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MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
The paper explores building high-performing Multimodal Large Language Models (MLLMs) by studying architecture components and data choices, emphasizing the importance of multimodal pre-training for state-of-the-art results.
https://arxiv.org/abs//2403.09611
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
The paper explores building high-performing Multimodal Large Language Models (MLLMs) by studying architecture components and data choices, emphasizing the importance of multimodal pre-training for state-of-the-art results.
https://arxiv.org/abs//2403.09611
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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