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

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 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 emails 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

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 based on preset criteria 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

NLP support, with focus on transformers QQ octoML Core Infrastructure Refactors ee New Integer Analysis Infrastructure o_ Supports the ability to handle nested division and modulus o_ Improves the ability implementation httpsJigithub,comlapachelincubator-tvmipull4274 remumn dming data AutoTYM 二 QQ octoML Coming Soon to HTVM (Self-Hosted Models) Host

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 计算平台事业部 COMPUTING PLATFORM COMPUTING PLATFORM INT8 Inference on PAI- 引FTe[= PAI-Blade Model Analysis Graph optimization Blade Graph Optimizer TensorRT Customized OptimizeT TAO Compiler (XLA)

出的逻辑性、知识深度和创新 度 即学即用：复杂任务的提示语链设计实战 需要考虑的因素 任务目标、目标受众、文章类型、字数要求、特殊要求 在分析阶段，首先明确 任务目标和关键问题 通过四个关键步骤：分析（Analysis）、构思（Ideation）、发展（Development） 和评估（Assessment），为提示语链的设计提供系统化的指导。 构思阶段注重创新性思 维，探索多种解决方案 在发展阶段，逐步深化

出的逻辑性、知识深度和创新 度 即学即用：复杂任务的提示语链设计实战 需要考虑的因素 任务目标、目标受众、文章类型、字数要求、特殊要求 在分析阶段，首先明确 任务目标和关键问题 通过四个关键步骤：分析（Analysis）、构思（Ideation）、发展（Development） 和评估（Assessment），为提示语链的设计提供系统化的指导。 构思阶段注重创新性思 维，探索多种解决方案 在发展阶段，逐步深化

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