error-prone, 
 for example performing vendor security reviews. 03 Heavy reliance on unstructured data: Scenarios that involve interpreting natural language, 
 extracting meaning from documents, or interacting redundant definitions. Broadly speaking, agents need three types of tools: Type Description Examples Data Enable agents to retrieve context and information necessary for executing the workflow. Query transaction ticket to a human. Orchestration Agents themselves can serve as tools for other agents—see the Manager Pattern in the Orchestration section. Refund agent, Research agent, Writing agent. 9 A practical

memory load latency 。 storage align to reduce bank conflicts of shared memory 。 Virtual threads for data reuse (on going) Performance on V100 (FP16) 计算平台事业部 COMPUTING PLATFORM 512, 16, 512 512, 32, 512 下和全于由 loss = loss_fn() opt = tf.Adamoptimizer(learning_rate=...) # Choose a 1oss Scale manager which decides how to pick the right loss scale # throughout the training process. 1oss_scale_manger original optimizer in a LossScale0ptimizer . loss_scale_optimizer = LossScaleOptimizer(opt，1oss_scale_manager) # Call minimize() on the loss scale optimizer. train_op = loss_scale_optimizer.minimize(1oss) Loss

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

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