RRepoGEO

REPOGEO REPORT · LITE

ModelTC/LightCompress

Default branch main · commit 86f564dd · scanned 6/3/2026, 11:17:13 AM

GitHub: 721 stars · 80 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 ModelTC/LightCompress, 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
    Clarify README's opening to counter mis-categorization

    Why:

    COPY-PASTE FIX
    Insert this sentence immediately after the "Notice" section: "LightCompress is a powerful toolkit for **large generative AI model compression**, specifically targeting **Large Language Models (LLMs), Vision-Language Models (VLMs), and generative video models**, *not* general-purpose video file compression."
  • mediumhomepage#2
    Add homepage URL

    Why:

    COPY-PASTE FIX
    https://llmc-en.readthedocs.io/en/latest/
  • lowreadme#3
    Add a 'Key Features' section to reinforce core purpose

    Why:

    COPY-PASTE FIX
    Add a 'Key Features' section to the README, perhaps as a bulleted list, explicitly mentioning:
- Comprehensive toolkit for compressing LLMs, VLMs, and generative video models.
- Supports state-of-the-art compression algorithms like quantization, pruning, and token merging.
- Designed for efficient deployment and reduced model size without compromising performance.

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 ModelTC/LightCompress
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/optimum
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/optimum · recommended 2×
  2. microsoft/onnxruntime · recommended 2×
  3. pytorch/pytorch · recommended 2×
  4. AutoGPTQ/AutoGPTQ · recommended 1×
  5. vllm-project/vllm · recommended 1×
  • CATEGORY QUERY
    How to reduce the size of large language models for efficient deployment?
    you: not recommended
    AI recommended (in order):
    1. AutoGPTQ (AutoGPTQ/AutoGPTQ)
    2. optimum (huggingface/optimum)
    3. vLLM (vllm-project/vllm)
    4. bitsandbytes (TimDettmers/bitsandbytes)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. Hugging Face Transformers (huggingface/transformers)
    7. DistilBERT
    8. NNCF (openvinotoolkit/nncf)
    9. PyTorch (pytorch/pytorch)
    10. Mistral 7B
    11. Mixtral 8x7B
    12. Phi-2
    13. Phi-3 Mini
    14. Gemma 2B
    15. Gemma 7B
    16. ALBERT

    AI recommended 16 alternatives but never named ModelTC/LightCompress. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools to apply quantization and pruning to large generative AI models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum (huggingface/optimum)
    2. OpenVINO Toolkit (openvinotoolkit/openvino)
    3. NVIDIA TensorRT (NVIDIA/TensorRT)
    4. PyTorch Quantization (native) (pytorch/pytorch)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. Intel Neural Compressor (INC) (intel/neural-compressor)
    7. DeepSpeed (microsoft/DeepSpeed)

    AI recommended 7 alternatives but never named ModelTC/LightCompress. 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 ModelTC/LightCompress?
    pass
    AI named ModelTC/LightCompress explicitly

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

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

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

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ModelTC/LightCompress — 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