Economical, and Efficient Mixture-of-Experts Language Model DeepSeek-AI research@deepseek.com Abstract We present DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training Evaluations on Math and Code 33 G Evaluation Formats 34 3 1. Introduction In the past few years, Large Language Models (LLMs) (Anthropic, 2023; Google, 2023; OpenAI, 2022, 2023) have undergone rapid development to tackle this problem, we introduce DeepSeek-V2, a strong open-source Mixture-of-Experts (MoE) language model, characterized by economical training and efficient inference through an innovative Transformer

large language models (LLMs) that – in effect – found freedom with the November 2022 launch of OpenAI’s ChatGPT with its extremely easy-to-use / speedy user interface. In addition, relatively new AI company to adapt to this evolving journey as knowledge – and its distribution – get leveled up rapidly in new ways. Special thanks to Grant Watson and Keeyan Sanjasaz and BOND colleagues who helped steer ideas cost-per-token declines, open-source proliferation and chip performance improvements are making new tech advances increasingly more powerful, accessible, and economically viable. OpenAI’s ChatGPT –

Summary 66 Endnotes 68 Prompt Engineering February 2025 6 Introduction When thinking about a large language model input and output, a text prompt (sometimes accompanied by other modalities such as image evaluating a prompt’s writing style and structure in relation to the task. In the context of natural language processing and LLMs, a prompt is an input provided to the model to generate a response or prediction such as text summarization, information extraction, question and answering, text classification, language or code translation, code generation, and code documentation or reasoning. Please feel free to

OctoML is a new company building DL deployment solutions using the Apache (incubating) TVM project. A goal is to nurture the TVM community and contribute new infrastructure and features. octom|.ai @octoml folks) o_ Improved 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 multi-language support for expPosing runtime, and |IRs. QQ octoML Unified Object Protocol vm::Object NDArray | Rd | tuplelclosure AST Nodes Cross language suppPort Easy to introduce new runtime

AI in the Enterprise Lessons from seven frontier companiesContents A new way to work 3 Executive summary 5 Seven lessons for enterprise AI adoption Start with evals 6 Embed AI into your products developers 18 Set bold automation goals 21 Conclusion 22 More resources 24 2 AI in the EnterpriseA new way 
 to work As an AI research and deployment company, OpenAI prioritizes partnering with global software or deploying cloud apps. The most successful companies are often those who treat it as a new paradigm. This leads to an experimental 
 mindset and an iterative approach that gets to value faster

Introduction Large language models are becoming increasingly capable of handling complex, multi-step tasks. Advances in reasoning, multimodality, and tool use have unlocked a new category of LLM-powered security reviews. 03 Heavy reliance on unstructured data: Scenarios that involve interpreting natural language, 
 extracting meaning from documents, or interacting with 
 users conversationally, for example or search the web. Action Enable agents to interact with systems to take actions such as adding new information to databases, updating records, or sending messages. Send emails and texts, update

kernel, strides, padding, dilation, layout, out_dtype): #Describe algorithm with tensor expression language'; #Return the out operation w How to compute. @autotvm.register_ topi_schedule(schedule_conv2d_nchw，pvr

零基础必知 为了更深入理解 DeepSeek-R1，首先需要掌握 LLM 的基础知识，包括其工 作原理、架构、训练方法。 近年来，人工智能（AI）技术的快速发展催生了大型语言模型（（Large Language Model, LLM））的兴起。LLM 在自然语言处理（NLP）领域 发挥着越来越重要的作用，广泛应用于智能问答、文本生成、代码编写、机 器翻译等任务。LLM 是一种基于深度学习的人工智能模型，其核心目标是

optimized DNN operator library FrameworksLimitations of Existing Approach cuDNN Frameworks New operator introduced by operator fusion optimization potential benefit: 1.5x speedup Engineering Learning System High-level data flow graph and optimizations Directly generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation is the Future Clear winner for Future Hardware Current TVM Stack New NPU Runtime TSIM Driver TSIM Binary New Hardware Design in Verilog VerilatorToward Unified IR InfraOverview of New IR Infra Single unified module/pass, type

for different terminals. 。 To help traditional car firms AliOS互联网汽车 共创智能网联汽车 共建未来出行生态 embrace new "connected' era by acting as the IT “chassis' of ROEWE负风 auto industry /NiiOS ! 驱动万物智能 TVM Timeline w,v4.w) V0.w vadd(v30.w,v31,.w) Vvmem( rO++#1) = V0.new } r@ = #0; jumpr r31 vrmpy(v2.ub,v9.ub) vSsptLat(r2) = V1 Vmem( rO++#1) = V3.new )} nop r2 = memw(rl++#4) } :endLoop0 Vv2.uw = vrmpy(v2 vo.ub) v30 = vsptLat(r2) Vvmem( rO++#1) = V2.new v1.uw = vrmpy(v1.ub,vo.ub) Vmem( rO++#1) = V1.new Vv31.uw = vrmpy(v30.ub,vo.ub) Vmem( rO++#1) = V31.new 上 r0 = #0; jumpr r31 } PART FOUR Alios