RRepoGEO

REPOGEO REPORT · LITE

Cerebras/modelzoo

Default branch main · commit f1fd1e09 · scanned 6/25/2026, 12:52:32 PM

GitHub: 1,163 stars · 166 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
30 /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
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 Cerebras/modelzoo, 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 'About' description

    Why:

    COPY-PASTE FIX
    A collection of deep learning models and utilities optimized to run on Cerebras hardware, including reference implementations for NLP, vision, and multimodal models.
  • mediumreadme#2
    Strengthen README introduction with core differentiator

    Why:

    CURRENT
    The Cerebras Model Zoo is a collection of deep learning models and utilities optimized to run on Cerebras hardware. The repository provides reference implementations, configuration files, and utilities that demonstrate best practices for training and deploying models using Cerebras systems.
    COPY-PASTE FIX
    The Cerebras Model Zoo is a collection of deep learning models and utilities specifically designed and optimized to run on Cerebras Systems' specialized AI hardware (e.g., the Wafer-Scale Engine and CS-2 system). It provides reference implementations, configuration files, and utilities that demonstrate best practices for training and deploying models using Cerebras systems.

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 Cerebras/modelzoo
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. PyTorch Hub · recommended 1×
  3. TensorFlow Hub · recommended 1×
  4. Keras Applications · recommended 1×
  5. OpenVINO Model Zoo · recommended 1×
  • CATEGORY QUERY
    Where can I find a collection of optimized deep learning models for NLP and vision tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Hub
    3. TensorFlow Hub
    4. Keras Applications
    5. OpenVINO Model Zoo
    6. ONNX Model Zoo

    AI recommended 6 alternatives but never named Cerebras/modelzoo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to streamline deep learning model training and deployment on high-performance AI systems?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA AI Enterprise
    2. Kubeflow (kubeflow/kubeflow)
    3. MLflow (mlflow/mlflow)
    4. PyTorch Lightning (Lightning-AI/lightning)
    5. TensorFlow Extended (TFX) (tensorflow/tfx)
    6. Ray (ray-project/ray)
    7. Hugging Face Accelerate (huggingface/accelerate)

    AI recommended 7 alternatives but never named Cerebras/modelzoo. 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 Cerebras/modelzoo?
    pass
    AI named Cerebras/modelzoo explicitly

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

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

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

Embed your GEO score

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Cerebras/modelzoo — 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