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

ever-growing digital datasets that have been in the making for over three decades; breakthrough large language models (LLMs) that – in effect – found freedom with the November 2022 launch of OpenAI’s ChatGPT 260% Annual Growth Over Fifteen Years of… Data to Train AI Models Led To… Note: Only “notable” language models shown (per Epoch AI, includes state of the art improvement on a recognized benchmark, >1K FLOPs are often used to estimate the computational cost of training or running a model. Note: Only language models shown (per Epoch AI, includes state of the art improvement on a recognized benchmark, >1K

multiple employees to contribute to TVML. ee Today we'ltouch on a few of those contribution areas: o Core Infrastructure Improvements to TVM o_uTVM: support for microcontrollers in TVM o_ Virtual Machine dynamic NNs support (w/ AWS 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 groundwork forimproved 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

foundations 7 Guardrails 24 Conclusion 32 2 Practical guide to building agents Introduction Large language models are becoming increasingly capable of handling complex, multi-step tasks. Advances in reasoning chatbots, single-turn LLMs, or sentiment classifiers—are not agents. More concretely, an agent possesses core characteristics that allow it to act reliably and consistently on behalf of a user: 01 It leverages 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

MobileNetv2 LaneNet 图TFLite1core 图TFLite4core 国QNNPACK 1core 四QNNPACK4core 四TVM1core 四TVM4core AiOS 1驱动万物智能 Alios TVM @ ARM CPU FP32 。，NHWC layout 。 For pointwise

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

Sparse 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 lines of Relay IR) - A few days of work - TVM sampling model running in 30us on single server CPU core - Beat hand-written, highly optimized baselines (https://github.com/mozilla/LPCNet) by ~40% - Bonus:

they could offer more and better insights to clients.
 They started with three model evals: 01 Language translation Measuring the accuracy and quality of translations produced 
 by a model. 02 Summarization 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 and messages to jobseekers their 
 AI application builds. Verdi integrates language models, Python nodes, and APIs to create a scalable, consistent platform that uses natural language as a central interface. Developers now build consistently

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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