REPOGEO REPORT · LITE

PrunaAI/pruna

Default branch main · commit e54baae1 · scanned 6/19/2026, 12:26:28 PM

GitHub: 1,221 stars · 91 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 PrunaAI/pruna, 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

Add a clear, direct opening sentence to the README

Why:

COPY-PASTE FIX

Pruna is an open-source deep learning model optimization framework designed for developers, enabling you to build and deploy faster, smaller, and more efficient AI models.

mediumreadme#2

Add a 'Why Pruna?' or 'Comparison' section to the README

Why:

COPY-PASTE FIX

## Why Pruna?
While tools like NVIDIA TensorRT and OpenVINO offer specialized optimization, Pruna provides a unified, developer-centric framework for comprehensive deep learning model optimization across various architectures (LLMs, Diffusion, Vision Transformers, Speech Recognition). It simplifies complex techniques like quantization, pruning, distillation, and compilation into a few lines of code, making advanced model efficiency accessible to all developers.

lowabout#3

Refine the repository description for stronger keyword alignment

Why:

CURRENT

Pruna is a model optimization framework built for developers, enabling you to deliver faster, more efficient models with minimal overhead.

COPY-PASTE FIX

Pruna is an open-source deep learning model optimization framework for developers, providing a comprehensive suite of techniques (quantization, pruning, distillation, compilation) to deliver faster, smaller, and more efficient AI models with minimal overhead.

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 PrunaAI/pruna

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×
OpenVINO Toolkit · recommended 2×
ONNX Runtime · recommended 2×
TensorFlow Lite · recommended 2×
PyTorch · recommended 2×

CATEGORY QUERY
How to make AI models run faster and more efficiently for deployment?
you: not recommended
AI recommended (in order):
1. NVIDIA TensorRT
2. OpenVINO Toolkit
3. ONNX Runtime
4. Apache TVM
5. TensorFlow Lite
6. PyTorch
7. TensorFlow Model Optimization Toolkit
8. DeepSpeed
9. FairScale
AI recommended 9 alternatives but never named PrunaAI/pruna. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective frameworks for reducing deep learning model size and inference cost?
you: not recommended
AI recommended (in order):
1. PyTorch
2. TensorFlow
3. TensorFlow Lite
4. TensorFlow Model Optimization Toolkit
5. TensorFlow Extended (TFX)
6. ONNX Runtime
7. ONNX Optimizer
8. ONNX Quantization
9. NVIDIA TensorRT
10. OpenVINO Toolkit
11. Model Optimizer
12. Inference Engine
13. DeepSpeed
14. ZeRO (Zero Redundancy Optimizer)
15. Hugging Face Optimum
AI recommended 15 alternatives but never named PrunaAI/pruna. 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 PrunaAI/pruna?
pass
AI named PrunaAI/pruna explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts PrunaAI/pruna in production, what risks or prerequisites should they evaluate first?
pass
AI named PrunaAI/pruna 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 PrunaAI/pruna solve, and who is the primary audience?
pass
AI named PrunaAI/pruna 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 PrunaAI/pruna. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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

HTML

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

Pro

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PrunaAI/pruna — 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