and supports a context length of 128K tokens. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly boosts the maximum generation throughput to 5.76 times. We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT) and Reinforcement Introduction 4 2 Architecture 6 2.1 Multi-Head Latent Attention: Boosting Inference Efficiency . . . . . . . . . . . . . 6 2.1.1 Preliminaries: Standard Multi-Head Attention . . . . . . . . . . . .

agents 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 a single model equipped with appropriate tools and instructions executes workflows in a loop 02 Multi-agent systems, where workflow execution is distributed across multiple coordinated agents Let’s explore the capital of the USA?" This concept of a while loop is central to the functioning of an agent. In multi-agent systems, as you’ll see next, you can have a sequence of tool calls and handoffs between agents

browse up to hundreds of websites on your behalf, think through its findings, and create insightful multi-page, reports that you can turn into engaging podcast-style conversations… …It’s a step towards Research Today we’re launching deep research in ChatGPT, a new agentic capability that conducts multi-step research on the internet for complex tasks. It accomplishes in tens of minutes what would step-change forward. These are intelligent long-running processes that can reason, act, and complete multi-step tasks on a user’s behalf. They don’t just answer questions – they execute: booking meetings

supported/not supported, pattern matching graph colorization - Choices how to partition especially for multi-branch networks (i.e. YOLOv3, SSD)© Copyright 2018 Xilinx TVM Graph Partitioning/Fusion >> 8 Subgraph (https://github.com/Xilinx/ml-suite/blob/master/examples/deployment_modes/mp_classify.py) Streamlined multi-process pipeline using shared memory Usually need >4 Pre-Process cores running to keep up with FPGA

2 Moore’s Law is Slowing Down MOTIVATION©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 3 Multi-Vendor Support MOTIVATION©2019 HARMAN INTERNATIONAL INDUSTRIES, INCORPORATED 4 Terasic DE10-Nano

truncating division. e Unified Object and Node system for TVM runtime o Lays groundwork forimproved multi-language support for expPosing runtime, and |IRs. QQ octoML Unified Object Protocol vm::Object

更强的长距离依赖建模能力。Transformer 由多个关键组件组成：1. 自注意 力机制（Self-Attention）：模型在处理文本时，会自动关注句子中的重要单 词，理解不同词语间的联系。2. 多头注意力（Multi-Head Attention）：使用 多个注意力头同时分析不同的语义信息，使得模型的理解能力更强。3. 前 馈神经网络（FFN）：非线性变换模块，提升模型的表达能力。4. 位置编码 （Positional