runtime resources of the container by including additional flags and settings that are used with the command. These flags and settings are described in Running A Container. ‣ The GPUs are explicitly defined 0 through v1.2.1 exposes a Regular Expression Denial of Service (ReDOS) vulnerability. ‣ Known security vulnerabilities: ‣ CVE-2022-32212, CVE-2022-43548, CVE-2023-0286, CVE-2022-32223, CVE-2023-0286 0 through v1.2.1 exposes a Regular Expression Denial of Service (ReDOS) vulnerability. ‣ Known security vulnerabilities: ‣ CVE-2022-25882 for ONNX<1.13.0 PyTorch RN-08516-001_v23.07 | 61 Chapter

families support a maximum of 32K context window size. import torch from llama_index.core import Settings from llama_index.core.node_parser import SentenceSplitter from llama_index.llms.huggingface import Set Qwen1.5 as the language model and set generation config (续下页) 42 Chapter 1. 文档 Qwen (接上页) Settings.llm = HuggingFaceLLM( model_name="Qwen/Qwen1.5-7B-Chat", tokenizer_name="Qwen/Qwen1.5-7B-Chat", device_map="auto", ) # Set embedding model Settings.embed_model = HuggingFaceEmbedding( model_name = "BAAI/bge-base-en-v1.5" ) # Set the size of the text chunk for retrieval Settings.transformations = [SentenceSp

tokenizer word2vec Elmo pb ckpt H5 (Keras) RESTful API RPC API Function test Concurrence test Security test Multi class Multi label preprocessor Traditional --->simple Char replacement Synonym

improvement on many image classification tasks with different model architectures and data augmentation settings when using SAM. For instance, on the ImageNet task and the ResNet-152 model architecture trained techniques. Similarly, we might find that techniques like distillation might not be as helpful in certain settings. Subclass distillation in the next subsection can help us in some of these cases. Let’s find out

like the figure below. Note that the figures may be slightly different under different parameter settings. 10 20 30 40 50 60 70 Exam 1 score 40 50 60 70 80 90 100 Exam 2 score Admitted Not admitted

) cannot reflect the nonlinear pattern in the data Kernels: Make linear model work in nonlinear settings By mapping data to higher dimensions where it exhibits linear patterns Apply the linear model in

demonstrated in Fig. 4. The basic idea of kernel method is to make linear model work in nonlinear settings by introducing kernel functions. In particular, by mapping the data into a higher-dimensional feature

cheaper operations like addition and subtraction, these gains need to be evaluated in practical settings because they require support from the underlying hardware. Moreover, multiplications and divisions

，如 图16.3.6顶部所示。在本例中，我 们保留“3. Configure Instance”（3. 配置实例）、“5. Add Tags”（5. 添加标签）和“6. Configure Security Group”（6. 配置安全组）步骤的默认配置。点击“4.添加存储”并将默认硬盘大小增加到64GB( 图16.3.6中 的红色框标记)。请注意，CUDA本身已经占用了4GB空间。 图16