RRepoGEO

REPOGEO REPORT · LITE

Cerebras/modelzoo

Default branch main · commit f1fd1e09 · scanned 5/14/2026, 10:42:29 PM

GitHub: 1,154 stars · 162 forks

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

3 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 for high-performance AI systems, specifically designed to run on Cerebras hardware.
  • hightopics#2
    Add relevant topics for categorization

    Why:

    COPY-PASTE FIX
    deep-learning, ai-models, model-zoo, cerebras, wafer-scale-engine, nlp, computer-vision, high-performance-computing, machine-learning
  • mediumreadme#3
    Refine the README's introductory sentence

    Why:

    CURRENT
    The Cerebras Model Zoo is a collection of deep learning models and utilities optimized to run on Cerebras hardware.
    COPY-PASTE FIX
    The Cerebras Model Zoo is a specialized collection of deep learning models and utilities for high-performance AI systems, specifically optimized to run on Cerebras hardware.

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
NVIDIA NGC
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA NGC · recommended 1×
  2. huggingface/transformers · recommended 1×
  3. TensorFlow Hub · recommended 1×
  4. PyTorch Hub · recommended 1×
  5. ONNX Model Zoo · recommended 1×
  • CATEGORY QUERY
    Looking for a collection of optimized deep learning models for high-performance AI systems.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NGC
    2. Hugging Face Transformers (huggingface/transformers)
    3. TensorFlow Hub
    4. PyTorch Hub
    5. ONNX Model Zoo
    6. OpenVINO Model Zoo

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

    Show full AI answer
  • CATEGORY QUERY
    What are good tools for streamlining deep learning model training and data preparation workflows?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Weights & Biases (W&B) (wandb/wandb)
    3. DVC (Data Version Control) (iterative/dvc)
    4. Kubeflow (kubeflow/kubeflow)
    5. PyTorch Lightning (Lightning-AI/lightning)
    6. Apache Airflow (apache/airflow)
    7. Ray (ray-project/ray)

    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?

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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