RRepoGEO

REPOGEO REPORT · LITE

huggingface/huggingface-llama-recipes

Default branch main · commit 7f5ab801 · scanned 5/30/2026, 12:18:06 PM

GitHub: 700 stars · 85 forks

AI VISIBILITY SCORE
17 /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
1 / 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 huggingface/huggingface-llama-recipes, 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
  • highlicense#1
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root to clearly state the licensing terms for the code recipes.
  • highabout#2
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    Minimal, ready-to-use code recipes for fine-tuning, inference, and experimentation with Llama 3.x models (Llama 3.1, 3.2, 3.3) using Hugging Face Transformers.
  • hightopics#3
    Add relevant repository topics

    Why:

    COPY-PASTE FIX
    llama, llama-3, llama-3-1, llama-3-2, llama-3-3, llm, large-language-models, generative-ai, transformers, huggingface, recipes, fine-tuning, inference, peft, qlora

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 huggingface/huggingface-llama-recipes
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ollama/ollama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ollama/ollama · recommended 2×
  2. huggingface/transformers · recommended 2×
  3. LM Studio · recommended 1×
  4. jan-ai/jan · recommended 1×
  5. TimDettmers/bitsandbytes · recommended 1×
  • CATEGORY QUERY
    How can I quickly set up and run large language models for local experimentation?
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. LM Studio
    3. Jan AI (jan-ai/jan)
    4. Hugging Face `transformers` library (huggingface/transformers)
    5. `bitsandbytes` (TimDettmers/bitsandbytes)
    6. Llama.cpp (ggerganov/llama.cpp)
    7. `llama-cpp-python` (abetlen/llama-cpp-python)

    AI recommended 7 alternatives but never named huggingface/huggingface-llama-recipes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for minimal code examples to deploy and interact with open-source generative AI models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. Ollama (ollama/ollama)
    3. Hugging Face Inference Endpoints
    4. vLLM (vllm-project/vllm)
    5. LM Studio (lmstudio-ai/lmstudio)

    AI recommended 5 alternatives but never named huggingface/huggingface-llama-recipes. 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 huggingface/huggingface-llama-recipes?
    pass
    AI did not name huggingface/huggingface-llama-recipes — 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 huggingface/huggingface-llama-recipes in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huggingface/huggingface-llama-recipes 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 huggingface/huggingface-llama-recipes solve, and who is the primary audience?
    pass
    AI did not name huggingface/huggingface-llama-recipes — 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?

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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