RRepoGEO

REPOGEO REPORT · LITE

modelscope/modelscope-classroom

Default branch main · commit f40bf861 · scanned 6/26/2026, 3:48:36 AM

GitHub: 1,429 stars · 173 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
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 modelscope/modelscope-classroom, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise English description to the About section

    Why:

    COPY-PASTE FIX
    Comprehensive tutorials and practical guides for large language models (LLMs) and deep learning, covering training, inference, deployment, and application development on the ModelScope platform.
  • mediumhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://github.com/modelscope/modelscope-classroom

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 modelscope/modelscope-classroom
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PEFT · recommended 1×
  3. Accelerate · recommended 1×
  4. Hugging Face Inference API · recommended 1×
  5. 🤗 Inference Endpoints · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive tutorials for large language model training, deployment, and application?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. Accelerate
    4. Hugging Face Inference API
    5. 🤗 Inference Endpoints
    6. PyTorch
    7. TensorFlow
    8. NVIDIA NeMo
    9. TensorRT-LLM
    10. OpenAI API

    AI recommended 10 alternatives but never named modelscope/modelscope-classroom. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical guides for deploying quantized large language models and advanced inference techniques.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Optimum
    3. bitsandbytes
    4. llama.cpp
    5. NVIDIA TensorRT-LLM
    6. OpenVINO
    7. vLLM
    8. ONNX Runtime

    AI recommended 8 alternatives but never named modelscope/modelscope-classroom. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 modelscope/modelscope-classroom?
    pass
    AI did not name modelscope/modelscope-classroom — likely talking about a different project

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

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

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

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modelscope/modelscope-classroom — 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