RRepoGEO

REPOGEO REPORT · LITE

kmeng01/rome

Default branch main · commit 0874014c · scanned 6/12/2026, 5:17:51 PM

GitHub: 764 stars · 165 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 kmeng01/rome, 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's opening to clarify direct model editing vs. RAG

    Why:

    CURRENT
    This repository provides an implementation of Rank-One Model Editing (ROME) on auto-regressive transformers (GPU-only).
    COPY-PASTE FIX
    This repository implements Rank-One Model Editing (ROME), a novel method for directly and precisely updating factual knowledge within pre-trained auto-regressive transformer models (like GPT-2/J). Unlike Retrieval-Augmented Generation (RAG) systems or full model fine-tuning, ROME directly modifies specific weights within the model to update factual associations, providing a precise and efficient method for targeted knowledge editing.
  • mediumtopics#2
    Add specific model/knowledge editing topics

    Why:

    CURRENT
    gpt, interpretability, pytorch, transformers
    COPY-PASTE FIX
    gpt, interpretability, pytorch, transformers, llm-editing, knowledge-editing, model-editing, factual-editing
  • mediumreadme#3
    Strengthen and highlight active maintenance status in README

    Why:

    CURRENT
    Feel free to open an issue if you find any problems; we are actively developing this repository and will monitor tickets closely.
    COPY-PASTE FIX
    This project is actively maintained and under continuous development. We welcome issues and contributions.

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 kmeng01/rome
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. LangChain · recommended 1×
  3. LlamaIndex · recommended 1×
  4. Weaviate · recommended 1×
  5. Elasticsearch · recommended 1×
  • CATEGORY QUERY
    How can I update specific factual knowledge within a pre-trained transformer model?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Pinecone
    4. Weaviate
    5. Elasticsearch
    6. Hugging Face Transformers library
    7. PyTorch Lightning
    8. TensorFlow Keras
    9. ROME (Rank-One Editing)
    10. MEND (Model Editor Networks for Task-Specific Editing)

    AI recommended 10 alternatives but never named kmeng01/rome. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow direct modification of factual memories in large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers library (huggingface/transformers)
    2. PEFT (Parameter-Efficient Fine-Tuning) library (huggingface/peft)
    3. Axolotl (OpenAccess-AI-Collective/axolotl)
    4. LangChain (langchain-ai/langchain)
    5. LlamaIndex (run-llama/llama_index)
    6. Haystack (deepset-ai/haystack)
    7. Weaviate (weaviate/weaviate)
    8. Pinecone
    9. OpenAI API Playground
    10. Anthropic Claude API
    11. Hugging Face Inference API/Spaces
    12. PyTorch (pytorch/pytorch)
    13. TensorFlow (tensorflow/tensorflow)

    AI recommended 13 alternatives but never named kmeng01/rome. 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 kmeng01/rome?
    pass
    AI named kmeng01/rome explicitly

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

  • If a team adopts kmeng01/rome in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kmeng01/rome 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 kmeng01/rome solve, and who is the primary audience?
    pass
    AI named kmeng01/rome 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite