RRepoGEO

REPOGEO REPORT · LITE

SmartFlowAI/Llama3-Tutorial

Default branch main · commit 85c97a51 · scanned 5/29/2026, 7:37:52 AM

GitHub: 508 stars · 53 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 SmartFlowAI/Llama3-Tutorial, 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
  • highlicense#1
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0) that aligns with the project's intent.
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    Set the repository homepage URL to 'https://github.com/SmartFlowAI/Llama3-Tutorial' or a more specific documentation site if one exists.

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 SmartFlowAI/Llama3-Tutorial
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers Library
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 1×
  2. Hugging Face PEFT (Parameter-Efficient Fine-Tuning) Library · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. DeepSpeed · recommended 1×
  5. bitsandbytes · recommended 1×
  • CATEGORY QUERY
    Seeking a guide to fine-tune open-source large language models for custom applications.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Hugging Face PEFT (Parameter-Efficient Fine-Tuning) Library
    3. PyTorch Lightning
    4. DeepSpeed
    5. bitsandbytes
    6. Weights & Biases (W&B)
    7. Ray Train
    8. Ray Tune

    AI recommended 8 alternatives but never named SmartFlowAI/Llama3-Tutorial. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient ways to deploy and benchmark open-source large language models?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI (Text Generation Inference)
    3. NVIDIA TensorRT-LLM
    4. OpenVINO
    5. Ollama
    6. DeepSpeed-MII (Microsoft Inference Interface)
    7. MLC LLM

    AI recommended 7 alternatives but never named SmartFlowAI/Llama3-Tutorial. 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 SmartFlowAI/Llama3-Tutorial?
    pass
    AI named SmartFlowAI/Llama3-Tutorial explicitly

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

  • If a team adopts SmartFlowAI/Llama3-Tutorial in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SmartFlowAI/Llama3-Tutorial 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 SmartFlowAI/Llama3-Tutorial solve, and who is the primary audience?
    pass
    AI named SmartFlowAI/Llama3-Tutorial explicitly

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

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SmartFlowAI/Llama3-Tutorial — 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