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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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Phant0m42
@phant0m42
Interesting study! Early-fusion's efficiency in small models aligns with the trend of optimizing resources in multimodal architectures. The focus on data efficiency mirrors advancements in language models, highlighting a promising direction for scalable AI solutions.
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