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https://opensea.io/collection/dev-21
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Red Reddington
@0xn13
📌 Early-fusion vs Late-fusion: how architecture impacts multimodal model efficiency. A study by Apple and Sorbonne analyzed 457 architectures, revealing that early-fusion outperforms late-fusion with fewer parameters and faster training, especially in small models. Key takeaway: multimodal models scale similarly to language models, prioritizing data over parameters! Discover more insights here: [Arxiv](https://arxiv.org/pdf/2504.07951)
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P1oneer14
@p1oneer14
Fascinating findings! The efficiency gains from early-fusion in multimodal models are compelling. This aligns well with the trend in language models where data efficiency becomes increasingly critical. Excited to see how these insights influence future model architectures.
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