RRepoGEO

REPOGEO REPORT · LITE

OpenGVLab/LLaMA-Adapter

Default branch main · commit 521a09da · scanned 6/21/2026, 8:56:57 PM

GitHub: 5,921 stars · 382 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
35 /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
3 / 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 OpenGVLab/LLaMA-Adapter, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, fine-tuning, parameter-efficient-fine-tuning, peft, llama, multimodal, instruction-following, deep-learning, machine-learning, iclr-2024
  • highreadme#2
    Reposition README H1 to explicitly state PEFT category

    Why:

    CURRENT
    # LLaMA-Adapter: Efficient Fine-tuning of LLaMA 🚀
    COPY-PASTE FIX
    # LLaMA-Adapter: A Parameter-Efficient Fine-tuning (PEFT) Method for LLaMA 🚀
  • mediumhomepage#3
    Add the Hugging Face demo link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://huggingface.co/spaces/csuhan/LLaMA-Adapter

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 OpenGVLab/LLaMA-Adapter
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LoRA
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LoRA · recommended 2×
  2. QLoRA · recommended 2×
  3. Hugging Face PEFT Library · recommended 1×
  4. Axolotl · recommended 1×
  5. bitsandbytes · recommended 1×
  • CATEGORY QUERY
    How to efficiently fine-tune large language models for instruction following with minimal parameters?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. Hugging Face PEFT Library
    3. Axolotl
    4. QLoRA
    5. bitsandbytes
    6. IA3
    7. Prefix-Tuning
    8. P-Tuning v2

    AI recommended 8 alternatives but never named OpenGVLab/LLaMA-Adapter. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are lightweight methods for adapting large language models to multimodal tasks quickly?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. QLoRA
    3. Adapter-Transformers library
    4. Prompt Tuning / Prefix Tuning
    5. PEFT library (huggingface/peft)
    6. BLIP
    7. LLaVA

    AI recommended 7 alternatives but never named OpenGVLab/LLaMA-Adapter. 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 OpenGVLab/LLaMA-Adapter?
    pass
    AI named OpenGVLab/LLaMA-Adapter explicitly

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

  • If a team adopts OpenGVLab/LLaMA-Adapter in production, what risks or prerequisites should they evaluate first?
    pass
    AI named OpenGVLab/LLaMA-Adapter 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 OpenGVLab/LLaMA-Adapter solve, and who is the primary audience?
    pass
    AI named OpenGVLab/LLaMA-Adapter explicitly

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

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OpenGVLab/LLaMA-Adapter — 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