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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

93,403 subscribers

18,211 просмотров • 11 дней назад •via X (Twitter)

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Комментарии: 3

Фото профиля 戴卫得
戴卫得11 дней назад

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

Фото профиля Adam 🤗
Adam 🤗11 дней назад

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

Фото профиля Kieren Day
Kieren Day11 дней назад

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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A friendly reminder to all Speed Racer fans that this film exists
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If we want crypto to win, we have to accept that most teams will fail Unfortunately; this is needed Does that mean it’s right for teams to extract? Absolutely not But you as the user made the decision to invest in extremely speculative startups Let’s try to learn from this
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