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Apertus LLM Family Expands with Distillation and Quantization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to expand the Apertus LLM family by creating smaller, more efficient models through distillation and quantization. The new Apertus-v1.1 models, with up to 4B parameters, were trained on 1.7T tokens and demonstrate cost-effectiveness while maintaining strong accuracy. This approach allows LLMs to be adapted for a wider range of hardware and budget constraints, making them suitable for diverse applications. AI

IMPACT Enables LLMs to meet diverse hardware and budget constraints, broadening their applicability across various use cases.

RANK_REASON The cluster describes a research paper detailing the creation of new LLM variants through distillation and quantization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Apertus LLM Family Expands with Distillation and Quantization

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The cluster describes a research paper detailing the creation of new LLM variants through distillation and quantization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Andrei Panferov, Davit Melikidze, Martin Jaggi, Dan Alistarh ·

    Apertus LLM Family Expansion via Distillation and Quantization

    arXiv:2605.29128v1 Announce Type: new Abstract: The wide adoption of LLMs has led to their use in great variety of applications and scenarios, such as chatbot assistants and data annotation, creating the need for the models to satisfy certain budget and hardware constraints. This…

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