multimodality across audio, visual, & text inputs 7/24: Apple releases Apple Intelligence, an AI system integrated into its devices, for developers 12/24: OpenAI announces o3, its highest-ever designate particularly influential models within the AI/machine learning ecosystem. Epoch maintains a database of 900 AI models released since the 1950s, selecting entries based on criteria such as state-of-the-art Unprecedented41 AI Performance = In 2024… Surpassed Human Levels of Accuracy & Realism, per Stanford HAI AI System Performance on MMLU Benchmark Test – 2019-2024, per Stanford HAI Note: The MMLU (Massive Multitask

Well-documented, thoroughly tested, and reusable tools improve discoverability, simplify version management, and prevent redundant definitions. Broadly speaking, agents need three types of tools: Type Description complicated instructions 
 or consistently select incorrect tools, you may need to further divide your system and introduce more distinct agents. Practical guidelines for splitting agents include: Complex r] ) order_management_agent = Agent( name= , instructions=( "Technical Support Agent", "You provide expert assistance with resolving technical issues, system outages, or product

Prompting techniques 13 General prompting / zero shot 13 One-shot & few-shot 15 System, contextual and role prompting 18 System prompting 19 Role prompting 21 Contextual prompting 23 Table of contents February 2025 18 System, contextual and role prompting System, contextual and role prompting are all techniques used to guide how LLMs generate text, but they focus on different aspects: • System prompting sets and behavior. There can be considerable overlap between system, contextual, and role prompting. E.g. a prompt that assigns a role to the system, can also have a context. However, each type of prompt serves

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of NPS surveys. 16 AI in the EnterpriseAnd the wins continue to spread across Marketing, Risk Management, Operations, and beyond. All because they got AI in the hands of the people who know how to apply thousands of tasks every month, freeing people to do more high-impact work. Not surprisingly, the system is now spreading across other departments. It happened because we set bold automation goals from

方法为语言任务提供了前所未有的性能，以此为基础，多模态融合的应用使得 LLM 更全面地处理各种 信息，支持更广泛的应用领域。 图源：https://postgresml.org/docs/.gitbook/assets/ml_system.svg 4 / 32 LLM 基础设施 01 03 02 04 向量数据库/数据库向量支持 为大模型提供高效的存储和检索能力 大模型框架及微调 (Fine Tuning) 年前四个月，向量数据库公司融资额 ，超过了 2022 年的总和 （图源：https://www.cbinsights.com/research/generative-ai-infrastructure- vector-database/） 7 / 32 LLM 基础设施：大模型框架及微调 (Fine Tuning) 大模型框架指专门设计用于构建、训练和部署大型机器 学习模型和深度学习模型的软件框架。这些框架提供了 必

Zhuang, Y. Sheng, L. Zheng, C. H. Yu, J. E. Gonzalez, H. Zhang, and I. Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium n&=480/30=\boxed{16} \end{align*} Final Answer: The final answer is $16$. I hope it is correct. Problem: If the system of equations \begin{align*} 6x-4y&=a,\\ 6y-9x &=b. \end{align*}has a solution $(x, y)$ where $x$

TRUE); if (ret == WAIT_OBJECT_0) { cout << " Thread " << GetCurrentThreadId() << "writing to database...\n" << endl; } else if (ret == WAIT_ABANDONED) { cout << "Thread failed ...\n" << endl; }

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How Would That Look Like?© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM Inc. or its Affiliates. All rights reserved. Partition the Relay IR graph ● No user involvement System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM Inc. or its Affiliates. All rights reserved. Partition the Relay IR graph ● No user involvement System Overview Relay IR Graph Annotation with Your Annotator Graph Partitioning Your Codegen LLVM