RRepoGEO

REPOGEO REPORT · LITE

idanshen/Self-Distillation

Default branch main · commit d7757321 · scanned 6/12/2026, 9:33:39 AM

GitHub: 614 stars · 71 forks

AI VISIBILITY SCORE
17 /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
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 idanshen/Self-Distillation, 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
    TRL-based code for reproducing On-Policy Self-Distillation Fine-Tuning (SDFT) to enable continual learning in large foundation models.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    continual-learning, self-distillation, foundation-models, large-language-models, llm-fine-tuning, pytorch, trl
  • highlicense#3
    Add a LICENSE file and clarify its terms

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root. If the license is custom or compound, explicitly state the terms in the README's 'License' section.

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 idanshen/Self-Distillation
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Elastic Weight Consolidation (EWC)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Elastic Weight Consolidation (EWC) · recommended 1×
  2. Learning without Forgetting (LwF) · recommended 1×
  3. Synaptic Intelligence (SI) · recommended 1×
  4. Experience Replay · recommended 1×
  5. Gradient Episodic Memory (GEM) · recommended 1×
  • CATEGORY QUERY
    How to prevent catastrophic forgetting when continually fine-tuning large foundation models?
    you: not recommended
    AI recommended (in order):
    1. Elastic Weight Consolidation (EWC)
    2. Learning without Forgetting (LwF)
    3. Synaptic Intelligence (SI)
    4. Experience Replay
    5. Gradient Episodic Memory (GEM)
    6. A-GEM (Averaged Gradient Episodic Memory)
    7. Progressive Neural Networks (PNNs)
    8. Continual Learning with Transformers (CL-Transformers)
    9. Adapter Layers
    10. Houlsby Adapters
    11. Pfeiffer Adapters
    12. Task-specific heads
    13. PackNet
    14. Soft Masking

    AI recommended 14 alternatives but never named idanshen/Self-Distillation. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective self-distillation strategies for continual learning in large language models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Hugging Face Transformers
    4. Diffusers
    5. Avalanche
    6. Hugging Face PEFT library

    AI recommended 6 alternatives but never named idanshen/Self-Distillation. 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 idanshen/Self-Distillation?
    pass
    AI did not name idanshen/Self-Distillation — 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?

  • If a team adopts idanshen/Self-Distillation in production, what risks or prerequisites should they evaluate first?
    pass
    AI named idanshen/Self-Distillation 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 idanshen/Self-Distillation solve, and who is the primary audience?
    pass
    AI did not name idanshen/Self-Distillation — 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?

Embed your GEO score

Drop this badge into the README of idanshen/Self-Distillation. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/idanshen/Self-Distillation.svg)](https://repogeo.com/en/r/idanshen/Self-Distillation)
HTML
<a href="https://repogeo.com/en/r/idanshen/Self-Distillation"><img src="https://repogeo.com/badge/idanshen/Self-Distillation.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

idanshen/Self-Distillation — 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