RRepoGEO

REPOGEO REPORT · LITE

OpenGVLab/LLaMA-Adapter

Default branch main · commit 521a09da · scanned 5/11/2026, 4:02:25 PM

GitHub: 5,923 stars · 382 forks

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 relevant topics to improve discoverability

    Why:

    COPY-PASTE FIX
    LLM, fine-tuning, parameter-efficient-tuning, PEFT, LLaMA, instruction-following, multimodal, deep-learning, machine-learning, AI, ICLR-2024
  • highreadme#2
    Clarify the core problem LLaMA-Adapter solves and its unique approach in the README's opening

    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 a homepage URL to the repository's 'About' section

    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. IA³ · recommended 1×
  4. Prefix-Tuning · recommended 1×
  5. P-Tuning v2 · 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. QLoRA
    3. IA³
    4. Prefix-Tuning
    5. P-Tuning v2
    6. Houlsby Adapters
    7. Pfeiffer Adapters
    8. BitFit

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

    Show full AI answer
  • CATEGORY QUERY
    Looking for efficient ways to create instruction-following or multimodal large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Accelerate (huggingface/accelerate)
    3. PyTorch Lightning (Lightning-AI/lightning)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. JAX (google/jax)
    6. Flax (google/flax)
    7. OpenAI API
    8. LoRA
    9. QLoRA
    10. Hugging Face PEFT (huggingface/peft)

    AI recommended 10 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?

Embed your GEO score

Drop this badge into the README of OpenGVLab/LLaMA-Adapter. 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/OpenGVLab/LLaMA-Adapter.svg)](https://repogeo.com/en/r/OpenGVLab/LLaMA-Adapter)
HTML
<a href="https://repogeo.com/en/r/OpenGVLab/LLaMA-Adapter"><img src="https://repogeo.com/badge/OpenGVLab/LLaMA-Adapter.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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