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Together AI unveils Parcae, a stable looped model architecture

By PulseAugur Editorial · [1 sources] ·

Together AI has introduced Parcae, a novel stable architecture for looped language models. This new design allows models to achieve the quality of larger Transformers while using significantly fewer parameters, by increasing recurrence rather than solely scaling data. Parcae demonstrates improved stability over previous looped models and establishes the first scaling laws for this type of architecture, suggesting a more efficient frontier for training memory-constrained on-device models. AI

IMPACT Introduces a more parameter-efficient model architecture, potentially enabling higher quality on-device AI with reduced memory footprints.

RANK_REASON The cluster describes a new model architecture and its training methodology, including new scaling laws, presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Together AI blog →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Together AI unveils Parcae, a stable looped model architecture

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The cluster describes a new model architecture and its training methodology, including new scaling laws, presented in a research paper. [lever_c_demoted from research: ic=1 ai=1.0]
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model release, paper, infra
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192 days old
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COVERAGE [1]

  1. Together AI blog TIER_1 English(EN) ·

    Parcae: Doing more with fewer parameters using stable looped models

    Parcae is a stable looped language model that matches the quality of a Transformer twice its size — a 770M model reaching 1.3B-level performance. We introduce the first scaling laws for looping and show that increasing recurrence, not just data, is a compute-efficient path to bet

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