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的输入,这里的prompt 一般是分词之后的内容 Input Embedding: Transformer无法理解文本,他只做矩阵计算,所以,这里必须要有这一...
从BERT的介绍我们已经知道了encoder-only就是所有输出token都能看到过去和未来的所有输入token,这个对于NLU任务天然友好,但是对于seq2seq任务,如机器翻译,这个结构就不是特别匹配,因为比较难直接用做翻译结果的生成 一种直接的办法就是加上decoder做预测生成,这就形成了encoder-decoder架构,如下所示 Classic Transformer B...
Speeding Up the Vision Transformer with BatchNorm Deep Learning How integrating Batch Normalization in an encoder-only Transformer architecture can lead to reduced training time… Anindya Dey, PhD August 6, 2024 28 min read The Math Behind Keras 3 Optimizers: Deep Understanding and Application ...
另一个是T5的论文《Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer》都拼了老命证明Encoder-Decoder架构相比于Decoder-only的优势，但是它们遭人诟病的在于，这两篇论文的模型尺度都还不算大，以及多数的LLM确实都是在做Decoder-only的，所以这个优势能否延续到更大尺度的LLM以及...
Not just GPT-3, the previous versions, GPT and GPT-2, too, utilised a decoder only architecture. The original Transformer model is made of both encoder and decoder, where each forms a separate stack. This architecture fits well with its primary application – machine translation. The authors...
(KV) caches that are reused by the cross-decoder via cross-attention. The overall model behaves like a decoder-only Transformer, although YOCO only caches once. The design substantially reduces GPU memory demands, yet retains global attention capability. Additionally, the comput...
Self-Attention Networks.Typically for decoderonly LLMs like Llama2 (Touvron et al., 2023b), self-attention networks (SANs) map queriesQ, keysK, and valuesVinto an output, as delineated in the following equations, whereMdenotes anL×Lmasking matrix, facilitating the currenti-th token to atten...
In this blog, we will get acquainted briefly with the ChatGPT stack and then implement a simple decoder-only transformer to train on Shakespeare. Creating ChatGPT models consists of four main stages:1. Pretraining:2. Supervised Fine Tuning3. Reward modeling4. Reinforcement learning The Pre...
使用Decoder-only的Transformer进行时序预测,包含SwiGLU和RoPE(Rotary Positional Embedding),Time series prediction using Decoder-only Transformer, Including SwiGLU and RoPE(Rotary Positional Embedding) time-series pytorch transformer rope time-series-prediction decoder-only rotary-positional-embedding swiglu Upda...
In the literature, there are three main Transformer variants for NLG: full Transformer, Encoder-Only (only using the encoder part of the Transformer), and Decoder-Only (only using the decoder part). A natural question to ask is: which architecture is the best choice. According to previous ...