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Hugging Face paper: Encoder-free multimodal models show promise at scale

By PulseAugur Editorial · [1 sources] ·

A new paper from Hugging Face explores the potential of encoder-free multimodal large language models (MLLMs). The research indicates that removing the traditional visual encoder shifts the optimal compute allocation towards larger models and suggests that encoder-free architectures can catch up to encoder-based models at approximately 10^22 FLOPs. The study also found that without a dedicated visual encoder, the language model itself adapts to perform this role, with benefits increasing at larger scales. AI

IMPACT Suggests encoder-free architectures may become more competitive with encoder-based models at larger scales, potentially simplifying multimodal model design.

RANK_REASON Research paper detailing scaling laws for a specific type of AI model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

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

Hugging Face paper: Encoder-free multimodal models show promise at scale

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Research paper detailing scaling laws for a specific type of AI model architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

    How Far Are We from Removing the Visual Encoder? Scaling Laws for Encoder-Free Multimodal Pretraining

    Most modern multimodal large language models (MLLMs) build on a pretrained visual encoder that provides a strong visual prior. Encoder-free MLLMs instead learn visual representations directly from raw pixels, offering a simple and unified architecture, but their scaling behavior …

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