RRepoGEO

REPOGEO REPORT · LITE

modelscope/evalscope

Default branch main · commit 639eb334 · scanned 5/25/2026, 5:16:50 PM

GitHub: 2,844 stars · 340 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 modelscope/evalscope, 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 introduction to emphasize comprehensive LLM/VLM benchmarking

    Why:

    CURRENT
    EvalScope is a one-stop LLM evaluation framework built by the ModelScope Community. Just one command to start — it supports model capability evaluation, inference performance stress testing, and result visualization.
    COPY-PASTE FIX
    EvalScope is a comprehensive, one-stop framework for **large model (LLM, VLM, AIGC) evaluation and performance benchmarking**. Built by the ModelScope Community, it streamlines model capability assessment, inference performance stress testing, and result visualization with just one command.
  • mediumreadme#2
    Expand on 'inference performance' in README to clarify AI model focus

    Why:

    COPY-PASTE FIX
    Add a new bullet point or expand an existing one under 'Key Features': '⚡ **Dedicated Inference Performance Benchmarking**: Conduct rigorous stress testing and visualize inference performance specifically for large language models (LLMs, VLMs, AIGC), identifying bottlenecks and optimizing deployment.'
  • lowtopics#3
    Add 'benchmarking' and 'aigc' to topics

    Why:

    CURRENT
    evaluation, llm, performance, rag, vlm
    COPY-PASTE FIX
    evaluation, llm, performance, rag, vlm, benchmarking, aigc

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/evalscope
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
EleutherAI/lm-evaluation-harness
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. EleutherAI/lm-evaluation-harness · recommended 1×
  2. Open LLM Leaderboard · recommended 1×
  3. HELM · recommended 1×
  4. DeepEval · recommended 1×
  5. LangChain Evaluation · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for comprehensive LLM capability evaluation and performance benchmarking?
    you: not recommended
    AI recommended (in order):
    1. LM-Harness (EleutherAI/lm-evaluation-harness)
    2. Open LLM Leaderboard
    3. HELM
    4. DeepEval
    5. LangChain Evaluation
    6. Ragas

    AI recommended 6 alternatives but never named modelscope/evalscope. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I efficiently stress test and visualize inference performance for large language models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server (triton-inference-server/server)
    2. Model Analyzer (triton-inference-server/model_analyzer)
    3. Locust (locustio/locust)
    4. Apache JMeter (apache/jmeter)
    5. K6 (grafana/k6)
    6. Prometheus (prometheus/prometheus)
    7. Grafana (grafana/grafana)
    8. TensorBoard (tensorflow/tensorboard)

    AI recommended 8 alternatives but never named modelscope/evalscope. 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 modelscope/evalscope?
    pass
    AI named modelscope/evalscope explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite