almogtavor

almogtavor / SE-KD3x

Public

Efficient LLM distillation via student-entropy selection across three axes.

20
1
100% credibility
Found Feb 03, 2026 at 13 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

SE-KD3X enables efficient training of smaller language models by selectively distilling knowledge from larger teacher models, focusing on high-uncertainty tokens for major speed and memory savings.

How It Works

1
🔍 Discover efficient AI training

You find a smart way to train smaller AI assistants by copying only the most helpful lessons from bigger ones, saving time and computer power.

2
📦 Get the tools ready

You add the simple kit to your computer with one easy command.

3
🤖 Choose your AI teachers and students

Pick a big expert AI to learn from and a smaller one to improve, plus some example texts to practice on.

4
🚀 Start smart training

Hit go, and it automatically focuses on the trickiest parts where the small AI needs the most help, training way faster.

5
⏱️ Watch it speed through

See your training finish quicker with less memory used, while getting great results.

6
🧪 Test your new AI

Run quick checks to confirm your smaller AI performs as well or better.

🎉 Enjoy your efficient AI

Celebrate having a fast, smart assistant that rivals the big ones but runs on everyday hardware.

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Star Growth

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AI-Generated Review

What is SE-KD3x?

SE-KD3x is a Python toolkit for efficient LLM distillation, selecting only the top-20% highest-entropy tokens from the student model across position, vocabulary class, and sample axes to match full knowledge distillation performance. It slashes training wall time by 70%, peak memory by 18%, and storage by 99.96% using PyTorch and Transformers, via a simple CLI like `python run_distillation.py --distill_type top-k-tok --k_percent 20 --datasets fineweb`. Researchers distill 1.7B models from 8B teachers on 80M tokens, evaluating zero-shot accuracy and perplexity.

Why is it gaining traction?

It stands out in efficient deep learning GitHub repos by focusing compute on informative tokens, beating dense baselines on benchmarks like IFEval while enabling efficient LLM inference setups. The hook is plug-and-play efficiency for resource-constrained training, with offline caching for teacher logits and quick baselines via one command—no need for custom efficient ML GitHub hacks across axes.

Who should use this?

ML engineers distilling LLMs for edge deployment on CPUs, where efficient LLM inference via chunked prefills or dynamic pruning matters. Researchers running efficient LLM adaptation using a single gradient step on 100 samples, or exploring efficient LLM scheduling by learning to rank in low-VRAM environments.

Verdict

Worth trying for efficient LLM projects—paper-backed gains are real, CLI is solid. But at 18 stars and 1.0% credibility, it's early: expect tweaks for production. (187 words)

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