RRepoGEO

REPOGEO REPORT · LITE

Tencent/AngelSlim

Default branch main · commit 48c4adb1 · scanned 6/7/2026, 12:51:49 AM

GitHub: 1,281 stars · 145 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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
    Reposition README H3 to emphasize inference optimization

    Why:

    CURRENT
    A more accessible, comprehensive, and efficient toolkit for large model compression.
    COPY-PASTE FIX
    A more accessible, comprehensive, and efficient toolkit for large model compression, accelerating inference and reducing memory footprint for LLMs and VLMs.
  • mediumtopics#2
    Add outcome-focused topics for better categorization

    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, llm-inference, model-optimization, inference-acceleration, model-deployment
  • mediumreadme#3
    Clarify existing license(s) in README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Specify License Name(s) and terms, e.g., 'a custom license. See the LICENSE file for details.']

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 Tencent/AngelSlim
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA TensorRT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA TensorRT · recommended 1×
  2. OpenVINO · recommended 1×
  3. ONNX Runtime · recommended 1×
  4. PyTorch · recommended 1×
  5. TensorFlow Lite · 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. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. PyTorch
    5. TensorFlow Lite
    6. Hugging Face Transformers
    7. DistilBERT
    8. TinyBERT
    9. TensorFlow Model Optimization Toolkit
    10. Mistral 7B
    11. Phi-2
    12. Gemma
    13. TinyLlama
    14. DeepSpeed

    AI recommended 14 alternatives but never named Tencent/AngelSlim. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective toolkits for compressing and quantizing large AI models for deployment?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit (openvinotoolkit/openvino)
    2. NVIDIA TensorRT (NVIDIA/TensorRT)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. PyTorch Quantization (pytorch/pytorch)
    5. TensorFlow Lite (tensorflow/tensorflow)
    6. Apache TVM (apache/tvm)
    7. Neural Network Compression Framework (NNCF) (openvinotoolkit/nncf)

    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?

Embed your GEO score

Drop this badge into the README of Tencent/AngelSlim. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/Tencent/AngelSlim.svg)](https://repogeo.com/en/r/Tencent/AngelSlim)
HTML
<a href="https://repogeo.com/en/r/Tencent/AngelSlim"><img src="https://repogeo.com/badge/Tencent/AngelSlim.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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