RRepoGEO

REPOGEO REPORT · LITE

llm-jp/awesome-japanese-llm

Default branch main · commit 972f155e · scanned 6/21/2026, 3:18:35 PM

GitHub: 1,409 stars · 46 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
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 llm-jp/awesome-japanese-llm, 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.

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 llm-jp/awesome-japanese-llm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
rinna/japanese-gpt-neox-3.6b
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. rinna/japanese-gpt-neox-3.6b · recommended 2×
  2. ELYZA-japanese-Llama-2-7b · recommended 1×
  3. Japanese StableLM Alpha (7B) · recommended 1×
  4. CyberAgent/calm2-7b · recommended 1×
  5. LINE-DistilBERT-base-japanese · recommended 1×
  • CATEGORY QUERY
    What are some highly-rated open-source large language models optimized for Japanese language tasks?
    you: not recommended
    AI recommended (in order):
    1. ELYZA-japanese-Llama-2-7b
    2. Japanese StableLM Alpha (7B)
    3. rinna/japanese-gpt-neox-3.6b
    4. CyberAgent/calm2-7b
    5. LINE-DistilBERT-base-japanese

    AI recommended 5 alternatives but never named llm-jp/awesome-japanese-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive overview of generative AI models supporting Japanese text processing?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Models
    2. rinna/japanese-gpt-neox-3.6b
    3. cyberagent/open-calm-7b
    4. stabilityai/japanese-stablelm-instruct-beta-70b
    5. GPT-2
    6. GPT-NeoX
    7. LLaMA
    8. Papers With Code
    9. PaLM 2
    10. LaMDA
    11. GPT-3.5
    12. GPT-4
    13. Claude models
    14. Claude 2
    15. Claude 3

    AI recommended 15 alternatives but never named llm-jp/awesome-japanese-llm. 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 llm-jp/awesome-japanese-llm?
    pass
    AI did not name llm-jp/awesome-japanese-llm — 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 llm-jp/awesome-japanese-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named llm-jp/awesome-japanese-llm 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 llm-jp/awesome-japanese-llm solve, and who is the primary audience?
    pass
    AI did not name llm-jp/awesome-japanese-llm — 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 llm-jp/awesome-japanese-llm. 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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llm-jp/awesome-japanese-llm — 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