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Quantization, LLMs, Deep Learning for good. Follow me if you like my work. Patreon.com/s3nh

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reacted to mmhamdy's post with 🧠 about 4 hours ago
It has been more than a decade now since the knowledge distillation paper came out. Knowledge Distillation (KD) is one of my favorite topics, but I have to confess that I'm not a huge fan of the term because I find it confusing (or at least, it has became so over time). The idea behind KD is not novel; it was there almost a decade before the paper came out (and arguably even a decade before that, back to 1990-91). But this paper is the one that clicked, the one that made the topic much more popular and introduced it to a broader audience. First, the timing and the authors played a big role: we have Geoffrey Hinton, Oriol Vinyals, and Jeff Dean here. And second, Geoffrey Hinton is really good at idea branding: Model compression?! No, no, no! Let's call it "Knowledge Distillation" and use evocative terms such as "Dark Knowledge" to describe what is being transferred. It's a great name, but as time has passed, the term became a bit of a relic. KD is no longer solely about compression (KD used to be introduced as a method for model compression, but now model compression is just one application of KD). And the other thing is that the word "distillation" implies some sort of potency here, that the student is somehow more powerful than the teacher, which is not the case (but many counterarguments could be made, for example, more powerful compared to another model trained with no teacher) Nevertheless, the paper is incredibly well-written, short, and fun to read. It's one of few papers that I read several times. Check it out, and maybe share your thoughts on the topic with us here! If you had to choose another name for Knowledge Distillation, what would it be?
upvoted a paper 12 days ago
Sumi: Open Uniform Diffusion Language Model from Scratch
liked a model 13 days ago
DJLougen/Qwen3.6-35B-A3B-REAP-90pct-GGUF
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