RRepoGEO

REPOGEO REPORT · LITE

microsoft/Llama-2-Onnx

Default branch main · commit f43af732 · scanned 5/26/2026, 7:32:04 AM

GitHub: 1,027 stars · 95 forks

AI VISIBILITY SCORE
23 /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
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 microsoft/Llama-2-Onnx, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the About section

    Why:

    COPY-PASTE FIX
    Optimized Llama 2 large language models converted to ONNX format for efficient, cross-platform inference, including Microsoft's contributions.
  • mediumreadme#2
    Add a dedicated 'License' section in the README

    Why:

    CURRENT
    The license information is currently embedded in the introductory paragraph.
    COPY-PASTE FIX
    ## License
This project is governed by the Llama Community License Agreement, which can be found in the `LICENSE` file in this repository. Microsoft's contributions to the optimized version are also subject to the restrictions and disclaimers outlined in that agreement.

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 microsoft/Llama-2-Onnx
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVINO Toolkit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVINO Toolkit · recommended 1×
  2. TensorRT · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. TFLite (TensorFlow Lite) · recommended 1×
  5. Core ML · recommended 1×
  • CATEGORY QUERY
    How can I efficiently deploy and run large language models on edge devices?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. TensorRT
    3. ONNX Runtime
    4. TFLite (TensorFlow Lite)
    5. Core ML
    6. Apache TVM
    7. GGML / llama.cpp

    AI recommended 7 alternatives but never named microsoft/Llama-2-Onnx. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best options for running quantized or smaller LLMs with good performance?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. Ollama (ollama/ollama)
    3. Hugging Face Transformers (huggingface/transformers)
    4. bitsandbytes (TimDettmers/bitsandbytes)
    5. AutoGPTQ (PanQiWei/AutoGPTQ)
    6. MLC LLM (mlc-ai/mlc-llm)
    7. ONNX Runtime (microsoft/onnxruntime)
    8. TensorRT-LLM (NVIDIA/TensorRT-LLM)

    AI recommended 8 alternatives but never named microsoft/Llama-2-Onnx. 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 microsoft/Llama-2-Onnx?
    pass
    AI named microsoft/Llama-2-Onnx explicitly

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

  • If a team adopts microsoft/Llama-2-Onnx in production, what risks or prerequisites should they evaluate first?
    pass
    AI named microsoft/Llama-2-Onnx 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 microsoft/Llama-2-Onnx solve, and who is the primary audience?
    pass
    AI did not name microsoft/Llama-2-Onnx — 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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MARKDOWN (README)
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microsoft/Llama-2-Onnx — 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