google Transformer的实现,A TensorFlow Implementation of the Transformer: Attention Is All You Need Resources Readme License Apache-2.0 license Stars 0 stars Watchers 0 watching Forks 1.2k forks Releases No releases published Packages No packages published Languages Python 93.3% Perl 6.5% Shell 0.2% Footer © 2023 GitHub, Inc. Footer na...
C++ implementation of the Google logging module google.github.io/glog/ Resources Readme License BSD-3-Clause license Code of conduct Code of conduct Security policy Security policy Activity Custom properties Stars 7.3k stars Watchers 265 watching Forks 2.1k forks Report repository ...
此仓库是为了提升国内下载速度的镜像仓库,每日同步一次。 原始仓库:https://github.com/google-research/pegasus main 克隆/下载 git config --global user.name userName git config --global user.email userEmail 分支4 标签0 Jie RenInternal change1b492902年前 ...
We will soon be publishing a guide showing you how to correctly partition a Transformer model and write the 6 lines of partitioning setup above. It is not very long but it would not fit in this post. You will have noticed that layer partitionings are defined through regexes on layer names...
Transformer 结构大有“一统天下”之势（Bert, ViT），并且支持任意种类结构化数据的输入输出（Perceiver ...
Lion 在执行语言建模任务时在验证困惑度(perplexity)上节省了高达 2 倍的计算量(左:在 Wiki-40B 上,右:在 PG-19 上)。 Lion 在更大的transformer上获得更大的收益。 与Adafactor 相比,Lion 在训练LLM时获得更好的平均上下文学习能力。 在GLUE 上微调 T5 时 Lion 也更好。
HuggingFace & Github: 人工智能与技术创新 Qwen2大型语言模型介绍:Qwen2是一个新系列的语言模型,参数范围从0.5到72亿,包括基础语言模型和经过指导训练的语言模型。Qwen2在语言理解、语言生成、多语言能力、编码、数学、推理等方面表现出色,基于Transformer架构,具有SwiGLU激活、注意力QKV偏置、组查询注意力等特点。Qwe...
BertModelis the basic BERT Transformer model with a layer of summed token, position and sequence embeddings followed by a series of identical self-attention blocks (12 for BERT-base, 24 for BERT-large). The inputs and output are identical to the TensorFlow model inputs and outputs. ...
代码地址:github.com/google/autom 1 简单、内存高效、运行速度更快 与AdamW 和各种自适应优化器需要同时保存一阶和二阶矩相比,Lion 只需要动量,将额外的内存占用减半。 这在训练大型模型和大Batch size时很有用。 例如,AdamW 需要至少 16 个 TPU V4 芯片来训练图像大小为 224、批量大小为 4,096 的 ViT-B/...
Transformers for Longer Sequences. Contribute to google-research/bigbird development by creating an account on GitHub.