RRepoGEO

REPOGEO REPORT · LITE

sail-sg/lorahub

Default branch main · commit df73afe5 · scanned 6/9/2026, 9:37:08 AM

GitHub: 671 stars · 43 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 sail-sg/lorahub, 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 the repository

    Why:

    COPY-PASTE FIX
    lora, llm, parameter-efficient-fine-tuning, peft, model-composition, machine-learning, deep-learning, generative-ai, adapter-composition, cross-task-generalization
  • highreadme#2
    Reposition the README's opening sentence to emphasize unique value

    Why:

    CURRENT
    The official repository which contains the code and pre-trained models for our paper LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition.
    COPY-PASTE FIX
    LoraHub is a novel framework for **efficient cross-task generalization via dynamic LoRA composition**, enabling large language models to perform well on unseen tasks by intelligently combining multiple LoRA modules without extra training. Unlike general PEFT libraries, LoRAHub focuses on the **composition and dynamic deployment of LoRA adapters** for robust, few-shot performance.
  • mediumreadme#3
    Add a sentence to the README differentiating LoRAHub from common alternatives

    Why:

    COPY-PASTE FIX
    Unlike general parameter-efficient fine-tuning (PEFT) libraries or adapter hubs, LoRAHub specifically focuses on the **dynamic composition and efficient deployment of multiple LoRA modules** to achieve superior cross-task generalization and few-shot performance on unseen tasks.

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 sail-sg/lorahub
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PEFT (Parameter-Efficient Fine-Tuning)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PEFT (Parameter-Efficient Fine-Tuning) · recommended 1×
  2. LoRAX (LoRA eXchange) · recommended 1×
  3. AdapterHub · recommended 1×
  4. OpenLoRA · recommended 1×
  5. PyTorch/TensorFlow · recommended 1×
  • CATEGORY QUERY
    Seeking a framework for dynamically composing low-rank adaptations across diverse tasks.
    you: not recommended
    AI recommended (in order):
    1. PEFT (Parameter-Efficient Fine-Tuning)
    2. LoRAX (LoRA eXchange)
    3. AdapterHub
    4. OpenLoRA
    5. PyTorch/TensorFlow

    AI recommended 5 alternatives but never named sail-sg/lorahub. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to efficiently generalize large language models to unseen tasks using existing LoRA?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face `peft` library (huggingface/peft)
    2. `mergekit` (cg123/mergekit)
    3. `learn2learn` (learn2learn/learn2learn)
    4. AdaLoRA
    5. QLoRA
    6. IA3
    7. Prefix Tuning

    AI recommended 7 alternatives but never named sail-sg/lorahub. 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 sail-sg/lorahub?
    pass
    AI named sail-sg/lorahub explicitly

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

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

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

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sail-sg/lorahub — 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