RRepoGEO

REPOGEO REPORT · LITE

nunchaku-ai/deepcompressor

Default branch main · commit 69f3473f · scanned 6/8/2026, 8:03:37 AM

GitHub: 787 stars · 95 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 nunchaku-ai/deepcompressor, 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
  • hightopics#1
    Add specific topics for better categorization

    Why:

    COPY-PASTE FIX
    llm, large-language-models, diffusion-models, model-compression, quantization, pruning, deep-learning, pytorch, machine-learning-optimization, efficient-ai
  • highreadme#2
    Strengthen README's opening paragraph to highlight unique value

    Why:

    CURRENT
    DeepCompressoris an open source model compression toolbox for large language models and diffusion models based on PyTorch. DeepCompressor currently supports fake quantization with any integer and floating-point data type within 8 bits, e.g., INT8, INT4 and FP4_E2M1.
    COPY-PASTE FIX
    DeepCompressor is a research-oriented, open-source model compression toolbox for large language models and diffusion models, built on PyTorch. It provides a unified framework for implementing and evaluating state-of-the-art quantization and pruning algorithms, focusing on reproducibility and ease of use for efficient AI deployment. DeepCompressor currently supports fake quantization with any integer and floating-point data type within 8 bits, e.g., INT8, INT4 and FP4_E2M1.
  • mediumhomepage#3
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    https://nunchaku-ai.github.io/deepcompressor

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 nunchaku-ai/deepcompressor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Optimum
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Optimum · recommended 2×
  2. ONNX Runtime · recommended 2×
  3. Intel OpenVINO · recommended 2×
  4. NVIDIA TensorRT · recommended 2×
  5. Intel Neural Compressor · recommended 1×
  • CATEGORY QUERY
    How to reduce the size of large language models for faster inference?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum
    2. Intel Neural Compressor
    3. ONNX Runtime
    4. TensorRT
    5. PyTorch Quantization Toolkit
    6. PyTorch Pruning API
    7. TensorFlow Model Optimization Toolkit
    8. Hugging Face Transformers
    9. DistilBERT
    10. TinyBERT
    11. MobileNet
    12. EfficientNet
    13. LightSeq
    14. ALBERT

    AI recommended 14 alternatives but never named nunchaku-ai/deepcompressor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for quantizing large language models or diffusion models?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes
    2. Hugging Face Optimum
    3. ONNX Runtime
    4. Intel OpenVINO
    5. NVIDIA TensorRT
    6. GPTQ
    7. AWQ
    8. NVIDIA TensorRT
    9. Intel OpenVINO
    10. PyTorch Quantization API

    AI recommended 10 alternatives but never named nunchaku-ai/deepcompressor. 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 nunchaku-ai/deepcompressor?
    pass
    AI named nunchaku-ai/deepcompressor explicitly

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

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

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

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nunchaku-ai/deepcompressor — 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