RRepoGEO

REPOGEO REPORT · LITE

jankais3r/LLaMA_MPS

Default branch main · commit fb098cfe · scanned 6/11/2026, 8:02:41 AM

GitHub: 582 stars · 46 forks

AI VISIBILITY SCORE
28 /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
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 jankais3r/LLaMA_MPS, 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
  • highreadme#1
    Reposition the README's opening to highlight its PyTorch/MPS developer focus

    Why:

    CURRENT
    Run LLaMA (and Stanford-Alpaca) inference on Apple Silicon GPUs.
    COPY-PASTE FIX
    A Python-native, PyTorch-centric solution for running LLaMA and Stanford-Alpaca inference directly on Apple Silicon GPUs, leveraging Metal Performance Shaders (MPS) for developers and researchers.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a relevant URL for the project's homepage (e.g., a project page, documentation, or a blog post).
  • mediumtopics#3
    Add more specific topics to clarify the project's technical stack and audience

    Why:

    CURRENT
    alpaca, apple-silicon, chat, chatbot, chatgpt, llama, llms, macos, metal, ml, mps, stanford-alpaca, torch
    COPY-PASTE FIX
    alpaca, apple-silicon, chat, chatbot, chatgpt, llama, llms, macos, metal, ml, mps, stanford-alpaca, torch, pytorch, inference-engine, gpu-acceleration, developer-tool

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 jankais3r/LLaMA_MPS
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ollama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 2×
  2. LM Studio · recommended 2×
  3. Jan · recommended 2×
  4. LocalAI · recommended 2×
  5. llama.cpp · recommended 2×
  • CATEGORY QUERY
    How to run local large language model inference on my Mac?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Jan
    4. LocalAI
    5. llama.cpp
    6. MLC LLM

    AI recommended 6 alternatives but never named jankais3r/LLaMA_MPS. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a solution to deploy open-source conversational AI models on macOS.
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Jan
    4. LocalAI
    5. llama.cpp
    6. llama-cpp-python
    7. MLC LLM

    AI recommended 7 alternatives but never named jankais3r/LLaMA_MPS. 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 jankais3r/LLaMA_MPS?
    pass
    AI named jankais3r/LLaMA_MPS explicitly

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

  • If a team adopts jankais3r/LLaMA_MPS in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jankais3r/LLaMA_MPS 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 jankais3r/LLaMA_MPS solve, and who is the primary audience?
    pass
    AI did not name jankais3r/LLaMA_MPS — 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?

Embed your GEO score

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jankais3r/LLaMA_MPS — 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