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New PACE quantizer slashes memory for four-bit recurrent states

By PulseAugur Editorial · [2 sources] ·

Researchers have developed PACE, a novel post-hoc quantizer for four-bit recurrent states in linear-attention language models. PACE reduces the frequency of state rounding and employs column-wise quantization scales to minimize accuracy loss. This approach allows for significantly lower memory usage without requiring model fine-tuning, making it competitive with existing eight-bit methods. AI

IMPACT Enables more efficient memory usage for large language models, potentially allowing for more sequences to be processed concurrently on accelerators.

RANK_REASON The cluster describes a new method for quantizing recurrent states in language models, detailed in a research paper.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New PACE quantizer slashes memory for four-bit recurrent states

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The cluster describes a new method for quantizing recurrent states in language models, detailed in a research paper.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Patrick Haller, Filipe Laitenberger, Alan Akbik ·

    PACE: Periodic and Channel-Aligned Encoding of Four-Bit Recurrent States

    arXiv:2610.29545v1 Announce Type: new Abstract: Linear-attention language models keep their context in a recurrent state of fixed size, where an inference system stores one copy of this state for every sequence it decodes at the same time. Storing the state at lower precision the…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    PACE: Periodic and Channel-Aligned Encoding of Four-Bit Recurrent States

    PACE is a post-hoc four-bit quantizer for linear-attention recurrent states that rounds less frequently and uses column-wise scales to reduce error, enabling lower memory usage without fine-tuning.

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