decoder与encoder的唯一区别就是decoder使用的是masked-self-attention即每一个词向量表示的计算仅依赖于其之前的词向量。 next token prediction 对于next token prediction训练目标,decoder-only架构(masked self attention)是必须的。发布于 2024-05-26 17:00・上海
项目主页:MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers 论文地址:nihalsid.github.io/mesh-gpt/static/MeshGPT.pdf 该文章的核心贡献就是给出了一种3D结构的sequence formulation,并成功使用Decoder-only Transformer实现了auto-regressive的生成模式。 该工作总共分为两个步骤: 从大量3D meshes中...
它主要适用于理解任务,如文本分类、情感分析等。代表模型是BERT(Bidirectional Encoder Representations from Transformers),通过双向注意力机制捕捉丰富的上下文信息。 工作原理:Encoder-Only架构利用编码器对输入序列进行编码,提取其特征和语义信息。在BERT模型中,采用双向注意力机制,能够同时关注序列中的前后词语,从而获得更...
Transformers. LLMs are primarily realized as decoder-only transformers (Vaswani et al., 2017; Touvron et al., 2023a,b), incorporating an input embedding layer and multiple decoder layers. Each layer contains a self-attention network and a feedforward network with normalization modules. Crucially,...
BERT(Bidirectional Encoder Representations from Transformers):一个预训练的语言表示模型,通过双向Transformer编码器来捕捉单词的上下文信息。 Decoder-Only(仅解码器) 1. 定义与用途 Decoder-Only模型则是指那些只包含解码器部分的模型。这类模型通常用于生成输出序列,但它们不依赖于显式的编码器来生成这个序列;相反,它...
This paper reveals a key insight that a one-layer decoder-only Transformer is equivalent to a two-layer Recurrent Neural Network (RNN). Building on this insight, we propose ARC-Tran, a novel approach for verifying the robustness of decoder-only Transformers against arbitrary perturbation spaces. ...
Projects Security Insights Additional navigation options main BranchesTags Code README License MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers arXiv|Video|Project Page This repository contains the implementation for the paper: MeshGPT: Generating Triangle Meshes with Decoder-Only Transformer...
仅解码器转换器实际上不使用任何内存，因为它不像编码器-解码器转换器那样存在编码器-解码器自关注。一...
pip3 install transformers[deepspeed] Note : When you optimize inference also in DeepSpeed, install CUDA 10.2 instead. After installation, please logout and login again to take effect for "jupyter" path. Then, start Jupyter notebook as follows. ...
与Transformers相比,YOCO具有更好的推理效率和竞争性能。实验结果表明,在各种设置下,YOCO在大型语言模型上取得了良好的效果,即扩大训练词元数量,扩大模型大小,将上下文长度扩大到1M词元。分析结果还表明,YOCO将推理效率提高了几个数量级,特别是对于长序列建模