RRepoGEO

REPOGEO REPORT · LITE

neuralmagic/sparseml

Default branch main · commit 1c04a016 · scanned 6/21/2026, 7:02:16 PM

GitHub: 2,143 stars · 155 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
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.

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. ONNX Runtime · recommended 1×
  4. TensorFlow Model Optimization Toolkit · recommended 1×
  5. PyTorch's `torch.nn.utils.prune` · recommended 1×
  • CATEGORY QUERY
    How can I reduce the size and inference time of my deep learning models?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime
    4. TensorFlow Model Optimization Toolkit
    5. PyTorch's `torch.nn.utils.prune`
    6. Hugging Face Transformers
    7. MobileNet
    8. EfficientNet
    9. SqueezeNet
    10. NVIDIA TensorRT
    11. OpenVINO
    12. TVM
    13. PyTorch Automatic Mixed Precision (AMP)
    14. TensorFlow Mixed Precision

    AI recommended 14 alternatives but never named neuralmagic/sparseml. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help apply sparsification recipes to neural networks in PyTorch or TensorFlow?
    you: not recommended
    AI recommended (in order):
    1. torch.nn.utils.prune
    2. torch.quantization
    3. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    4. NVIDIA Apex (NVIDIA/apex)
    5. OpenVINO Toolkit (openvinotoolkit/openvino)
    6. ONNX Runtime (microsoft/onnxruntime)
    7. DeepSparse (neuralmagic/deepsparse)
    8. Sparsify (neuralmagic/sparsify)

    AI recommended 8 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?

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neuralmagic/sparseml — 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