RRepoGEO

REPOGEO REPORT · LITE

AdaBit-AI/parameter_efficient_instruction_tuning

Default branch validation_ni_adapterpeft · commit d7178497 · scanned 6/7/2026, 10:17:52 AM

GitHub: 600 stars · 74 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 AdaBit-AI/parameter_efficient_instruction_tuning, 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
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    An empirical study systematically comparing various parameter-efficient fine-tuning (PEFT) methods for instruction tuning tasks, using the NI dataset as a benchmark.
  • mediumreadme#2
    Clarify README's opening statement to emphasize comparison

    Why:

    CURRENT
    ## Parameter efficient instruction tuning: an Empirical Study
This repository serves as an effort to systematically to compare different parameter efficient fine-tuning methods on instruction tuning task. We use the NI dataset as the benchmark dataset. The technical report can be found here
    COPY-PASTE FIX
    ## Parameter-Efficient Instruction Tuning: An Empirical Study and Comparison
This repository presents an empirical study systematically comparing various existing parameter-efficient fine-tuning (PEFT) methods on instruction tuning tasks. It is *not* a new PEFT method, but rather a benchmark and analysis framework. We use the NI dataset as the benchmark dataset. The technical report can be found here.

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 AdaBit-AI/parameter_efficient_instruction_tuning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LoRA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LoRA · recommended 1×
  2. QLoRA · recommended 1×
  3. IA3 · recommended 1×
  4. Prefix-Tuning · recommended 1×
  5. P-Tuning v2 · recommended 1×
  • CATEGORY QUERY
    Which parameter-efficient fine-tuning methods are most effective for instruction tuning tasks?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. QLoRA
    3. IA3
    4. Prefix-Tuning
    5. P-Tuning v2
    6. Houlsby Adapters
    7. Pfeiffer Adapters

    AI recommended 7 alternatives but never named AdaBit-AI/parameter_efficient_instruction_tuning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an empirical comparison of various PEFT techniques for instruction-following models.
    you: not recommended
    AI recommended (in order):
    1. PEFT (Hugging Face Library)

    AI recommended 1 alternative but never named AdaBit-AI/parameter_efficient_instruction_tuning. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 AdaBit-AI/parameter_efficient_instruction_tuning?
    pass
    AI named AdaBit-AI/parameter_efficient_instruction_tuning explicitly

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

  • If a team adopts AdaBit-AI/parameter_efficient_instruction_tuning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AdaBit-AI/parameter_efficient_instruction_tuning 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 AdaBit-AI/parameter_efficient_instruction_tuning solve, and who is the primary audience?
    pass
    AI did not name AdaBit-AI/parameter_efficient_instruction_tuning — 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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AdaBit-AI/parameter_efficient_instruction_tuning — 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