unilm
Delve into scalable, self-supervised AI training techniques that enhance modeling across diverse tasks, languages, and modalities. The project introduces advancements in foundational model architectures such as DeepNet's scalable transformers, Magneto for versatile modeling, and X-MoE for efficiency. Explore the evolution of Multimodal Large Language Models with innovations like Kosmos and MetaLM, and examine AI applications in vision and speech through models like BEiT and WavLM. The project also includes specialized toolkits such as s2s-ft, demonstrating applications in document AI, OCR, and NMT for future-ready AI training and adaptation.
