Oct 12, 2023 · LEMON, a recipe to initialize scaled models using the weights of their smaller but pre-trained counterparts. This is followed by model training.

Nov 17, 2023 · We propose LEMON, a method that initializes large model with pretrained small model to save computational resources.

This is the unofficial PyTorch implementation of LEMON: lossless model expansion. We provide our reimplemented code for Folder cnn : ResNet on ImageNet.

Oct 12, 2023 · We propose LEMON, a suite of algorithms designed for lossless model expansion across a variety of architectures, ensuring compatibility with ...

This work presents a recipe to initialize scaled models using the weights of their smaller but pre-trained counterparts using an optimized learning rate ...

LEMON is a new method to make training larger deep learning models, like Transformers, faster and easier. Instead of starting from scratch, LEMON lets us ...

LEMON: Lossless model expansion · 1 code implementation • 12 Oct 2023 • Yite ... Our empirical results demonstrate that LEMON reduces computational costs by 56.

May 6, 2024 · We will present our latest work, "LEMON: Lossless Model Expansion," at ICLR 2024 in Vienna, Austria. Join Jiahao Su on Tuesday, May 7, ...

LEMON: Lossless model expansion ... Scaling of deep neural networks, especially Transformers, is pivotal for their surging performance and has further led to the ...

ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models. ... LEMON: Lossless model expansion Yite Wang, Jiahao ...