RRepoGEO

REPOGEO REPORT · LITE

VainF/Torch-Pruning

Default branch master · commit e80127d7 · scanned 5/8/2026, 9:12:00 PM

GitHub: 3,302 stars · 382 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 VainF/Torch-Pruning, 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's opening to highlight advanced structural pruning and DepGraph

    Why:

    CURRENT
    Torch-Pruning (TP) is a framework for structural pruning with the following features:
    COPY-PASTE FIX
    Torch-Pruning (TP) is a **comprehensive and general-purpose framework for advanced structural pruning** of deep neural networks in PyTorch, featuring the innovative ⚡ **DepGraph** algorithm for automatic dependency tracking and removal of coupled parameters. It provides unparalleled support for modern architectures, including Large Language Models (LLMs), Vision Foundation Models, and Transformers.
  • mediumtopics#2
    Add more specific topics to clarify the framework's focus

    Why:

    CURRENT
    efficient-deep-learning, llm, model-compression, pruning, transformers, vision
    COPY-PASTE FIX
    efficient-deep-learning, llm, model-compression, pruning, transformers, vision, pytorch, structural-pruning, deep-learning-optimization, depgraph
  • lowhomepage#3
    Update homepage to point to an interactive demo

    Why:

    CURRENT
    https://arxiv.org/abs/2301.12900
    COPY-PASTE FIX
    https://colab.research.google.com/drive/1TRvELQDNj9PwM-EERWbF3IQOyxZeDepp?usp=sharing

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 VainF/Torch-Pruning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch Pruning (torch.nn.utils.prune)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch Pruning (torch.nn.utils.prune) · recommended 1×
  2. huggingface/optimum · recommended 1×
  3. NVIDIA TensorRT · recommended 1×
  4. openvinotoolkit/openvino · recommended 1×
  5. AMC (Automated Machine Compression) · recommended 1×
  • CATEGORY QUERY
    How can I structurally prune large language models or vision transformers to make them more efficient?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Pruning (torch.nn.utils.prune)
    2. Hugging Face Optimum (huggingface/optimum)
    3. NVIDIA TensorRT
    4. OpenVINO Toolkit (Intel) (openvinotoolkit/openvino)
    5. AMC (Automated Machine Compression)
    6. DeepSpeed (Microsoft) (microsoft/DeepSpeed)
    7. Neural Network Distiller (Intel) (IntelAI/distiller)

    AI recommended 7 alternatives but never named VainF/Torch-Pruning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a general-purpose toolkit for advanced structural pruning of deep learning models in PyTorch.
    you: not recommended
    AI recommended (in order):
    1. PyTorch's Built-in Pruning (torch.nn.utils.prune)
    2. Neural Network Compression Framework (NNCF) by Intel
    3. PyTorch-Pruning (by Vainachek)
    4. OpenVINO Training Extensions (OTE) by Intel
    5. Distiller by Intel

    AI recommended 5 alternatives but never named VainF/Torch-Pruning. 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 VainF/Torch-Pruning?
    pass
    AI named VainF/Torch-Pruning explicitly

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

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

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

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VainF/Torch-Pruning — 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