Keras i Keras ii About the Tutorial Keras is an open source deep learning framework for python. It has been developed by an artificial intelligence researcher ........................................................................................... 17 Model ................................................................................................. ............................................................................... 58 10. Keras ― Model Compilation .....................................................................................

Qwen Team 2024 年 05 月 11 日 快速开始 1 文档 3 i ii Qwen Qwen is the large language model and large multimodal model series of the Qwen Team, Alibaba Group. Now the large language models have been upgraded AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-7B-Chat") # Instead of using model.chat(), we directly use model.generate() # But you need to use tokenizer.apply_chat_template() to format your inputs

you'll go.” ― Dr. Seuss Model quality is an important benchmark to evaluate the performance of a deep learning model. A language translation application that uses a low quality model would struggle with consumer helping them communicate effectively with others who speak different languages. An application that employs a high quality model with a reasonable translation accuracy would garner better consumer support. In picked to benchmark learning techniques. It is followed by a short discussion on exchanging model quality and model footprint. An in-depth discussion of data augmentation and distillation follows right after

in ANALOG magazine (1991) So far, we have discussed generic techniques which are agnostic to the model architecture. These techniques can be applied in NLP, vision, speech or other domains. However, owing challenges. What good is a model that cannot be deployed in practical applications! Efficient Architectures aim to improve model deployability by proposing novel ways to reduce model footprint and improve running on mobile and edge devices. We have also set up a couple of programming projects for a hands-on model optimization experience using these efficient layers and architectures. Let’s start our journey with

techniques can help us meet our model quality goals. Techniques like distillation and data augmentation improve the model quality, without increasing the footprint of the model (size, latency, etc). And as reliable, human labeling gets very expensive very quickly. Even after that it is likely that the model might not be able to capture the intricacies of your task well. Self-Supervised learning helps to efficacy through a colab. Finally, we introduce miscellaneous techniques to help you improve your model’s quality metrics without taking a hit on any of the footprint metrics. These techniques might get

Can we optimally prune the network connections, remove extraneous nodes, etc. while retaining the model’s performance? In this chapter we introduce the intuition behind sparsity, different possible methods methods of picking the connections and nodes to prune, and how to prune a given deep learning model to achieve storage and latency gains with a minimal performance tradeoff. Next, the chapter goes over weight learn about these techniques together! Model Compression Using Sparsity Sparsity or Pruning refers to the technique of removing (pruning) weights during the model training to achieve smaller models. Such

49 4.3.1 Model 类 API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.2 Model 的实用属性 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.3 Model 类模型方法 . . . . . . . . . . . . . . . 239 20.8 plot_model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 20.9 multi_gpu_model . . . . . . . . . . . . . . . . . . . . . . Keras 的核心数据结构是 model，一种组织网络层的方式。最简单的模型是 Sequential 顺 序模型，它是由多个网络层线性堆叠的栈。对于更复杂的结构，你应该使用 Keras 函数式 API， 它允许构建任意的神经网络图。 Sequential 顺序模型如下所示： from keras.models import Sequential model = Sequential()

assumes that you want to pull the latest container. docker pull nvcr.io/nvidia/pytorch:23.07-py3 2. Open a command prompt and paste the pull command. Running PyTorch PyTorch RN-08516-001_v23.07 | 4 --shm-size= in the command line to docker run --gpus all To pull data and model descriptions from locations outside the container for use by PyTorch or save results to locations not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. GPU Requirements

together? We have four options: none, quantization, clustering, and both. We would need to train a model with each of these four options to make an informed decision. Blessed with a large research community multiple parameters. Figure 7-1: The plethora of choices that we face when training a deep learning model in the computer vision domain. A Search Space for n parameters is a n-dimensional region such that understand this using the earlier example for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering

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