RRepoGEO

REPOGEO REPORT · LITE

neuralmagic/sparseml

Default branch main · commit 1c04a016 · scanned 5/11/2026, 2:07:58 PM

GitHub: 2,144 stars · 156 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 neuralmagic/sparseml, 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
    Emphasize training-time sparsification in README H3

    Why:

    CURRENT
    Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
    COPY-PASTE FIX
    Libraries for applying sparsification recipes *during neural network training* with a few lines of code, enabling faster and smaller models
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., to an archive of Neural Magic's site, or a page detailing the project's history) to the 'Homepage' field in the repository's 'About' section.
  • mediumreadme#3
    Add a concise statement about continued utility to the EOL announcement

    Why:

    CURRENT
    The existing EOL announcement.
    COPY-PASTE FIX
    Insert the following sentence immediately after the H3 and before the detailed EOL announcement: 'While development has ceased, SparseML's existing codebase and recipes remain a valuable resource for researchers exploring sparsification techniques or for maintaining existing projects that do not require ongoing updates.'

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 neuralmagic/sparseml
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Lite
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Lite · recommended 1×
  2. PyTorch Mobile · recommended 1×
  3. microsoft/onnxruntime · recommended 1×
  4. NVIDIA TensorRT · recommended 1×
  5. tensorflow/model-optimization · recommended 1×
  • CATEGORY QUERY
    How can I reduce the size and improve inference speed of my deep learning models?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime (microsoft/onnxruntime)
    4. NVIDIA TensorRT
    5. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    6. NVIDIA Apex (NVIDIA/apex)
    7. Hugging Face Transformers (huggingface/transformers)
    8. MobileNet
    9. EfficientNet
    10. TensorFlow Lite Micro (tensorflow/tflite-micro)
    11. OpenVINO Toolkit (openvinotoolkit/openvino)
    12. TVM (apache/tvm)

    AI recommended 12 alternatives but never named neuralmagic/sparseml. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help apply sparsification or pruning to PyTorch and TensorFlow models?
    you: not recommended
    AI recommended (in order):
    1. torch.nn.utils.prune
    2. TensorFlow Model Optimization Toolkit
    3. NVIDIA Apex
    4. DeepSpeed
    5. Intel Neural Compressor
    6. OpenVINO Post-training Optimization Toolkit

    AI recommended 6 alternatives but never named neuralmagic/sparseml. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 neuralmagic/sparseml?
    pass
    AI named neuralmagic/sparseml explicitly

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

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

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

Embed your GEO score

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite