DeepSeek-V2: A Strong, 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 model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens. DeepSeek-V2 innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while

Intelligence,’ a term he coined 1/62: Arthur Samuel, an IBM computer scientist, creates a self-learning program that proves capable of defeating a top USA checkers champion AI ‘Winter1’ (1967-1996) Shakey, the first general- purpose mobile robot that can reason about its own actions 5/97: Deep Blue, IBM’s chess- playing computer, defeats Garry Kasparov, the world chess champion Trending = Unprecedented37 Machine-Learning Model* Trending = In 2015... Industry Surpassed Academia as Data + Compute + Financial Needs Rose *Machine Learning = A subset of AI where machines learn

step. How it started Morgan Stanley’s first eval focused on making their financial advisors more efficient and effective. The premise was simple: If advisors could access information faster and reduce the people. AI amplifies our potential and helps us be more efficient and creative. Elena Alfaro Head of Global AI Adoption Product Note: With deep research, ChatGPT can do work independently. Give it a prompt employee productivity and gives them access to deep, detailed research on any topic in minutes. In an internal evaluation by experts across domains, deep research saved an average of 4 hours per complex

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 effective prompt can be complicated 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 2025 7 When you chat with temperature control can be understood in a similar way to the softmax function used in machine learning. A low temperature setting mirrors a low softmax temperature (T), emphasizing a single, preferred

TVM: Where are we going Tianqi ChenCurrent Deep Learning Landscape Frameworks and Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated automated end-to- end optimization framework for deep learning.TVM Stack High-Level Differentiable IR Tensor Expression and Optimization Search Space LLVM, CUDA, Metal VTA Edge FPGA Cloud FPGA FPGA ASIC Optimization AutoTVM Device FleetExisting Deep Learning Frameworks High-level data flow graph Hardware Primitive Tensor operators such as Conv2D eg. cuDNN Offload to heavily optimized

Neo, Deep Engine Science Bring Your Own Codegen to TVM AWS AI© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Considering You... Design and manufacture a deep learning chip

16th, 2019Bringing together the Arm ecosystemLinaro AI Initiative Provide the best-in-class Deep Learning performance by leveraging Neural Network acceleration in IP and SoCs from the Arm ecosystem,

Compiler Tensor Graph Optimization Framework Tensor Graph to Xilinx Tensor Graph Frontend Deep Learning Frameworks https://github.com/xilinx© Copyright 2018 Xilinx TVM as Unified ML Front End >>

. . . . . . 7 2.3.2 监督微调（Supervised Fine-Tuning, SFT） . . . . . . 7 2.3.3 强化学习（Reinforcement Learning, RL） . . . . . . . 7 3 DeepSeek-R1 精华图解 . . . . . . . . . . . . . . . . . . . . . . . 7 3.1 DeepSeek-R1 据集，让模型在特定任务上优化表现。调整参数，使其更符合人类需求，如 问答、对话生成等任务。 2.3.3 强化学习（Reinforcement Learning, RL） 采用强化学习（RL）方法进行优化，主要通过人类反馈强化学习（RLHF, Reinforcement Learning from Human Feedback）： 强化学习（RLHF）优化过程 • 步骤 1：人类标注者提供高质量回答。 • 虽然展现出惊人的推理能力提升，但是也出现了回复时 语言混合，非推理任务回复效果差的问题，为了解决这些问题，DeepSeek 提出通用强化学习训练框架。 如图7所示，通用强化学习（General Reinforcement Learning）基于 SFT- checkpoint，模型进行通用强化学习（RL）训练，优化其在推理任务和其他 教程作者：郭震，工作 8 年目前美国 AI 博士在读，公众号：郭震 AI，欢迎关注获取更多原创教程。资

垂直领域深耕 「核心功能」 多步骤自主研究、端到端强化学习、深度信息整合 实际使用 图源@宝玉 在 ChatGPT 中，选择「message composer」中的 deep research 并输入 查询 可以附加文件或电子表格，为问题添 加上下文。一旦开始运行，侧边栏将 显示所采取的步骤和使用的来源摘要。 1.多步骤 自主研究 2.端到端强化学习 3.深度信息整合 GAIA测试 准确率是此前 OpenAI o1 模型的近三倍 来源：https://openai.com/index/introducing-deep-research 基准测试：精度提升，行业领先 与GPT-4o对比 相比传统GPT-4o模型，Deep Research在多步推理、数据验证、处理 速度和信息追溯性方面表现出明显优势。这些提升有助于模型在复杂 任务中的表现更好，特别是在需要高可靠性和高效执行场景中。 2.关键临床试验数据 3.汇总技术路线对比图谱 4.待突破方向预测 5.符合APA格式的参考文献库 科研场景实测: 获得: 学术研究案例：明确需求，报告生成 通过百度网盘分享的文件：deep Research功能深度研究.docx 链接: https://pan.baidu.com/s/1pyaygXqFXvRe-In7gn5gOA?pwd=fn7s 提取码: fn7s  团队自测案例