RRepoGEO

REPOGEO REPORT · LITE

luogen1996/LaVIN

Default branch main · commit dd0a1bfc · scanned 6/13/2026, 11:51:50 AM

GitHub: 523 stars · 39 forks

AI VISIBILITY SCORE
30 /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
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 luogen1996/LaVIN, 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 the repository

    Why:

    COPY-PASTE FIX
    vision-language, multimodal-llm, instruction-tuning, large-language-models, llm, deep-learning, neurips-2023, efficient-ai
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the root of the repository.
  • mediumreadme#3
    Explicitly state LaVIN's core category and differentiator in the README's opening

    Why:

    CURRENT
    This repository contains the implementation of the NeurIPS 2023 paper:
> **Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models**
    COPY-PASTE FIX
    LaVIN is an efficient, cost-effective framework for **vision-language instruction tuning of large language models (LLMs)**, designed to enhance multimodal reasoning. This repository provides the official implementation of our NeurIPS 2023 paper:
> **Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models**

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 luogen1996/LaVIN
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4V (ision)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4V (ision) · recommended 1×
  2. Google Gemini (Pro/Ultra) · recommended 1×
  3. LLaVA (Large Language and Vision Assistant) · recommended 1×
  4. BLIP-2 (Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation) · recommended 1×
  5. InstructBLIP · recommended 1×
  • CATEGORY QUERY
    Seeking efficient methods to integrate visual understanding into large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4V (ision)
    2. Google Gemini (Pro/Ultra)
    3. LLaVA (Large Language and Vision Assistant)
    4. BLIP-2 (Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation)
    5. InstructBLIP
    6. MiniGPT-4
    7. CLIP (Contrastive Language-Image Pre-training)

    AI recommended 7 alternatives but never named luogen1996/LaVIN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I perform cost-effective instruction tuning for multi-modal large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT library
    2. Axolotl
    3. DeepSpeed
    4. PyTorch FSDP
    5. FlashAttention-2
    6. xFormers
    7. PyTorch Automatic Mixed Precision (AMP)
    8. AWS EC2 Spot Instances
    9. Google Cloud Preemptible VMs
    10. Azure Spot Virtual Machines

    AI recommended 10 alternatives but never named luogen1996/LaVIN. 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 luogen1996/LaVIN?
    pass
    AI named luogen1996/LaVIN explicitly

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

  • If a team adopts luogen1996/LaVIN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named luogen1996/LaVIN 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 luogen1996/LaVIN solve, and who is the primary audience?
    pass
    AI named luogen1996/LaVIN explicitly

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

Embed your GEO score

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luogen1996/LaVIN — 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