you use, the model’s training data, the model configurations, your word-choice, style and tone, structure, and context all matter. Therefore, prompt engineering is an iterative process. Inadequate prompts to find the best prompt, optimizing prompt length, and evaluating a prompt’s writing style and structure in relation to the task. In the context of natural language processing and LLMs, a prompt is an between top-K, top-P, temperature, and the number of tokens to generate, depends on the specific application and desired outcome, and the settings all impact one another. It’s also important to make sure

frameworks for modeling & training, inference optimization, dataset engineering, & model evaluation. Application development = custom AI-powered applications (varied use cases). Source: Chip Hyuen via GitHub long lead times. That shift is accelerating the rise of custom silicon – especially ASICs, or application-specific integrated circuits. Unlike GPUs, which are designed to support a wide range of workloads Assessing The ROI Equation’ (2/25) TPUs were purpose-built specifically for AI. TPUs are an application-specific integrated circuit (ASIC), a chip designed for a single, specific purpose: running the

affect thedynamics of a specific trophic level as well as thedynamics of the entire community structure. 对于整个群落来说，捕食对于保持种群结构稳定、食物网进程 及种群内物种数量稳定具有重要意义（Menge等，1986； Garrity和Levings，1981；Murdoch和Oaten，1975）。 1975）。 For the entire community, pedation is crucial formaintaining population structure stability, food webprocesses, and stable species numbers within thepopulation (Menge et al, 1986; Garrity and

1. TASTE框架 • Task (任务): 定义模型主要任务或生成内容。 • Audience (目标受众): 明确说明目标受众。 • Structure (结构): 为输出的内容提供明确的组织结 构，包括段落安排、论点展开顺序或其他逻辑关系。 • Tone (语气): 指定模型回答时的语气或风格。 • Example (示例):例子或模板可帮助模型理解输出风 Novelty: 要求结合最新的环境数据，提出新颖的观点和解 决方案。 示例 • Task: 写一篇关于数据隐私的重要性的简短博客文章。 • Audience: 普通的互联网用户，非技术背景。 • Structure: 文章需要有明确的开头、中间讨论和结尾， 开头提出问题，中间介绍原因和影响，结尾提供建议。 • Tone: 采用友好、易懂的语气。 • Example: 类似于《纽约时报》科技专栏的风格。

1. TASTE框架 • Task (任务): 定义模型主要任务或生成内容。 • Audience (目标受众): 明确说明目标受众。 • Structure (结构): 为输出的内容提供明确的组织结 构，包括段落安排、论点展开顺序或其他逻辑关系。 • Tone (语气): 指定模型回答时的语气或风格。 • Example (示例):例子或模板可帮助模型理解输出风 Novelty: 要求结合最新的环境数据，提出新颖的观点和解 决方案。 示例 • Task: 写一篇关于数据隐私的重要性的简短博客文章。 • Audience: 普通的互联网用户，非技术背景。 • Structure: 文章需要有明确的开头、中间讨论和结尾， 开头提出问题，中间介绍原因和影响，结尾提供建议。 • Tone: 采用友好、易懂的语气。 • Example: 类似于《纽约时报》科技专栏的风格。

code-generation techniques (TVM, Xbyak, etc) - Interesting new tradeoffs - how const are parameters? - structure specialization trades off icache/ dcache - also available today in FBGEMMPyTorch and TVM - Lots

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sensitive nature of the work. The answer was to conduct intensive evals for every proposed application. An eval is simply a rigorous, structured process for measuring how AI models actually perform GPT-4o and GPT-4o mini. Today, it helps their 17,000 developers unify and accelerate their 
 AI application builds. Verdi integrates language models, Python nodes, and APIs to create a scalable, consistent

autotvm on Ubuntu 2. Use the .log from step1 on Windows to generate the .dll for deployment 3. For application on 32bits, no support of 32bit tensorflow , a workround from FrozenGene a. python/tvm/contrib/ndk