REPOGEO REPORT · LITE

huggingface/optimum

Default branch main · commit 153ba0d4 · scanned 5/13/2026, 10:11:47 PM

GitHub: 3,390 stars · 639 forks

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 README opening to highlight unified API for Hugging Face model optimization

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 is the unified API and abstraction layer for applying various hardware-specific and general optimization techniques (like quantization, pruning, and compilation) specifically to 🤗 Transformers, Diffusers, TIMM, and Sentence-Transformers models. It provides a set of easy-to-use tools to enable maximum efficiency for training and running models on targeted hardware.

mediumtopics#2

Add broader, more descriptive topics to improve category matching

Why:

CURRENT

graphcore, habana, inference, intel, onnx, onnxruntime, optimization, pytorch, quantization, tflite, training, transformers

COPY-PASTE FIX

ai-optimization, model-deployment, hardware-acceleration, unified-api, deep-learning-optimization, graphcore, habana, inference, intel, onnx, onnxruntime, optimization, pytorch, quantization, tflite, training, transformers

lowabout#3

Refine repository description to emphasize unified optimization API

Why:

CURRENT

🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools

COPY-PASTE FIX

🚀 A unified API for accelerating inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy-to-use hardware optimization tools.

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

Of all named tools, what % are you?

Top rival

NVIDIA TensorRT

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

NVIDIA TensorRT · recommended 2×
openvinotoolkit/openvino · recommended 1×
microsoft/onnxruntime · recommended 1×
microsoft/DeepSpeed · recommended 1×
Lightning-AI/lightning · recommended 1×

CATEGORY QUERY
Seeking tools to accelerate deep learning model inference and training performance.
you: not recommended
AI recommended (in order):
1. NVIDIA TensorRT
2. OpenVINO Toolkit (openvinotoolkit/openvino)
3. ONNX Runtime (microsoft/onnxruntime)
4. DeepSpeed (microsoft/DeepSpeed)
5. PyTorch Lightning (Lightning-AI/lightning)
6. XLA (tensorflow/tensorflow)
7. Apache TVM (apache/tvm)
AI recommended 7 alternatives but never named huggingface/optimum. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How to optimize AI models for efficient deployment on various hardware platforms?
you: not recommended
AI recommended (in order):
1. ONNX Runtime
2. OpenVINO
3. NVIDIA TensorRT
4. Apache TVM
5. Core ML Tools
6. TensorFlow Lite (TFLite)
7. TensorFlow Model Optimization Toolkit
8. PyTorch Mobile
9. TorchScript
10. Edge TPU Compiler
AI recommended 10 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?

Embed your GEO score

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

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/huggingface/optimum.svg)](https://repogeo.com/en/r/huggingface/optimum)

HTML

<a href="https://repogeo.com/en/r/huggingface/optimum"><img src="https://repogeo.com/badge/huggingface/optimum.svg" alt="RepoGEO" /></a>

Pro

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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