REPOGEO REPORT · LITE

FLHonker/Awesome-Knowledge-Distillation

Default branch main · commit 355fc31e · scanned 5/25/2026, 8:13:08 AM

GitHub: 2,664 stars · 332 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 FLHonker/Awesome-Knowledge-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

highreadme#1

Clarify repository's nature as an 'Awesome List' in README

Why:

CURRENT

# Awesome Knowledge-Distillation

COPY-PASTE FIX

# Awesome Knowledge-Distillation

A curated list of research papers, code, and resources on Knowledge Distillation, categorized for easy navigation by researchers and practitioners.

highlicense#2
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Create a `LICENSE` file in the repository root with the MIT License text.
```

mediumtopics#3

Add 'awesome-list' to repository topics

Why:

CURRENT

deep-learning, distillation, kd, knowldge-distillation, model-compression, transfer-learning

COPY-PASTE FIX

deep-learning, distillation, kd, knowldge-distillation, model-compression, transfer-learning, awesome-list

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 FLHonker/Awesome-Knowledge-Distillation

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 2×
TensorFlow Lite · recommended 1×
PyTorch Mobile · recommended 1×
microsoft/onnxruntime · recommended 1×
NVIDIA TensorRT · recommended 1×

CATEGORY QUERY
How can I compress large deep learning models for faster inference on edge devices?
you: not recommended
AI recommended (in order):
1. TensorFlow Lite
2. PyTorch Mobile
3. ONNX Runtime (microsoft/onnxruntime)
4. NVIDIA TensorRT
5. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
6. Hugging Face Transformers (huggingface/transformers)
7. DistilBERT
8. TinyBERT
9. Google Cloud AutoML
10. Microsoft Azure Machine Learning
11. NNI (Neural Network Intelligence) (microsoft/nni)
12. AutoKeras (keras-team/autokeras)
13. MobileNet
14. EfficientNet
15. SqueezeNet
16. ShuffleNet
AI recommended 16 alternatives but never named FLHonker/Awesome-Knowledge-Distillation. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective techniques for transferring knowledge from a large teacher model to a smaller student?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PyTorch (pytorch/pytorch)
3. TensorFlow (tensorflow/tensorflow)
4. MMDetection (open-mmlab/mmdetection)
5. Keras (keras-team/keras)
6. OpenAI's GPT-3/GPT-4
7. Google's AutoAugment/RandAugment (tensorflow/models)
8. AllenNLP (allenai/allennlp)
9. PyTorch Lightning (Lightning-AI/lightning)
10. DeepMind's AlphaFold (deepmind/alphafold)
AI recommended 10 alternatives but never named FLHonker/Awesome-Knowledge-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
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 FLHonker/Awesome-Knowledge-Distillation?
pass
AI did not name FLHonker/Awesome-Knowledge-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 FLHonker/Awesome-Knowledge-Distillation in production, what risks or prerequisites should they evaluate first?
pass
AI named FLHonker/Awesome-Knowledge-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 FLHonker/Awesome-Knowledge-Distillation solve, and who is the primary audience?
pass
AI did not name FLHonker/Awesome-Knowledge-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 FLHonker/Awesome-Knowledge-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.

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MARKDOWN (README)

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FLHonker/Awesome-Knowledge-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