Issue #104 LLMs Edwin Dong, Lalit Julapalli
Multi-Token Prediction, Simply Explained
Free editionPRO edition
This week’s PRO issue includes a Colab notebook, diving into building a multi-token prediction pipeline from scratch. We’re going to compare MTP inference to regular autoregressive inference and build a beautiful interactive visualization. Please do read the full article before attempting the code tutorial.
As always, the code in this issue can be executed on a normal home PC and does not require a large amount of VRAM.
The monthly interview question bank can be found here, and has been updated for the month of June. This month, we’ve incorporated more company-specific interview questions.
These are industry standard questions that are tested across major tech companies, AI research firms, and more. The questions are sorted by difficulty and topic.
Check back frequently for more questions (as soon as next week), and let us know what type of questions you would like to see next!
When humans read text, we don't only process the words one at a time, pausing to guess the next word before continuing.
We read ahead, absorbing chunks of words at a time, anticipating where the thought is heading before it arrives, forming a broader sense of direction as we read.
This "reading ahead" is more than a speed trick, as it helps you understand the deeper structure of language and its presentation over a span of language.
If you’re familiar with large language models (LLMs), you’ll know that the dominant training paradigm has actually been the opposite: perform next-token prediction (NTP) by generating a single token at a time until completion.
In April 2024, researchers at Meta's FAIR lab published a paper, "Better & Faster Large Language Models via Multi-token Prediction," proposing an inference approach where LLMs adopt this human-like reading technique.
By training models to simultaneously predict multiple future tokens at every position in a sequence, they unlocked meaningful gains in overall performance, efficiency, and inference speed.
Incredibly, a deeper semantic and contextual understanding came without increasing the training cost.
This edition is for PRO members.
