RRepoGEO

REPOGEO REPORT · LITE

huggingface/optimum

Default branch main · commit 3d1f5e74 · scanned 6/24/2026, 9:07:09 AM

GitHub: 3,423 stars · 653 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
40 /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
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 huggingface/optimum, 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 paragraph to highlight its unified abstraction layer

    Why:

    CURRENT
    Optimum is an extension of Transformers 🤖 Diffusers 🧨 TIMM 🖼️ and Sentence-Transformers 🤗, providing a set of optimization tools and enabling maximum efficiency to train and run models on targeted hardware, while keeping things easy to use.
    COPY-PASTE FIX
    🤗 Optimum provides a unified, hardware-agnostic abstraction layer for optimizing and accelerating deep learning models from 🤗 Transformers, Diffusers, TIMM, and Sentence-Transformers. It offers a comprehensive set of tools to achieve maximum efficiency for training and inference across diverse hardware, simplifying deployment while maintaining ease of use.
  • mediumtopics#2
    Expand repository topics with broader optimization and deployment terms

    Why:

    CURRENT
    graphcore, habana, inference, intel, onnx, onnxruntime, optimization, pytorch, quantization, tflite, training, transformers
    COPY-PASTE FIX
    graphcore, habana, inference, intel, onnx, onnxruntime, optimization, pytorch, quantization, tflite, training, transformers, deep-learning-optimization, model-deployment, inference-acceleration, hardware-acceleration, neural-network-optimization
  • lowcomparison#3
    Add a 'Why Choose Optimum?' or 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example: `## Why Choose Optimum?` This section should explain how Optimum provides a unified, high-level API to leverage tools like TensorRT, OpenVINO, and ONNX Runtime, simplifying complex hardware-specific optimizations for deep learning models.

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/optimum
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorRT · recommended 2×
  2. TensorFlow Lite · recommended 2×
  3. OpenVINO Toolkit · recommended 2×
  4. ONNX Runtime · recommended 2×
  5. NVIDIA CUDA-X · recommended 1×
  • CATEGORY QUERY
    How to accelerate deep learning model inference and training on diverse hardware?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA-X
    2. cuDNN
    3. cuBLAS
    4. TensorRT
    5. PyTorch
    6. TorchScript
    7. TorchServe
    8. TensorFlow
    9. TensorFlow Lite
    10. TensorFlow Serving
    11. OpenVINO Toolkit
    12. ONNX Runtime
    13. TVM (Apache TVM)
    14. DeepSpeed
    15. FairScale

    AI recommended 15 alternatives but never named huggingface/optimum. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help optimize large neural networks for efficient deployment and execution?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. TensorRT
    3. ONNX Runtime
    4. Apache TVM
    5. PyTorch Mobile
    6. TensorFlow Lite
    7. NNCF

    AI recommended 7 alternatives but never named huggingface/optimum. 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 huggingface/optimum?
    pass
    AI named huggingface/optimum explicitly

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

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

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

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huggingface/optimum — 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