RRepoGEO

REPOGEO REPORT · LITE

PaulPauls/llama3_interpretability_sae

Default branch main · commit 6ee4596f · scanned 6/5/2026, 9:37:50 PM

GitHub: 635 stars · 38 forks

AI VISIBILITY SCORE
22 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
1 / 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 PaulPauls/llama3_interpretability_sae, 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 opening to state core value proposition

    Why:

    CURRENT
    The current README starts with a general explanation of SAEs after the title.
    COPY-PASTE FIX
    Add this sentence immediately after the main title: 'This repository provides a complete, end-to-end, and fully reproducible PyTorch pipeline for Llama 3.2 interpretability using Sparse Autoencoders (SAEs).'
  • mediumtopics#2
    Enhance topics with specific interpretability and pipeline keywords

    Why:

    CURRENT
    feature-extraction, feature-steering, llama3, llm-interpretability, open-research, pytorch, sparse-autoencoder
    COPY-PASTE FIX
    feature-extraction, feature-steering, interpretability-pipeline, llama3, llm-interpretability, mechanistic-interpretability, open-research, pytorch, sparse-autoencoder
  • lowreadme#3
    Add a differentiator statement to the README

    Why:

    COPY-PASTE FIX
    Add a sentence to the 'Project Overview' or a new 'Key Features' section, such as: 'Unlike more general interpretability libraries, this project provides a dedicated, end-to-end pipeline specifically optimized for Llama 3.2, ensuring full reproducibility and a streamlined workflow for SAE-based interpretability.'

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 PaulPauls/llama3_interpretability_sae
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. TransformerLens · recommended 1×
  3. PyTorch · recommended 1×
  4. scikit-learn · recommended 1×
  5. TensorBoard · recommended 1×
  • CATEGORY QUERY
    How can I understand internal representations of large language models using sparse autoencoders?
    you: not recommended
    AI recommended (in order):
    1. TransformerLens
    2. PyTorch
    3. Hugging Face Transformers
    4. scikit-learn
    5. TensorBoard
    6. Weights & Biases (W&B)
    7. NumPy

    AI recommended 7 alternatives but never named PaulPauls/llama3_interpretability_sae. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a PyTorch-based pipeline to extract and steer features in large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. peft
    3. accelerate
    4. LitGPT
    5. DeepSpeed
    6. PyTorch Lightning
    7. transformers_interpret
    8. captum

    AI recommended 8 alternatives but never named PaulPauls/llama3_interpretability_sae. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 PaulPauls/llama3_interpretability_sae?
    pass
    AI named PaulPauls/llama3_interpretability_sae explicitly

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

  • If a team adopts PaulPauls/llama3_interpretability_sae in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name PaulPauls/llama3_interpretability_sae — 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?

  • In one sentence, what problem does the repo PaulPauls/llama3_interpretability_sae solve, and who is the primary audience?
    pass
    AI did not name PaulPauls/llama3_interpretability_sae — 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?

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PaulPauls/llama3_interpretability_sae — 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