arXiv:2409.01990
공식 코드 · Papers with Code
Designing Large Foundation Models for Efficient Training and Inference: A Survey
원문 초록
This paper focuses on modern efficient training and inference technologies on foundation models and illustrates them from two perspectives: model and system design. Model and System Design optimize LLM training and inference from different aspects to save computational resources, making LLMs more efficient, affordable, and more accessible. The paper list repository is available at https://github.com/NoakLiu/Efficient-Foundation-Models-Survey.
함께 살펴볼 논문
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Efficient Speech Translation through Model Compression and Knowledge Distillation
From Tiny Machine Learning to Tiny Deep Learning: A Survey
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix Approximation
Structured Pruning and Quantization for Learned Image Compression
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models
토론
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