RRepoGEO

REPOGEO REPORT · LITE

shawwn/llama-dl

Default branch main · commit 51368a06 · scanned 5/25/2026, 10:37:51 AM

GitHub: 4,126 stars · 399 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 shawwn/llama-dl, 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 categorize the repository

    Why:

    COPY-PASTE FIX
    llama-model, llm-download, generative-ai, facebook-llama, model-downloader, large-language-models
  • highreadme#2
    Reposition the README's introduction to clarify its role as a downloader

    Why:

    CURRENT
    This repository contains a high-speed download of LLaMA, Facebook's 65B parameter model that was recently made available via torrent.
    COPY-PASTE FIX
    This repository provides a high-speed, reliable method to download LLaMA, Facebook's 65B parameter model. It is designed specifically for efficiently *obtaining* the model files, serving as the essential first step before using tools like `llama.cpp` or Ollama for local inference.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/shawwn/llama-dl

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 shawwn/llama-dl
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. Hugging Face transformers · recommended 2×
  4. bitsandbytes · recommended 2×
  5. llama.cpp · recommended 2×
  • CATEGORY QUERY
    How can I quickly obtain and set up a powerful open-source language model?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LM Studio
    3. Hugging Face transformers
    4. bitsandbytes
    5. Text Generation WebUI (oobabooga/text-generation-webui)
    6. llama.cpp

    AI recommended 6 alternatives but never named shawwn/llama-dl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient ways to run large generative AI models on a local machine?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. LM Studio
    3. Jan
    4. Ollama
    5. Hugging Face transformers
    6. bitsandbytes
    7. accelerate
    8. PyTorch torch.compile
    9. ONNX Runtime
    10. TensorRT

    AI recommended 10 alternatives but never named shawwn/llama-dl. 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 shawwn/llama-dl?
    pass
    AI named shawwn/llama-dl explicitly

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

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

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

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shawwn/llama-dl — 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