本文的方法SiRA,将 Sparse MOE 和 lora 结合起来,相比 lora 收敛更快。相比 MoLoRA 节约了计算资源。 方法倒是很直接。也考虑到了 MoE 常规会考虑的Token Capacity 和 Auxiliary Loss. 效果的话: 其实也是半斤八俩,略好。 不过作者也说了 倒是挺合适。
🎉 This is the implementation of EMNLP 2023 paper:Sparse Low-rank Adaptation of Pre-trained Language Models Requirements To run our code, please install all the dependency packages by using the following command: pip install -r requirements.txt ...
either through low-rank adaptation or factorization. While effective for fine-tuning, low-rank structures are generally less suitable for pretraining because they restrict parameters to a low-dimensional subspace. In this work, we propose to parameterize the w...
According to the proposed technique, noise signals are assumed as low-rank components because noise spectra within different time frames are usually highly correlated with each other; while the speech signals are considered as sparse components because they are relatively sparse in time–frequency ...
Domain adaptation tries to mitigate this degradation. This chapter presents an overview of recent domain adaptation methods based on sparse and low-rank representations.doi:10.1142/9789813144552_0004Rama ChellappaVishal M. Patel
Shao M, Kit D, Fu Y (2014) Generalized transfer subspace learning through low-rank constraint. Int J Comput Vis 109:74–93 Article MathSciNet Google Scholar Jhuo I-H, Liu D, Lee D, Chang S-F (2012) Robust visual domain adaptation with low-rank reconstruction. In: 2012 IEEE Conferen...
The proposed method can capture the global mixture of the clustering structure (by the sparseness and low rankness) and the locally consistent structure (by the local graph regularization) as well as the distribution difference (by the distribution adaptation) of the domains data. Hence, the ...
rank that might not always be the ideal choice. Recognizing the need for more flexible adaptation, we extend the methodology of LoRA to an innovative approach we call sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. We achieve...
We found this less effective empirically using the example of LoRA that introducing more trainable parameters does not help. Motivated by this we investigate the importance of leveraging "sparse" computation and propose SiRA: sparse mixture of low rank adaption. SiRA leverages the Sparse Mixture of ...
This paper describes a new exponential language model that decomposes the model parameters into one or more low-rank matrices that learn regularities in the training data and one or more sparse matrices that learn exceptions (e.g., keywords). The low-rank matrices induce continuous-space represent...