uncertainty accommodates scenarios where a rigid, precise temperature may not be essential like for example when experimenting with creative outputs. Top-K and top-P Top-K and top-P (also known as nucleus Language) in Vertex AI,6 which provides a playground to test prompts. In Table 1, you will see an example zero-shot prompt to classify movie reviews. The table format as used below is a great way of documenting unchecked. I wish there were more movies like this masterpiece. Sentiment: Output POSITIVE Table 1. An example of zero-shot prompting When zero-shot doesn’t work, you can provide demonstrations or examples in

prompts, exhibits unique characteristics that are distinct from the training on general data. For example, the mathematical and coding abilities of our model can keep improving over a longer period of training slightly worse on the test sets that are closely associated with specific regional cultures. For example, when evaluated on MMLU, although DeepSeek-V2 achieves comparable or superior performance on the 正确的是选项：(A)三者都 存在于蓝藻中(B)三者都含有DNA (C)三者都是ATP 合成的场所(D)三者的膜结 构中都含有蛋白质 答案：从A到D, 我们应选择 Table 12 | An example of AGIEval. 34 PROMPT Question: A sample in a cylindrical container has a cylindrical shape and

to workflows where traditional deterministic and rule-based approaches fall short. Consider the example of payment fraud analysis. A traditional rules engine works like a checklist, flagging transactions decision-making: Workflows involving nuanced judgment, exceptions, or 
 context-sensitive decisions, for example refund approval 
 in customer service workflows. 02 Difficult-to-maintain rules: Systems that have become unwieldy due to extensive and intricate rulesets, making updates costly or error-prone, 
 for example performing vendor security reviews. 03 Heavy reliance on unstructured data: Scenarios that involve

whether Witness A or Witness B was an AI system. Results: The conversation on the left is an example Turing Test carried out in 3/25 using GPT-4.5. During the test, participants incorrectly identified centers…are, in fact, AI factories. That race is moving faster than many expected. The most striking example may be xAI’s Colossus facility in Memphis, Tennessee which went from a gutted factory to a fully applying/using these models – known as inference – is falling quickly. Hardware is improving – for example, NVIDIA’s 2024 Blackwell GPU consumes 105,000x less energy per token than its 2014 Kepler GPU predecessor

Dense Your Chip Your Chip© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example showcase: Intel MKL-DNN (DNNL) library 1. Import packages import numpy as np from tvm import relay build_extern(mod, “dnnl”)© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example: Annotate an Entire Graph After Annotation op op op op data weight1 weight3 weight2 output the external function© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Example: Dispatch Codegen Built Shared Library runtime::PackedFunc DNNLModule::GetFunction( const std::string&

in const_range(len(inputs)): out[i] += inputs[j][i] return out Shape function example Use hybrid script to write shape function Input shape tensors Type checking Data independent© independent© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Shape function example @script def _arange_shape_func(start, stop, step): out = output_tensor((1,), "int64") out[0] = 16) (17, 32) ...© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. API Example input_name = "data" input_shape = [tvm.relay.Any(), 3, 224, 224] dtype = "float32" block = g

OpenAI, we live with AI every day, so we’re often spotting new ways to automate our own work. An example: Our support teams were getting bogged down, spending time accessing systems, trying to understand the front lines of AI to help guide your own thinking. Product Note: Operator Operator is an example of OpenAI’s agentic approach. Leveraging its own virtual browser, Operator can navigate the web

明确说明目标受众。 • Structure (结构): 为输出的内容提供明确的组织结 构，包括段落安排、论点展开顺序或其他逻辑关系。 • Tone (语气): 指定模型回答时的语气或风格。 • Example (示例):例子或模板可帮助模型理解输出风 格或格式。 2. ALIGN框架 • Aim (目标): 明确任务的最终目标。 • Level (难度级别): 定义输出的难度级别。 • Input 普通的互联网用户，非技术背景。 • Structure: 文章需要有明确的开头、中间讨论和结尾， 开头提出问题，中间介绍原因和影响，结尾提供建议。 • Tone: 采用友好、易懂的语气。 • Example: 类似于《纽约时报》科技专栏的风格。 三重概率：多层互动 逐层精炼 AIGC的三层概率交互的内容生成体系，描述了

明确说明目标受众。 • Structure (结构): 为输出的内容提供明确的组织结 构，包括段落安排、论点展开顺序或其他逻辑关系。 • Tone (语气): 指定模型回答时的语气或风格。 • Example (示例):例子或模板可帮助模型理解输出风 格或格式。 2. ALIGN框架 • Aim (目标): 明确任务的最终目标。 • Level (难度级别): 定义输出的难度级别。 • Input 普通的互联网用户，非技术背景。 • Structure: 文章需要有明确的开头、中间讨论和结尾， 开头提出问题，中间介绍原因和影响，结尾提供建议。 • Tone: 采用友好、易懂的语气。 • Example: 类似于《纽约时报》科技专栏的风格。 三重概率：多层互动 逐层精炼 AIGC的三层概率交互的内容生成体系，描述了

'], attrs['model_name'], outs[0], *ins ), name=name) return out >> 10© Copyright 2018 Xilinx Example of FPGA node in TVM graph { "nodes": [ { "op": "null", "name": "data", "inputs": [] }, { "op":