🚀 The feature, motivation and pitch DeepSeek V3 is trained with MTP. This has potential to increase the throughput by 2-3x dependent on how many extra tokens are generated. Paper: https://github.com/deepseek-
共享的 f_s:如前文所述,这种方式只需单次前向传播即可获得 z_{t:1},从而生成 n 个词元,相比传统的 next-token prediction 具有更高的计算效率。 共享的解嵌入矩阵 f_u:由于解嵌入矩阵非常大,维度数量为 d×V(d 为隐藏层维度,V 为词表大小,通常为 5 万~ 20 万),共享参数能大大减少参数量且对性能...
为解决这些问题,多词元预测(multi-token prediction) 应运而生。 1.2 先前的多词元预测方法 在文献[3]中,作者将 next-token prediction 扩展为一种多词元预测机制: 其中,给定相同的输入序列,模型将通过单次前向传播生成从 x_{t+1} 到 x_{t+n} 的 n 个 tokens。 请注意,这并不意味着在单个 Softmax ...
Accelerating Codec-based Speech Synthesis with Multi-Token Prediction and Speculative Decoding 2024.10.18https://arxiv.org/pdf/2410.13839v1keywords: 自回归tts,推理加速出版单位:韩国科学技术院Demo page:Demo:https://multpletokensprediction.github.io/multipletokensprediction.github.io/快速阅读: 本文重新构建...
要理解 DeepSeek 的多词元预测(multi-token prediction),我们首先需要仔细了解大语言模型(LLMs)如何生成文本。 1.1 Next-Token Prediction LLMs 通常通过自回归(autoregressive)的方式生成文本,即在给定历史 tokens 序列的前提下,通过逐 token 预测下一个最可能的 token 来生成文本。
link: https://arxiv.org/pdf/2404.19737v1TL, DR:在生成式模型中,如果每一步预测多个token效果也许会更好 思路比较简单,就是多使用几个预测头,多预测几个token: 在计算loss的时候,直接求和就行了 然而,因…
Transformers made simple with training, evaluation, and prediction possible with one line each. Currently supports Sequence Classification (binary, multiclass, multilabel, sentence pair), Token Classification (NER), Question Answering, Language Modeling, Regression, Conversational AI, and Multi-Modal tasks...
future tokens rather than just one. This research investigates a new pretraining method called Future Token Prediction (FTP). In FTP, a large transformer encoder generates top layer embedding vectors for each token position, which, instead of being passed to a language head, are linearly and ...
We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality sequences. We pretrain multiple Transfusion models up...
The Transformer architecture and versatile CNN backbones have led to advanced progress in sequence modeling and dense prediction tasks. A critical development is the incorporation of different token mixing modules such as ConvNeXt, Swin Transformer. However, findings within the MetaFormer framework ...