by the number of elements, regardless of the storage precision. For DeepSeek-V2, ?? is set to 4?ℎ and ?? ℎ is set to ?ℎ 2 . So, its KV cache is equal to GQA with only 2.25 groups, but its performance to-expert affinity; e? is the centroid of the ?-th routed expert in this layer; and Topk(·, ?) denotes the set comprising ? highest scores among the affinity scores calculated for the ?-th token and all routed Hyper-Parameters. We set the number of Transformer layers to 60 and the hidden dimension to 5120. All learnable parameters are randomly initialized with a standard deviation of 0.006. In MLA, we set the number of

Docker 是一个开源的容器化平台，旨在开发、部署和运行应用。它利用容器来 隔离软件，使其在不同环境中都能一致运行。Docker 提供轻量级虚拟化，能快 速部署并且易于管理应用。 Docker 的优势： 1. 快速部署：Docker 容器可以在几秒钟内启动，提高了开发和部署的效率。 2. 一致性：确保应用在开发、测试和生产环境中具有一致的运行环境。 3. 可移植性：容器可以在任何支持 Docker 的系统上运行，实现跨平台的可 移植性。 4. 易于扩展：Docker 可以方便地扩展并支持微服务架构的部署。 基本概念： 1. 容器（Container）：轻量级、独立的可执行软件包，包含了运行所需的代 码、运行时、系统工具、系统库和设置。 2. 镜像（Image）：用于创建容器的只读模板。一个镜像可以包含完整的操作 系统环境。 3. Dockerfile：定义镜像内容的文本文件，包含了构建镜像的所有指令。 Dockerfile 目录中运行：docker build -t . 常用命令： 1. 列出正在运行的容器：docker ps 2. 列出所有容器：docker ps -a 3. 停止一个容器：docker stop 4. 删除一个容器：docker rm 20 4.2.2 下载 docker docker 下载地址:

tokens and what the LLM has seen during its training. When you write a prompt, you are attempting to set up the LLM to predict the right sequence of tokens. Prompt engineering is the process of designing Engineering February 2025 12 • If you set temperature to 0, top-K and top-P become irrelevant–the most probable token becomes the next token predicted. If you set temperature extremely high (above 1–generally predicted token. • If you set top-K to 1, temperature and top-P become irrelevant. Only one token passes the top-K criteria, and that token is the next predicted token. If you set top-K extremely high,

where smaller models succeed or fail. In summary, the principles for choosing a model are simple: 01 Set up evals to establish a performance baseline 02 Focus on meeting your accuracy target with the best in writing instructions for an LLM agent. Convert the following help center document into a clear set of instructions, written in a numbered list. The document will be a policy followed by an LLM. Ensure prompt leaks) or reputational risks (for example, enforcing brand aligned model behavior). 
 You can set up guardrails that address risks you’ve already identified for your use case and layer 
 in additional

Customize and fine-tune your models 13 Get AI in the hands of experts 16 Unblock your developers 18 Set bold automation goals 21 Conclusion 22 More resources 24 2 AI in the EnterpriseA new way 
 to work your
 developers Automating the software development lifecycle can multiply 
 AI dividends. 07 Set bold 
 automation goals Most processes involve a lot of rote work, ripe for automation. Aim high solutions. BBVA, the global banking leader, has more than 125,000 employees, each with a unique set of challenges and opportunities. They decided to get AI into the hands of employees—working closely

Intelligence (AI) May 30, 2025 Mary Meeker / Jay Simons / Daegwon Chae / Alexander Krey2 Context We set out to compile foundational trends related to AI. A starting collection of several disparate datapoints Public Launch (Google = 9/98, ChatGPT = 11/22)21 In 1998, tapping emerging Internet access, Google set out to ‘organize the world’s information and make it universally accessible and useful.’ Nearly For TiVo, we use the launch of consumer sales on 3/31/99, when TiVo charged $499 for its 14-hour box set. We do not count TiVo subscription costs. We also use the iPhone 1’s 4GB entry level price of $499

Xilinx Cloud DPU Processor (xDNNv3) >> 3 ˃ Configurable Overlay Processor ˃ DNN Specific Instruction Set Convolution, Max Pool etc. ˃ Any Network, Any Image Size ˃ High Frequency & High Compute Efficiency Xilinx Inference Flow >> 5 MxNet CPU Layers FPGA Layers Runtime Image Model Weights Calibration Set Quantizer Compiler Tensor Graph Optimization Framework Tensor Graph to Xilinx Tensor Graph Frontend

matters a lot - Heterogenous computing environment - High variety of workloads - Ever-increasing set of primitives (over 500 aten kernels) - Interpreter methods not delivering generalized performance

咏 2018.12 | 2019.6 ee 2019.10 Alios TVM Team Set up TFLite Quantized Support 1.61X MobilenetVl TFlite

dy/dx’等关键词。” 场景2：文科生快速上手编程 加载数据集：使用datasets库加载SQuAD数据集，这个数据 集包含了大量基于2020年之前数据生成的问答对。 提取问题：从数据集中提取问题，并使用set去重。 检查问题数量：确保提取的问题数量至少为10万个。 保存问题：将问题保存到CSV文件生成的真实答案问题.csv中。 要生成10万个存在真实答案的问题，并且基于2020年之前的 数据，可以使用现有的公开问答数据集（如SQuAD