RRepoGEO

REPOGEO REPORT · LITE

Tencent/AngelSlim

Default branch main · commit f76d316c · scanned 5/7/2026, 11:02:19 PM

GitHub: 1,031 stars · 106 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 Tencent/AngelSlim, 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
    Add a direct opening sentence to the README

    Why:

    CURRENT
    English | [简体中文](README_cn.md)

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/logos/angelslim_logo_light.png">
    
  </picture>
</p>

<h3 align="center">
A more accessible, comprehensive, and efficient toolkit for large model compression.
</h3>
    COPY-PASTE FIX
    English | [简体中文](README_cn.md)

Tencent/AngelSlim is a comprehensive toolkit for large model compression, focusing on quantization and efficiency for LLMs and VLMs.

<p align="center">
  <picture>
    <source media="(prefers-color-scheme: dark)" srcset="./docs/source/assets/logos/angelslim_logo_light.png">
    
  </picture>
</p>

<h3 align="center">
A more accessible, comprehensive, and efficient toolkit for large model compression.
</h3>
  • hightopics#2
    Expand topics with broader model optimization terms

    Why:

    CURRENT
    audio, deepseek, dflash, diffusion, eagle, fp4, hunyuan, llm, llm-compression, quantization, qwen, speculative-decoding, vlm
    COPY-PASTE FIX
    audio, deepseek, dflash, diffusion, eagle, fp4, hunyuan, llm, llm-compression, quantization, qwen, speculative-decoding, vlm, model-optimization, deep-learning-optimization, ai-acceleration, inference-optimization, model-quantization
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    ## License

This project is licensed under the terms specified in the [LICENSE](LICENSE) file.

Category GEO backends resolved for this scan: google/gemini-2.0-flash-001, deepseek/deepseek-chat

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.0-flash-001. 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 Tencent/AngelSlim
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorRT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorRT · recommended 2×
  2. microsoft/onnxruntime · recommended 1×
  3. huggingface/optimum · recommended 1×
  4. GPTQ · recommended 1×
  5. neuralmagic/sparseml · recommended 1×
  • CATEGORY QUERY
    How can I reduce the memory footprint and inference latency of large language models?
    you: not recommended
    AI recommended (in order):
    1. TensorRT
    2. ONNX Runtime (microsoft/onnxruntime)
    3. Optimum (huggingface/optimum)
    4. GPTQ
    5. SparseML (neuralmagic/sparseml)
    6. Neural Magic DeepSparse Engine (neuralmagic/deepsparse)
    7. TensorFlow (tensorflow/tensorflow)
    8. PyTorch (pytorch/pytorch)
    9. DeepSpeed (microsoft/deepspeed)
    10. Megatron-LM (NVIDIA/Megatron-LM)
    11. PyTorch DistributedDataParallel (DDP)
    12. PEFT library from Hugging Face (huggingface/peft)
    13. NVIDIA GPUs
    14. Google TPUs
    15. AWS Inferentia
    16. AWS Trainium
    17. Habana Gaudi
    18. FlashAttention (Dao-AILab/flash-attention)
    19. Linear Attention
    20. NVIDIA Triton Inference Server (triton-inference-server/server)

    AI recommended 20 alternatives but never named Tencent/AngelSlim. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive toolkit for quantizing large models to improve deployment efficiency.
    you: not recommended
    AI recommended (in order):
    1. ONNX Runtime
    2. TensorRT
    3. PyTorch (torch.quantization)
    4. TensorFlow Model Optimization Toolkit
    5. Intel Neural Compressor
    6. Optimum (Hugging Face)
    7. TVM

    AI recommended 7 alternatives but never named Tencent/AngelSlim. 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 Tencent/AngelSlim?
    pass
    AI named Tencent/AngelSlim explicitly

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

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

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

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Tencent/AngelSlim — 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