├── .gitignore ├── 00-序-AGI之路 ├── assest │ ├── 大家都谈的ScalingLaw是什么 │ │ ├── 0.png │ │ ├── 000.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ └── 4.png │ └── 智能涌现和AGI的起源 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ └── 6.png ├── 大家都在谈的ScalingLaw是什么.md └── 智能涌现和AGI的起源.md ├── 01-第一章-预训练 ├── 10分钟了解什么是多模态大模型.md ├── 10分钟搞清楚为什么Transformer中使用LayerNorm而不是BatchNorm.md ├── FlashAttentionv2相比于v1有哪些更新？.md ├── assest │ ├── 10分钟了解什么是多模态大模型（Multimodal LLMs） │ │ ├── 0.png │ │ ├── 00.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ └── 4.png │ ├── 10分钟搞清楚为什么Transformer中使用LayerNorm而不是BatchNorm │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ └── 3.png │ ├── FlashAttention v2相比于v1有哪些更新？ │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ └── 4.png │ ├── 一文了解Deepseek系列中的MLA技术 │ │ ├── 0.PNG │ │ ├── 1.PNG │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 2.PNG │ │ ├── 3.PNG │ │ ├── 4.PNG │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 为什么会发展出Multi Query Attention和Group Query Attention │ │ └── 1.PNG │ ├── 为什么大型语言模型都在使用 SwiGLU 作为激活函数？ │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ └── 4.png │ ├── 什么是perplexity │ │ ├── 0.PNG │ │ ├── 1.PNG │ │ └── 2.PNG │ ├── 什么是大模型的位置编码Position Encoding │ │ ├── 02.png │ │ ├── 03.png │ │ ├── 04.png │ │ ├── 1.PNG │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ └── 7.png │ ├── 从欧拉公式的美到旋转位置编码RoPE │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 复变函数在大模型位置编码中的应用 │ │ ├── 1.PNG │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ └── 7.png │ ├── 大模型分布式训练并行技术（一）概述 │ │ ├── 0.png │ │ ├── 00.png │ │ ├── 2.png │ │ ├── 4.png │ │ └── 9.png │ ├── 大模型分布式训练并行技术（三）流水线并行 │ │ ├── 1.png │ │ ├── 2.png │ │ └── 3.PNG │ ├── 大模型分布式训练并行技术（二）数据并行 │ │ ├── 0.png │ │ └── 1.png │ ├── 大模型分布式训练并行技术（五）混合并行 │ │ ├── 10.jpeg │ │ ├── 8.png │ │ └── 9.png │ ├── 大模型分布式训练并行技术（四）张量并行 │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ └── 7.png │ ├── 大模型并行策略[中文翻译] │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 10.jpeg │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 大模型训练框架（一）：综述 │ │ ├── 0.png │ │ ├── 2.png │ │ ├── 4.png │ │ └── 5.png │ ├── 大模型训练框架（三）DeepSpeed │ │ ├── 0.png │ │ ├── 2.png │ │ └── 4.png │ ├── 大模型训练框架（二）FSDP │ │ ├── 0.png │ │ ├── 1.png │ │ └── 2.png │ ├── 大模型训练框架（五）Accelerate │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ └── 3.png │ ├── 大模型训练框架（四）Megatron-LM │ │ ├── 0.png │ │ ├── 00.png │ │ └── 1.png │ ├── 搞懂大模型的分词器（一） │ │ ├── 0.png │ │ └── 1.png │ ├── 搞懂大模型的分词器（三） │ │ ├── 0.png │ │ └── 1.png │ ├── 搞懂大模型的分词器（二） │ │ ├── 0.png │ │ └── 1.png │ ├── 搞懂大模型的分词器（五） │ │ ├── 0.png │ │ └── 1.webp │ ├── 搞懂大模型的分词器（六） │ │ ├── 0.png │ │ └── 1.png │ ├── 搞懂大模型的分词器（四） │ │ ├── 0.png │ │ └── 1.png │ ├── 最简单的方式理解Mamba（中文翻译） │ │ ├── 0.png │ │ ├── 033.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 2.png │ │ ├── 2.webp │ │ ├── 3.gif │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 最美的数学公式-欧拉公式 │ │ ├── 1.png │ │ └── 2.png │ ├── 混合专家模型 (MoE) 详解（节选） │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ └── 5.png │ ├── 看懂FlashAttention需要的数学储备是？高考数学最后一道大题！ │ │ ├── 0.png │ │ ├── 1.PNG │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 2.PNG │ │ ├── 3.PNG │ │ ├── 4.PNG │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 神经网络的优化器（一）概述 │ │ ├── 0.gif │ │ ├── 00.png │ │ ├── 1.gif │ │ ├── 3.png │ │ └── 4.png │ ├── 神经网络的优化器（七）AdaDelta │ │ └── 0.png │ ├── 神经网络的优化器（三）Momentum │ │ ├── 0.png │ │ └── 1.png │ ├── 神经网络的优化器（九）Adam │ │ └── 0.png │ ├── 神经网络的优化器（二）SGD │ │ ├── 0.png │ │ ├── 00.png │ │ └── 01.png │ ├── 神经网络的优化器（五）Rprop │ │ └── 0.png │ ├── 神经网络的优化器（八）RMSprop │ │ └── 0.png │ ├── 神经网络的优化器（六）AdaGrad │ │ └── 0.png │ ├── 神经网络的优化器（十一）AdamW │ │ └── 0.png │ ├── 神经网络的优化器（十二）RAdam │ │ └── 0.png │ ├── 神经网络的优化器（十）Nadam │ │ └── 0.png │ ├── 神经网络的优化器（四）ASGD │ │ ├── 0.png │ │ └── 1.png │ └── 神经网络的激活函数 │ │ ├── 0.png │ │ ├── 00.png │ │ ├── 01.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 2.1.png │ │ ├── 2.png │ │ ├── 3.1.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.1.png │ │ ├── 6.2.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ ├── 9.png │ │ └── Logistic-curve.svg ├── 一文了解Deepseek系列中的MLA技术.md ├── 为什么会发展出Multi-Query-Attention和Group-Query-Attention.md ├── 为什么大型语言模型都在使用SwiGLU作为激活函数？.md ├── 什么是大模型的位置编码Position-Encoding.md ├── 从欧拉公式的美到旋转位置编码RoPE.md ├── 全网最全的大模型分词器（Tokenizer）总结.md ├── 全网最全的神经网络优化器optimizer总结.md ├── 复变函数在大模型位置编码中的应用.md ├── 大模型分布式训练并行技术（一）概述.md ├── 大模型分布式训练并行技术（三）流水线并行.md ├── 大模型分布式训练并行技术（二）数据并行.md ├── 大模型分布式训练并行技术（五）混合并行.md ├── 大模型分布式训练并行技术（四）张量并行.md ├── 大模型并行策略[中文翻译].md ├── 大模型训练框架（一）综述.md ├── 大模型训练框架（三）DeepSpeed.md ├── 大模型训练框架（二）FSDP.md ├── 大模型训练框架（五）Accelerate.md ├── 大模型训练框架（四）Megatron-LM.md ├── 搞懂大模型的分词器（一）.md ├── 搞懂大模型的分词器（三）.md ├── 搞懂大模型的分词器（二）.md ├── 搞懂大模型的分词器（五）.md ├── 搞懂大模型的分词器（六）.md ├── 搞懂大模型的分词器（四）.md ├── 最简单的方式理解Mamba（中文翻译）.md ├── 最美的数学公式-欧拉公式.md ├── 混合专家模型MoE详解节选.md ├── 看懂FlashAttention需要的数学储备是？高考数学最后一道大题！.md ├── 神经网络的优化器（一）概述.md ├── 神经网络的优化器（七）AdaDeleta.md ├── 神经网络的优化器（三）Momentum.md ├── 神经网络的优化器（九）Adam.md ├── 神经网络的优化器（二）SGD.md ├── 神经网络的优化器（五）Rprop.md ├── 神经网络的优化器（八）RMSprop.md ├── 神经网络的优化器（六）AdaGrad.md ├── 神经网络的优化器（十一）AdamW.md ├── 神经网络的优化器（十二）RAdam.md ├── 神经网络的优化器（十）Nadam.md ├── 神经网络的优化器（四）ASGD.md ├── 神经网络的激活函数（一）概述.md ├── 神经网络的激活函数（三）ReLU和它的变种.md ├── 神经网络的激活函数（二）Sigmiod、Softmax和Tanh.md ├── 神经网络的激活函数（五）门控系列-GLU、Swish和SwiGLU.md ├── 神经网络的激活函数（六）GELU和Mish.md └── 神经网络的激活函数（四）ELU和它的变种SELU.md ├── 02-第二章-部署与推理 ├── 10分钟私有化部署大模型到本地.md ├── DevOps，AIOps，MLOps，LLMOps，这些Ops都是什么？.md ├── assest │ ├── 10分钟私有化部署大模型到本地 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── DevOps, AIOps, MLOps, LLMOps，这些Ops都是什么？ │ │ ├── 0.png │ │ ├── 00.png │ │ ├── 01.png │ │ ├── 02.png │ │ ├── 03.png │ │ ├── 04.png │ │ ├── 05.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── vLLM 使用PagedAttention轻松、快速且廉价地提供LLM服务（中文版翻译） │ │ ├── 0.png │ │ ├── 00.png │ │ ├── 1.png │ │ ├── 2.gif │ │ ├── 3.gif │ │ ├── 4.gif │ │ ├── 5.gif │ │ └── 6.png │ ├── 大模型推理框架（一）综述 │ │ ├── 0.PNG │ │ ├── 1.png │ │ ├── 2.png │ │ └── 3.png │ ├── 大模型推理框架（三）Text generation inference (TGI) │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ └── 7.gif │ ├── 大模型推理框架（二）vLLM │ │ ├── 0.gif │ │ ├── 1.gif │ │ ├── 2.gif │ │ ├── 3.gif │ │ └── 4.png │ ├── 大模型推理框架（五）Ollama │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 大模型推理框架（四）TensorRT-LLM │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ └── 4.PNG │ ├── 大模型的latency（延迟）和throughput（吞吐量）有什么区别 │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 如何评判大模型的输出速度？首Token延迟和其余Token延迟有什么不同？ │ │ ├── 0.png │ │ ├── 1.PNG │ │ ├── 2.PNG │ │ ├── 3.PNG │ │ └── 4.PNG │ └── 私有化部署大模型性能估计 │ │ ├── 0.PNG │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.PNG │ │ └── 5.png ├── inference_speed.ipynb ├── vLLM使用PagedAttention轻松、快速且廉价地提供LLM服务（中文版翻译）.md ├── 大模型output-token为什么比input-token贵？.md ├── 大模型推理框架（一）综述.md ├── 大模型推理框架（三）Text generation inference (TGI).md ├── 大模型推理框架（二）vLLM.md ├── 大模型推理框架（五）Ollama.md ├── 大模型推理框架（四）TensorRT-LLM.md ├── 大模型的latency（延迟）和throughput（吞吐量）有什么区别.md ├── 如何评判大模型的输出速度？首Token延迟和其余Token延迟有什么不同？.md └── 模型部署不求人！从TTFT到Throughput的性能估算终极公式.md ├── 03-第三章-微调 ├── assest │ ├── 10分钟教你套壳（不是）Llama-3，小白也能上手 │ │ ├── 0.png │ │ ├── 1.PNG │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 2.PNG │ │ ├── 3.png │ │ ├── 4.PNG │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 大模型微调之Adapters（一）概述 │ │ ├── 0.gif │ │ ├── 1.png │ │ └── 2.png │ ├── 大模型微调之Adapters（三）QLoRA │ │ └── 0.png │ ├── 大模型微调之Adapters（二）LoRA │ │ ├── 1.png │ │ └── lora_animated (1).gif │ ├── 大模型微调之Adapters（四）AdaLoRA │ │ └── 0.png │ ├── 大模型微调之Soft prompts（一）概述 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ └── 3.png │ ├── 大模型微调之Soft prompts（三）Prefix-tuning │ │ └── 0.png │ ├── 大模型微调之Soft prompts（二）Prompt-tuning │ │ └── 0.png │ ├── 大模型微调之Soft prompts（五）Multitask prompt tuning │ │ ├── 0.png │ │ └── 1.png │ ├── 大模型微调之Soft prompts（四）P-tuning │ │ └── 0.png │ ├── 大模型微调框架（一）综述 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ └── 4.png │ ├── 大模型微调框架（三）Llama-Factory │ │ ├── 3.png │ │ └── webui_0.webp │ ├── 大模型微调框架（二）Huggingface-PEFT │ │ ├── 0.png │ │ └── 1.png │ └── 大模型的参数高效微调（PEFT），LoRA微调以及其它 │ │ ├── 0.9.png │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 2.1.png │ │ ├── 2.2.png │ │ ├── 2.3.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.1.png │ │ ├── 4.png │ │ ├── 5.png │ │ └── 6.png ├── 大模型微调之Adapters（一）概述.md ├── 大模型微调之Adapters（三）QLoRA.md ├── 大模型微调之Adapters（二）LoRA.md ├── 大模型微调之Adapters（四）AdaLoRA.md ├── 大模型微调之Soft prompts（一）概述.md ├── 大模型微调之Soft prompts（三）Prefix-Tuning.md ├── 大模型微调之Soft prompts（二）Prompt Tuning.md ├── 大模型微调之Soft prompts（五）Multitask prompt tuning.md ├── 大模型微调之Soft prompts（四）P-Tuning.md ├── 大模型微调框架（一）综述.md ├── 大模型微调框架（三）LLama-Factory.md ├── 大模型微调框架（二）Huggingface-PEFT.md └── 大模型的参数高效微调（PEFT），LoRA微调以及其它.md ├── 04-第四章-量化 ├── 10分钟理解大模型的量化.md ├── assest │ ├── 10分钟理解大模型的量化 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ └── 大模型量化认知的三重境界 │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ └── 7.png └── 大模型量化认知的三重境界.md ├── 05-第五章-显卡与并行 ├── Transformer架构的GPU并行和之前的NLP算法有什么不同？.md ├── assest │ ├── AGI时代人人都可以看懂的显卡知识 │ │ ├── 0.PNG │ │ ├── 1.PNG │ │ ├── 2.PNG │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── Transformer架构的GPU并行和之前的NLP算法有什么不同 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ └── 3.png │ └── 大模型部署三要素：显存、计算与通信深度解析 │ │ ├── 0.PNG │ │ ├── 1.PNG │ │ └── 2.png └── 大模型部署三要素：显存、计算与通信深度解析.md ├── 06-第六章-Prompt Engineering ├── COT思维链，TOT思维树，GOT思维图，这些都是什么.md ├── assest │ ├── COT思维链，TOT思维树，GOT思维图，这些都是什么？ │ │ ├── 0.png │ │ ├── 01.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ └── hallucinations │ │ │ ├── 1.png │ │ │ └── 2.png │ ├── 万字长文 Prompt Engineering-解锁大模型的力量 │ │ ├── 0.PNG │ │ ├── 1.PNG │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.jpg │ │ ├── 2.PNG │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 25.png │ │ ├── 26.png │ │ ├── 27.png │ │ ├── 28.png │ │ ├── 29.png │ │ ├── 3.png │ │ ├── 30.png │ │ ├── 31.png │ │ ├── 32.png │ │ ├── 33.png │ │ ├── 34.png │ │ ├── 35.png │ │ ├── 36.png │ │ ├── 37.png │ │ ├── 38.png │ │ ├── 39.png │ │ ├── 4.png │ │ ├── 40.png │ │ ├── 41.png │ │ ├── 42.png │ │ ├── 43.png │ │ ├── 44.png │ │ ├── 45.png │ │ ├── 46.png │ │ ├── 47.png │ │ ├── 48.png │ │ ├── 49.png │ │ ├── 5.png │ │ ├── 50.png │ │ ├── 51.png │ │ ├── 52.png │ │ ├── 53.png │ │ ├── 54.png │ │ ├── 55.png │ │ ├── 56.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ └── 过去式就能越狱大模型？一文了解大模型安全攻防战！ │ │ ├── 0.png │ │ ├── 1.PNG │ │ ├── 2.PNG │ │ ├── 3.PNG │ │ └── 4.PNG ├── 万字长文Prompt-Engineering-解锁大模型的力量.md └── 过去式就能越狱大模型？一文了解大模型安全攻防战.md ├── 07-第七章-Agent ├── 10分钟了解如何进行多模态RAG.md ├── Agent设计范式与常见框架.md ├── GraphRAG解锁大模型对叙述性私人数据的检索能力（中文翻译）.md ├── LLM应用落地之Text-2-SQL（三）.md ├── LLM应用落地之架构设计（二）.md ├── LLM应用落地指南之应用的分类(一).md ├── MCP：基础概念、快速应用和背后原理.md ├── assest │ ├── 10分钟了解如何进行多模态RAG │ │ ├── 0.PNG │ │ ├── 1.PNG │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ └── 5.png │ ├── Agent设计范式与常见框架 │ │ ├── 1.PNG │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 2.PNG │ │ ├── 3.PNG │ │ ├── 4.PNG │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── GraphRAG 解锁大模型对叙述性私人数据的检索能力（中文翻译） │ │ ├── 0.png │ │ ├── 1.png │ │ └── 2.jpg │ ├── LLM应用落地指南 │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── MCP：基础概念、快速应用和背后原理 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── langchain向左coze向右 │ │ ├── 0.png │ │ ├── 1.webp │ │ ├── 2.webp │ │ ├── 3.webp │ │ └── 4.webp │ ├── 向量数据库拥抱大模型 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ └── 3.PNG │ ├── 如何设计智能体架构：参考OpenAI还是Anthropic? │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ └── 6.png │ ├── 干货： 落地企业级RAG的实践指南 │ │ ├── 0.png │ │ ├── 00.PNG │ │ ├── 01.png │ │ ├── 02.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 25.png │ │ ├── 26.png │ │ ├── 27.png │ │ ├── 28.png │ │ ├── 29.png │ │ ├── 3.png │ │ ├── 30.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png │ ├── 开发大模型or使用大模型 │ │ ├── 3.PNG │ │ ├── 4.png │ │ ├── 5.PNG │ │ ├── 6.PNG │ │ ├── 7.png │ │ ├── 8.PNG │ │ └── 微调 │ │ │ ├── 0.png │ │ │ ├── 12.png │ │ │ └── 13.png │ └── 搭配Knowledge Graph的RAG架构 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ └── 8.PNG ├── langchain向左coze向右.md ├── 向量数据库拥抱大模型.md ├── 如何设计智能体架构：参考OpenAI还是Anthropic?.md ├── 干货-落地企业级RAG的实践指南.md ├── 开发大模型or使用大模型.md └── 搭配Knowledge-Graph的RAG架构.md ├── 08-第八章-大模型企业落地 ├── CRUDETL工程师的末日从NL2SQL到ChatBI.md ├── assest │ ├── CRUDETL工程师的末日从NL2SQL到ChatBI │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ └── hallucination │ │ │ ├── 2.png │ │ │ ├── 3.PNG │ │ │ ├── 4.png │ │ │ ├── 5.PNG │ │ │ ├── 6.PNG │ │ │ ├── 7.png │ │ │ └── 8.PNG │ ├── 大模型复读机问题 │ │ ├── 0.png │ │ ├── 00.png │ │ └── 1.png │ ├── 大模型落地难点之幻觉 │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.PNG │ │ ├── 4.png │ │ ├── 5.PNG │ │ ├── 6.PNG │ │ ├── 7.png │ │ └── 8.PNG │ ├── 大模型落地难点之结构化输出 │ │ ├── 0.png │ │ ├── 0.webp │ │ └── 1.png │ ├── 大模型落地难点之输出的不确定性 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ └── 7.png │ └── 用红队测试（Red teaming）发现大模型应用的漏洞 │ │ ├── 0.png │ │ ├── 00.png │ │ ├── 01.png │ │ ├── 02.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 25.png │ │ ├── 26.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png ├── 大模型复读机问题.md ├── 大模型应用涌现出的新工作机会-红队测试Red-teaming.md ├── 大模型落地难点之幻觉.md ├── 大模型落地难点之结构化输出.md └── 大模型落地难点之输出的不确定性.md ├── 09-第九章-评估指标 ├── assest │ ├── 大模型性能评测之大海捞针 │ │ └── 0.png │ ├── 大模型性能评测之数星星 │ │ ├── 0.png │ │ ├── 1.png │ │ └── 2.png │ └── 大模型有哪些评估指标？ │ │ ├── 01.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 12.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png ├── 大模型性能评测之大海捞针.md ├── 大模型性能评测之数星星.md └── 大模型有哪些评估指标？.md ├── 10-第十章-热点 ├── assest │ ├── 9.11大于9.9？大模型怎么又翻车了？ │ │ ├── 0.png │ │ ├── 1.PNG │ │ └── 2.PNG │ ├── Llama 3.1 405B 为什么这么大？ │ │ ├── 0.png │ │ ├── 1.PNG │ │ ├── 2.PNG │ │ └── 3.png │ ├── 我是怎么通过2022下半年软考高级：系统架构设计师考试的 │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ └── 5.png │ ├── 用Exploit and Explore解决不知道吃什么的选择困难症 │ │ ├── 0.png │ │ ├── 00.png │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.1.png │ │ ├── 3.png │ │ └── 4.webp │ └── 韩国“N 号房”事件因Deep Fake再现，探究背后的技术和应对方法 │ │ ├── 0.png │ │ ├── 00.png │ │ ├── 01.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png ├── 我是怎么通过2022下半年软考高级：系统架构设计师考试的.md ├── 用Exploit-and-Explore解决不知道吃什么的选择困难症.md └── 韩国“N 号房”事件因Deep-Fake再现，探究背后的技术和应对方法.md ├── 11-第十一章-数学 ├── Probability&Statistics │ ├── 0基础学习AI大模型必备数学知识之概率统计（一）贝叶斯定理和概率分布.md │ ├── 0基础学习AI大模型必备数学知识之概率统计（三）中心极限定理.md │ ├── 0基础学习AI大模型必备数学知识之概率统计（二）概率分布的描述方法.md │ └── Probability&Statistics │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 12.png │ │ ├── 13.png │ │ ├── 14.png │ │ ├── 15.png │ │ ├── 16.png │ │ ├── 17.png │ │ ├── 18.png │ │ ├── 19.png │ │ ├── 2.png │ │ ├── 20.png │ │ ├── 21.png │ │ ├── 22.png │ │ ├── 23.png │ │ ├── 24.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.webp │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png ├── calculus │ ├── 0基础学习AI大模型必备数学知识之微积分（一）.md │ ├── 0基础学习AI大模型必备数学知识之微积分（二）.md │ └── assest │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 10.png │ │ ├── 11.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ ├── 5.png │ │ ├── 6.png │ │ ├── 7.png │ │ ├── 8.png │ │ └── 9.png └── linear-algebra │ ├── 0基础学习AI大模型必备数学知识之线性代数（一）.md │ ├── 0基础学习AI大模型必备数学知识之线性代数（三）.md │ ├── 0基础学习AI大模型必备数学知识之线性代数（二）.md │ ├── 0基础学习AI大模型必备数学知识之线性代数（五）.md │ ├── 0基础学习AI大模型必备数学知识之线性代数（四）.md │ └── assest │ ├── 1.jpeg │ ├── 10.png │ ├── 11.png │ ├── 12.png │ ├── 13.png │ ├── 14.png │ ├── 15.png │ ├── 16.png │ ├── 17.png │ ├── 18.png │ ├── 19.png │ ├── 2.jpeg │ ├── 20.png │ ├── 21.png │ ├── 22.png │ ├── 23.png │ ├── 24.png │ ├── 25.png │ ├── 26.png │ ├── 27.png │ ├── 28.png │ ├── 29.png │ ├── 3.png │ ├── 30.png │ ├── 31.png │ ├── 32.png │ ├── 33.png │ ├── 34.png │ ├── 4.png │ ├── 5.png │ ├── 6.png │ ├── 7.jpeg │ ├── 8.png │ └── 9.png ├── LICENSE ├── README.md ├── ReAct └── PromptEngineeringReAct.ipynb ├── draft ├── AssertForCommonUse │ ├── agent.jfif │ └── agent2.png ├── assest │ ├── 0微信公众号 │ │ ├── small.png │ │ └── wechat.png │ ├── Agent │ │ ├── 0.png │ │ ├── 00.png │ │ └── 1.png │ ├── repo │ │ ├── 1.PNG │ │ ├── 2.PNG │ │ ├── 3.PNG │ │ ├── 4.jpg │ │ └── stars │ │ │ ├── 1.PNG │ │ │ ├── 2.png │ │ │ ├── 3.png │ │ │ ├── 4.png │ │ │ ├── 5.png │ │ │ ├── 6.png │ │ │ ├── 7.png │ │ │ ├── 8.png │ │ │ └── 9.png │ └── 在坚冰还盖着北海的时候，我看到了怒放的梅花 │ │ ├── 0.png │ │ ├── 1.png │ │ ├── 2.png │ │ └── 3.png └── md │ ├── repo.md │ ├── template.md │ ├── 在坚冰还盖着北海的时候，我看到了怒放的梅花.md │ └── 智能体Agent的5个层次.md └── pic └── 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