## 深度学习-Vision Transformer (ViT) 黄海广 副教授 2023年06月 ## 本章目录 01 背景知识 02 模型介绍 03 模型训练策略 04 模型的缺点与改进 05 模型的代码实现 ### 1. 背景知识 01 背景知识 02 模型介绍 03 模型训练策略 04 模型的缺点与改进 05 模型的代码实现 ### 1. 背景知识 架构，名为Transformer；论文实现的任务是机器翻译。 ## Transformer结构  ### 1. 背景知识 ## 为什么需要用transformer Transformer原本是用来做NLP的工作的，所以ViT的首要任务 _2.jpg) ### 1. 背景知识 ## 为什么需要用transformer CNN（如ResNet）是图像分类的最佳解决方案。 如果预训练的数据集足够大（至少一亿张图像），则Vision Transformer（ViT）将击败CNN（小幅度） Vision Transformer（ViT）实际上就是Transformer的encode网络。 Image Classification

深度学习-Transformer 黄海广 副教授 2023年05月 ## 本章目录 01 Transformer介绍 02 Transformer的工作流程 03 Transformer的训练 04 BERT ### 1 \.Transformer介绍 01 Transformer介绍 02 Transformer的工作流程 03 Transformer的训练 04 BERT ### 1 \.Transformer介绍 ## 为什么需要用transformer 在没有transformer的时候，我们都是用什么来完成这系列的任务 的呢？ 其实在之前我们使用的是RNN（或者是其的单向或者双向变种LSTM/GRU等）来作为编解码器。RNN模块每次只能够吃进一个输入token和前一次的隐藏状态，然后得到输出。它的时序结构使得这个模型能够得到长距离的 低。  ### 1 \.Transformer介绍 ## Seq2Seq任务 Seq2Seq 任务指的是输入和输出都是序列的任务，输出的长度不确定时采用的模型，这种情况一般是在机器翻译的任务中出现，将一句中文翻译成英文，那么这句英文

compression and acceleration on mobile devices[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 784–800. [11] Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020. Graph [4] https://mp.weixin.qq.com/s/RwWuZBSaoVXVmZpnyg7FHg [5] https://tech.meituan.com/2020/04/16/transformer-in-meituan.html. [6] https://tech.meituan.com/2021/07/08/multi-business-modeling.html. [7] Tang Wasserstein contrastive representation distillation[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 16296–16305. [10] https://arxiv.org/abs/2207.03073 [11] Liu Y, Jia

## 扫码查看《2023 LLM 技术报告》 ## LLM 技术背景 Transformer 架构和预训练与微调策略是 LLM 技术的核心，随着大规模语言数据集的可用性和计算能力的提升，研究者们开始设计更大规模的神经网络，以提高对语言复杂性的理解。 GPT (Generative Pre-trained Transformer) 的提出标志着 LLM 技术的飞速发展，其预训练和微调的方法为语言任 message/bus 平台及通用套件 platform/common 系统管理 memory/thread/driver 语音识别Istm/rnn 自然语言处理 bert/transformer 用户接口python 静态执行 define and run 图优化 graph optimization 自动梯度求导 autograd 自动放置 auto placement testing + 345 deployments ## AutoGPT: build & use AI agents Languages AutoGPT is the vision of the power of AI accessible to everyone, to use and to build on. Our mission is to provide the

techniques which are agnostic to the model architecture. These techniques can be applied in NLP, vision, speech or other domains. However, owing to their incremental nature, they offer limited gains. Sometimes backbone of the state of the art NLP model architectures such as the Transformer, which is now showing great promise in computer vision applications as well! ## Learn Long-Term Dependencies Using Attention al. introduced a novel architecture called Transformer which achieved state of the art performance for common NLP tasks primarily using attention. A transformer replaces the recurrent units in the RNN Encoder-Decoder

AlexNet开启深度学习时代 Vision Transformer 突破CNN局限 20世纪60年代，边缘检测算法作为计算机视觉的基础技术之一被提出，它通过识别图像中的边缘来帮助机器理解物体的形状和结构。 2012年，AlexNet在ImageNet大规模视觉识别挑战赛（ILSVRC）上取得了突破性的成绩，标志着深度学习在图像识别领域的崛起。 2020年，Vision Transformer（ViT）模型的

Image Generation Retrieval Video Caption ImageInput VideoInput TextInput Data Aug Resnet Vit Swin Bert RPNHead RCNNHead MaskHead SeqHead CrossEntropy SmoothL1 Mnrdk gcp DiceLoss Contrasive Normalization: bn, gn, In? 激活函数: relu, leaky_relu, swish ? Backbone: resnet, hrnet, mobilenet, transformer? 多任务模型: share-bottom, mmoe, ple? 特征选择/生成: Age, sex, comment, click... 问题: 黑盒 1. 参数太多 / 参数敏感 jpg)  M6模型 Transformer模型: RapidFormer ➢ 人脸分类模型: 超大softmax ## M6模型 3D卷积模型 ![Image](/uploads/documents/1/d/a/d/1d

be the backend to support inference on trn1/inf2 instances. Follow the steps below to install transformer-neuronx package and its dependencies. # Install Python venv sudo apt-get install -y python3.10-venv embedding) # list of float of len 4096 ## 1.10.20 OpenAI Vision API Client Source https://github.com/vllm-project/vllm/blob/main/examples/openai_vision_api_client.py. ```python """An example buildkite/download-images.sh` to download them def run_phi3v(): model_path = "microsoft/Phi-3-vision-128k-instruct" # Note: The default setting of max_num_seqs (256) and # max_model_len

be the backend to support inference on trn1/inf2 instances. Follow the steps below to install transformer-neuronx package and its dependencies. ```bash # Install Python venv sudo apt-get install -y startswith(answer) ``` ## 1.10.22 Offline Inference Vision Language Source https://github.com/vllm-project/vllm/blob/main/examples/offline_inference_vision_language.py. ```python """ This example example shows how to use vLLM for running offline inference with the correct prompt format on vision language models. For most models, the prompt format should follow corresponding example on HuggingFace

be the backend to support inference on trn1/inf2 instances. Follow the steps below to install transformer-neuronx package and its dependencies. # Install Python venv sudo apt-get install -y python3.10-venv # list of float of len 4096 ``` ## 1.10.23 OpenAI Vision API Client Source https://github.com/vllm-project/vllm/blob/main/examples/openai_vision_api_client.py. ```python """An example from vllm.assets.image import ImageAsset def run_phi3v(): model_path = "microsoft/Phi-3-vision-128k-instruct" # Note: The default setting of max_num_seqs (256) and # max_model_len