稀疏注意力计算:sliding window attention 理解LLM位置编码:RoPE 大模型算法题(1)大模型算法题(2)大模型...
SAMBA is a hybrid language model architecture that combines state space models (SSMs) and sliding window attention (SWA) to efficiently process long sequences of text with improved memory recall. Stanislav Karzhev 10 min tutorial Getting Started With Mixtral 8X22B Explore how Mistral AI's Mixtral...
Microstructure and Meso-Mechanical Properties of Asphalt Mixture Modified by Rubber Powder under a Multi-Scale Effect The applications of rubber-modified asphalt and its mixtures have received widespread attention due to the environmental and economic benefits of such materials. However, studies on the ...
When attempting to simulate the compaction, particular attention should be paid to the roles of these factors. In the following texts, the simulation of the compaction process using DEM and analysis of the internal structure of the compacted mixture will be discussed. 5.1. Material specifications ...
Sliding Window Attention 7BsdOQ 目的：提高推理速度，缓解O(n^2)的时间复杂度 SWA（滑动窗口注意力）...
The target in the traditional detection result is usually labeled with a bounding box, and no attention is given to each component. For example, an airplane is generally composed of a head, an airframe, two wings and a tail. However, traditional methods have not investigated the latent ...
Li et al. [14] suggested a novel approach to perform multimodal fusion through the utilization of multimodal interactive attention network (MIA-Net). They only considered the modality that had the most impact on the emotion to be the primary modality, with every other modality termed as auxiliar...
Raman spectroscopy; deep learning; feature fusion; attention mechanism; convolutional generative adversarial networks1. Introduction Raman spectroscopy technology, as a powerful tool for revealing the internal structure of matter, relies on analyzing information on various vibration frequencies and vibration ...
It is fed in parallel to the input of the LSTM model with a dropout layer at the output and to the transformer block, consisting of several layers (the number of layers is a hyperparameter). Each transformer layer consists of attention heads, dropout, and Layernorm layers, as well as FC...
It is fed in parallel to the input of the LSTM model with a dropout layer at the output and to the transformer block, consisting of several layers (the number of layers is a hyperparameter). Each transformer layer consists of attention heads, dropout, and Layernorm layers, as well as FC...