RRepoGEO

REPOGEO REPORT · LITE

Beomi/KoAlpaca

Default branch main · commit fb5c84e2 · scanned 6/23/2026, 9:48:06 PM

GitHub: 1,576 stars · 227 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
67 /100
Needs work
Category recall
2 / 2
Avg rank #5.5 when recommended
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 Beomi/KoAlpaca, 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, prominent value proposition to the README's opening

    Why:

    COPY-PASTE FIX
    Add the following sentence at the very top of the README, before any update logs or detailed sections: 'KoAlpaca is a leading open-source large language model designed to understand and follow instructions in Korean, offering pre-trained models and practical guides for efficient fine-tuning on consumer GPUs.'
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add the URL for the KoAlpaca Hugging Face model page (e.g., `https://huggingface.co/beomi/KoAlpaca-Polyglot-5.8B`) or a dedicated project website to the repository's 'About' section.
  • lowtopics#3
    Expand repository topics to include fine-tuning and GPU-specific terms

    Why:

    CURRENT
    alpaca, chatkoalpaca, koalpaca, korean-nlp, llama, polyglot-ko
    COPY-PASTE FIX
    Add `fine-tuning`, `qlora`, `peft`, `consumer-gpu`, `llm-training` to the existing topics. The full list should be: `alpaca`, `chatkoalpaca`, `koalpaca`, `korean-nlp`, `llama`, `polyglot-ko`, `fine-tuning`, `qlora`, `peft`, `consumer-gpu`, `llm-training`.

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
2 / 2
100% of queries surface Beomi/KoAlpaca
Avg rank
#5.5
Lower is better. #1 = top recommendation.
Share of voice
13%
Of all named tools, what % are you?
Top rival
Polyglot-Ko
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Polyglot-Ko · recommended 1×
  2. KULLM · recommended 1×
  3. Open-Ko-LLaMA · recommended 1×
  4. Hugging Face Hub · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    Looking for an open-source large language model that understands instructions in Korean.
    you: #2
    AI recommended (in order):
    1. Polyglot-Ko
    2. KoAlpaca ← you
    3. KULLM
    4. Open-Ko-LLaMA
    5. Hugging Face Hub
    Show full AI answer
  • CATEGORY QUERY
    How to fine-tune an instruction-following language model for the Korean language on consumer GPUs?
    you: #9
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. LoRA
    4. bitsandbytes
    5. DeepSpeed
    6. Accelerate
    7. PyTorch
    8. Polyglot-ko
    9. KoAlpaca ← you
    10. SKT-AI/KoGPT
    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 Beomi/KoAlpaca?
    pass
    AI did not name Beomi/KoAlpaca — 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 Beomi/KoAlpaca in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Beomi/KoAlpaca 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 Beomi/KoAlpaca solve, and who is the primary audience?
    pass
    AI named Beomi/KoAlpaca 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 Beomi/KoAlpaca. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Beomi/KoAlpaca.svg)](https://repogeo.com/en/r/Beomi/KoAlpaca)
HTML
<a href="https://repogeo.com/en/r/Beomi/KoAlpaca"><img src="https://repogeo.com/badge/Beomi/KoAlpaca.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

Beomi/KoAlpaca — 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