    4. BERT-base-Chinese
    5. ELECTRA-base-Chinese
    6. MiniCPM

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

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

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charent/ChatLM-mini-Chinese — 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