蓝色分支,Decoder-only框架(也叫Auto-Regressive),典型代表如GPT系列/LLaMa/PaLM等 Harnessing the Power of LLMs in Practice 刚听这三种框架名称可能会有点懵逼,不用担心,先感性认识一下。如下所示 横轴代表了输入token,纵轴代表相对应每个位置的输出token 左图为encoder-only,输出token都能看到所有输入token。例如...
本文主要内容:介绍 LLM 最底层的 Transformer 架构原理,重点介绍所有大语言模型都在用的解码器专用Transformer架构的原理! 1.Transformer 架构 先放一张网上已经包浆的图(好用好懂才会包浆): Transformer最常见的架构图 接下来我们从下往上,一点点看看图片中每一个元素的含意与作用 Input(prompt): 作为Transformer的...
编码器-解码器架构(Encoder-Decoder Architecture):Transformer采用了标准的编码器-解码器结构,其中,编码器负责理解输入序列,将其转换成高级语义表示;解码器则依据编码器的输出,结合自身产生的隐状态逐步生成目标序列。在解码过程中,解码器还应用了自注意力机制以及一种称为“掩码”(Masking)的技术来防止提前看到未来要预...
Apart from the various interesting features of this model, one feature that catches the attention is its decoder-only architecture. In fact, not just PaLM, some of the most popular and widely used language models are decoder-only.
DETR是DEtection TRANSformer的缩写,可能会改变计算机视觉领域。该框架是解决对象检测问题的创新有效方法。而且DETR极其快捷,高效,这是数据科学专业人员的梦想!
In this paper, we propose a deep multi-task encoder-transformer-decoder architecture (ChangeMask) designed by exploring two important inductive biases: sematic-change causal relationship and temporal symmetry. ChangeMask decouples the SCD into a temporal-wise semantic segmentation and a BCD, and then...
Transformer模型基于自注意力机制(self-attention mechanism),可以并行处理输入序列,避免了传统循环神经网络(RNN)和卷积神经网络(CNN)在处理长距离依赖关系时的局限性。Transformer模型由编码器(encoder)和解码器(decoder)组成,每个编码器和解码器都包含多层的自注意力机制和前馈神经网络。
In NLP, encoder and decoder are two important components, with the transformer layer becoming a popular architecture for both components. FasterTransformer implements a highly optimized transformer layer for both the encoder and decoder for inference. On Volta, Turing and Ampere GPUs, the computing po...
DeepMind’s RETRO Transformer uses cross-attention to incorporate the database retrived sequencesCode example: HuggingFace BERT (key, value are from the encoder, while query is from the decoder)CrossVit - here only simplified cross-attention is usedOn the Strengths of Cross-Attention in Pretrained...
Decoder only 一些研究侧重于对语言建模的Transformer解码器进行预训练。例如,生成式预训练 Transformer系列,即GPT、GPT-2和GPT-3,专门用于缩放预训练的Transformer解码器,并且最近的研究工作表明大规模PTM可以通过将任务和示例作为构造提示输入模型来实现令人印象深刻的性能。 Encoder-Decoder 也有采用Transformer编码器-解码...