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  • pdf文档 Trends Artificial Intelligence

    – 5/23-4/25, per Adobe The release of the Adobe FireFly video model in February, a commercially safe generative AI video model, has been very positively received by brands and creative professionals… over 80% of Fortune 500 companies. We’ve heard from business leaders that they’d like a simple and safe way of deploying it in their organization. Early users of ChatGPT Enterprise…are redefining how thinking and reasoning capabilities of large language models through the innovative application of scaled reinforcement learning. - Alibaba Qwen 2.5 Press Release, 1/25 USA vs. China in Technology = China’s
    0 码力 | 340 页 | 12.14 MB | 5 月前
    3
  • pdf文档 OpenAI 《A practical guide to building agents》

    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 agent Call initiate_
 refund function ‘is_safe’ True Reply to user User input User AgentSDK gpt-4o-mini Hallucination/ relevence gpt-4o-mini
 (FT) 
 safe/unsafe LLM Moderation API Rules-based protections Moderation Flags harmful or inappropriate inputs (hate speech, harassment, violence) to maintain safe, respectful interactions. Tool safeguards Assess the risk of each tool available to your agent by
    0 码力 | 34 页 | 7.00 MB | 6 月前
    3
  • pdf文档 Google 《Prompt Engineering v7》

    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 hordes of aggressive zombies, featuring intense close-quarters combat and puzzle-solving to find safe passage. 5. **Underwater Research Facility**: A deep-sea laboratory flooded with water, filled with hordes of aggressive zombies, featuring intense close-quarters combat and puzzle-solving to find safe passage. 5. **Underwater Research Facility**: A deep-sea laboratory flooded with water, filled with
    0 码力 | 68 页 | 6.50 MB | 6 月前
    3
  • pdf文档 DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

    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
    0 码力 | 52 页 | 1.23 MB | 1 年前
    3
  • pdf文档 TVM Meetup: Quantization

    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
    0 码力 | 19 页 | 489.50 KB | 5 月前
    3
  • pdf文档 清华大学 DeepSeek+DeepResearch 让科研像聊天一样简单

    electrochemical performance, the search for sustainable anode materials that provide lithium-ion batteries with safe and stable cyclic performance, while providing high capacity and high voltage curves, has sparked sustainable anode materials. The goal is to find materialsthat not only ensure lithium-ion batteries have a safe and stable cyclic performance, but also offer high capacity and high voltage curves. Among various
    0 码力 | 85 页 | 8.31 MB | 8 月前
    3
  • pdf文档 清华大学第二弹:DeepSeek赋能职场

    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
    0 码力 | 35 页 | 9.78 MB | 8 月前
    3
  • pdf文档 TVM: Where Are We Going

    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
    0 码力 | 31 页 | 22.64 MB | 5 月前
    3
  • pdf文档 XDNN TVM - Nov 2019

    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©
    0 码力 | 16 页 | 3.35 MB | 5 月前
    3
  • pdf文档 TVM@AliOS

    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
    0 码力 | 27 页 | 4.86 MB | 5 月前
    3
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