Mastodawn

Apple przyspiesza działanie modeli AI nawet 5 razy

Apple opublikowało badania opisujące nową technikę, która pozwala modelom językowym (LLM) generować odpowiedzi nawet pięć razy szybciej, bez utraty jakości.

Tradycyjnie modele LLM tworzą tekst token po tokenie (autoregresja), co spowalnia proces. Apple odkryło, że modele – mimo trenowania na przewidywanie jednego tokena – mają wiedzę o kilku kolejnych. Na tej podstawie powstał framework Multi-Token Prediction (MTP), w którym model przewiduje naraz kilka tokenów.

Badacze wprowadzili specjalne tokeny maskujące w treści promptów (np. „Kot jest ”), które model wypełnia w jednym kroku („bardzo puszysty”). Jeśli przewidywanie nie jest zgodne z klasycznym trybem, system wraca do standardowej metody. Dzięki temu zachowana jest wysoka dokładność.

Testy z modelem open-source Tulu3-8B pokazały:

2–3 razy szybsze działanie w typowych zadaniach (Q&A, czat)
do 5 razy szybsze w przewidywalnych domenach, takich jak programowanie i matematyka
brak utraty jakości dzięki technice gated LoRA adaptation

Pełny artykuł naukowy dostępny jest na stronach arXiv.

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HackerNoon Jul 25

Explore the sophisticated mechanisms driving multi-token prediction. This section rigorously explains its edge via information-theoretic mutual information https://hackernoon.com/decoding-the-magic-multi-token-predictions-information-theoretic-edge-and-beyond #multitokenprediction

Decoding the Magic: Multi-Token Prediction's Information-Theoretic Edge & Beyond | HackerNoon

Explore the sophisticated mechanisms driving multi-token prediction. This section rigorously explains its edge via information-theoretic mutual information

HackerNoon Jul 23

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Discover how multi-token prediction improves LLM algorithmic reasoning, potentially by learning to allocate computational resources more efficiently

HackerNoon Jul 23

This figure illustrates the profound impact of training scale on multi-token prediction models' performance on GSM8K, highlighting critical data efficiency https://hackernoon.com/strategic-llm-training-multi-token-predictions-data-efficiency-in-mathematical-reasoning #multitokenprediction

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This figure illustrates the profound impact of training scale on multi-token prediction models' performance on GSM8K, highlighting critical data efficiency

HackerNoon Jul 22

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Explore Table S5 revealing multi-token prediction's remarkable training efficiency across LLM sizes (0.3B-13B)

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We conclude our work on multi-token prediction as a superior method for training LLMs, delivering enhanced performance for generative/reasoning tasks

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Explore the landscape of language modeling losses, multi-token prediction, and self-speculative decoding. highlights.

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Exploring Alternative Architectures for Multi-Token LLM Prediction | HackerNoon

Dive into the design space beyond our core multi-token prediction architecture, comparing approaches like replicated unembeddings and linear heads

HackerNoon Jul 18

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Dive into the core reasons behind multi-token prediction's superior LLM performance, exploring how it mitigates distributional discrepancy

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Explore how multi-token prediction fundamentally alters LLM capabilities, dramatically improving induction and algorithmic reasoning