RRepoGEO

REPOGEO REPORT · LITE

nlpxucan/WizardLM

Default branch main · commit cd4b4cc0 · scanned 5/14/2026, 11:46:42 PM

GitHub: 9,483 stars · 749 forks

AI VISIBILITY SCORE
23 /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
2 / 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 nlpxucan/WizardLM, 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
    Clarify the project's licensing terms in the README

    Why:

    COPY-PASTE FIX
    Add a section like '## Licensing' to your README, explicitly stating the license(s) under which WizardLM and its components (e.g., models, data) are distributed, referencing any inherited licenses from projects like Stanford Alpaca or LLaMA.
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://wizardlm.github.io/

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 nlpxucan/WizardLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Llama 3
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Llama 3 · recommended 2×
  2. Mixtral 8x7B Instruct · recommended 1×
  3. Gemma · recommended 1×
  4. Mistral 7B Instruct · recommended 1×
  5. Zephyr · recommended 1×
  • CATEGORY QUERY
    What open-source large language models excel at following complex, nuanced instructions?
    you: not recommended
    AI recommended (in order):
    1. Llama 3
    2. Mixtral 8x7B Instruct
    3. Gemma
    4. Mistral 7B Instruct
    5. Zephyr
    6. OpenHermes 2.5

    AI recommended 6 alternatives but never named nlpxucan/WizardLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an LLM that can generate high-quality code and solve advanced mathematical problems.
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3 Opus
    3. Gemini 1.5 Pro
    4. Llama 3
    5. Code Llama

    AI recommended 5 alternatives but never named nlpxucan/WizardLM. 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 nlpxucan/WizardLM?
    pass
    AI did not name nlpxucan/WizardLM — likely talking about a different project

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

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

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

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nlpxucan/WizardLM — 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