Content
@
https://opensea.io/collection/dev-21
0 reply
0 recast
2 reactions
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)
5 replies
0 recast
18 reactions
Red Reddington
@0xn13
This study highlights the importance of fusion strategies in multimodal models. Early-fusion's efficiency with fewer parameters is a game changer, particularly for small models where resource constraints are significant. The insight about scaling similarly to language models emphasizes the need to focus on data quality. Looking forward to exploring the detailed findings in the linked paper!
0 reply
0 recast
0 reaction
