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What exactly is model distillation — and why did DeepSeek suddenly make everyone talk about it? Think of it as a teacher and a student. The teacher is a massive model: very smart, very expensive, very slow. The student is a much smaller one: cheap and fast, but not as capable. Instead of training the small model only on raw internet data, you let the big model write the answers — the worked examples, the reasoning, step by step. Those outputs become the student's training data. The student never copies the teacher's weights. It learns the pattern. The result isn't as smart as the teacher. But it's dramatically cheaper and faster to run — and surprisingly strong. DeepSeek did exactly this with R1: they used the big model to generate reasoning data, then trained smaller Qwen and Llama models on it, from 1.5B all the way to 70B. Their own report found that distilling from R1 worked better than making those small models discover the reasoning themselves through reinforcement learning. Then the controversy. OpenAI has accused DeepSeek of using outputs from OpenAI models — accounts, programmatic access, bypassed restrictions. DeepSeek hasn't confirmed it. Two things to keep apart: ✅ DeepSeek distilling its own R1 into smaller models — documented. ⚠️ DeepSeek distilling from OpenAI models — an allegation, not an established fact. And distillation itself is not shady. It's a standard machine learning method. DeepSeek's own MIT licence explicitly allows using R1 outputs for fine-tuning and distillation. The real question is permission. If the model owner allows it, distillation is completely normal. If the terms prohibit building a competing model, doing it anyway is a very different issue. The one-line version: the biggest model discovers the capability. Distillation squeezes as much of it as possible into something smaller. Save this for the next time someone says

What exactly is model distillation — and why did DeepSeek suddenly make everyone talk about it? Think of it as a teacher and a student. The teacher is a massive model: very smart, very expensive, very slow. The student is a much smaller one: cheap and fast, but not as capable. Instead of training the small model only on raw internet data, you let the big model write the answers — the worked examples, the reasoning, step by step. Those outputs become the student's training data. The student never copies the teacher's weights. It learns the pattern. The result isn't as smart as the teacher. But it's dramatically cheaper and faster to run — and surprisingly strong. DeepSeek did exactly this with R1: they used the big model to generate reasoning data, then trained smaller Qwen and Llama models on it, from 1.5B all the way to 70B. Their own report found that distilling from R1 worked better than making those small models discover the reasoning themselves through reinforcement learning. Then the controversy. OpenAI has accused DeepSeek of using outputs from OpenAI models — accounts, programmatic access, bypassed restrictions. DeepSeek hasn't confirmed it. Two things to keep apart: ✅ DeepSeek distilling its own R1 into smaller models — documented. ⚠️ DeepSeek distilling from OpenAI models — an allegation, not an established fact. And distillation itself is not shady. It's a standard machine learning method. DeepSeek's own MIT licence explicitly allows using R1 outputs for fine-tuning and distillation. The real question is permission. If the model owner allows it, distillation is completely normal. If the terms prohibit building a competing model, doing it anyway is a very different issue. The one-line version: the biggest model discovers the capability. Distillation squeezes as much of it as possible into something smaller. Save this for the next time someone says "distilled model" like you're supposed to know what it means. #modeldistillation #deepseek #machinelearning #aiexplained #llm

ЕГОР НАСТОЯЩИЙ — АУРА БАТТЕЛ

ЕГОР НАСТОЯЩИЙ — АУРА БАТТЕЛ

Download wallpapers from link in bio 🔗 #wallpaper #wallpapers #lockscreenwallpaper #foryou #fyp

Download wallpapers from link in bio 🔗 #wallpaper #wallpapers #lockscreenwallpaper #foryou #fyp

Darr Hai Tujhe Main Kho Na Doon #foryou #viral #viraltiktok #lyrics #bollywood #hindisong #arjitsingh #sadsong

Darr Hai Tujhe Main Kho Na Doon #foryou #viral #viraltiktok #lyrics #bollywood #hindisong #arjitsingh #sadsong

стереограмма 3d секрет #стереограмма #викторина #головоломка #игра #логика

стереограмма 3d секрет #стереограмма #викторина #головоломка #игра #логика

Al Mumtaz 🥹😭💔#fyyyyyyyyyyyyyyyyyyy #foryoupage

Al Mumtaz 🥹😭💔#fyyyyyyyyyyyyyyyyyyy #foryoupage

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