RRepoGEO

REPOGEO REPORT · LITE

tairov/llama2.mojo

Default branch master · commit 3c556c31 · scanned 5/27/2026, 12:37:36 AM

GitHub: 2,124 stars · 136 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 tairov/llama2.mojo, 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 README opening to highlight Mojo's performance advantage

    Why:

    CURRENT
    Have you ever wanted to inference a baby Llama 2 model in pure Mojo? No? Well, now you can!
    COPY-PASTE FIX
    Achieve unparalleled Llama 2 inference performance on CPU with `llama2.mojo`, a pure Mojo implementation that leverages SIMD and vectorization to significantly outperform `llama.cpp` and `llama2.c`. This project is for developers seeking cutting-edge, high-performance AI solutions and exploring the capabilities of the Mojo language.
  • mediumreadme#2
    Add a dedicated 'Why llama2.mojo?' or 'Comparison' section to README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., '## Why `llama2.mojo`? Outperforming C/C++' or '## `llama2.mojo` vs. `llama.cpp` and others'. This section should explicitly state the performance gains and the benefits of Mojo over traditional C/C++ or Python implementations, referencing the existing benchmarks.
  • lowtopics#3
    Refine topics to emphasize Mojo's unique performance and language aspects

    Why:

    CURRENT
    inference, llama, llama2, modular, mojo, parallelize, performance, simd, tensor, transformer-architecture, vectorization
    COPY-PASTE FIX
    llama2-inference, mojo-lang, high-performance, cpu-inference, simd, vectorization, llama-models, ai-acceleration, modular-lang

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 tairov/llama2.mojo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ggerganov/llama.cpp
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ggerganov/llama.cpp · recommended 2×
  2. microsoft/onnxruntime · recommended 2×
  3. openvinotoolkit/openvino · recommended 2×
  4. pytorch/pytorch · recommended 2×
  5. abetlen/llama-cpp-python · recommended 1×
  • CATEGORY QUERY
    How to achieve high-performance Llama 2 inference on CPU using a modern language?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. llama-cpp-python (abetlen/llama-cpp-python)
    3. Ollama (ollama/ollama)
    4. Hugging Face Transformers (huggingface/transformers)
    5. Optimum (huggingface/optimum)
    6. ONNX Runtime (microsoft/onnxruntime)
    7. Intel OpenVINO (openvinotoolkit/openvino)
    8. PyTorch (pytorch/pytorch)
    9. torch.compile
    10. TensorFlow (tensorflow/tensorflow)
    11. TensorFlow Lite
    12. XLA
    13. llm (rustformers/llm)

    AI recommended 13 alternatives but never named tairov/llama2.mojo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking efficient CPU inference for small Llama models with strong performance improvements.
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. OpenVINO (openvinotoolkit/openvino)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    5. PyTorch (pytorch/pytorch)

    AI recommended 5 alternatives but never named tairov/llama2.mojo. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 tairov/llama2.mojo?
    pass
    AI did not name tairov/llama2.mojo — 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 tairov/llama2.mojo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tairov/llama2.mojo 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 tairov/llama2.mojo solve, and who is the primary audience?
    pass
    AI named tairov/llama2.mojo explicitly

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

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tairov/llama2.mojo — 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