RRepoGEO

REPOGEO REPORT · LITE

SimpleBerry/LLaMA-O1

Default branch main · commit a007894d · scanned 6/13/2026, 12:43:08 PM

GitHub: 803 stars · 47 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 SimpleBerry/LLaMA-O1, 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

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of the Apache-2.0 license.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, llm, reasoning-models, pytorch, huggingface, llm-framework, training, finetuning, evaluation
  • highabout#3
    Refine the repository's 'About' description

    Why:

    CURRENT
    Large Reasoning Models
    COPY-PASTE FIX
    Open-source framework for training, inference, and evaluation of large reasoning models with PyTorch and HuggingFace.

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 SimpleBerry/LLaMA-O1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. PyTorch Lightning · recommended 1×
  3. DeepSpeed · recommended 1×
  4. JAX/Flax · recommended 1×
  5. Megatron-LM · recommended 1×
  • CATEGORY QUERY
    How can I set up an open-source framework for training and finetuning large reasoning models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. DeepSpeed
    4. JAX/Flax
    5. Megatron-LM
    6. OpenAssistant (OASST1)

    AI recommended 6 alternatives but never named SimpleBerry/LLaMA-O1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best PyTorch frameworks for deploying and evaluating large reasoning models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. TorchServe (pytorch/serve)
    5. TensorRT (NVIDIA/TensorRT)
    6. Ray Serve (ray-project/ray)
    7. FastAPI (tiangolo/fastapi)
    8. Uvicorn (encode/uvicorn)
    9. Gunicorn (benoitc/gunicorn)

    AI recommended 9 alternatives but never named SimpleBerry/LLaMA-O1. 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 SimpleBerry/LLaMA-O1?
    pass
    AI named SimpleBerry/LLaMA-O1 explicitly

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

  • If a team adopts SimpleBerry/LLaMA-O1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SimpleBerry/LLaMA-O1 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 SimpleBerry/LLaMA-O1 solve, and who is the primary audience?
    pass
    AI named SimpleBerry/LLaMA-O1 explicitly

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

Embed your GEO score

Drop this badge into the README of SimpleBerry/LLaMA-O1. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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SimpleBerry/LLaMA-O1 — 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
SimpleBerry/LLaMA-O1 — RepoGEO report