RRepoGEO

REPOGEO REPORT · LITE

ELS-RD/transformer-deploy

Default branch main · commit 6b88e24a · scanned 5/23/2026, 5:12:07 PM

GitHub: 1,685 stars · 152 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 ELS-RD/transformer-deploy, 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 the README's opening statement to clarify its role as an inference server

    Why:

    CURRENT
    Optimize and deploy in **production** 🤗 Hugging Face Transformer models in a single command line.
    COPY-PASTE FIX
    ELS-RD/transformer-deploy is an efficient, scalable, and enterprise-grade CPU/GPU inference server specifically designed for 🤗 Hugging Face Transformer models, offering up to 10X faster inference by leveraging ONNX Runtime and NVIDIA TensorRT.
  • mediumabout#2
    Enhance the repository description to include key optimization technologies

    Why:

    CURRENT
    Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀
    COPY-PASTE FIX
    Efficient, scalable, and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models, leveraging ONNX Runtime and NVIDIA TensorRT for up to 10X faster inference 🚀
  • lowtopics#3
    Add specific technology topics to improve keyword matching

    Why:

    CURRENT
    deep-learning, deployment, inference, machine-learning, natural-language-processing, server
    COPY-PASTE FIX
    deep-learning, deployment, inference, machine-learning, natural-language-processing, server, onnx, tensorrt, triton

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 ELS-RD/transformer-deploy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
triton-inference-server/server
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. triton-inference-server/server · recommended 1×
  2. microsoft/onnxruntime · recommended 1×
  3. tensorflow/serving · recommended 1×
  4. pytorch/serve · recommended 1×
  5. kserve/kserve · recommended 1×
  • CATEGORY QUERY
    What is the best way to deploy deep learning transformer models for production inference?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server (triton-inference-server/server)
    2. ONNX Runtime (microsoft/onnxruntime)
    3. TensorFlow Serving (tensorflow/serving)
    4. TorchServe (pytorch/serve)
    5. KServe (kserve/kserve)
    6. AWS SageMaker Endpoints
    7. Google Cloud Vertex AI Endpoints

    AI recommended 7 alternatives but never named ELS-RD/transformer-deploy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I optimize the CPU/GPU inference speed of large NLP models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. DeepSpeed-MII
    5. vLLM
    6. Hugging Face Optimum
    7. TorchServe
    8. TensorFlow Serving

    AI recommended 8 alternatives but never named ELS-RD/transformer-deploy. 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 ELS-RD/transformer-deploy?
    pass
    AI named ELS-RD/transformer-deploy explicitly

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

  • If a team adopts ELS-RD/transformer-deploy in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ELS-RD/transformer-deploy 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 ELS-RD/transformer-deploy solve, and who is the primary audience?
    pass
    AI did not name ELS-RD/transformer-deploy — 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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ELS-RD/transformer-deploy — 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