00 Deepseek官方提示词
社会 #### 使用说明 - 输入 :一段新闻文本。 - 输出 :只输出新闻文本所属的种类,不需要额外解释。 USER 美国太空探索技术公司(SpaceX)的猎鹰 9 号运载火箭(Falcon 9)在经历美国联邦航空管理局(Federal Aviation Administration,FAA)短暂叫停发射后,于当地时间 8 月 31 日凌晨重启了发射任务。 11. 宣传标语生成:让模型生成贴合商品信息的宣传标语。0 码力 | 4 页 | 7.93 KB | 7 月前3TVM 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 Parser0 码力 | 19 页 | 489.50 KB | 5 月前3DeepSeek-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 underlined0 码力 | 52 页 | 1.23 MB | 1 年前3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 | 7 月前3Google 《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 To see this in action, you need to write some code. In code Snippet 1 I am using the langchain framework for Python, together with VertexAI (google-cloud-aiplatform) and the google-search-results pip0 码力 | 68 页 | 6.50 MB | 6 月前3TVM: 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 Optimization0 码力 | 31 页 | 22.64 MB | 5 月前3XDNN 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3TVM@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 Alios0 码力 | 27 页 | 4.86 MB | 5 月前3OpenAI 《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 use0 码力 | 34 页 | 7.00 MB | 5 月前3
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