RRepoGEO

REPOGEO REPORT · LITE

Instruction-Tuning-with-GPT-4/GPT-4-LLM

Default branch main · commit 80cda626 · scanned 5/20/2026, 5:52:51 AM

GitHub: 4,336 stars · 309 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
20 /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
0 / 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 Instruction-Tuning-with-GPT-4/GPT-4-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.

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
    Update README H1 and description to include 'GPT-4-LLM' and explicitly state 'dataset'

    Why:

    CURRENT
    Description: "Instruction Tuning with GPT-4"
README H1: "# Instruction Tuning with GPT-4"
    COPY-PASTE FIX
    Description: "GPT-4-LLM: High-quality instruction-following dataset generated by GPT-4 for LLM fine-tuning."
README H1: "# GPT-4-LLM: Instruction-Following Dataset Generated by GPT-4"
  • mediumreadme#2
    Clarify data license in README's license section

    Why:

    COPY-PASTE FIX
    ## License
The code in this repository is licensed under Apache-2.0.
The instruction-following dataset (English and Chinese) is licensed under CC BY NC 4.0 (Creative Commons Attribution-NonCommercial 4.0 International License), allowing non-commercial research use only. Models trained using this dataset should also be used for research purposes only.
  • lowtopics#3
    Add 'dataset' and 'multilingual' to topics

    Why:

    CURRENT
    alpaca, chatgpt, gpt-4, instruction-tuning, llama
    COPY-PASTE FIX
    alpaca, chatgpt, gpt-4, instruction-tuning, llama, dataset, multilingual

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 Instruction-Tuning-with-GPT-4/GPT-4-LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Alpaca (Stanford Alpaca)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Alpaca (Stanford Alpaca) · recommended 1×
  2. Dolly 2.0 (Databricks Dolly 2.0) · recommended 1×
  3. ShareGPT (ShareGPT.com) · recommended 1×
  4. OpenAssistant Conversations Dataset (OASST1) · recommended 1×
  5. FLAN (Fine-tuned LAnguage Net) · recommended 1×
  • CATEGORY QUERY
    Need high-quality instruction-following datasets to train my own large language model for research.
    you: not recommended
    AI recommended (in order):
    1. Alpaca (Stanford Alpaca)
    2. Dolly 2.0 (Databricks Dolly 2.0)
    3. ShareGPT (ShareGPT.com)
    4. OpenAssistant Conversations Dataset (OASST1)
    5. FLAN (Fine-tuned LAnguage Net)
    6. P3 (Public Pool of Prompts)

    AI recommended 6 alternatives but never named Instruction-Tuning-with-GPT-4/GPT-4-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking diverse instruction-response data to improve custom language model performance across languages.
    you: not recommended
    AI recommended (in order):
    1. xLAM Dataset
    2. FLORES-200 Dataset
    3. XNLI Dataset
    4. WikiLingua
    5. TyDi QA
    6. mT5 Pre-training Data

    AI recommended 6 alternatives but never named Instruction-Tuning-with-GPT-4/GPT-4-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 Instruction-Tuning-with-GPT-4/GPT-4-LLM?
    pass
    AI did not name Instruction-Tuning-with-GPT-4/GPT-4-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 Instruction-Tuning-with-GPT-4/GPT-4-LLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name Instruction-Tuning-with-GPT-4/GPT-4-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?

  • In one sentence, what problem does the repo Instruction-Tuning-with-GPT-4/GPT-4-LLM solve, and who is the primary audience?
    pass
    AI did not name Instruction-Tuning-with-GPT-4/GPT-4-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?

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Instruction-Tuning-with-GPT-4/GPT-4-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