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 Engineering February 2025 19 Distinguishing between system, contextual, and role prompts provides a framework for designing prompts with clear intent, allowing for flexible combinations and making it easier are some benefits in returning JSON objects from a prompt that extracts data. In a real-world application I don’t need to manually create this JSON format, I can already return the data in a sorted order

computer-use models to interact directly with those applications and systems through web and application UIs—just as a human would. Each tool should have a standardized definition, enabling flexible condition is met. An effective strategy for managing complexity without switching to a multi-agent framework is to use prompt templates. Rather than maintaining numerous individual prompts for distinct use

dialect© 2019, Amazon Web Services, Inc. or its Affiliates. All rights reserved. TVM Overview Framework Graph Mxnet TF …. parsers Relay Graph Target-independent Relay passes Target-optimized graph .. More targets AutoTVM – Tuning the kernels Optimized Binary Codegen – LLVM, Cuda, C, … Framework Parsers Graph level optimizations Tensor-level optimizations Machine code generation© 2019, Amazon reserved. Quantization Appraoches in TVM Framework FP32 Graph MXNet Parser TF parser …. Relay FP32 Graph Relay Automatic Quantization Relay Int8 Graph Framework Pre-quantized Graph MXNet Parser TF Parser

tokens. We optimize the attention modules and Feed-Forward Networks (FFNs) within the Trans- former framework (Vaswani et al., 2017) with our proposed Multi-head Latent Attention (MLA) and DeepSeekMoE. (1) Infrastructures DeepSeek-V2 is trained based on the HAI-LLM framework (High-flyer, 2023), an efficient and light-weight training framework developed internally by our engineers. It employs a 16-way zero-bubble in English and Chinese. Our evaluation is based on our internal evaluation framework integrated 13 in our HAI-LLM framework. Included benchmarks are categorized and listed as follows, where underlined

作为智能体 ü 角色 ü 功能 ü 技能 ü 约束 ü 工作流程 ü 输出格式 "全维度智能体提示框架" (Comprehensive Agent Prompting Framework, CAP Framework) 核心层: 1.身份定义 (Identity) •角色属性 •专业背景 •交互特征 执行层: 2. 能力矩阵 (Capability Matrix) •功能范围

Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated end-to- end optimization framework for deep learning.TVM Stack High-Level Differentiable IR Tensor Expression and Optimization

Runtime Image Model Weights Calibration Set Quantizer Compiler Tensor Graph Optimization Framework Tensor Graph to Xilinx Tensor Graph Frontend Deep Learning Frameworks https://github.com/xilinx©

nests marked as pipeline 。， Implement complete Hexagon runtime based on community PR. ADSPRPC Framework Applications Processor | | DSP Processor /NiiOS ! 驱动万物智能 Alios

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

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