向transformer架构中的每一层,注入可训练的 rank decomposition matrices-(低)秩分解矩阵,从而可以显著地减少下游任务所需要的可训练参数的规模。 效果举例: 相比于使用Adam的gpt3 175B,LoRA可以降低可训练参数规模,到原来的1/10000,以及GPU内存的需求是原来的1/3。 GitHub - microsoft/LoRA: Code for loralib, ...
即LoRA的微调方式是这样做的:它冻住了预训练模型的权重,并且在Transformer架构中的每一层都注入了一个可分解的低秩矩阵(Rank Decomposition Matrix)(这个是什么意思后面再说) 而且这种方式能够保证no additional inference latency,即和原模型相比,采用这种方式微调后的模型没有加大任何推演时间 Introduction 如上图,蓝色...
3) Matrix Decomposition 矩阵分解 1. Packet scheduling based on matrix decomposition in optical switches; 基于矩阵分解的光交换机分组调度算法 2. Direction finding in the presence of coherent signals based on data matrix decomposition; 基于数据矩阵分解的相干源方向估计新方法 3. In order to solve ...
H. Goller, Shorted operators and rank decomposition matrices, Linear Algebra Appl. 81 (1986), 207-236.H. Goller, Shorted operators and rank decomposition matrices, Linear Algebra Appl. 81 (1986) 207-236.Shorted operators and rank decomposition matrices - Goller - 1986 () Citation Context ......
Tensor rank decomposition
The 蠒J蠒J polar decomposition of matrices with rank 2Datoon, JMerino, DParas, a
Analysis of individual differences in multidimensional scaling via an n-way generalization of Eckart-Yong decomposition Psychometrika, 35 (1970), pp. 283-319 Google Scholar 7 H.H. Crapo, Gian-Carlo Rota On the Foundations of Combinatorial Theory: Combinatorial Geometries (prelim. ed.), M.I.T....
For example, LoRA [15] introduces trainable low-rank decomposition matrices into LLMs, enabling the model to adapt to a new task while preserving the integrity of the original LLMs and retaining the acquired knowledge. Fundamentally, this approach is built upon the assumption that updates to the...
For matrices, the solution is conceptually obtained by truncation of the singular value decomposition (SVD); however, this approach does not have a straightforward multilinear counterpart. We discuss higher-order generalizations of the power method and the orthogonal iteration method. 展开 ...
(LoRA) approach. LoRA allows us to train some dense layers in a neural network indirectly by optimizing rank decomposition matrices of the dense layers’ change during adaptation instead, while keeping the pre-trained weights frozen, as shown in Figure 1. Using GPT-3 175B as an example, we...