classification tasks, mix up the classes 59 Adapt to model updates 60 Experiment with output formats 60 JSON Repair 61 Working with Schemas 62 Experiment together with other prompt engineers 63 CoT Best practices as image prompts) is the input the model uses to predict a specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most complicated. Many aspects of your prompt affect its efficacy: the model you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore

= relay.create_executor(“vm”, mod=mod, ctx=tvm.cpu(0)) data = np.random.uniform(size=(1, 3, 224, 224)).astype(“float32”) out = exe.evaluate()(data, **params) How Would That Look Like?© 2019, Amazon Web (inputs) can be checked as well Return True/False for this op After Annotation op op op op data weight1 weight3 weight2 output Subgraph begin Subgraph end© 2019, Amazon Web Services, Inc. or Affiliates. All rights reserved. Example: Annotate an Entire Graph After Annotation op op op op data weight1 weight3 weight2 output Subgraph begin Subgraph end class WholeGraphAnnotator(ExprMutator):

Upstream Master ) 。， Optimize on INT8 & FP32 AiiOS ! 驱动万物智能 Alios TVM @ ARM CPU INT8 * Cache 芍四 Data FO Data FOData … QNNPACK Convolution 。，NHWC layout Cach， 浆百 FeU Cach- 区下 。， re 。 Tensorize GEMM Cache 大站 Fe Data FO Data … FOData QNNPACK /NiiOS ! 驱动万物智能 P Cache 浆加 Data FO Data FOData … NHWC L2 da … FL2 da Alios TVM @ ARM CPU INT8 TVM /QNNPACK Hexagon DSsP AiOS 1驱动万物智能 Alios TVM @ Hexagon DSP 人NiOS ! 驱动万物知 Tensorflow deploy.so / deploy.json / deploy.bin | NNVM / Relay 让 Graph Optimization 站 站 Compile | libtvm_hexagon_runtime.so

Prompts（指令） 描述 Can you load and preview the data? 加载，预览一下数据 Can you list the top 10 key points? 最重要的十个要点 What are the trends shown in this data? 找趋势 Can you describe the data? 描述数据 Show me the top trends in a using this data? 创建一个热力图 Can you segment this data and create a table? 切分数据 Can you create a graph using this data? 制作一个图 Can you create a world cloud? 做一个词云 Can you create a chart using this data? 画一个图表 graphs more beautiful? 把图美化一下 Can you write a one sentence recap of this data? 快速回顾一下 Create a visual chart, based on this data. 做一个视觉图表 What’s the main takeaway from this dataset? 找出最主要的信息 Can you explain

USER 假设诸葛亮死后在地府遇到了刘备，请模拟两个人展开一段对话。 5. 结构化输出 ：将内容转化为 Json，来方便后续程序处理 SYSTEM 用户将提供给你一段新闻内容，请你分析新闻内容，并提取其中的关键信息，以 JSON 的形式输出，输出的 JSON 需遵守以下的格式： { "entiry": <新闻实体>, "time": < 新闻时间，格式为 YYYY-mm-dd

and run apps/vta_rpc/start_rpc_server.sh Step 8: Configure vta/config/de10nano_config.json to vta_config.json Step 9: Go to vta/hardware/intel and run make command Step 10: Get the generated .sof file

rights reserved. Amazon Trademark Deploy GluonCV Models GluonCV Models MXNet Computational Graph Json Acyclic Graph Export As-is Optimize with TVM© 2019, Amazon Web Services, Inc. or its Affiliates.

datapoints turned into this beast. As soon as we updated one chart, we often had to update another – a data game of whack-a-mole… a pattern that shows no sign of stopping…and will grow more complex as competition related to the artificial intelligence technology evolution is indeed unprecedented, as supported by the data. This document is filled with user, usage and revenue charts that go up-and-to-the-right… often supported Threats = Rising Competition + Open-Source Momentum + China’s Rise • AI & Physical World Ramps = Fast + Data-Driven • Global Internet User Ramps Powered by AI from Get-Go = Growth We Have Not Seen Likes of

Experimental Setups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.1 Data Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.1.2 Hyper-Parameters MLA and MHA . . . . . . . . . . . . . . . . . . . . . . . . . 31 E Discussion About Pre-Training Data Debiasing 32 F Additional Evaluations on Math and Code 33 G Evaluation Formats 34 3 1. Introduction previous release) (DeepSeek-AI, 2024), this corpus features an extended amount of data, especially Chinese data, and higher data quality. We first pretrain DeepSeek-V2 on the full pre-training corpus. Then

shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control flow: concatenate within a while loop Limitation of TVM/graph modes (op_attrs, input_tensors, out_ndims) -> out_shape_tensors ○ Data dependent (op_attrs, input_data, out_ndims) -> out_shape_tensors ○ Data independent (op_attrs, input_shapes, out_ndims) -> out_shape_tensors© out_shape_tensors ○ Data dependent (op_attrs, input_data, out_ndims) -> out_shape_tensors ○ Data independent (op_attrs, input_shapes, out_ndims) -> out_shape_tensors ● Why? ○ Fuse data independent shape