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friendly reminder: this is on Monday 🤝 → learn about speculative decoding to speed-up inference → if you're a beginner and not interested, just use that does this for you automatically 🦙

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merve

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17,586 görüntüleme • 2 gün önce •via X (Twitter)

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戴卫得 profil fotoğrafı
戴卫得2 gün önce

The tool that handles speculative decoding automatically is the bit I'd try first. Let me feel the speedup before the lecture 😂

Adam 🤗 profil fotoğrafı
Adam 🤗2 gün önce

you're making me want the weekend to end faster 😔💔

Kieren Day profil fotoğrafı
Kieren Day2 gün önce

The speedup lives or dies on the acceptance rate. Code and structured output are where a small draft model guesses right most often, so that's where the big wins show up. On open-ended chat, or once you're serving large batches, the gain shrinks fast.

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While exploring diffusion language models (DLMs), we kept asking ourselves: given speculative decoding, a lossless acceleration technique, why do we still need DLMs? 🤔 Our latest work, ORL, tries to answer this question: AR optimizes intelligence 👆, speculative decoding optimizes speed 👉, and DLMs optimize the Pareto frontier of speed and intelligence 🚀. Through co-design across the algorithms, the training and inference engine, and the kernels, ORL pushes out a new Pareto frontier of intelligence and speed. Similar performance, much faster than speculative decoding. You say lossy spec? ORL-DLM is better too! 😎 🌐 Project + live demos:
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