Years Since 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 lot of information is indeed digitized / accessible / useful. The AI-driven evolution of how we access and move information is happening much faster… …AI is a compounder – on internet infrastructure benefits: put simply, our entire civilization is the product of our human intelligence. If we have access to substantially greater machine intelligence, the [ceiling of our] ambitions is raised substantially

a code change, 
 or generating a report. Applications that integrate LLMs but don’t use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are not agents its actions if needed. In case 
 of failure, it can halt execution and transfer control back to the user. 02 It has access to various tools to interact with external systems—both to gather context and orchestrate a network of specialized agents seamlessly through tool calls. Instead of losing context or control, the manager intelligently delegates tasks to the right agent at the right time, effortlessly synthesizing

regularly, getting feedback, and improving performance and safety at every step. The result: users access new advancements in AI early and often—and your feedback shapes future products and models. 4 their financial advisors more efficient and effective. The premise was simple: If advisors could access information faster and reduce the time spent on repetitive tasks, they could offer more and better 6 AI in the EnterpriseHow it’s going Today, 98% of Morgan Stanley advisors use OpenAI every day; access to documents has jumped from 20% to 80%, with dramatically reduced search time; and advisors spend

Design with simplicity 55 Be specific about the output 56 Use Instructions over Constraints 56 Control the max token length 58 Use variables in prompts 58 Experiment with input formats and writing styles model within Vertex AI or by using the API, because by prompting the model directly you will have access to the configuration such as temperature etc. This whitepaper discusses prompt engineering in detail need to figure out the model configuration. Most LLMs come with various configuration options that control the LLM’s output. Effective prompt engineering requires setting these configurations optimally for

dynamism ● Control flow (if, loop, etc) ● Dynamic shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control flow:

镜像的最新版本下载到你的本地系 统，以便你可以使用它创建和运行 Docker 容器。 然后再运行一条命令就可以了： docker run -d --name lobe-chat -p 10084:3210 -e ACCESS_CODE=lobe66 lobehub/lobe-chat:latest 22 解释下这条命令，它用于以守护进程模式（后台）运行一个名为 lobe-chat 的 Docker 容器，并设置一些特定参数： 映 射 到 容 器 的 3210 端 口。 这 样， 主 机 的 10084 端 口 的 请 求 会 被 转 发 到 容 器 的 3210 端 口。 -e ACCESS_CODE=lobe66 ： 设 置 环 境 变 量 ACCESS_CODE 的 值 为 lobe66 ， 这 通 常 是 用 于 在 容 器 内 配 置 应 用 程 序 的 参 数。 lobehub/lobe-chat:latest llama3。网站里提供很多 助手，选择某个助手，进入会话状态，如图 25。 24 Figure 25: 自带很多助手 4.5 部署常见问题 4.5.1 权限问题 Windows 系统安装，错误提示中带有 Access is denied. 如图 26所示。 Figure 26: ollama 部署权限错误 解决方法：Ollama 默认安装的路径： C:\Users\Wb\AppData\Local\Temp

Visit the body of ComputeOp to get the indices of input matrices: inadexO, indexI 。 Compare the access indices with the axis/reduce_axis of ComputeOp n matrix_b [idx0, idxl] k “UN1T1a:111T1a SUMT1C(G 了引包cf =“c=1JoalB)ioat人+C XC6CT6IT6032 。 Find the HeryBrto scale according to the access indices of fragment registers Jorfintk_inner_inner=0K_inner_inner<16;++k inner_ innerf Jorfintjc

AST Nodes Cross language suppPort Easy to introduce new runtime objects (trees, graphs) Direct access from other languages QQ octoML HTVM Overview *。 Plug directly into TVYM as a backend *，Target

"num_outputs": "1" }, "inputs": [[1, 0, 0]] }, >> 11 Calls XDNN’s TVM registered function to access the FPGA runtime APIs© Copyright 2018 Xilinx Registering TVM op in Python at runtime File contrib_xlnx

cost. As we employ expert parallelism during training, we also devise supplementary mechanisms to control communication overheads and ensure load balance. By combining these two techniques, DeepSeek-V2 features