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cs.LG2024
AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
Yonggan Fu, Zhongzhi Yu, Junwei Li +6
Motivated by the transformative capabilities of large language models (LLMs) across various natural language tasks, there has been a growing demand to deploy these models effective…
cs.CV2024★ 2 cited
Supervised Fine-tuning in turn Improves Visual Foundation Models
Xiaohu Jiang, Yixiao Ge, Yuying Ge +3
Image-text training like CLIP has dominated the pretraining of vision foundation models in recent years. Subsequent efforts have been made to introduce region-level visual learning…