Prompt Engineering 40 Code prompting 42 Prompts for writing code 42 Prompts for explaining code 44 Prompts for translating code 46 Prompts for debugging and reviewing code 48 What about multimodal information extraction, question and answering, text classification, language or code translation, code generation, and code documentation or reasoning. Please feel free to refer to Google’s prompting guides2 ‘providing an additional task to the system’. For example, you could use a system prompt to generate a code snippet that is compatible with a specific programming language, or you could use a system prompt

. . . 31 E Discussion About Pre-Training Data Debiasing 32 F Additional Evaluations on Math and Code 33 G Evaluation Formats 34 3 1. Introduction In the past few years, Large Language Models (LLMs) corpus. Then, we collect 1.5M conversational sessions, which encompass various domains such as math, code, writing, reasoning, safety, and more, to perform Supervised Fine-Tuning (SFT) for DeepSeek-V2 Chat include GSM8K (Cobbe et al., 2021), MATH (Hendrycks et al., 2021), and CMath (Wei et al., 2023). Code datasets include HumanEval (Chen et al., 2021), MBPP (Austin et al., 2021), and CRUXEval (Gu et al

(5 days to secure 1MM users). Generative AI = AI that can create content – text, images, audio, or code – based on learned patterns. Source: OpenAI Generative AI – Public Launch of ChatGPT 2022* Knowledge Computing-Related* Patents Granted Annually – 1960-2024, per USPTO *Uses Cooperative Patent Classification (CPC) code G06, which corresponds to computing, calculating or counting patents. Google patents data changes above show average accuracy of top-performing AI models in each calendar year. Source: Papers With Code via Nestor Maslej et al., ‘The AI Index 2025 Annual Report,’ AI Index Steering Committee, Stanford

whether that's resolving a customer service issue, booking a restaurant reservation, committing a code change, 
 or generating a report. Applications that integrate LLMs but don’t use them to control Explicit guidelines and guardrails defining how the 
 agent behaves Here’s what this looks like in code when using OpenAI’s Agents SDK. You can also implement the same concepts using your preferred library learning of specialized domain-specific languages. In contrast, the Agents SDK adopts a more flexible, code-first approach. Developers can 
 directly express workflow logic using familiar programming constructs

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： 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 ：

other languages QQ octoML HTVM Overview *。 Plug directly into TVYM as a backend *，Target C to emit code for microcontrollers that is device- agnostic AuroTYM QQ octoML AutoTVM on HTVM DTYM Runtime send compiler VM runtime VM serialization Dynamic Shape Support Dynamic Shape Allocation o Dynamic Shape Code generation ee Looking for more contributions in this part of the systeml e Haichen and | will discuss

TensorizationVTA: Open & Flexible Deep Learning Accelerator • Runtime JIT compile accelerator micro code • Support heterogenous devices, 10x better than CPU on the same board. • Move hardware complexity Incubated as Apache TVM recently. Independent governance, allowing competitors to collaborate. Open Code Open Development Open GovernanceAcknowledgement Apache (incubating) TVM community Our awesome

space (TensorCore + non-TensorCore) Our >olution 1 “Generate TensorCore code directly from Ta ea normal thread-level schedule Se 一人一 -Dscm1p寻Ya7179 SCTDLt -DawtioHXeaomec' som=true 三Dloss5ca/9g=gsca/e ctom7 No need to modify or add any line of code. 计算平台事业部 COMPUTING PLATFORM Loss Scaling in TF 下和全于由 loss = loss_fn() opt

often prune models to 80%+ sparsity(with retraining) - Massive speedups combined with specialized code-generation techniques (TVM, Xbyak, etc) - Interesting new tradeoffs - how const are parameters

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