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

Transformers, etc - Reduce precision with int8/float16 - very helpful to maintain model in core-private L1 dcaches - Use rational approximations for transcendentals (exp, tanh, erf, etc) - very general

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

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 responses, and can hinder the model’s ability to provide meaningful output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. Prompt Engineering February

= 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):

employees can focus on 
 the things only people can do. And because AI can process huge amounts of data from many sources, it can create customer experiences that feel more human because they’re more relevant need to explain to the candidate why this specific job was recommended to them. Indeed uses the data analysis and natural language capabilities of GPT-4o mini to shape these ‘why’ statements in their function. With thousands of suppliers, Lowe’s often has to work with incomplete or inconsistent product data. 13 AI in the EnterpriseThe key is in accurate product descriptions and tagging. But it also requires

VGG16 ResNet-50 GoogleNet-V3 Aristotle on 7020 FPGA Iphone8plus Kirin 970 CPU MEM CONTROLLER BUS Data Mover IMG WR SCHEDULER WEIGHTS WR SCHEDULER SMART MEM FABRIC IMG RD SCHEDULER WEIGHTS RD node in TVM graph { "nodes": [ { "op": "null", "name": "data", "inputs": [] }, { "op": "tvm_op", "name": "xdnn0", "attrs": { "flatten_data": "0", "func_name": “accel_fused", "num_inputs": "1", "num_outputs": "num_outputs": "1" }, "inputs": [[0, 0, 0]] }, { "op": "tvm_op", "name": "flatten0", "attrs": { "flatten_data": "0", "func_name": "fuse_flatten", "num_inputs": "1", "num_outputs": "1" }, "inputs": [[1, 0, 0]]

Quantize Operator fn (%input_data: Tensor[(2, 5), float32]) { qnn.quantize(%input_data, out_dtype="uint8", output_zero_point=127, output_scale=0.5f) } def @main(%input_data: Tensor[(2, 5), float32]) -> -> Tensor[(2, 5), uint8] { %0 = divide(%input_data, 0.5f /* ty=float32 */) /* ty=Tensor[(2, 5), float32] */; %1 = round(%0) /* ty=Tensor[(2, 5), float32] */; %2 = cast(%1, dtype="int32") /* ty=Tensor[(2 conv2d fn (%data: Tensor[(1, 3, 2, 3), uint8], %weight: Tensor[(3, 3, 2, 2), uint8]) { qnn.conv2d(%data, %weight, … , out_dtype="int32", input_zero_point=1, kernel_zero_point=1)} def @main(%data: Tensor[(1

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