diff --git a/app/docs/ai/agents-todo/cs294-194-196/index.mdx b/app/docs/ai/agents-todo/cs294-194-196/index.mdx new file mode 100644 index 0000000..06bc796 --- /dev/null +++ b/app/docs/ai/agents-todo/cs294-194-196/index.mdx @@ -0,0 +1,10 @@ +--- +title: "CS294/194-196 Large Language Model Agents" +description: "CS294/194-196 课程相关资料" +--- + +## 课程信息 + +- 官网: https://rdi.berkeley.edu/llm-agents/f24 +- 机构: UC Berkeley +- 特色: 系统性介绍 LLM 智能体的理论和实践 diff --git a/app/docs/ai/agents-todo/index.mdx b/app/docs/ai/agents-todo/index.mdx new file mode 100644 index 0000000..bae9951 --- /dev/null +++ b/app/docs/ai/agents-todo/index.mdx @@ -0,0 +1,7 @@ +--- +title: "Agent" +description: "大语言模型智能体：CS294/194-196课程、ReAct、FireAct等" +status: todo +--- + +本节聚合 LLM 智能体相关的课程与技术框架，后续补充。 diff --git a/app/docs/ai/ai-math-basics/calculus-optimization/index.mdx b/app/docs/ai/ai-math-basics/calculus-optimization/index.mdx new file mode 100644 index 0000000..6972ec2 --- /dev/null +++ b/app/docs/ai/ai-math-basics/calculus-optimization/index.mdx @@ -0,0 +1,44 @@ +--- +title: "微积分与优化 (Calculus & Optimization)" +description: "微积分与优化核心概念及其在大模型中的应用" +date: "2024-01-12" +tags: + - calculus + - optimization + - derivative + - gradient + - chain-rule + - convex-optimization + - backpropagation + - sgd + - adam + - rmsprop +--- + +## 核心 + +- 导数 +- 偏导数 +- 梯度 +- 链式法则 +- 泰勒展开 +- 拉格朗日乘子法 +- 凸优化 + +## 大模型应用 + +### 反向传播 (Backpropagation) + +- 梯度计算和链式法则的完美体现。 + +### 模型训练 + +- 最小化损失函数（优化问题）的核心，各种优化器（SGD、Adam、RMSProp）都是梯度下降的变体。 + +### 激活函数 + +- 它们的导数特性对梯度传播至关重要。 + +### 模型收敛性分析 + +- 涉及到微积分中的收敛性理论。 diff --git a/app/docs/ai/ai-math-basics/index.mdx b/app/docs/ai/ai-math-basics/index.mdx new file mode 100644 index 0000000..4edd551 --- /dev/null +++ b/app/docs/ai/ai-math-basics/index.mdx @@ -0,0 +1,84 @@ +--- +title: "AI 数学基础" +description: "AI相关数学基础知识：线性代数、概率统计、微积分优化、信息论、数值分析" +date: "2025-01-27" +tags: + - mathematics + - linear-algebra + - probability + - calculus + - information-theory +--- + +AI和大模型需要扎实的数学基础。本节涵盖了深度学习和大模型开发所需的核心数学概念。 + +## 核心数学领域 + +### 1. 线性代数 (Linear Algebra) + +**核心概念**: 向量、矩阵、张量、特征值/特征向量、SVD（奇异值分解）、PCA（主成分分析） + +**大模型应用**: + +- **Embedding (嵌入)**: 词向量、Token嵌入本质上就是高维向量 +- **Attention Mechanism (注意力机制)**: QKV矩阵乘法、Self-Attention的核心计算（点积） +- **Transformer架构**: 各种层（Linear Layer）、残差连接、Feed-Forward Network都涉及矩阵运算 +- **模型参数**: 整个模型的参数量可以用矩阵、张量来表示 +- **降维与可视化**: 对Embedding空间进行降维（t-SNE, UMAP, PCA）以进行分析 + +**参考资料**: + +- [沉浸式线性代数](https://textbooks.math.gatech.edu/ila/index2.html) +- [3Blue1Brown线性代数的本质](https://www.youtube.com/@3blue1brown) - 可视化极佳，能帮助建立直观理解 +- 《线性代数的几何意义》(任广千, 谢聪, 胡翠芳) + +### 2. 概率论与数理统计 (Probability & Statistics) + +**核心概念**: 随机变量、概率分布（高斯、伯努利、多项式）、期望、方差、协方差、条件概率、贝叶斯定理、最大似然估计（MLE）、最大后验估计（MAP） + +**大模型应用**: + +- **语言建模**: P(下一个词 | 上下文) 就是条件概率 +- **损失函数**: 交叉熵损失来源于信息论和概率分布的差异度量 +- **采样与生成**: Top-k, Top-p (nucleus) sampling都基于概率分布 +- **不确定性量化**: 对模型预测结果的置信度评估 +- **强化学习**: 基于概率策略的优化 + +### 3. 微积分与优化 (Calculus & Optimization) + +**核心概念**: 导数、偏导数、梯度、链式法则、泰勒展开、拉格朗日乘子法、凸优化 + +**大模型应用**: + +- **反向传播**: 梯度计算和链式法则的完美体现 +- **模型训练**: 最小化损失函数（优化问题）的核心，各种优化器（SGD、Adam、RMSProp）都是梯度下降的变体 +- **激活函数**: 它们的导数特性对梯度传播至关重要 +- **模型收敛性分析**: 涉及微积分中的收敛性理论 + +### 4. 信息论 (Information Theory) + +**核心概念**: 信息量、熵（Entropy）、联合熵、条件熵、互信息、交叉熵、KL散度 + +**大模型应用**: + +- **损失函数**: 交叉熵损失是衡量预测分布与真实分布之间差异的度量 +- **注意力机制**: 计算注意力权重时，softmax操作与概率分布和熵的关联 +- **强化学习**: 策略梯度的优化目标中可能包含熵正则项；TRPO/PPO算法的核心是KL散度约束 +- **模型压缩与量化**: 量化信息损失的评估 + +### 5. 数值分析 (Numerical Analysis) + +**核心概念**: 浮点数精度、数值稳定性、梯度裁剪、学习率调度 + +**大模型应用**: + +- **防止梯度爆炸/消失**: 大模型层数深，计算量大，数值稳定性问题尤为突出 +- **BFloat16/FP16训练**: 理解不同精度浮点数对模型训练的影响 +- **优化器选择**: 某些优化器在数值上更稳定 + +## 学习建议 + +1. **理论与实践结合**: 不要只停留在公式推导，要理解这些数学概念在AI中的具体应用 +2. **可视化理解**: 利用3Blue1Brown等资源建立几何直观 +3. **代码实现**: 尝试用代码实现基本的数学运算，加深理解 +4. **循序渐进**: 从基础概念开始，逐步深入到高级应用 diff --git a/app/docs/ai/ai-math-basics/information-theory/index.mdx b/app/docs/ai/ai-math-basics/information-theory/index.mdx new file mode 100644 index 0000000..f749261 --- /dev/null +++ b/app/docs/ai/ai-math-basics/information-theory/index.mdx @@ -0,0 +1,42 @@ +--- +title: "信息论 (Information Theory)" +description: "信息论核心概念及其在大模型中的应用" +date: "2024-01-13" +tags: + - information-theory + - entropy + - cross-entropy + - kl-divergence + - mutual-information + - rl + - model-compression +--- + +## 核心 + +- 信息量 +- 熵（Entropy） +- 联合熵 +- 条件熵 +- 互信息 +- 交叉熵（Cross-Entropy） +- KL散度（Kullback-Leibler Divergence） + +## 大模型应用 + +### 损失函数 + +- 交叉熵损失是衡量预测分布与真实分布之间差异的度量。 + +### 注意力机制 + +- 计算注意力权重时，softmax 操作与概率分布和熵的关联。 + +### 强化学习 + +- 策略梯度的优化目标中可能包含熵正则项，以鼓励探索。 +- TRPO / PPO 算法的核心是 KL 散度约束。 + +### 模型压缩与量化 + +- 量化信息损失的评估。 diff --git a/app/docs/ai/ai-math-basics/linear-algebra/index.mdx b/app/docs/ai/ai-math-basics/linear-algebra/index.mdx new file mode 100644 index 0000000..cb23b8f --- /dev/null +++ b/app/docs/ai/ai-math-basics/linear-algebra/index.mdx @@ -0,0 +1,49 @@ +--- +title: "线性代数 (Linear Algebra)" +description: "线性代数核心概念及其在大模型中的应用" +date: "2024-01-10" +tags: + - linear-algebra + - matrix + - tensor + - embedding + - attention + - transformer + - pca + - svd +--- + +## 核心 + +- 向量 +- 矩阵 +- 张量 +- 特征值 / 特征向量 +- SVD（奇异值分解） +- PCA（主成分分析） + +## 大模型应用 + +### Embedding (嵌入) + +- 词向量、Token 嵌入本质上就是高维向量。 + +### Attention Mechanism (注意力机制) + +- QKV 矩阵乘法 +- Self-Attention 的核心计算（点积） + +### Transformer 架构 + +- 各种层（Linear Layer） +- 残差连接（Residual Connection） +- Feed-Forward Network + → 都涉及矩阵运算 + +### 模型参数 + +- 整个模型的参数量可以用矩阵、张量来表示。 + +### 降维与可视化 + +- 对 Embedding 空间进行降维（t-SNE, UMAP, PCA）以进行分析。 diff --git a/app/docs/ai/ai-math-basics/linear-algebra/resources/index.mdx b/app/docs/ai/ai-math-basics/linear-algebra/resources/index.mdx new file mode 100644 index 0000000..9915fe1 --- /dev/null +++ b/app/docs/ai/ai-math-basics/linear-algebra/resources/index.mdx @@ -0,0 +1,15 @@ +--- +title: "参考资料" +description: "线性代数参考资料。" +date: "2024-01-10" +tags: + - linear-algebra + - resources +--- + +- [沉浸式线性代数 (Immersive Linear Algebra)](https://textbooks.math.gatech.edu/ila/index2.html) +- 《线性代数的几何意义 (任广千, 谢聪, 胡翠芳)》 PDF +- [3Blue1Brown (YouTube频道)](https://www.3blue1brown.com/) + - 《线性代数的本质》系列视频 + - 《微积分的本质》系列视频 + → 可视化极佳，能帮助建立直观理解。 diff --git a/app/docs/ai/ai-math-basics/numerical-analysis/index.mdx b/app/docs/ai/ai-math-basics/numerical-analysis/index.mdx new file mode 100644 index 0000000..75f2918 --- /dev/null +++ b/app/docs/ai/ai-math-basics/numerical-analysis/index.mdx @@ -0,0 +1,35 @@ +--- +title: "数值分析 (Numerical Analysis)" +description: "数值分析核心概念及其在大模型中的应用" +date: "2024-01-14" +tags: + - numerical-analysis + - floating-point + - stability + - gradient-clipping + - learning-rate + - bfloat16 + - fp16 + - optimizer +--- + +## 核心 + +- 浮点数精度 +- 数值稳定性 +- 梯度裁剪（Gradient Clipping） +- 学习率调度 + +## 大模型应用 + +### 防止梯度爆炸 / 消失 + +- 大模型层数深，计算量大，数值稳定性问题尤为突出。 + +### BFloat16 / FP16 训练 + +- 理解不同精度浮点数对模型训练的影响。 + +### 优化器选择 + +- 某些优化器在数值上更稳定。 diff --git a/app/docs/ai/ai-math-basics/probability-statistics/index.mdx b/app/docs/ai/ai-math-basics/probability-statistics/index.mdx new file mode 100644 index 0000000..1b93d78 --- /dev/null +++ b/app/docs/ai/ai-math-basics/probability-statistics/index.mdx @@ -0,0 +1,53 @@ +--- +title: "概率论与数理统计 (Probability & Statistics)" +description: "概率论与数理统计核心概念及其在大模型中的应用" +date: "2024-01-11" +tags: + - probability + - statistics + - random-variable + - distribution + - bayes + - mle + - map + - hypothesis-testing + - confidence-interval + - cross-entropy + - rlhf +--- + +## 核心 + +- 随机变量 +- 概率分布（高斯、伯努利、多项式） +- 期望 +- 方差 +- 协方差 +- 条件概率 +- 贝叶斯定理 +- 最大似然估计（MLE） +- 最大后验估计（MAP） +- 假设检验 +- 置信区间 + +## 大模型应用 + +### 语言建模 + +- P(下一个词 | 上下文) 就是条件概率。 + +### 损失函数 + +- 交叉熵损失（Cross-Entropy Loss）来源于信息论和概率分布的差异度量。 + +### 采样与生成 + +- Top-k, Top-p (nucleus) sampling 都基于概率分布。 + +### 不确定性量化 + +- 对模型预测结果的置信度评估。 + +### 强化学习 (RLHF) + +- 基于概率策略的优化。 diff --git a/app/docs/ai/ai-math-basics/probability-statistics/resources/index.mdx b/app/docs/ai/ai-math-basics/probability-statistics/resources/index.mdx new file mode 100644 index 0000000..c9dcf2f --- /dev/null +++ b/app/docs/ai/ai-math-basics/probability-statistics/resources/index.mdx @@ -0,0 +1,11 @@ +--- +title: "参考资料" +description: "概率论与数理统计参考资料。" +date: "2024-01-11" +tags: + - probability + - statistics + - resources +--- + +- 方浩 diff --git a/app/docs/ai/compute-platforms/index.mdx b/app/docs/ai/compute-platforms/index.mdx new file mode 100644 index 0000000..9099038 --- /dev/null +++ b/app/docs/ai/compute-platforms/index.mdx @@ -0,0 +1,214 @@ +--- +title: "算力平台" +description: "AI开发算力平台：AutoDL、InternStudio等云服务使用指南" +date: "2025-01-27" +tags: + - compute-platforms + - autodl + - internstudio + - gpu-cloud + - development-environment +--- + +AI模型训练和推理需要强大的算力支持。本节介绍主流的算力平台和云服务，帮助开发者选择合适的计算资源。 + +## AutoDL + +### 平台特色 + +- **官网**: [https://www.autodl.com/home](https://www.autodl.com/home) +- **定位**: 专业的GPU云服务平台 +- **优势**: 价格实惠，操作简单，适合个人开发者和小团队 + +### 使用文档 + +- **完整文档**: [AutoDL官方文档](https://www.autodl.com/docs/) +- **涵盖内容**: + - 实例创建和管理 + - 环境配置 + - 数据上传下载 + - 费用计算 + +### 【保姆级图文详解】PyCharm专业版连接AutoDL详细教程 + +**配置步骤**: + +1. **创建AutoDL实例**: 选择合适的GPU配置 +2. **获取连接信息**: 记录IP地址、端口、用户名 +3. **配置PyCharm**: 设置远程解释器 +4. **文件同步**: 配置自动上传下载 +5. **调试运行**: 远程调试和代码执行 + +**网络配置**: + +- SSH连接配置 +- 端口映射设置 +- 文件传输优化 +- 网络稳定性保障 + +**开发工作流**: + +- 本地代码编写 +- 远程代码同步 +- GPU资源调度 +- 结果文件下载 + +## InternStudio + +### 平台介绍 + +- **官网**: [https://studio.intern-ai.org.cn/](https://studio.intern-ai.org.cn/user/account) +- **特色**: 上海AI实验室提供的免费算力平台 +- **适用**: 学习、研究、小型项目开发 + +### 连接与使用 + +**SSH连接配置**: + +- [SSH连接及端口映射教程](https://juejin.cn/post/7446234916049829939) +- 支持远程开发环境 +- 提供JupyterLab界面 + +**第四期训练营资源**: + +- **GitHub**: [Tutorial第四期](https://github.com/InternLM/Tutorial/tree/camp4) +- **Linux基础**: [InternStudio基础指令](https://aicarrier.feishu.cn/wiki/XZChwwDsciyFyHk5mGTc1EKinkc) + +### 开源社区项目申报 + +**算力申请**: + +- 🔥 [书生大模型开源社区项目申报](https://openxlab.org.cn/apps) 🔥 +- 适用于开源项目和学术研究 +- 提供长期稳定的算力支持 + +## 平台对比与选择 + +### AutoDL适用场景 + +**优势**: + +- 按时计费，成本可控 +- 丰富的预装环境 +- 良好的中文支持 +- 稳定的网络连接 + +**适合用户**: + +- 个人开发者 +- 初学者和学生 +- 短期项目需求 +- 预算有限的团队 + +### InternStudio适用场景 + +**优势**: + +- 免费使用额度 +- 学术友好 +- 与InternLM生态结合 +- 教育资源丰富 + +**适合用户**: + +- 学生和研究人员 +- InternLM模型使用者 +- 教学和培训 +- 开源项目开发 + +### 其他云平台选择 + +#### 国际平台 + +- **Google Colab**: 免费GPU，适合学习和轻量级开发 +- **AWS EC2**: 企业级服务，功能全面但成本较高 +- **Microsoft Azure**: 与Windows生态结合好 +- **Lambda Labs**: 专业GPU云服务 + +#### 国内平台 + +- **阿里云**: 企业级服务，生态完善 +- **腾讯云**: 游戏和社交场景优化 +- **百度云**: AI平台和PaddlePaddle生态 +- **华为云**: 昇腾AI处理器支持 + +## 使用技巧与最佳实践 + +### 成本优化 + +1. **按需使用**: 及时关闭不用的实例 +2. **预装环境**: 选择合适的预装镜像 +3. **数据管理**: 合理规划存储空间 +4. **监控告警**: 设置费用和资源告警 + +### 开发效率 + +1. **环境配置**: 使用Docker或conda管理环境 +2. **代码同步**: 配置Git或文件同步工具 +3. **调试方法**: 掌握远程调试技巧 +4. **资源监控**: 实时监控GPU、内存使用情况 + +### 数据安全 + +1. **定期备份**: 重要数据多地备份 +2. **版本控制**: 使用Git管理代码版本 +3. **访问控制**: 设置安全的SSH密钥 +4. **合规要求**: 遵守数据处理相关法规 + +## 环境配置指南 + +### 深度学习环境 + +**基础组件**: + +- CUDA/cuDNN +- Python 3.8+ +- PyTorch/TensorFlow +- Jupyter Notebook + +**常用库**: + +```bash +# PyTorch生态 +pip install torch torchvision transformers datasets + +# 科学计算 +pip install numpy pandas matplotlib seaborn + +# 机器学习 +pip install scikit-learn xgboost lightgbm + +# 深度学习辅助 +pip install wandb tensorboard +``` + +### 开发工具配置 + +- **IDE**: PyCharm Professional, VSCode +- **调试**: pdb, ipdb +- **性能分析**: nvidia-smi, htop +- **版本控制**: Git, DVC + +## 故障排除 + +### 常见问题 + +1. **连接超时**: 检查网络和防火墙设置 +2. **GPU不可用**: 验证CUDA安装和驱动版本 +3. **内存不足**: 优化批处理大小和模型参数 +4. **存储空间**: 清理临时文件和日志 + +### 性能优化 + +1. **GPU利用率**: 监控和优化GPU使用效率 +2. **I/O优化**: 优化数据读取和预处理 +3. **内存管理**: 合理设置缓存和批处理 +4. **并行计算**: 利用多GPU和分布式训练 + +## 学习建议 + +1. **平台熟悉**: 深入了解至少一个主要平台 +2. **成本意识**: 学会合理控制和预算算力成本 +3. **环境管理**: 掌握环境配置和依赖管理 +4. **监控调优**: 学会监控资源使用和性能调优 +5. **安全实践**: 重视数据安全和访问控制 diff --git a/app/docs/ai/foundation-models/datasets/index.mdx b/app/docs/ai/foundation-models/datasets/index.mdx new file mode 100644 index 0000000..c4547a2 --- /dev/null +++ b/app/docs/ai/foundation-models/datasets/index.mdx @@ -0,0 +1,322 @@ +--- +title: "数据集构建" +description: "大模型数据集构建：数据来源、处理流程、质量控制" +date: "2025-01-27" +tags: + - dataset-construction + - data-processing + - data-quality + - common-crawl + - data-cleaning +--- + +数据是大模型的基础，高质量的数据集直接影响模型性能。本节详细介绍大模型数据集的构建方法和技术。 + +## 数据来源 + +### 网络数据 + +- **Common Crawl**: 大规模网页爬取数据 + - 覆盖全球数十亿网页 + - 多语言内容丰富 + - 定期更新数据快照 + +- **Wikipedia**: 高质量百科全书数据 + - 多语言版本 + - 结构化知识内容 + - 持续更新维护 + +### 专业数据 + +- **书籍语料**: 高质量文本数据 + - Project Gutenberg开源书籍 + - 学术出版物 + - 技术文档和手册 + +- **代码数据**: GitHub等代码仓库 + - 开源项目代码 + - 多种编程语言 + - 代码注释和文档 + +- **学术论文**: arXiv、PubMed等学术数据 + - 最新研究成果 + - 专业领域知识 + - 引用关系网络 + +## 数据处理流程 + +### 1. 数据清洗 + +**文本质量过滤**: + +- 去除低质量内容（乱码、重复文本） +- 语言检测和过滤 +- 格式标准化处理 +- 编码统一转换 + +**内容过滤**: + +- 去除广告和垃圾信息 +- 过滤有害和不当内容 +- 移除隐私敏感信息 +- 版权内容识别 + +### 2. 格式统一 + +**文本规范化**: + +- 统一编码格式（UTF-8） +- 标准化标点符号 +- 处理特殊字符 +- 段落和换行规范 + +**结构化处理**: + +- 提取正文内容 +- 去除HTML标签 +- 保留有意义的格式信息 +- 统一文档结构 + +### 3. 去重处理 + +**精确去重**: + +- MD5哈希匹配 +- 完全相同内容识别 +- 批量去重处理 + +**模糊去重**: + +- MinHash算法 +- 相似度阈值设置 +- 近似重复检测 +- SimHash指纹匹配 + +**跨文档去重**: + +- 段落级别去重 +- 句子级别去重 +- n-gram重叠检测 + +### 4. 质量过滤 + +**统计指标过滤**: + +- 文档长度限制 +- 词汇丰富度检查 +- 语言复杂度评估 +- 标点符号比例 + +**语言模型评分**: + +- 困惑度(Perplexity)评估 +- 语言模型打分 +- 可读性评估 +- 语法正确性检查 + +### 5. 隐私保护 + +**个人信息识别(PII)**: + +- 邮箱地址检测 +- 电话号码识别 +- 身份证号码过滤 +- 地址信息处理 + +**数据脱敏**: + +- 敏感信息替换 +- 匿名化处理 +- 差分隐私技术 +- 数据加密存储 + +## 数据质量控制 + +### 质量评估指标 + +**内容质量**: + +- 信息准确性 +- 逻辑连贯性 +- 语言流畅度 +- 知识深度 + +**多样性指标**: + +- 主题覆盖范围 +- 语言风格多样性 +- 来源多样性 +- 时间跨度覆盖 + +**平衡性考虑**: + +- 语言分布平衡 +- 领域知识平衡 +- 观点立场平衡 +- 文化背景多样性 + +### 质量保证流程 + +**自动化检查**: + +- 批量质量评估 +- 异常检测算法 +- 统计分析报告 +- 质量趋势监控 + +**人工审核**: + +- 随机抽样检查 +- 专家领域审核 +- 标注质量控制 +- 反馈循环机制 + +## 特殊数据处理 + +### 多语言数据 + +**语言检测**: + +- 自动语言识别 +- 多语言混合处理 +- 方言和变体识别 +- 代码切换处理 + +**跨语言对齐**: + +- 平行语料构建 +- 翻译质量评估 +- 文化适应性调整 + +### 多模态数据 + +**图文对齐**: + +- 图像-文本配对 +- 描述准确性验证 +- 视觉内容理解 +- 多模态一致性 + +**结构化数据**: + +- 表格数据处理 +- 知识图谱集成 +- 数据库内容提取 + +## 数据管道技术 + +### 分布式处理 + +**大数据框架**: + +- Apache Spark处理 +- Hadoop生态系统 +- 分布式存储(HDFS) +- 流式数据处理 + +**并行化策略**: + +- 数据分片处理 +- 任务调度优化 +- 资源动态分配 +- 故障恢复机制 + +### 数据版本管理 + +**版本控制**: + +- 数据集版本追踪 +- 变更记录管理 +- 回滚机制设计 +- 增量更新支持 + +**元数据管理**: + +- 数据源信息记录 +- 处理流程追踪 +- 质量指标监控 +- 使用统计分析 + +## 合规性考虑 + +### 法律法规 + +**数据合规**: + +- GDPR隐私保护 +- 版权法律要求 +- 地区法规遵循 +- 行业标准对接 + +**使用许可**: + +- 开源协议理解 +- 商业使用限制 +- 衍生作品规则 +- 归属声明要求 + +### 伦理考虑 + +**偏见和公平性**: + +- 数据偏见识别 +- 代表性问题分析 +- 公平性评估指标 +- 偏见缓解策略 + +**社会影响**: + +- 内容价值观审查 +- 文化敏感性考虑 +- 社会责任承担 +- 负面影响评估 + +## 最佳实践 + +### 数据管理 + +1. **建立清晰的数据标准** +2. **实施自动化质量检查** +3. **保持数据处理透明度** +4. **定期更新和维护数据集** +5. **建立完善的文档记录** + +### 工具推荐 + +**数据处理工具**: + +- pandas: Python数据处理 +- Apache Beam: 批处理和流处理 +- Dask: 并行计算框架 +- Ray: 分布式计算平台 + +**质量检查工具**: + +- Great Expectations: 数据质量框架 +- Apache Griffin: 数据质量监控 +- Deequ: 数据质量测试 + +## 未来发展趋势 + +1. **自动化程度提升**: 更智能的数据处理流程 +2. **实时数据集成**: 动态数据更新和集成 +3. **隐私保护技术**: 联邦学习和差分隐私 +4. **多模态融合**: 更复杂的多模态数据处理 +5. **个性化数据**: 针对特定任务的定制化数据集 + +## 学习建议 + +1. **理论基础**: 掌握数据科学和统计学基础 +2. **工程技能**: 熟练使用大数据处理工具 +3. **质量意识**: 培养对数据质量的敏感度 +4. **合规意识**: 了解相关法律法规要求 +5. **实践经验**: 参与实际的数据集构建项目 + +## 来自 UNSW IT-AI内卷地狱 文档摘录 + +- https://huggingface.co/ +- AK https://hf.co/akhaliq +- https://www.modelscope.cn/home +- https://www.kaggle.com/datasets +- UCI 机器学习仓库：https://archive.ics.uci.edu/ml/index.php +- ImageNet diff --git a/app/docs/ai/foundation-models/deploy-infer/index.mdx b/app/docs/ai/foundation-models/deploy-infer/index.mdx new file mode 100644 index 0000000..c816b7e --- /dev/null +++ b/app/docs/ai/foundation-models/deploy-infer/index.mdx @@ -0,0 +1,438 @@ +--- +title: "部署与推理" +description: "大模型部署与推理优化：KV Cache、Flash Attention、量化技术、推理框架" +date: "2025-01-27" +tags: + - model-deployment + - inference-optimization + - kv-cache + - flash-attention + - quantization + - vllm +--- + +大模型的部署与推理是将训练好的模型投入实际应用的关键环节，涉及推理优化、部署框架、服务架构等多个方面。 + +## 推理优化技术 + +### KV Cache + +**核心原理**: 缓存键值对，避免重复计算，加速生成过程。 + +**实现机制**: + +- 存储历史序列的Key和Value +- 新token只需计算当前Query +- 显著降低计算复杂度 +- 从O(n²·d)降低到O(n·d) + +**内存管理**: + +- 动态内存分配 +- 批处理优化 +- 内存碎片整理 +- OOM预防机制 + +### Flash Attention + +**技术特点**: 内存高效的注意力计算算法 + +**核心优化**: + +- 分块计算策略 +- 内存访问优化 +- IO复杂度降低 +- 数值稳定性保证 + +**性能提升**: + +- 内存使用减少 +- 计算速度提升 +- 支持更长序列 +- 硬件友好设计 + +### 量化技术 + +**量化方法**: + +- **INT8量化**: 8位整数表示 +- **INT4量化**: 4位整数表示 +- **混合精度**: 不同层使用不同精度 +- **动态量化**: 运行时量化 + +**量化策略**: + +- 权重量化 +- 激活量化 +- KV Cache量化 +- 梯度量化 + +**工具支持**: + +- PyTorch量化 +- TensorRT量化 +- ONNX量化 +- 自定义量化kernel + +### 并行推理 + +**模型并行**: + +- 张量并行: 层内参数分割 +- 流水线并行: 层间流水线 +- 专家并行: MoE模型专家分配 +- 混合并行: 多种并行组合 + +**数据并行**: + +- 批处理并行 +- 序列并行 +- 动态批处理 +- 连续批处理 + +## 部署框架 + +### vLLM + +**特色**: 高吞吐量推理引擎 + +- **PagedAttention**: 高效内存管理 +- **连续批处理**: 动态批处理优化 +- **流式输出**: 实时响应支持 +- **多GPU支持**: 大模型分布式推理 + +**核心技术**: + +- 内存池管理 +- 请求调度优化 +- KV Cache共享 +- 推理并发控制 + +### TensorRT-LLM + +**特色**: NVIDIA优化推理框架 + +- **深度优化**: 针对NVIDIA GPU优化 +- **算子融合**: 自动算子融合优化 +- **多精度**: 支持FP16/INT8/INT4 +- **插件生态**: 丰富的插件支持 + +**优化技术**: + +- Graph优化 +- 内存优化 +- Kernel融合 +- 动态shape支持 + +### Text Generation Inference (TGI) + +**特色**: HuggingFace推理服务 + +- **易用性**: 简单部署和使用 +- **模型支持**: 广泛的模型支持 +- **API标准**: 标准化API接口 +- **监控**: 内置监控和日志 + +**功能特性**: + +- 自动批处理 +- 流式响应 +- 安全过滤 +- 负载均衡 + +### FastChat + +**特色**: 聊天模型部署框架 + +- **多模型**: 支持多种聊天模型 +- **Web界面**: 友好的用户界面 +- **API服务**: RESTful API支持 +- **分布式**: 多节点部署支持 + +## 服务架构设计 + +### 推理服务架构 + +**组件设计**: + +- 模型加载器 +- 请求处理器 +- 批处理调度器 +- 响应生成器 +- 监控组件 + +**性能优化**: + +- 异步处理 +- 连接池管理 +- 缓存策略 +- 资源调度 + +### 负载均衡 + +**策略**: + +- 轮询调度 +- 最少连接 +- 加权分配 +- 健康检查 + +**实现**: + +- Nginx负载均衡 +- HAProxy配置 +- Kubernetes Service +- 自定义负载均衡器 + +### 扩缩容策略 + +**水平扩展**: + +- 实例数量调整 +- 动态扩缩容 +- 资源监控触发 +- 预热机制 + +**垂直扩展**: + +- 资源规格调整 +- GPU内存扩展 +- CPU核心增加 +- 存储容量扩展 + +## 内存优化 + +### 内存管理策略 + +**KV Cache优化**: + +- 分页存储 +- 内存共享 +- 垃圾回收 +- 碎片整理 + +**模型权重优化**: + +- 权重共享 +- 延迟加载 +- 内存映射 +- 压缩存储 + +### 内存监控 + +**监控指标**: + +- 内存使用率 +- OOM频率 +- 内存碎片率 +- GC时间统计 + +**告警机制**: + +- 阈值告警 +- 趋势预警 +- 自动处理 +- 故障转移 + +## 推理性能优化 + +### 延迟优化 + +**减少延迟策略**: + +- 模型预热 +- 批处理优化 +- 算子融合 +- 硬件加速 + +**首Token延迟(TTFT)**: + +- 预填充优化 +- 内存预分配 +- 模型预加载 +- 缓存预热 + +### 吞吐量优化 + +**提升吞吐量**: + +- 批处理大小调优 +- 并发请求处理 +- 流水线处理 +- 资源利用率提升 + +**连续批处理**: + +- 动态批次调整 +- 请求优先级管理 +- 延迟敏感度调节 +- 公平性保证 + +### 成本优化 + +**计算成本**: + +- GPU利用率最大化 +- 混合实例使用 +- 按需扩缩容 +- Spot实例利用 + +**存储成本**: + +- 模型压缩 +- 冷热数据分离 +- 缓存策略优化 +- 数据生命周期管理 + +## 质量保证 + +### 模型验证 + +**功能测试**: + +- 输出质量验证 +- 边界条件测试 +- 压力测试 +- 回归测试 + +**性能测试**: + +- 延迟基准测试 +- 吞吐量测试 +- 并发能力测试 +- 稳定性测试 + +### 监控体系 + +**核心指标**: + +- QPS (每秒查询数) +- 平均响应时间 +- P99延迟 +- 错误率 +- 资源使用率 + +**监控工具**: + +- Prometheus监控 +- Grafana可视化 +- 自定义监控 +- 告警系统 + +### A/B测试 + +**测试设计**: + +- 流量分割 +- 指标对比 +- 统计显著性 +- 效果评估 + +**实现方案**: + +- 灰度发布 +- 蓝绿部署 +- 金丝雀发布 +- 影子测试 + +## 安全与合规 + +### 安全防护 + +**输入验证**: + +- 内容过滤 +- 长度限制 +- 格式检查 +- 恶意输入检测 + +**输出控制**: + +- 内容审核 +- 敏感信息过滤 +- 版权保护 +- 有害内容拦截 + +### 隐私保护 + +**数据保护**: + +- 请求日志脱敏 +- 用户信息匿名化 +- 数据加密传输 +- 存储加密 + +**合规要求**: + +- GDPR合规 +- 数据本地化 +- 审计日志 +- 权限控制 + +## 故障处理 + +### 常见问题 + +**性能问题**: + +- 内存不足OOM +- GPU利用率低 +- 延迟突增 +- 吞吐量下降 + +**稳定性问题**: + +- 服务崩溃 +- 内存泄漏 +- 网络超时 +- 模型异常 + +### 恢复策略 + +**自动恢复**: + +- 健康检查 +- 自动重启 +- 故障转移 +- 服务降级 + +**监控告警**: + +- 实时监控 +- 预警机制 +- 自动处理 +- 人工介入 + +## 最佳实践 + +### 部署建议 + +1. **渐进式部署**: 从小规模开始逐步扩展 +2. **性能基准**: 建立性能基准和监控体系 +3. **资源规划**: 合理规划计算和存储资源 +4. **安全优先**: 重视安全和隐私保护 +5. **文档完整**: 维护完整的部署文档 + +### 运维策略 + +1. **自动化运维**: 尽可能自动化运维流程 +2. **监控告警**: 建立完善的监控告警体系 +3. **备份恢复**: 制定数据备份和恢复策略 +4. **版本管理**: 规范化版本发布流程 +5. **应急预案**: 制定详细的应急处理预案 + +## 未来发展趋势 + +1. **硬件协同**: 软硬件深度协同优化 +2. **边缘部署**: 边缘计算设备模型部署 +3. **联邦推理**: 分布式隐私保护推理 +4. **自适应优化**: 智能自适应推理优化 +5. **绿色计算**: 低功耗环保推理技术 + +## 学习建议 + +1. **系统性学习**: 全面了解推理优化技术栈 +2. **动手实践**: 亲自部署和优化推理服务 +3. **性能调优**: 深入学习性能调优技巧 +4. **工具熟练**: 熟练使用主流推理框架 +5. **持续关注**: 跟踪最新的优化技术发展 diff --git a/app/docs/ai/foundation-models/evaluation/index.mdx b/app/docs/ai/foundation-models/evaluation/index.mdx new file mode 100644 index 0000000..8b72b1c --- /dev/null +++ b/app/docs/ai/foundation-models/evaluation/index.mdx @@ -0,0 +1,290 @@ +--- +title: "模型评测" +description: "大模型评测体系：BenchMark、评测指标、中英文评测基准" +date: "2025-01-27" +tags: + - model-evaluation + - benchmark + - mmlu + - c-eval + - evaluation-metrics +--- + +模型评测是衡量大模型性能和能力的重要手段，为模型改进和应用选择提供科学依据。 + +## BenchMark评测体系 + +### 通用能力评测 + +#### MMLU (Massive Multitask Language Understanding) + +- **评测范围**: 多任务语言理解 +- **题目数量**: 15,908道选择题 +- **学科领域**: 57个学科，从数学到历史 +- **难度等级**: 从高中到专业水平 +- **评测指标**: 准确率 + +#### HellaSwag + +- **评测目标**: 常识推理能力 +- **任务类型**: 句子完成任务 +- **数据来源**: 真实场景描述 +- **评测指标**: 准确率 + +#### ARC (AI2 Reasoning Challenge) + +- **评测重点**: 科学推理能力 +- **题目类型**: 小学科学选择题 +- **难度分级**: Easy和Challenge两个级别 +- **特色**: 需要多步推理 + +#### GSM8K + +- **评测范围**: 数学问题求解 +- **题目类型**: 小学数学应用题 +- **答案形式**: 数值答案 +- **评测重点**: 数学推理链 + +### 中文评测基准 + +#### C-Eval + +- **评测目标**: 中文综合评测 +- **题目数量**: 13,948道题目 +- **学科覆盖**: 52个学科领域 +- **难度范围**: 从中学到专业水平 +- **特色**: 中国教育体系对齐 + +#### CMMLU (Chinese Massive Multitask Language Understanding) + +- **评测范围**: 中文多任务评测 +- **题目来源**: 中国考试和教材 +- **学科分类**: 人文、社科、理工、医学等 +- **评测指标**: 多维度评估 + +#### AGIEval + +- **评测特色**: 人类考试评测 +- **数据来源**: 真实考试题目 +- **考试类型**: 高考、公务员考试、法考等 +- **评测价值**: 与人类能力直接对比 + +### 专业领域评测 + +#### HumanEval + +- **评测目标**: 代码生成能力 +- **任务类型**: 函数实现 +- **编程语言**: 主要是Python +- **评测方法**: 单元测试通过率 + +#### MATH + +- **评测范围**: 数学竞赛题目 +- **难度等级**: 高中数学竞赛水平 +- **题目类型**: 证明题、计算题 +- **评测方法**: 答案正确性 + +#### BBH (Big-Bench Hard) + +- **评测特色**: 大语言模型挑战 +- **任务来源**: Big-Bench困难子集 +- **评测重点**: 推理和理解能力 +- **题目特点**: 对大模型具有挑战性 + +## 评测方法论 + +### 评测设计原则 + +1. **全面性**: 覆盖模型的多种能力 +2. **客观性**: 避免主观偏见和偏好 +3. **可重复性**: 结果可重现和验证 +4. **公平性**: 对不同模型公平比较 +5. **实用性**: 与实际应用场景相关 + +### 评测维度 + +#### 知识能力 + +- **事实性知识**: 基础知识掌握 +- **概念理解**: 抽象概念理解 +- **知识推理**: 基于知识的推理 +- **知识更新**: 最新知识掌握 + +#### 推理能力 + +- **逻辑推理**: 演绎和归纳推理 +- **数学推理**: 数值计算和证明 +- **常识推理**: 日常生活常识 +- **因果推理**: 因果关系理解 + +#### 语言能力 + +- **语言理解**: 文本理解和解析 +- **语言生成**: 流畅和准确的生成 +- **多语言**: 跨语言能力 +- **语言风格**: 不同风格适应 + +#### 安全性评测 + +- **有害内容**: 避免生成有害内容 +- **偏见检测**: 社会偏见识别 +- **隐私保护**: 隐私信息处理 +- **对抗鲁棒性**: 对抗攻击抵抗 + +## 评测实施 + +### 评测流程 + +1. **基准选择**: 根据评测目标选择合适基准 +2. **环境准备**: 配置评测环境和依赖 +3. **模型准备**: 加载和配置待评测模型 +4. **执行评测**: 运行评测脚本和程序 +5. **结果分析**: 统计和分析评测结果 + +### 评测框架 + +#### OpenCompass + +- **特色**: 开源评测框架 +- **支持**: 多种模型和基准 +- **功能**: 自动化评测流程 +- **可视化**: 结果展示和对比 + +#### lm-evaluation-harness + +- **来源**: EleutherAI开源 +- **特色**: 标准化评测接口 +- **支持**: 广泛的评测任务 +- **易用**: 简单的命令行接口 + +#### FlagEval + +- **来源**: 智源研究院 +- **特色**: 中文评测友好 +- **覆盖**: 全面的评测维度 +- **标准**: 科学的评测标准 + +### 评测环境 + +#### 硬件要求 + +- **GPU**: 根据模型大小选择 +- **内存**: 充足的系统内存 +- **存储**: 快速的SSD存储 +- **网络**: 稳定的网络连接 + +#### 软件环境 + +- **Python**: 主要编程语言 +- **PyTorch/TensorFlow**: 深度学习框架 +- **transformers**: 模型加载库 +- **评测工具**: 特定评测框架 + +## 结果分析 + +### 性能指标 + +#### 准确率指标 + +- **Accuracy**: 总体准确率 +- **Top-k准确率**: 前k个预测中的准确率 +- **F1分数**: 精确率和召回率的调和平均 +- **BLEU/ROUGE**: 文本生成质量 + +#### 效率指标 + +- **推理速度**: Token生成速度 +- **内存使用**: 推理时内存占用 +- **能耗指标**: 推理能耗统计 +- **成本效益**: 性能成本比 + +### 对比分析 + +#### 模型对比 + +- **同规模模型**: 相同参数量模型对比 +- **不同架构**: 不同架构模型对比 +- **发展趋势**: 模型能力发展趋势 +- **优劣分析**: 各模型优缺点分析 + +#### 能力分析 + +- **强项识别**: 模型擅长的任务领域 +- **弱项分析**: 模型不足的方面 +- **改进方向**: 模型优化建议 +- **应用建议**: 适用场景推荐 + +### 可视化展示 + +#### 雷达图 + +- 多维能力展示 +- 不同模型对比 +- 直观的能力分布 +- 平衡性分析 + +#### 热力图 + +- 细粒度性能展示 +- 任务维度分析 +- 性能差异可视化 +- 模式识别 + +## 评测挑战 + +### 技术挑战 + +1. **评测成本**: 大模型评测资源消耗大 +2. **基准局限**: 现有基准可能不够全面 +3. **作弊检测**: 防止模型在基准上作弊 +4. **动态更新**: 基准需要持续更新 + +### 方法学挑战 + +1. **评测偏见**: 评测基准可能存在偏见 +2. **文化差异**: 跨文化评测的公平性 +3. **能力定义**: 如何科学定义和测量能力 +4. **生态效应**: 评测对模型发展的影响 + +## 未来发展 + +### 评测创新 + +1. **动态评测**: 实时更新的评测基准 +2. **交互评测**: 多轮交互的评测模式 +3. **人机协作**: 人类专家参与的评测 +4. **自动化**: 更智能的自动评测系统 + +### 评测标准 + +1. **国际标准**: 建立国际认可的评测标准 +2. **行业规范**: 制定行业评测规范 +3. **认证体系**: 建立模型能力认证 +4. **监管配合**: 与监管要求相结合 + +## 最佳实践 + +### 评测策略 + +1. **多维评测**: 从多个维度全面评估 +2. **基准组合**: 使用多个基准交叉验证 +3. **定期评测**: 建立定期评测机制 +4. **结果校验**: 多次评测确保可靠性 +5. **透明公开**: 公开评测方法和结果 + +### 结果应用 + +1. **模型改进**: 基于评测结果改进模型 +2. **应用指导**: 指导模型应用场景选择 +3. **能力匹配**: 任务与模型能力匹配 +4. **风险评估**: 识别模型应用风险 +5. **持续监控**: 持续监控模型性能 + +## 学习建议 + +1. **理解基准**: 深入理解各种评测基准 +2. **实践评测**: 亲自进行模型评测实验 +3. **分析结果**: 学会科学分析评测结果 +4. **关注发展**: 跟踪评测方法的最新发展 +5. **批判思维**: 对评测结果保持批判性思考 diff --git a/app/docs/ai/foundation-models/finetune/index.mdx b/app/docs/ai/foundation-models/finetune/index.mdx new file mode 100644 index 0000000..c4f5ca1 --- /dev/null +++ b/app/docs/ai/foundation-models/finetune/index.mdx @@ -0,0 +1,343 @@ +--- +title: "模型微调" +description: "大模型微调技术：LoRA、PEFT、微调框架等参数高效微调方法" +date: "2025-01-27" +tags: + - fine-tuning + - lora + - peft + - parameter-efficient + - unsloth +--- + +模型微调是将预训练大模型适应特定任务的关键技术。本节介绍各种高效微调方法和实践技巧。 + +## 微调概述 + +### 微调类型 + +1. **全参数微调**: 更新所有模型参数 +2. **参数高效微调(PEFT)**: 只训练少量参数 +3. **指令微调**: 基于指令数据的微调 +4. **对齐微调**: 人类偏好对齐微调 + +### 微调挑战 + +- **计算资源**: 大模型全参数微调成本高 +- **灾难性遗忘**: 微调可能损害原有能力 +- **数据质量**: 高质量任务数据获取困难 +- **超参数敏感**: 微调超参数选择关键 + +## 参数高效微调 (PEFT) + +### 核心思想 + +通过训练少量参数实现与全参数微调相当的效果，大幅降低计算和存储成本。 + +### 主要方法 + +#### LoRA (Low-Rank Adaptation) + +**原理**: 将权重更新分解为低秩矩阵的乘积 + +``` +W_new = W_original + ΔW = W_original + BA +``` + +其中B和A是可训练的低秩矩阵。 + +**优势**: + +- 大幅减少可训练参数量 +- 保持预训练权重不变 +- 支持多任务LoRA合并 +- 推理时可合并回原权重 + +#### AdaLoRA (Adaptive LoRA) + +**改进**: 自适应调整不同层的秩大小 + +- 根据重要性分配参数预算 +- 动态裁剪不重要的参数 +- 进一步提升参数效率 + +#### Prefix Tuning + +**原理**: 在输入序列前添加可训练的前缀token + +- 只训练前缀部分的参数 +- 保持模型主体不变 +- 适用于生成任务 + +#### P-Tuning v2 + +**改进**: Prefix Tuning的深度版本 + +- 在每一层都添加可训练参数 +- 更好的任务适应能力 +- 适用于理解和生成任务 + +#### BitFit + +**原理**: 仅调优偏置参数 + +- 极少的参数量（小于0.1%） +- 适用于小规模任务微调 +- 计算成本极低 + +### 方法对比 + +| 方法 | 参数量 | 适用场景 | 优势 | 劣势 | +| ------------- | -------- | -------- | -------------- | -------------- | +| LoRA | 0.1-1% | 通用任务 | 效果好，易实现 | 需要选择秩大小 | +| Prefix Tuning | 0.1-3% | 生成任务 | 效果稳定 | 序列长度限制 | +| P-Tuning v2 | 0.1-5% | 理解任务 | 适应性强 | 参数略多 | +| BitFit | 小于0.1% | 简单任务 | 极少参数 | 表达能力有限 | + +## 微调框架与工具 + +### 推荐框架 + +#### LLaMA-Factory + +- **特色**: 全面的微调工具箱 +- **支持**: 多种模型和微调方法 +- **易用性**: Web界面和配置化 +- **文档**: 详细的使用教程 + +#### Hugging Face TRL + +- **特色**: 官方推荐框架 +- **支持**: RL微调、SFT、DPO +- **生态**: 与transformers深度集成 +- **更新**: 持续更新最新技术 + +#### Swift框架 + +- **来源**: 阿里巴巴开源 +- **特色**: 中文友好，支持多模态 +- **性能**: 针对国产硬件优化 +- **社区**: 活跃的中文社区 + +#### X-Tuner框架 + +- **来源**: MMDetection团队 +- **特色**: 轻量级，易扩展 +- **性能**: 内存优化出色 +- **集成**: 与MMX系列工具集成 + +### Unsloth - 高效微调框架 + +- **项目**: [GitHub链接](https://github.com/unslothai/unsloth) +- **特色**: 速度提升显著（2-5x） +- **优化**: 内存使用减少80% +- **支持**: 主流模型和方法 +- **易用**: 简单的API接口 + +## 微调实践要点 + +### 学习重点 + +**深入理解底层原理**: + +- 不能只会跑脚本，必须学会底层实现 +- 理解KV Cache机制和内存管理 +- 掌握Causal Mask的作用和实现 +- 了解梯度计算和反向传播 + +### 数据准备 + +**数据格式**: + +- 指令-回答对格式 +- 对话格式数据 +- 任务特定格式 +- 多轮对话处理 + +**数据质量**: + +- 数据清洗和去重 +- 质量评估和筛选 +- 数据平衡和增强 +- 领域数据收集 + +### 超参数调优 + +**关键参数**: + +- 学习率: 通常比预训练更小 +- LoRA秩(r): 平衡性能和效率 +- LoRA alpha: 控制适应强度 +- 批次大小: 根据硬件调整 + +**训练策略**: + +- 渐进式学习率调度 +- 早停策略避免过拟合 +- 梯度累积模拟大批次 +- 定期评估和保存 + +## 多任务微调 + +### 任务路由 + +**方法**: + +- 任务特定的LoRA模块 +- 专家混合(MoE)架构 +- 条件生成控制 +- 多头输出设计 + +### 模块化设计 + +**LoRA组合**: + +- 任务特定LoRA +- 领域通用LoRA +- 能力增强LoRA +- 动态组合策略 + +## 高级微调技术 + +### 指令微调 (Instruction Tuning) + +**数据构建**: + +- 多样化指令模板 +- 任务描述变体 +- 少样本示例 +- 负样本构造 + +**训练策略**: + +- 多任务混合训练 +- 课程学习策略 +- 对比学习增强 +- 元学习方法 + +### 强化学习微调 (RLHF) + +**流程**: + +1. 监督微调(SFT) +2. 奖励模型训练 +3. 强化学习优化 +4. 迭代改进 + +**关键技术**: + +- PPO算法优化 +- 奖励模型设计 +- 价值函数估计 +- 策略梯度计算 + +### 对齐微调 + +**方法**: + +- Constitutional AI +- DPO (Direct Preference Optimization) +- 人类反馈学习 +- 价值观对齐 + +## 评估与分析 + +### 评估指标 + +**任务性能**: + +- 准确率、F1分数 +- BLEU、ROUGE分数 +- 人工评估质量 +- 任务特定指标 + +**模型能力**: + +- 原有能力保持 +- 新任务适应能力 +- 泛化性能测试 +- 鲁棒性分析 + +### 分析工具 + +**可视化**: + +- 损失曲线分析 +- 注意力权重可视化 +- 参数变化追踪 +- 性能对比图表 + +**诊断**: + +- 过拟合检测 +- 灾难性遗忘分析 +- 参数重要性分析 +- 激活模式分析 + +## 部署与推理 + +### 模型合并 + +**LoRA合并**: + +```python +# 将LoRA权重合并回基础模型 +merged_model = base_model + lora_model.merge() +``` + +**多LoRA切换**: + +- 动态加载不同LoRA +- 任务特定路由 +- 内存高效切换 +- 批处理优化 + +### 推理优化 + +**内存优化**: + +- 量化技术应用 +- 梯度检查点 +- 动态批处理 +- KV Cache优化 + +**速度优化**: + +- 模型并行推理 +- 批处理优化 +- 硬件加速 +- 编译优化 + +## 最佳实践 + +### 实验设计 + +1. **基线建立**: 从简单方法开始 +2. **消融实验**: 验证每个组件的作用 +3. **超参数搜索**: 系统性调优 +4. **多次实验**: 确保结果可重复 +5. **详细记录**: 记录所有实验细节 + +### 工程技巧 + +1. **渐进式训练**: 从小数据到大数据 +2. **检查点管理**: 定期保存和恢复 +3. **监控机制**: 实时监控训练状态 +4. **错误处理**: 优雅处理训练异常 +5. **资源管理**: 合理分配计算资源 + +## 未来发展趋势 + +1. **自动化微调**: 自动选择微调策略和超参数 +2. **多模态微调**: 跨模态任务的统一微调 +3. **个性化微调**: 用户个性化的模型适应 +4. **联邦微调**: 隐私保护的分布式微调 +5. **持续学习**: 不遗忘的持续适应学习 + +## 学习建议 + +1. **理论基础**: 深入理解微调的数学原理 +2. **动手实践**: 从简单任务开始练习 +3. **代码阅读**: 阅读优秀框架的源码 +4. **实验对比**: 对比不同方法的效果 +5. **社区参与**: 活跃在开源社区和论坛 diff --git a/app/docs/ai/foundation-models/index.mdx b/app/docs/ai/foundation-models/index.mdx new file mode 100644 index 0000000..11cf9f7 --- /dev/null +++ b/app/docs/ai/foundation-models/index.mdx @@ -0,0 +1,122 @@ +--- +title: "基座大模型" +description: "基础模型的全生命周期：数据构建、训练、微调、部署与评测" +date: "2025-01-27" +tags: + - foundation-models + - llm-lifecycle + - model-development +--- + +基座大模型是现代 AI 系统的核心，本节涵盖从数据构建到部署评测的完整技术栈与生命周期管理。 + +## 核心组成部分 + +### 📊 数据集构建 + +- 前往: [数据集构建](./datasets/) +- 数据来源与获取策略 +- 数据清洗与质量控制 +- 隐私保护与合规处理 +- 多模态数据处理技术 + +### 🔧 模型训练 + +- 前往: [模型训练](./training/) +- 分布式训练技术 +- MoE 混合专家模型 +- 模型权重合并策略 +- 训练优化与稳定性 + +### 🎯 模型微调 + +- 前往: [模型微调](./finetune/) +- LoRA 低秩适应 +- PEFT 参数高效微调 +- 指令微调与对齐 +- 微调框架与工具 + +### 🚀 部署与推理 + +- 前往: [部署与推理](./deploy-infer/) +- KV Cache 优化 +- Flash Attention 加速 +- 量化与并行推理 +- 推理框架对比 + +### 📈 模型评测 + +- 前往: [模型评测](./evaluation/) +- Benchmark 评测体系 +- 中英文评测基准 +- 评测方法与指标 +- 结果分析与应用 + +### 🏗️ 模型结构 + +- 前往: [模型结构](./architectures/) +- Decoder-only 架构 +- 注意力机制设计 +- 位置编码方案 +- 架构演进趋势 + +### 💡 QKV 面试问题 + +- 前往: [QKV 面试问题](./qkv-interview/) +- KV Cache 工作原理 +- 注意力机制细节 +- 经典面试题解析 +- 技术要点深度剖析 + +## 学习路径 + +### 初学者路线 + +1. 理论基础：Transformer 架构与注意力机制 +2. 数据处理：了解数据集构建流程 +3. 微调实践：掌握 LoRA 等参数高效微调 +4. 评测理解：熟悉主流基准与指标 + +### 进阶开发 + +1. 训练优化：分布式训练与 MoE +2. 推理加速：KV Cache、Flash Attention 等 +3. 部署工程：vLLM、TensorRT 等推理框架 +4. 性能调优：系统级性能分析与优化 + +### 架构设计 + +1. 架构取舍：不同架构优缺点与场景 +2. 系统集成：端到端应用系统设计 +3. 成本优化：平衡性能、成本与资源 +4. 技术选型：面向场景的技术方案 + +## 重要概念 + +### Decoder-only 架构优势 + +- 注意力满足：因果注意力适配生成任务 +- 生成适配：天然适合自回归语言建模 +- 统一框架：多任务统一为文本生成 + +### KV Cache 核心原理 + +- 重复利用：历史 KV 复用降低计算 +- 复杂度降低：O(n²) → O(n) +- 内存权衡：空间换时间 + +## 技术发展趋势 + +1. 模型效率：参数高效训练与推理优化 +2. 多模态融合：文本/图像/音频统一 +3. 长序列处理：更长上下文支持 +4. 边缘部署：面向边缘设备的压缩 +5. 绿色 AI：降低能耗的计算技术 + +## 参考资料 + +- 动手学大模型（知乎专栏） +- 《Attention is All You Need》 +- 《Language Models are Few-Shot Learners》 + +> 💡 学习建议：栈面宽且更新快，依据角色与目标选择路线；理论与实践并重，持续跟进前沿。 diff --git a/app/docs/ai/foundation-models/qkv-interview/index.mdx b/app/docs/ai/foundation-models/qkv-interview/index.mdx new file mode 100644 index 0000000..cef38d5 --- /dev/null +++ b/app/docs/ai/foundation-models/qkv-interview/index.mdx @@ -0,0 +1,105 @@ +--- +title: "经典面试QKV问题" +description: "深入解析Transformer中QKV机制和KV Cache的经典面试问题" +date: "2025-01-27" +tags: + - interview-questions + - qkv + - kv-cache + - attention-mechanism + - transformer +--- + +# 经典面试 QKV 问题 + +Transformer中的QKV机制是大模型面试的热门话题。本节深入解析相关的经典面试问题。 + +## 核心面试问题 + +### 1. 在LLM推理时，为什么KV可以Cache？ + +**核心原因**: 自回归生成的特性决定了KV可以重复使用。 + +**详细解析**: + +1. **减少重复计算**: 历史序列的Key和Value在每次生成时都需要重新计算，通过缓存避免重复计算 +2. **提升推理速度**: 生成新token时只需计算当前token的Query，与缓存的KV进行注意力计算 +3. **降低计算复杂度**: 从O(n²·d)降低到O(n·d)，n是序列长度，d是向量维度 +4. **跨请求复用**: 相同前缀的多个请求可以共享KV Cache，提高系统整体吞吐量 + +### 2. 那为什么Q不可以Cache？ + +**关键理解**: Q不需要Cache，而不是不能Cache。 + +**原因分析**: + +1. **依赖性差异**: 每个新生成token的输出只依赖于当前token的Q，Q在下次推理时不会被再次使用 +2. **计算效率考虑**: 缓存Q不会带来效率提升，每个Q都是基于前面序列生成的，具有时序依赖性 +3. **自回归特性**: 每个Token生成仅依赖于之前所有Token，Q的计算本身就是基于历史序列的 + +### 3. 为什么需要三个不同的矩阵WQ、WK、WV？ + +**功能分离**: 将注意力机制分解为三个不同的功能： + +- **查询生成 (WQ)**: 生成"我要找什么" +- **键生成 (WK)**: 生成"我是什么" +- **值生成 (WV)**: 生成"我包含什么信息" + +**数学原理**: 不同的线性变换学习不同的表示空间，增加模型的表达能力和灵活性。 + +### 4. Multi-Head Attention的作用是什么？ + +**核心思想**: 并行的专门化，不同的头学习不同类型的注意力模式。 + +**具体作用**: + +1. **信息子空间**: 每个头关注不同的特征子空间 +2. **注意力多样性**: 同时捕获多种注意力模式 +3. **位置信息**: 不同头可能关注不同的位置关系 +4. **语义层次**: 不同头关注不同层次的语义信息 + +### 5. KV Cache的内存使用量如何计算？ + +**计算公式**: + +``` +KV Cache内存 = 2 × 序列长度 × 层数 × 隐藏维度 × 头数 × 精度字节数 +``` + +**优化策略**: + +- 量化: 使用INT8或INT4量化KV Cache +- 分页: PagedAttention的分页存储 +- 压缩: 动态压缩不活跃的Cache +- 共享: 多请求间的Cache共享 + +## 进阶技术问题 + +### Flash Attention优化原理 + +- **内存访问优化**: 分块计算注意力，减少HBM与SRAM间的数据传输 +- **算法改进**: IO复杂度从O(N²)降低到O(N²d²/M)，支持更长的序列长度 + +### 不同精度对KV Cache的影响 + +| 精度 | 内存占用 | 计算速度 | 精度损失 | +| ---- | -------- | -------- | -------- | +| FP16 | 50% | 1.5-2x | 极小 | +| INT8 | 25% | 2-3x | 小 | +| INT4 | 12.5% | 3-4x | 中等 | + +## 面试准备建议 + +### 技术深度 + +1. **理解原理**: 深入理解注意力机制的数学原理 +2. **实现细节**: 了解KV Cache的具体实现 +3. **优化技术**: 掌握相关的优化技术 +4. **性能分析**: 能够分析内存和计算开销 + +### 表达技巧 + +1. **结构化回答**: 按照原理→实现→优化的顺序 +2. **举例说明**: 用具体例子解释抽象概念 +3. **数据支撑**: 用具体数据说明优化效果 +4. **对比分析**: 对比不同方案的优缺点 diff --git a/app/docs/ai/foundation-models/training/index.mdx b/app/docs/ai/foundation-models/training/index.mdx new file mode 100644 index 0000000..269d480 --- /dev/null +++ b/app/docs/ai/foundation-models/training/index.mdx @@ -0,0 +1,288 @@ +--- +title: "模型训练" +description: "大模型训练技术：MoE、分布式训练、模型权重合并等" +date: "2025-01-27" +tags: + - model-training + - distributed-training + - moe + - model-merging + - training-optimization +--- + +大模型训练是一个复杂的工程问题，涉及分布式计算、内存优化、训练稳定性等多个方面。 + +## 训练基础 + +### 训练流程 + +1. **数据准备**: 分词、批处理、数据加载 +2. **模型初始化**: 权重初始化、架构配置 +3. **前向传播**: 计算损失函数 +4. **反向传播**: 梯度计算和更新 +5. **模型保存**: 检查点保存和恢复 + +### 关键技术 + +- **梯度累积**: 模拟大批次训练 +- **混合精度**: FP16/BF16训练加速 +- **梯度裁剪**: 防止梯度爆炸 +- **学习率调度**: 优化收敛过程 + +## 分布式训练 + +### 数据并行 + +- **原理**: 不同GPU处理不同数据批次 +- **实现**: PyTorch DDP, DeepSpeed +- **适用**: 模型较小但数据量大的场景 + +### 模型并行 + +- **张量并行**: 将模型层分割到不同GPU +- **流水线并行**: 将模型层按顺序分配 +- **专家并行**: MoE模型的专家分配 + +### 混合并行 + +- **3D并行**: 数据+张量+流水线并行 +- **Zero冗余优化**: 优化器状态分片 +- **激活重计算**: 用计算换内存 + +## MoE (Mixture of Experts) + +### 核心概念 + +**稀疏激活**: 每次只激活部分专家网络，实现计算效率和模型容量的平衡。 + +**路由机制**: 智能分配输入到合适的专家 + +- Top-k路由: 选择最相关的k个专家 +- 负载均衡: 确保专家使用的均衡性 +- 噪声注入: 提高路由的鲁棒性 + +**架构设计**: + +- 专家网络结构 +- 门控网络设计 +- 残差连接策略 + +### 技术挑战 + +1. **负载均衡**: 防止专家利用不均 +2. **通信开销**: 跨设备专家调用 +3. **训练稳定性**: 路由学习收敛 +4. **推理优化**: 稀疏模型推理加速 + +## 模型权重合并 + +📊 **权重合并技术表格**: 查看完整的权重合并方法对比 + +### 合并策略 + +**线性插值合并**: + +```python +merged_weight = alpha * weight_1 + (1 - alpha) * weight_2 +``` + +**SLERP (球面线性插值)**: + +- 适用于归一化权重 +- 保持权重向量的角度关系 +- 在embedding层特别有效 + +**Task Arithmetic**: + +- 基于任务向量的合并 +- 支持多任务能力组合 +- 可控的能力迁移 + +### 应用场景 + +- **多任务模型融合**: 组合不同任务的能力 +- **不同训练阶段整合**: 结合不同阶段的权重 +- **模型能力组合优化**: 平衡不同能力维度 + +## 训练优化技术 + +### 内存优化 + +**梯度检查点**: + +- 重计算激活值 +- 减少内存占用 +- 计算时间权衡 + +**零冗余优化器**: + +- ZeRO-1: 优化器状态分片 +- ZeRO-2: 添加梯度分片 +- ZeRO-3: 参数分片 + +**CPU卸载**: + +- 参数CPU存储 +- 动态加载到GPU +- 内存容量扩展 + +### 计算优化 + +**算子融合**: + +- LayerNorm融合 +- Attention算子优化 +- 自定义CUDA kernel + +**编译优化**: + +- TorchScript编译 +- TensorRT优化 +- ONNX转换 + +## 训练稳定性 + +### 数值稳定性 + +**损失缩放**: + +- 自动混合精度 +- 动态损失缩放 +- 梯度溢出检测 + +**权重初始化**: + +- Xavier/Kaiming初始化 +- 层次化初始化 +- 预训练权重加载 + +### 训练监控 + +**指标监控**: + +- 损失曲线跟踪 +- 梯度范数监控 +- 学习率变化 +- 内存使用情况 + +**异常检测**: + +- NaN/Inf检测 +- 梯度爆炸监控 +- 模型发散预警 + +## 大规模训练工程 + +### 硬件配置 + +**计算资源**: + +- GPU集群配置 +- 内存和带宽要求 +- 存储系统设计 +- 网络拓扑优化 + +**环境管理**: + +- Docker容器化 +- 环境一致性保证 +- 依赖管理 +- 版本控制 + +### 实验管理 + +**超参数搜索**: + +- 网格搜索 +- 贝叶斯优化 +- 早停策略 +- 资源预算管理 + +**实验追踪**: + +- MLflow实验记录 +- Weights & Biases监控 +- 实验结果对比 +- 可复现性保证 + +## 故障处理 + +### 常见问题 + +1. **内存不足**: 批次大小调整、梯度累积 +2. **收敛问题**: 学习率调整、架构优化 +3. **通信故障**: 网络配置、节点恢复 +4. **数据问题**: 数据验证、异常处理 + +### 恢复策略 + +**检查点机制**: + +- 定期保存模型状态 +- 优化器状态保存 +- 随机种子记录 +- 训练进度恢复 + +**容错设计**: + +- 节点故障检测 +- 自动任务重启 +- 弹性训练架构 +- 数据完整性校验 + +## 性能优化 + +### 系统层面 + +**I/O优化**: + +- 数据预加载 +- 多进程数据loading +- 内存映射文件 +- SSD存储优化 + +**通信优化**: + +- AllReduce算法优化 +- 通信拓扑设计 +- 带宽利用率提升 +- 延迟降低技术 + +### 算法层面 + +**训练策略**: + +- 渐进式训练 +- 课程学习 +- 对抗训练 +- 多阶段训练 + +**正则化技术**: + +- Dropout变体 +- 权重衰减 +- 标签平滑 +- 数据增强 + +## 最佳实践 + +1. **充分的实验设计**: 控制变量、可复现性 +2. **渐进式扩展**: 从小模型到大模型逐步验证 +3. **监控驱动**: 实时监控训练状态和资源使用 +4. **文档记录**: 详细记录实验配置和结果 +5. **团队协作**: 建立良好的实验分享机制 + +## 未来发展方向 + +1. **自动化训练**: 自动超参数调优、架构搜索 +2. **高效架构**: 更高效的模型架构设计 +3. **硬件协同**: 软硬件协同优化 +4. **绿色AI**: 降低训练能耗和碳排放 +5. **联邦学习**: 分布式协作训练范式 + +## 来自 UNSW IT-AI内卷地狱 文档摘录 + +- Swan Lab - AI 模型训练跟踪与可视化工具 +- 文档: https://docs.swanlab.cn/guide_cloud/general/what-is-swanlab.html +- 官网: https://swanlab.cn +- GitHub: https://github.com/swanhubx/swanlab diff --git a/app/docs/ai/generative-todo/index.mdx b/app/docs/ai/generative-todo/index.mdx new file mode 100644 index 0000000..a7356ca --- /dev/null +++ b/app/docs/ai/generative-todo/index.mdx @@ -0,0 +1,7 @@ +--- +title: "生成模型" +description: "生成模型：Diffusion 等生成技术" +status: todo +--- + +本节聚合生成模型资料（Diffusion 等），后续补充。 diff --git a/app/docs/ai/index.mdx b/app/docs/ai/index.mdx new file mode 100644 index 0000000..465f03d --- /dev/null +++ b/app/docs/ai/index.mdx @@ -0,0 +1,29 @@ +--- +title: "AI 知识库" +description: "UNSW IT-AI内卷地狱 - AI 学习资源全面整理" +date: "2025-01-27" +tags: + - ai + - machine-learning + - deep-learning + - llm +--- + +该文档致力于汇总高质量 AI 学习资源，减少信息分散与重复，便于协作维护与持续更新。 + +## 学习路径 + +本知识库按以下结构组织： + +- AI 数学基础：线性代数、概率统计、微积分与优化、信息论、数值分析 +- 大模型基础：深度学习、PyTorch、CUDA、Transformer、Embedding、入门课程 +- 强化学习：RL 基础、思维链 COT、GRPO +- 基座大模型：数据集、训练、微调、部署、评测、模型结构 +- 多模态大模型：LLaVA、QwenVL、ViT、MLLM +- 推荐系统：学习路径、实战项目、论文资源 +- Agent：大模型智能体 +- 生成模型：Diffusion Models +- 方法论学习：科研指南、论文阅读 +- 杂项工具：开发工具、平台使用 + +> 注意：论文合集可邀请到 Zotero 协作；“TODO” 代表待施工项目。 diff --git a/app/docs/ai/llm-basics/courses/index.mdx b/app/docs/ai/llm-basics/courses/index.mdx new file mode 100644 index 0000000..08bb531 --- /dev/null +++ b/app/docs/ai/llm-basics/courses/index.mdx @@ -0,0 +1,65 @@ +--- +title: "大模型入门课程" +description: "深度学习与大模型相关的课程资料汇总。" +--- + +# 入门课程 + +## CS224N 斯坦福《深度学习自然语言处理》 + +从 NLP 基础讲起，有 miniGPT + +- 官网：[CS224N Stanford](https://web.stanford.edu/class/cs224n/index.html#schedule) +- 视频：（2025 中英）斯坦福 CS224N《深度学习自然语言处理》 +- 课件与作业地址：[Quark 网盘](https://pan.quark.cn/s/1e43e9b25006) + +--- + +## CMU Advanced NLP + +- 课程主页：[CMU Advanced NLP Spring 2025](https://cmu-l3.github.io/anlp-spring2025/) +- 代码地址：[Github Repo](https://github.com/cmu-l3/anlp-spring2025-code/tree/main) +- 作业： + - **minLLaMA**： [HW1](https://github.com/cmu-l3/anlp-spring2025-hw1) （从零实现 LLaMA，质量超级高） + - **RAG**： [HW2](https://github.com/cmu-l3/anlp-spring2025-hw2) + +--- + +## NanoGPT —— 从零实现 GPT + +- 代码：[NanoGPT GitHub](https://github.com/karpathy/nanoGPT) +- 学习资料：【LLM 训练系列】NanoGPT 源码详解和中文 GPT 训练实践 + +--- + +## 斯坦福大学 CS336 《从头开始创造语言模型》 (Spring 2025) + +- 课程视频：[YouTube 播放列表](https://www.youtube.com/watch?v=SQ3fZ1sAqXI&list=PLoROMvodv4rOY23Y0BoGoBGgQ1zmU_MT_) +- 课程主页：[CS336 官方站点](https://stanford-cs336.github.io/spring2025/) +- 代码仓库：[GitHub Repo](https://github.com/stanford-cs336/spring2025-lectures) +- 视频翻译：[Bilibili](https://www.bilibili.com/video/BV13SV9zdEhX/?spm_id_from=333.337.search-card.all.click&vd_source=14245d272f6606a31fe299db9e47ca84) +- 翻译合集：[知乎专栏](https://zhuanlan.zhihu.com/p/1906315844386034284) +- 私有作业仓库：TODO + +### 作业说明 + +1. **作业 1**：实现 BPE 分词器、Transformer 架构、Adam 优化器，并在 TinyStories 和 OpenWebText 上训练模型（仅允许用 PyTorch 原语）。 +2. **作业 2**：在 Triton 中实现 Flash Attention 2、分布式数据并行 + 优化器分片。 +3. **作业 3**：Scaling Law。使用 IsoFLOP 拟合 Scaling Law，模拟有限计算预算的实验过程。 +4. **作业 4**：数据处理。将 Common Crawl HTML 转为文本，过滤（质量、有害内容、PII），去重。 +5. **作业 5**：对齐。实现监督微调、专家迭代、GRPO 与变体，在 Qwen 2.5 Math 1.5B 上运行 RL 提升 MATH 指标。 + +### 前置课程 + +- 数学：MATH 51、CME 100 +- 概率：CS 109 + +### 学习笔记 + +- CS336 学习笔记：TODO + +--- + +## Happy-LLM —— 从零构建 215M 大模型 + +- 代码仓库：[Happy-LLM GitHub](https://github.com/datawhalechina/happy-llm) diff --git a/app/docs/ai/llm-basics/cuda/index.mdx b/app/docs/ai/llm-basics/cuda/index.mdx new file mode 100644 index 0000000..302e1ea --- /dev/null +++ b/app/docs/ai/llm-basics/cuda/index.mdx @@ -0,0 +1,29 @@ +--- +title: "CUDA" +description: "CUDA 学习资料与大模型相关优化技术" +date: "2024-01-17" +tags: + - cuda + - gpu + - triton + - cutlass + - flashattention + - ring-attention + - profiling +--- + +## 推荐课程 + +- [CUDA Lectures（Github 课程，内容全面）](https://github.com/cuda-mode/lectures) + 涵盖 **profiling、Triton、Cutlass、FlashAttention、Ring Attention** 等实用主题。 + +## 相关文章 + +- [知乎专栏：CUDA 进阶学习](https://zhuanlan.zhihu.com/p/711304830) + +## 学习价值 + +- 理解 GPU 并行计算与内存优化 +- 掌握模型加速的核心组件（FlashAttention、Ring Attention） +- 学会利用 profiling 定位性能瓶颈 +- 熟悉 Triton / Cutlass 等新一代高性能 GPU 编程框架 diff --git a/app/docs/ai/llm-basics/deep-learning/d2l/index.mdx b/app/docs/ai/llm-basics/deep-learning/d2l/index.mdx new file mode 100644 index 0000000..9e89d9a --- /dev/null +++ b/app/docs/ai/llm-basics/deep-learning/d2l/index.mdx @@ -0,0 +1,24 @@ +--- +title: "李沐动手学深度学习" +description: "《动手学深度学习》相关笔记。" +date: "2024-01-18" +tags: + - d2l + - deep-learning + - pytorch + - notes +--- + +## 官方资源 + +- [动手学深度学习 (D2L 官方站点)](https://zh-v2.d2l.ai/) + +## 电子书 + +- [李沐 - 《动手学深度学习》.pdf] +- [动手学深度学习 (PyTorch 版).pdf] + +## 学习笔记 + +- 动手学深度学习笔记 TODO +- 夸克网盘：[动手学深度学习中文笔记](https://pan.quark.cn/s/9a7cf3f3eae2) diff --git a/app/docs/ai/llm-basics/deep-learning/index.mdx b/app/docs/ai/llm-basics/deep-learning/index.mdx new file mode 100644 index 0000000..980bd75 --- /dev/null +++ b/app/docs/ai/llm-basics/deep-learning/index.mdx @@ -0,0 +1,106 @@ +--- +title: "深度学习基础" +description: "深度学习基础知识：李沐动手学深度学习、NLP、机器学习资源" +date: "2025-01-27" +tags: + - deep-learning + - d2l + - nlp + - machine-learning +--- + +深度学习是大模型的理论基础，本节提供系统的学习资源和实践指导。 + +## 李沐动手学深度学习 + +### 核心资源 + +- **官方网站**: [https://zh-v2.d2l.ai/](https://zh-v2.d2l.ai/) - 中文版在线教程 +- **特色**: 理论与代码并重，提供PyTorch和MXNet实现 +- **内容**: 从基础的线性回归到高级的注意力机制 + +### 学习资料 + +- **PDF版本**: 李沐-《动手学深度学习》 +- **PyTorch版**: 动手学深度学习(Pytorch版) +- **笔记资源**: 动手学深度学习中文笔记 + - 夸克网盘链接：https://pan.quark.cn/s/9a7cf3f3eae2 + +### 学习特点 + +- **实践导向**: 每个概念都有对应的代码实现 +- **渐进式**: 从简单概念逐步构建复杂模型 +- **全面性**: 覆盖深度学习的主要领域 +- **更新及时**: 持续更新最新技术和方法 + +## 学习建议 + +### 学习顺序 + +1. **数学基础**: 线性代数、概率论、微积分 +2. **机器学习**: 传统机器学习算法理解 +3. **深度学习**: 神经网络基础和反向传播 +4. **现代架构**: Transformer和注意力机制 +5. **实践应用**: 具体任务的模型应用 + +### 实践建议 + +1. **理论实践并重**: 每学一个概念就动手实现 +2. **项目驱动**: 通过完整项目巩固知识 +3. **社区参与**: 加入学习社区，交流讨论 +4. **持续更新**: 跟踪最新的技术发展 + +### 常见挑战 + +1. **数学门槛**: 需要一定的数学基础 +2. **概念抽象**: 一些概念较为抽象，需要多实践 +3. **技术更新快**: 需要持续学习新技术 +4. **实践与理论结合**: 平衡理论学习和实践操作 + +## 进阶方向 + +### 理论深化 + +- 优化理论与算法 +- 信息论与深度学习 +- 统计学习理论 +- 贝叶斯深度学习 + +### 应用领域 + +- 计算机视觉 +- 自然语言处理 +- 语音识别与合成 +- 推荐系统 + +### 工程实践 + +- 大规模训练 +- 模型部署与优化 +- 分布式计算 +- MLOps实践 + +## 学习资源汇总 + +### 在线课程 + +- MIT 6.034 Artificial Intelligence +- Stanford CS229 Machine Learning +- Deep Learning Specialization (Coursera) +- Fast.ai Practical Deep Learning + +### 经典教材 + +- 《深度学习》(花书) +- 《机器学习》(西瓜书) +- 《统计学习方法》 +- 《模式识别与机器学习》 + +### 实践平台 + +- Kaggle竞赛平台 +- Google Colab +- Jupyter Notebook +- GitHub开源项目 + +这些资源为深度学习提供了从理论到实践的完整学习路径，建议根据个人基础和目标选择合适的学习方式。 diff --git a/app/docs/ai/llm-basics/deep-learning/misc/index.mdx b/app/docs/ai/llm-basics/deep-learning/misc/index.mdx new file mode 100644 index 0000000..ec6c493 --- /dev/null +++ b/app/docs/ai/llm-basics/deep-learning/misc/index.mdx @@ -0,0 +1,40 @@ +--- +title: "其他资料" +description: "深度学习杂项资料。" +--- + +# 其他重要资源 + +## 机器学习南瓜书 + +- **核心价值**: 周志华《机器学习》的公式推导解析 +- **适用人群**: 希望深入理解算法数学原理的学习者 +- **内容特色**: 详细的数学推导和证明过程 + +## 花书 (深度学习) + +- **作者**: Ian Goodfellow, Yoshua Bengio, Aaron Courville +- **特色**: 理论基础扎实，数学严谨 +- **内容**: 深度学习的理论基础和数学原理 +- **适用**: 研究人员和高级开发者 + +## MIT深度学习教材 + +- **官方网站**: [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/) +- **特色**: 权威理论参考，学术标准 +- **语言**: 英文原版，理论深度高 + +## 北大CSDIY + +- **资源链接**: [https://csdiy.wiki/](https://csdiy.wiki/) +- **特色**: 计算机自学指南，包含深度学习路径 +- **内容**: 课程推荐、学习路径、实践项目 + +## 视频课程资源 + +### 咕泡程序员AI课程 + +- **课程**: 【人工智能机器学习】2023最新系统教程 +- **内容**: 机器学习算法、机器学习实战 +- **链接**: [B站视频](https://www.bilibili.com/video/BV1hM4y1U7FV) +- **特色**: 系统性强，实战导向 diff --git a/app/docs/ai/llm-basics/deep-learning/nlp/index.mdx b/app/docs/ai/llm-basics/deep-learning/nlp/index.mdx new file mode 100644 index 0000000..290f009 --- /dev/null +++ b/app/docs/ai/llm-basics/deep-learning/nlp/index.mdx @@ -0,0 +1,19 @@ +--- +title: "NLP" +description: "自然语言处理基础。" +--- + +# NLP基础 + +## HuggingFace NLP课程 + +- **官方链接**: [https://huggingface.co/learn/llm-course/zh-CN/chapter7/2](https://huggingface.co/learn/llm-course/zh-CN/chapter7/2) +- **特色**: 实用的NLP工具与技术 +- **内容**: Transformers库使用、模型微调、部署等 + +## 学习路径 + +1. **基础概念**: 文本预处理、词向量、语言模型 +2. **经典模型**: RNN、LSTM、GRU、Attention +3. **现代架构**: Transformer、BERT、GPT系列 +4. **实践应用**: 文本分类、命名实体识别、机器翻译 diff --git a/app/docs/ai/llm-basics/embeddings/index.mdx b/app/docs/ai/llm-basics/embeddings/index.mdx new file mode 100644 index 0000000..3617cea --- /dev/null +++ b/app/docs/ai/llm-basics/embeddings/index.mdx @@ -0,0 +1,38 @@ +--- +title: "Embedding模型" +description: "Embedding/向量化相关资料。" +date: "2024-01-15" +tags: + - embedding + - vector + - representation + - nlp + - retrieval +--- + +**Embedding（嵌入/向量化）** 是一种将离散对象（如词语、句子、图片、用户行为等）映射到连续向量空间的技术。 +通过这种表示方式，语义上相近的对象在向量空间中往往距离也更近，从而便于计算和建模。 + +## 核心思想 + +- **离散 → 连续**：将符号化的输入转化为数值向量，便于神经网络处理。 +- **语义保留**：向量空间结构能够保留对象之间的语义关系。 +- **可计算性**：向量可以进行加法、点积、余弦相似度等运算，实现检索、聚类、分类等功能。 + +## 大模型中的应用 + +- **词/句子向量**：NLP 模型中最常见的表示方式（如 Word2Vec、BERT、GPT）。 +- **多模态表示**：将图像、音频、视频等信息映射到相同的向量空间，实现跨模态检索。 +- **检索与推荐**：基于向量相似度的语义检索（向量数据库、RAG）、个性化推荐系统。 +- **微调与融合**：通过 LoRA、SLERP 等方法优化特定任务的向量表示。 + +## 典型方法 + +- **早期方法**：Word2Vec、GloVe +- **上下文相关表示**：ELMo、BERT +- **生成式大模型的嵌入**：GPT 系列、Qwen Embedding、OpenAI Embedding API + +## 总结 + +Embedding 是现代机器学习和大模型应用的基础模块。 +它不仅连接了离散世界与连续世界，也是语义理解、检索增强生成（RAG）、多模态融合的核心工具。 diff --git a/app/docs/ai/llm-basics/embeddings/qwen3-embedding/index.mdx b/app/docs/ai/llm-basics/embeddings/qwen3-embedding/index.mdx new file mode 100644 index 0000000..e155520 --- /dev/null +++ b/app/docs/ai/llm-basics/embeddings/qwen3-embedding/index.mdx @@ -0,0 +1,23 @@ +--- +title: "Qwen3-embedding" +description: "Qwen3 Embedding 模型的关键点、深度解读与实战资料" +date: "2024-01-15" +tags: + - qwen3 + - embedding + - slerp + - lora + - fine-tuning +--- + +## 关键点 + +- SLERP 权重合并算法 + +## 深度解读 + +- [深度解读 Qwen3 Embedding](https://zhuanlan.zhihu.com/p/1916627276335945288) + +## 实战 + +- [Qwen3 Embedding 模型 LoRA 微调实战](https://zhuanlan.zhihu.com/p/1914711055654950451) diff --git a/app/docs/ai/llm-basics/index.mdx b/app/docs/ai/llm-basics/index.mdx new file mode 100644 index 0000000..2501e9e --- /dev/null +++ b/app/docs/ai/llm-basics/index.mdx @@ -0,0 +1,165 @@ +--- +title: "大模型基础" +description: "大模型基础知识体系：深度学习、PyTorch、CUDA、Transformer" +date: "2025-01-27" +tags: + - llm-basics + - deep-learning + - pytorch + - transformer +--- + +大模型基础涵盖从深度学习理论到实际开发的完整知识体系，为理解和开发大模型提供坚实基础。 + +## 核心学习模块 + +### 📚 深度学习基础 + +- 前往: [深度学习基础](./deep-learning/) +- 李沐动手学深度学习 +- NLP 基础课程 +- 机器学习经典教材 +- 理论与实践结合 + +### 🔧 PyTorch 框架 + +- 前往: [PyTorch框架](./pytorch/) +- 小土堆入门教程 +- 张量操作进阶 +- 面试要点总结 +- 项目实战指导 + +### CUDA 编程 + +- 前往: [CUDA编程](./cuda/) +- CUDA Mode 系统课程 +- GPU 并行计算原理 +- 性能优化技巧 +- FlashAttention 实现 + +### Transformer 架构 + +- 前往: [Transformer架构](./transformer/) +- Attention 机制详解 +- 多头注意力原理 +- 位置编码设计 +- 架构可视化学习 + +### 🎯 Embedding 模型 + +- 前往: [Embedding模型](./embeddings/) +- Qwen3-embedding 深度解读 +- SLERP 权重合并算法 +- 向量化表示技术 +- 相似度计算方法 + +### 📖 入门课程 + +- 前往: [入门课程](./courses/) +- CS224N 斯坦福 NLP 课程 +- CMU Advanced NLP +- NanoGPT 实现项目 +- CS336 语言模型课程 +- Happy-LLM 实践项目 + +## 学习路径规划 + +### 初学者路线 + +1. 数学基础：线性代数、概率论、微积分 +2. 深度学习：神经网络、反向传播、优化算法 +3. 框架掌握：PyTorch 基础操作和模型构建 +4. 架构理解：Transformer 和注意力机制 + +### 进阶开发 + +1. CUDA 编程：GPU 并行计算和性能优化 +2. 模型实现：从零实现 Transformer 架构 +3. 训练优化：大规模模型训练技术 +4. 部署应用：模型推理和服务化 + +### 研究导向 + +1. 理论深入：数学原理和算法创新 +2. 前沿跟踪：最新论文和技术趋势 +3. 实验设计：科学的实验方法论 +4. 代码复现：顶会论文复现能力 + +## 重要概念速览 + +### Transformer 核心 + +- Self-Attention：自注意力机制 +- Multi-Head：多头并行表示学习 +- Position Encoding：位置信息编码 +- Feed Forward：前馈神经网络 + +### PyTorch 要点 + +- 张量操作：多维数组的高效计算 +- 自动梯度：动态图与反向传播 +- 模块化设计：nn.Module 构建复杂模型 +- GPU 加速：CUDA 支持与内存管理 + +### CUDA 优化 + +- 并行计算：利用 GPU 大规模并行能力 +- 内存管理：全局/共享内存优化 +- 算子融合：减少内存访问与计算开销 +- 性能分析：Profile 工具定位瓶颈 + +## 实践项目建议 + +### 入门项目 + +- 手写数字识别 (MNIST) +- 文本分类器实现 +- 简单的 seq2seq 模型 +- 基础注意力机制 + +### 进阶项目 + +- miniGPT 从零实现 +- Transformer 机器翻译 +- BERT 模型微调 +- 大模型推理优化 + +### 高级项目 + +- 分布式训练系统 +- 自定义 CUDA 算子 +- 模型压缩与量化 +- 端到端大模型应用 + +## 学习资源推荐 + +### 在线课程 + +- 李沐《动手学深度学习》 +- 斯坦福 CS224N +- CMU Advanced NLP +- Fast.ai 实用深度学习 + +### 经典教材 + +- 《深度学习（花书）》 +- 《动手学深度学习》 +- 《机器学习（西瓜书）》 +- 《统计学习方法》 + +### 实践平台 + +- Google Colab +- Kaggle 竞赛 +- GitHub 开源项目 +- Hugging Face 模型库 + +## 学习建议 + +1. 循序渐进：从基础概念到复杂架构 +2. 理论实践并重：学一个概念就动手实现 +3. 项目驱动：用完整项目联结知识 +4. 社区参与：加入开源与技术讨论 +5. 持续更新：跟踪最新研究与技术 + +> 💡 核心理念：基础不只是“知识点”，更是解决复杂问题的能力。结合理论与实战，循序渐进建立完整体系。 diff --git a/app/docs/ai/llm-basics/pytorch/index.mdx b/app/docs/ai/llm-basics/pytorch/index.mdx new file mode 100644 index 0000000..ddca14a --- /dev/null +++ b/app/docs/ai/llm-basics/pytorch/index.mdx @@ -0,0 +1,125 @@ +--- +title: "PyTorch" +description: "PyTorch深度学习框架：入门教程、张量操作、面试要点" +date: "2025-01-27" +tags: + - pytorch + - deep-learning-framework + - tensor + - neural-networks +--- + +PyTorch是现代深度学习最重要的框架之一，特别在研究和大模型开发中占主导地位。 + +## 入门教程 + +### 小土堆PyTorch教程 + +- **视频地址**: [PyTorch深度学习快速入门教程](https://www.bilibili.com/video/BV1hE411t7RN/) +- **特色**: 绝对通俗易懂，适合零基础学习者 +- **内容覆盖**: + - PyTorch基础概念 + - 张量操作 + - 神经网络构建 + - 训练循环设计 + - 实际项目案例 + +### 官方资源 + +- **PyTorch官方中文教程**: [https://pytorch.ac.cn/tutorials/](https://pytorch.ac.cn/tutorials/) +- **菜鸟教程**: [https://www.runoob.com/pytorch/pytorch-tensor.html](https://www.runoob.com/pytorch/pytorch-tensor.html) +- **特色**: 权威性强，内容全面，持续更新 + +### PyTorch面试精华 + +- **资源链接**: [https://www.mstx.cn/pytorch.html](https://www.mstx.cn/pytorch.html) +- **覆盖内容**: + - PyTorch基础概念 + - 常见面试问题 + - 代码实现要点 + - 性能优化技巧 + +## 张量操作进阶 + +### einops库 + +- **GitHub**: [https://github.com/arogozhnikov/einops](https://github.com/arogozhnikov/einops) +- **特色**: 优雅的张量操作，代码可读性强 +- **核心功能**: `rearrange`, `reduce`, `repeat` + +### The Tensor Cookbook + +- **网站**: [https://tensorcookbook.com/](https://tensorcookbook.com/) +- **GitHub**: [https://github.com/thomasahle/tensorgrad](https://github.com/thomasahle/tensorgrad) +- **特色**: 可视化张量操作指南 +- **知乎讨论**: [张量食谱相关讨论](https://www.zhihu.com/search?type=content&q=%E5%BC%A0%E9%87%8F%E9%A3%9F%E8%B0%B1) + +## 经典教材 + +### Deep Learning with PyTorch Step-by-Step + +- **特色**: 从基础到高级的完整路径，逐步深入的PyTorch学习 +- **适用**: 有一定基础的学习者 +- **内容**: 包含完整的项目实战案例 + +## 核心概念 + +### 张量 (Tensor) + +PyTorch的核心数据结构，支持GPU加速的多维数组。 + +### 自动梯度 (Autograd) + +动态计算图和自动微分系统，深度学习训练的核心。 + +### 神经网络模块 (nn.Module) + +构建神经网络的基础类，支持模块化设计。 + +## 实践建议 + +### 学习路径 + +1. **基础入门**: 张量操作、自动梯度 +2. **模型构建**: nn.Module、损失函数 +3. **训练循环**: 优化器、学习率调度 +4. **高级特性**: 分布式、优化技术 +5. **项目实战**: 完整项目开发 + +### 开发技巧 + +1. **多动手**: 每个概念都要亲自实现 +2. **读源码**: 理解底层实现原理 +3. **做项目**: 通过项目巩固知识 +4. **参与社区**: 贡献开源项目 + +PyTorch作为深度学习的主流框架，掌握其核心概念和高级特性对于大模型开发至关重要。 + +1. [PyTorch 深度学习快速入门教程（绝对通俗易懂！）【小土堆】](https://www.bilibili.com/video/BV1hE411t7RN/?spm_id_from=333.337.search-card.all.click&vd_source=14245d272f6606a31fe299db9e47ca84) +2. [PyTorch 面试精华](https://www.mstx.cn/pytorch.html) +3. [菜鸟教程 - PyTorch](https://www.runoob.com/pytorch/pytorch-tensor.html) +4. [PyTorch 官方中文教程](https://pytorch.ac.cn/tutorials/) + +## 张量 (Tensor) + +### 张量工具与库 + +- [einops (Github)](https://github.com/arogozhnikov/einops) + +### Tensor Cookbook 及相关资料 + +- [The Tensor Cookbook（可视化张量）](https://tensorcookbook.com/) +- [知乎 - 张量食谱相关讨论](https://www.zhihu.com/search?type=content&q=%E5%BC%A0%E9%87%8F%E9%A3%9F%E8%B0%B1) +- [tensorgrad (Github)](https://github.com/thomasahle/tensorgrad) +- [Cookbook PDF] +- Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide (Daniel Voigt Godoy) PDF (Z-Library) + +## Jupyter 内核注册（来自群文档摘录） + +```bash +pip install ipykernel + +# 注册内核（示例） +python -m ipykernel install --user --name myenv +python -m ipykernel install --user --name yourname --display-name yourname +``` diff --git a/app/docs/ai/llm-basics/transformer/ai-by-hand/index.mdx b/app/docs/ai/llm-basics/transformer/ai-by-hand/index.mdx new file mode 100644 index 0000000..360d09a --- /dev/null +++ b/app/docs/ai/llm-basics/transformer/ai-by-hand/index.mdx @@ -0,0 +1,26 @@ +--- +title: "AI by Hand：手搓 AI 模型" +description: "AI by Hand 项目资源与介绍" +date: "2024-01-16" +tags: + - ai-by-hand + - transformer + - visualization + - tutorial +--- + +## 项目主页 + +- [AI by Hand 官方网站](https://www.byhand.ai/) + → 提供图解和交互式展示，帮助从零理解 AI 模型的计算过程。 + +## 相关介绍 + +- [CSDN 介绍文章](https://blog.csdn.net/qq_35591253/article/details/144326165) +- [Dongou Tech 博客 - AI by Hand ✍️ with Prof. Tom Yeh](https://dongou.tech/ai/dongou/ai-by-hand-%E2%9C%8D%EF%B8%8F-with-prof-tom-yeh-for-ai-professionals/) + +## 学习价值 + +- 通过“手算”方式理解 Transformer 等模型的核心运算 +- 可视化讲解 Attention、矩阵运算等机制 +- 适合想要从基础逻辑层面掌握大模型原理的学习者 diff --git a/app/docs/ai/llm-basics/transformer/index.mdx b/app/docs/ai/llm-basics/transformer/index.mdx new file mode 100644 index 0000000..f5c2096 --- /dev/null +++ b/app/docs/ai/llm-basics/transformer/index.mdx @@ -0,0 +1,44 @@ +--- +title: "Transformer" +description: "Transformer 架构与注意力机制相关资料" +date: "2024-01-16" +tags: + - transformer + - attention + - qkv + - multi-head-attention + - tokenizer + - positional-encoding + - feedforward + - normalization + - decoding +--- + +## 核心论文与代码 + +- [Attention is All You Need (原始论文, 2017)](https://arxiv.org/abs/1706.03762) +- [Github 代码复现（PyTorch）](https://github.com/jadore801120/attention-is-all-you-need-pytorch) + +## 关键概念 + +- Self-Attention 的 QKV 计算 +- Scaled Dot-Product 的作用 +- Multi-Head Attention 的原理 +- 分词与 Tokenizer +- 词嵌入（Word Embedding） +- 位置编码（Positional Encoding） +- 注意力机制（Attention Mechanism） +- 前馈网络（Feed Forward Network） +- 掩码（Masking） +- 层归一化（Normalization） +- 解码技术（Decoding Techniques） + +## 深入学习 + +- Transformer 论文逐段精读 【论文精读】 + +## 注意力机制学习资源 + +- 【高清中英字幕】2025 年吴恩达详细讲解 Transformer 工作原理 + - [原课程链接（DeepLearning.AI Short Courses）](https://www.deeplearning.ai/short-courses/how-transformer-llms-work/) +- 彻底弄清注意力机制 diff --git a/app/docs/ai/methodology/index.mdx b/app/docs/ai/methodology/index.mdx new file mode 100644 index 0000000..2e2dccb --- /dev/null +++ b/app/docs/ai/methodology/index.mdx @@ -0,0 +1,213 @@ +--- +title: "方法论学习" +description: "科研自救指南：研究方向、论文速读、代码复现、SCI投稿等方法论" +date: "2025-01-27" +tags: + - methodology + - research + - paper-reading + - code-reproduction + - sci-publication +--- + +科研不仅需要技术能力，更需要正确的方法论指导。本节提供从研究方向选择到论文发表的完整科研方法论。 + +## 科研自救指南系列 + +### 科研自救指南 | 如何找到研究方向2.0 + +**核心方法**: + +1. **文献调研**: 系统性阅读领域综述和顶会论文 +2. **问题识别**: 发现当前研究的不足和空白 +3. **技术趋势**: 跟踪前沿技术发展方向 +4. **实践验证**: 通过小规模实验验证想法可行性 + +**具体步骤**: + +- **第一步**: 广泛阅读，建立知识图谱 +- **第二步**: 深入特定子领域，找到兴趣点 +- **第三步**: 分析现有方法的局限性 +- **第四步**: 提出改进思路并验证 + +**选择标准**: + +- 个人兴趣和天赋匹配 +- 技术发展趋势 +- 实际应用价值 +- 研究资源可获得性 + +### 科研自救指南 | 如何使用大模型实现论文速读 + +**大模型辅助阅读策略**: + +**1. 论文结构化解读**: + +- 使用GPT等模型快速提取论文关键信息 +- 自动生成论文摘要和要点 +- 识别论文的创新点和贡献 + +**2. 批量文献处理**: + +- 批量处理PDF文档 +- 生成文献对比表格 +- 构建知识图谱 + +**3. 深度理解辅助**: + +- 解释复杂的技术概念 +- 分析数学公式和算法 +- 提供相关背景知识 + +**实用工具推荐**: + +- **ChatPDF**: 直接与PDF对话 +- **Perplexity**: 学术搜索和文献分析 +- **Claude**: 长文本处理专家 +- **自定义Prompt**: 设计专门的论文阅读提示词 + +### 科研自救指南 | 如何复现论文代码 + +**复现流程**: + +**1. 准备阶段**: + +- 仔细阅读论文和补充材料 +- 查找官方代码仓库 +- 分析实验设置和超参数 + +**2. 环境搭建**: + +- 复现论文中的软硬件环境 +- 安装依赖包和特定版本 +- 准备数据集和预处理 + +**3. 逐步实现**: + +- 从最简单的baseline开始 +- 逐个模块进行实现和测试 +- 对比中间结果确保正确性 + +**4. 调试优化**: + +- 分析复现结果与论文的差异 +- 调整超参数和实现细节 +- 记录调试过程和经验 + +**常见问题与解决**: + +- **环境不一致**: 使用Docker或虚拟环境 +- **数据不可用**: 寻找替代数据集或合成数据 +- **硬件限制**: 调整批处理大小和模型规模 +- **版本冲突**: 固定依赖包版本 + +### 科研自救指南 | 如何投稿人生第一篇SCI论文？ + +**投稿前准备**: + +**1. 论文质量检查**: + +- 创新性：确保有明确的技术贡献 +- 实验完整性：充分的实验验证和对比 +- 写作质量：逻辑清晰，表达准确 +- 格式规范：遵循目标期刊要求 + +**2. 期刊选择策略**: + +- 影响因子vs接收难度平衡 +- 领域匹配度 +- 审稿周期 +- 开放获取政策 + +**3. 投稿材料准备**: + +- Cover Letter：简明扼要介绍工作 +- Highlights：突出主要贡献 +- 图表优化：清晰美观的可视化 +- 补充材料：详细的实验和代码 + +**投稿流程管理**: + +- 时间规划：预留充分的修改时间 +- 状态跟踪：及时关注审稿进度 +- 回复准备：为可能的修改要求做准备 + +**应对审稿意见**: + +- 积极态度：将意见视为改进机会 +- 逐条回复：详细回应每个问题 +- 修改说明：清楚标注所有修改 +- 实验补充：根据要求补充必要实验 + +## 扩展学习资源 + +### 美团北斗大模型课题 + +- **资源链接**: [美团技术文章](https://mp.weixin.qq.com/s/kyrM35FYIdV1NJr3m7CGoA) +- **学习价值**: 工业界大模型应用实践 +- **核心内容**: 从技术方案到产品落地的完整流程 + +### 学术写作提升 + +**写作技巧**: + +- 结构化写作：金字塔原理 +- 科技英语：专业术语和表达 +- 图表设计：数据可视化最佳实践 +- 引用规范：正确的文献引用格式 + +**工具推荐**: + +- **语法检查**: Grammarly, LanguageTool +- **参考文献**: Zotero, Mendeley +- **图表制作**: matplotlib, plotly, tikz +- **协作编辑**: Overleaf, Google Docs + +## 研究能力培养 + +### 批判性思维 + +1. **质疑精神**: 不盲从权威，独立思考 +2. **逻辑分析**: 理性分析论证过程 +3. **证据评估**: 客观评价实验证据 +4. **创新思维**: 从不同角度思考问题 + +### 学术交流技能 + +1. **论文presentation**: 清晰准确的学术报告 +2. **海报展示**: 有效的可视化交流 +3. **学术讨论**: 建设性的学术对话 +4. **网络建设**: 建立学术合作关系 + +### 项目管理能力 + +1. **时间管理**: 合理安排研究计划 +2. **资源协调**: 有效利用各种资源 +3. **团队协作**: 与导师和同学协作 +4. **风险控制**: 识别和应对研究风险 + +## 心理调适与坚持 + +### 应对挫折 + +- **正常化**: 认识到失败是研究的常态 +- **学习机会**: 从失败中提取经验教训 +- **支持系统**: 寻求导师和同行支持 +- **心理健康**: 保持良好的心理状态 + +### 保持动力 + +- **短期目标**: 设定可实现的阶段性目标 +- **成就感**: 及时庆祝小的进步 +- **兴趣驱动**: 保持对研究问题的好奇心 +- **长期愿景**: 明确研究的意义和价值 + +## 实践建议 + +1. **系统性学习**: 建立完整的知识体系，而非碎片化学习 +2. **实践导向**: 将方法论应用到具体的研究项目中 +3. **持续改进**: 根据实践经验不断优化方法 +4. **经验分享**: 与同行交流经验和教训 +5. **终身学习**: 保持对新方法和工具的开放态度 + +> 💡 **核心思想**: 科研方法论不是死板的规则，而是需要根据具体情况灵活应用的指导原则。关键是要培养科学的思维方式和解决问题的能力。 diff --git a/app/docs/ai/misc-tools/index.mdx b/app/docs/ai/misc-tools/index.mdx new file mode 100644 index 0000000..073b19a --- /dev/null +++ b/app/docs/ai/misc-tools/index.mdx @@ -0,0 +1,118 @@ +--- +title: "杂项工具" +description: "AI开发与学习相关的实用工具集合" +date: "2025-01-27" +tags: + - tools + - zotero + - github + - perplexity + - productivity +--- + +AI学习和开发过程中需要各种实用工具来提高效率。本节整理了论文管理、代码开发、在线服务等方面的优质工具。 + +## 论文阅读与管理 + +### Zotero - 文献管理神器 + +**功能特色**: + +- 文献收集、整理、引用管理 +- 支持PDF标注和笔记 +- 多平台同步 +- 丰富的插件生态 + +**学习资源**: + +- [Zotero中文指南](https://zotero-chinese.com/user-guide/quick-start) - 快速上手指南 +- [翻译插件设置](https://zhuanlan.zhihu.com/p/11419778268) - 提升阅读效率 +- [DeepL Pro翻译](https://deepl-pro.com/#/translate) - 高质量翻译服务 + +**共享学习**: + +- **Intern-S1 Zotero群组** - 玩转Zotero，助力科研提效 +- 支持团队协作和文献共享 + +## 开发工具 + +### GitHub相关 + +**代理与镜像**: + +- [GitHub镜像站](https://github.akams.cn/) - 加速访问GitHub +- **基础操作教程** - 掌握Git和GitHub的基本使用 + +### 破解与资源获取 + +**CSDN内容获取**: + +- [CSDN文档破解](https://zhuanlan.zhihu.com/p/10504181475) - 绕过付费限制 +- [破解服务网站](https://cs.cuckooing.cn/) - 在线文档获取 + +> ⚠️ **注意**: 请合理使用破解工具，支持原创内容和知识付费 + +## AI辅助工具 + +### Perplexity - AI搜索助手 + +**服务特色**: + +- AI驱动的智能搜索 +- 实时信息检索 +- 学术文献查找 +- Literature Review辅助 + +**获取方式**: + +- [推荐链接](https://plex.it/referrals/HO4F6X9N) - 学生身份可免费领取一个月Pro会员 +- 特别适合文献调研和背景资料收集 + +### Node.js和NPM + +**环境搭建**: + +- [Node.js安装指南](https://blog.csdn.net/weixin_62818371/article/details/146175470) +- 推荐版本：Node.js v22.19.0 LTS + +### Jupyter环境配置 + +**内核管理**: + +```bash +# 安装ipykernel +pip install ipykernel + +# 注册内核 +python -m ipykernel install --user --name myenv + +# 自定义显示名称 +python -m ipykernel install --user --name yourname --display-name yourname +``` + +## 写作与分享 + +### 技术博客平台 + +**稀土掘金**: + +- [掘金写作平台](https://juejin.cn/) - 技术内容分享 +- 特色：技术氛围浓厚，读者质量高 +- 支持Markdown写作，排版美观 + +## 面试与求职 + +### 面试宝典 + +**推荐资源**: + +- 《从0到∞: 大语言模型知识面试一本通》 +- 《从1到∞: 多模态大模型知识面试一本通》 + +## 使用建议 + +1. **工具组合**: 根据工作流程选择合适的工具组合 +2. **自动化优先**: 能自动化的任务尽量自动化 +3. **持续优化**: 定期评估和优化工具使用效率 +4. **分享交流**: 与同行分享有用的工具和技巧 +5. **备份重要**: 重要数据和配置要做好备份 diff --git a/app/docs/ai/model-datasets-platforms/index.mdx b/app/docs/ai/model-datasets-platforms/index.mdx new file mode 100644 index 0000000..1b2541d --- /dev/null +++ b/app/docs/ai/model-datasets-platforms/index.mdx @@ -0,0 +1,192 @@ +--- +title: "模型数据集平台" +description: "主流AI模型、数据集和开发平台汇总" +date: "2025-01-27" +tags: + - platforms + - huggingface + - modelscope + - datasets + - models +--- + +现代AI开发离不开各种模型和数据集平台。本节汇总了主要的AI开发平台、模型库和数据集资源。 + +## 主流平台 + +### Hugging Face + +**平台特色**: + +- 全球最大的AI模型社区 +- 丰富的预训练模型库 +- 易用的transformers库 +- 强大的数据集生态 + +**核心功能**: + +- **模型库**: 数十万个预训练模型 +- **数据集**: 大规模数据集集合 +- **Spaces**: 在线模型演示平台 +- **Datasets库**: 高效的数据集处理工具 + +**官网**: [https://huggingface.co/](https://huggingface.co/) + +### Hugging Face Daily Papers + +**特色**: + +- AI领域最新论文每日更新 +- 论文摘要和关键信息提取 +- 社区讨论和分享 + +**访问方式**: + +- [Daily Papers](https://hf.co/papers) - 每日论文推荐 +- [AK推荐](https://hf.co/akhaliq) - 精选高质量论文 + +**使用价值**: [平台介绍文章](https://mp.weixin.qq.com/s/25fnXvyUPkp8YXmKnIOTyQ) + +### ModelScope (魔搭社区) + +**平台定位**: 中国版Hugging Face + +**核心优势**: + +- 阿里巴巴开源的模型社区 +- 专注中文模型和应用 +- 更好的国内访问速度 +- 丰富的中文数据集 + +**官网**: [https://www.modelscope.cn/](https://www.modelscope.cn/) + +## 数据集资源 + +### 通用数据集平台 + +**Kaggle**: + +- **网址**: [https://www.kaggle.com/datasets](https://www.kaggle.com/datasets) +- **特色**: 竞赛数据集、社区分享 +- **优势**: 高质量标注数据、实际业务场景 + +**UCI机器学习仓库**: + +- **网址**: [https://archive.ics.uci.edu/ml/index.php](https://archive.ics.uci.edu/ml/index.php) +- **特色**: 经典机器学习数据集 +- **适用**: 算法研究、教学实验 + +### 专业数据集 + +#### ImageNet + +经典的计算机视觉数据集，深度学习发展的重要里程碑。 + +**特点**: + +- 超过1400万张图像 +- 1000个类别分类任务 +- 计算机视觉模型的标准测试集 + +#### 其他重要数据集 + +- **COCO**: 目标检测和分割 +- **OpenImages**: 大规模图像数据集 +- **Common Crawl**: 网页爬取文本数据 +- **WMT**: 机器翻译数据集 + +## 开发与训练平台 + +### 使用教程 + +详细的平台使用指南: [教程链接](https://github.com/InternLM/Tutorial/tree/camp4/docs/L0/maas) + +### Swan Lab - AI模型训练跟踪 + +**功能特色**: + +- AI模型训练过程可视化 +- 实验管理和结果对比 +- 团队协作和分享 + +**访问方式**: + +- **官网**: [https://swanlab.cn](https://swanlab.cn) +- **GitHub**: [https://github.com/swanhubx/swanlab](https://github.com/swanhubx/swanlab) +- **文档**: [https://docs.swanlab.cn/](https://docs.swanlab.cn/guide_cloud/general/what-is-swanlab.html) + +**应用场景**: + +- 训练过程监控 +- 超参数调优记录 +- 模型性能对比 +- 团队实验分享 + +## 平台选择建议 + +### 国际平台 + +**Hugging Face适用场景**: + +- 需要最新的国际模型 +- 参与全球AI社区 +- 访问最全面的模型库 +- 英文项目开发 + +### 国内平台 + +**ModelScope适用场景**: + +- 中文NLP任务 +- 国内网络环境限制 +- 本土化AI应用 +- 合规性要求 + +### 数据集选择 + +**考虑因素**: + +1. **任务匹配度**: 数据集是否符合具体任务需求 +2. **数据质量**: 标注准确性和完整性 +3. **规模大小**: 是否满足模型训练需求 +4. **许可证**: 商业使用的法律限制 +5. **更新频率**: 数据的时效性 + +## 最佳实践 + +### 模型选择策略 + +1. **任务匹配**: 选择针对具体任务优化的模型 +2. **规模平衡**: 在性能和计算资源间找平衡 +3. **社区活跃度**: 选择维护良好的模型 +4. **文档完整性**: 确保有详细的使用说明 + +### 数据集使用规范 + +1. **版权合规**: 遵守数据集的许可证要求 +2. **数据预处理**: 标准化数据格式和质量 +3. **验证分割**: 合理划分训练、验证、测试集 +4. **偏差检查**: 识别和处理数据集偏差 + +### 平台集成 + +1. **多平台组合**: 结合不同平台的优势 +2. **本地缓存**: 重要模型和数据的本地备份 +3. **版本管理**: 记录使用的模型和数据集版本 +4. **自动化流程**: 建立模型下载和更新的自动化流程 + +## 发展趋势 + +1. **模型民主化**: 降低AI模型使用门槛 +2. **生态融合**: 平台间更好的互操作性 +3. **质量提升**: 更严格的模型和数据质量控制 +4. **本土化发展**: 各地区专业化平台的兴起 +5. **商业化成熟**: 从开源分享到商业服务的转化 + +## 学习建议 + +1. **多平台熟悉**: 掌握主要平台的使用方法 +2. **社区参与**: 积极参与模型和数据集的分享 +3. **质量意识**: 重视数据和模型的质量评估 +4. **版权意识**: 了解开源许可和商业使用规范 +5. **技术跟踪**: 关注平台功能更新和新技术集成 diff --git a/app/docs/ai/multimodal/courses/index.mdx b/app/docs/ai/multimodal/courses/index.mdx new file mode 100644 index 0000000..ab18a92 --- /dev/null +++ b/app/docs/ai/multimodal/courses/index.mdx @@ -0,0 +1,20 @@ +--- +title: "多模态基础课程" +description: "多模态学习课程。" +date: "2024-01-18" +tags: + - multimodal + - cmu + - course +--- + +## 卡内基梅隆大学 【多模态机器学习】 (CMU Multimodal Machine Learning) + +- [课程主页](https://cmu-multicomp-lab.github.io/mmml-course/) +- 主讲人：Louis-Philippe Morency 教授（CMU Language Technologies Institute） +- 内容覆盖： + - 多模态表示学习 (Representation Learning) + - 多模态对齐 (Alignment) + - 跨模态迁移与融合 (Fusion & Transfer) + - 应用场景：情感识别、对话系统、视频理解等 +- 特点：这是全球最系统的多模态机器学习课程之一，每年都会更新讲义与论文阅读清单。 diff --git a/app/docs/ai/multimodal/index.mdx b/app/docs/ai/multimodal/index.mdx new file mode 100644 index 0000000..ab0d3a2 --- /dev/null +++ b/app/docs/ai/multimodal/index.mdx @@ -0,0 +1,168 @@ +--- +title: "多模态大模型" +description: "多模态大模型技术体系：LLaVA、QwenVL、ViT 等主流框架" +date: "2025-01-27" +tags: + - multimodal + - vision-language + - llava + - qwen-vl +--- + +多模态大模型结合视觉、文本等多种模态信息，是 AI 向通用智能迈进的重要一步。 + +## 核心技术框架 + +### 🎯 LLaVA 框架 + +- 前往: [LLaVA 框架](./llava/) +- 视觉指令调优开创 +- ViT + 投影层 + LLM 架构 +- CLIP 基础技术详解 +- 复现项目实践指导 + +### 🚀 QwenVL 系列 + +- 前往: [QwenVL 系列](./qwenvl/) +- 中文多模态大模型标杆 +- Qwen2.5-VL 技术创新 +- 微调复现实践教程 +- 源码解读与简化实践 + +### 👁️ ViT 视觉编码器 + +- 前往: [ViT 视觉编码器](./vit/) +- Vision Transformer 原理 +- 模型压缩与优化技术 +- Token 合并策略研究 +- 学习笔记资源整理 + +### 🧠 MLLM 多模态大模型 + +- 前往: [MLLM 多模态大模型](./mllm/) +- 主流模型技术对比 +- 细粒度感知技术 +- 长视频理解方案 +- 多轮对话交互设计 + +### 📹 视频大模型 + +- 前往: [视频大模型](./video-mm-todo/) +- 时空建模技术挑战 +- 长视频理解问题 +- 多粒度理解方案 +- 实时处理技术 + +### 📚 多模态课程 + +- 前往: [多模态课程](./courses/) +- 理论基础与实践结合 +- 模态对齐与融合技术 +- 协同学习方法 + +## 技术发展脉络 + +### 架构演进 + +``` +单模态模型 → 简单多模态融合 → 深度跨模态对齐 → 统一多模态架构 +``` + +### 关键突破 + +1. CLIP：对比学习跨模态预训练 +2. LLaVA：视觉指令调优范式 +3. QwenVL：中文多模态能力 +4. Qwen2.5-VL：长视频与动态分辨率 + +## 学习路径 + +### 初学者路线 + +1. 视觉基础：计算机视觉基本概念 +2. CLIP 理解：跨模态对比学习原理 +3. LLaVA 入门：多模态框架基础 +4. 简单应用：图像描述与问答 + +### 进阶开发 + +1. 架构深入：多模态模型设计原理 +2. QwenVL 实践：工业级模型微调 +3. 性能优化：推理加速与模型压缩 +4. 创新应用：复杂任务开发 + +## 重要概念 + +### 跨模态对齐 + +- 语义对齐：不同模态语义映射 +- 时序对齐：视频/音频时间同步 +- 空间对齐：图像区域与文本对齐 + +### 融合策略 + +- 早期融合：特征级融合 +- 晚期融合：决策级融合 +- 深度融合：多层交互融合 + +### 指令调优 + +- 视觉指令：基于图像的任务指令 +- 多轮对话：连续多模态交互 +- 任务泛化：跨任务能力迁移 + +## 应用场景 + +### 内容理解 + +- 图像/视频描述生成 +- 多模态问答系统 +- 文档智能分析 +- 场景理解推理 + +### 创作辅助 + +- 图文内容创作 +- 视频脚本生成 +- 设计灵感提供 +- 营销素材制作 + +### 教育培训 + +- 可视化教学辅导 +- 个性化学习指导 +- 作业批改助手 +- 知识图谱构建 + +### 工业应用 + +- 质量检测分拣 +- 监控视频理解 +- 医疗影像分析 +- 自动驾驶感知 + +## 技术挑战与方向 + +### 核心难题 + +1. 模态差异：表示鸿沟 +2. 数据对齐：高质量配对数据 +3. 计算复杂：训练/推理开销 +4. 泛化能力：跨域/跨任务泛化 + +### 解决方向 + +1. 更好预训练：大规模自监督 +2. 高效架构：轻量化多模态模型 +3. 数据增强：合成与扩充 +4. 持续学习：增量学习与适应 + +## 发展趋势 + +1. 模型统一化：更统一的多模态架构 +2. 效率优化：降低开销，提升速度 +3. 能力泛化：跨模态/跨任务泛化 +4. 实时交互：支持实时多模态交互 +5. 边缘部署：适配移动与边缘设备 + +> 💡 学习建议：从 CLIP 和 LLaVA 入手，逐步深入最新进展；重视动手实践与应用开发。 diff --git a/app/docs/ai/multimodal/llava/index.mdx b/app/docs/ai/multimodal/llava/index.mdx new file mode 100644 index 0000000..b89da28 --- /dev/null +++ b/app/docs/ai/multimodal/llava/index.mdx @@ -0,0 +1,118 @@ +--- +title: "LLaVA" +description: "LLaVA多模态大模型框架：架构解析、CLIP基础、论文精读、复现实践" +date: "2025-01-27" +tags: + - llava + - multimodal + - vision-language + - clip + - visual-instruction-tuning +--- + +LLaVA (Large Language and Vision Assistant) 是多模态大模型的开创性框架，开启了视觉指令调优的新范式。 + +![](/images/word/word-img-03.png) + +## 核心架构 + +### 基本结构 + +``` +ViT视觉编码器 → 投影层跨模态对齐 → LLM语言生成 +``` + +![](/images/word/word-img-04.png) + +![](/images/word/word-img-05.png) + +### 技术特点 + +- **视觉编码**: 使用预训练的Vision Transformer处理图像 +- **跨模态对齐**: 通过投影层将视觉特征映射到语言空间 +- **语言生成**: 基于LLM进行多模态理解和生成 +- **指令调优**: 开创了视觉指令调优的新范式 + +## 学习资源 + +### 核心论文 + +- **论文**: [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) +- **代码**: [LLaVA GitHub](https://github.com/haotian-liu/LLaVA) +- **特色**: 首次提出视觉指令调优概念 + +### CLIP基础 + +**CLIP (Contrastive Language-Image Pre-training)** 是多模态学习的重要基础技术。 + +**架构设计**: + +- **双塔结构**: Text Encoder + Image Encoder +- **对比学习**: 通过(image, text)数据对进行预训练 +- **零样本能力**: 强大的图文匹配和分类能力 + +**学习资源**: + +- **论文**: [Learning Transferable Visual Representations](https://arxiv.org/abs/2103.00020) +- **代码**: [OpenAI CLIP](https://github.com/openai/CLIP) + +### LLaVA复现项目 + +计划复现LLaVA模型，深入理解多模态模型的训练流程和技术细节。 + +## 技术深度解析 + +### 视觉指令调优 + +**核心思想**: 让模型学会理解和执行基于图像的指令。 + +**数据构建**: + +- 图像描述任务 +- 视觉问答任务 +- 复杂推理任务 +- 指令遵循任务 + +### 跨模态对齐 + +**对齐挑战**: 视觉和语言模态的语义空间差异 + +**解决方案**: + +- 线性投影层映射 +- 对比学习预训练 +- 多任务联合训练 +- 渐进式对齐策略 + +## 应用场景 + +### 图像理解 + +- **图像描述**: 自动生成图像的详细描述 +- **视觉问答**: 基于图像内容回答问题 +- **场景分析**: 理解复杂场景和行为 +- **细节检测**: 识别图像中的关键细节 + +### 教育辅助 + +- **视觉教学**: 基于图像的知识讲解 +- **作业辅导**: 帮助理解图表和示例 +- **创意启发**: 基于视觉内容的创意引导 +- **学习评估**: 视觉化学习效果评估 + +### 内容创作 + +- **故事创作**: 基于图像创作故事 +- **营销文案**: 商品图像的描述生成 +- **社交媒体**: 图片配文和hashtag生成 +- **创意设计**: 设计思路和概念阐释 + +## 学习建议 + +1. **CLIP基础**: 理解跨模态预训练 +2. **论文精读**: 深入研究LLaVA技术细节 +3. **代码分析**: 阅读官方实现代码 +4. **复现实践**: 尝试简化版本实现 +5. **应用开发**: 构建实际应用场景 + +LLaVA作为多模态大模型的里程碑工作，为理解视觉语言交互和构建智能多模态系统提供了重要基础。 diff --git a/app/docs/ai/multimodal/mllm/index.mdx b/app/docs/ai/multimodal/mllm/index.mdx new file mode 100644 index 0000000..6c8c9b3 --- /dev/null +++ b/app/docs/ai/multimodal/mllm/index.mdx @@ -0,0 +1,4 @@ +--- +title: "MLLM 多模态大模型" +description: "多模态大模型（MLLM）资料" +--- diff --git a/app/docs/ai/multimodal/qwenvl/index.mdx b/app/docs/ai/multimodal/qwenvl/index.mdx new file mode 100644 index 0000000..392b34c --- /dev/null +++ b/app/docs/ai/multimodal/qwenvl/index.mdx @@ -0,0 +1,232 @@ +--- +title: "QwenVL" +description: "QwenVL多模态大模型系列：发展历程、Qwen2.5-VL技术创新、微调实践" +date: "2025-01-27" +tags: + - qwen-vl + - multimodal + - vision-language + - chinese-model + - mrope +--- + +QwenVL是阿里巴巴开源的多模态大模型系列，在中文多模态理解方面表现出色，技术不断迭代升级。 + +## 发展历程 + +### Qwen-VL (第一代) + +- **论文**: [https://arxiv.org/abs/2308.12966](https://arxiv.org/abs/2308.12966) +- **代码**: [https://github.com/QwenLM/Qwen-VL](https://github.com/QwenLM/Qwen-VL) +- **特色**: 中文多模态能力的开创性工作 +- **能力**: 图像理解、OCR、文档分析 + +### Qwen2-VL (第二代) + +- **论文**: [https://arxiv.org/abs/2409.12191](https://arxiv.org/abs/2409.12191) +- **项目**: [HuggingFace文档](https://huggingface.co/docs/transformers/main/model_doc/qwen2_vl) +- **改进**: 架构优化、性能提升 +- **新功能**: 视频理解、多轮对话增强 + +### Qwen2.5-VL (最新一代) + +- **论文**: [https://arxiv.org/abs/2502.13923](https://arxiv.org/abs/2502.13923) +- **项目**: [https://github.com/QwenLM/Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL) +- **突破**: 多项技术创新和性能飞跃 + +## Qwen2.5-VL技术创新 + +### 核心技术突破 + +#### 1. 窗口注意力 (Window Attention) + +- **目标**: 提升长序列处理效率 +- **原理**: 将注意力计算限制在局部窗口内 +- **优势**: 降低计算复杂度，支持更长序列 +- **应用**: 长文档理解、高分辨率图像处理 + +#### 2. 绝对时间编码 (Absolute Time Encoding) + +- **功能**: 增强时序建模能力 +- **应用**: 视频理解、时间序列分析 +- **优势**: 更好的时间关系建模 +- **创新**: 结合绝对和相对时间信息 + +#### 3. 动态分辨率处理 (Dynamic Resolution) + +- **特点**: 适应不同尺寸输入 +- **技术**: 自适应图像分割和处理 +- **优势**: 保持图像细节，提升处理效率 +- **应用**: 任意分辨率图像理解 + +#### 4. 长视频理解 (Long Video Understanding) + +- **能力**: 支持长时间视频内容理解 +- **技术**: 时序建模和内存优化 +- **应用**: 电影分析、监控视频理解 +- **挑战**: 计算效率和内存管理 + +#### 5. 多模态旋转位置编码 (MROPE) + +- **创新**: 改进的位置编码机制 +- **优势**: 更好的空间和时间位置建模 +- **应用**: 多模态序列理解 +- **技术**: 结合旋转位置编码和多模态特性 + +## 微调复现实践 + +### 学习资源 + +#### 视频教程 + +- **详细教程**: [B站Qwen2.5-VL微调教程](https://www.bilibili.com/video/BV1djF2emEcS) +- **内容覆盖**: 环境配置、数据准备、训练过程、效果评估 +- **适用对象**: 希望实践多模态模型微调的开发者 + +#### 目标检测微调 + +- **专项教程**: [Grounding任务微调指南](https://zhuanlan.zhihu.com/p/1910287556211352282) +- **任务特点**: 结合目标检测和语言理解 +- **应用场景**: 视觉定位、物体识别、场景理解 +- **技术要点**: 边界框预测、多任务学习 + +### 微调实践步骤 + +#### 1. 环境准备 + +```bash +# 安装依赖 +pip install torch transformers +pip install qwen-vl-utils + +# 配置GPU环境 +export CUDA_VISIBLE_DEVICES=0 +``` + +#### 2. 数据准备 + +- **数据格式**: 图文对话格式 +- **质量要求**: 高质量标注数据 +- **预处理**: 图像缩放、文本清洗 +- **增强策略**: 数据增强和平衡 + +#### 3. 模型配置 + +- **基础模型**: 选择合适的预训练权重 +- **微调策略**: LoRA或全参数微调 +- **超参数**: 学习率、批次大小等 +- **硬件配置**: GPU内存和计算要求 + +#### 4. 训练监控 + +- **损失曲线**: 监控训练和验证损失 +- **性能指标**: 准确率、BLEU分数等 +- **可视化**: 训练过程可视化分析 +- **早停策略**: 防止过拟合 + +## 源码解读 + +### 预训练三阶段设计 + +#### 第一阶段：视觉预训练 + +- **目标**: 训练视觉编码器 +- **数据**: 图像标题、视觉知识、OCR数据 +- **策略**: 仅训练ViT，冻结语言模型 +- **效果**: 建立基础的视觉理解能力 + +#### 第二阶段：多模态预训练 + +- **目标**: 跨模态对齐和理解 +- **数据**: 交错数据、VQA、视频、智能体数据 +- **策略**: 解冻所有参数，联合训练 +- **重点**: 视觉-语言对齐学习 + +#### 第三阶段：长上下文预训练 + +- **目标**: 增强长序列处理能力 +- **数据**: 视频数据、智能体交互数据 +- **策略**: 增加序列长度，优化注意力机制 +- **创新**: 长视频理解和复杂推理 + +### 技术详解资源 + +- **深度解析**: [Qwen2.5-VL源码解读](https://zhuanlan.zhihu.com/p/1921289925552210138) +- **内容**: 架构设计、训练策略、优化技巧 +- **价值**: 深入理解工业级多模态模型实现 + +## 简化版实现 + +### 手撕Qwen2.5项目 + +通过简化版实现深入理解模型架构和关键技术点。 + +#### 实现要点 + +1. **注意力机制**: 窗口注意力的简化实现 +2. **位置编码**: MROPE的核心逻辑 +3. **多模态融合**: 图文特征对齐机制 +4. **动态处理**: 可变分辨率输入处理 + +#### 学习价值 + +- 掌握多模态模型核心原理 +- 理解工程实现的技术细节 +- 积累模型开发实战经验 +- 为创新研究打下基础 + +## 应用场景 + +### 文档理解 + +- **OCR增强**: 结合文字识别和理解 +- **表格分析**: 复杂表格数据提取 +- **版面分析**: 文档结构理解 +- **多语言**: 中英文混合文档处理 + +### 视频分析 + +- **内容理解**: 视频内容自动摘要 +- **时序分析**: 动作识别和事件检测 +- **多模态问答**: 基于视频的问答系统 +- **实时处理**: 流式视频分析 + +### 智能助手 + +- **多轮对话**: 基于视觉的对话系统 +- **任务执行**: 视觉引导的任务完成 +- **创意协作**: 设计和创作辅助 +- **教育应用**: 个性化学习辅导 + +## 技术发展趋势 + +### 效率优化 + +- 模型压缩和量化 +- 推理加速技术 +- 边缘设备部署 +- 实时交互能力 + +### 能力扩展 + +- 三维视觉理解 +- 视频生成能力 +- 多模态推理 +- 跨语言理解 + +### 应用深化 + +- 行业专业化 +- 个性化定制 +- 安全可控性 +- 伦理合规性 + +## 学习建议 + +1. **循序渐进**: 从Qwen-VL开始，逐步深入最新版本 +2. **动手实践**: 完成微调项目，积累实战经验 +3. **源码研读**: 深入理解工业级实现细节 +4. **社区参与**: 关注开源社区动态和技术讨论 +5. **应用创新**: 结合具体场景开发创新应用 + +QwenVL系列代表了中文多模态大模型的最高水平，学习其技术实现和应用实践对于多模态AI开发具有重要价值。 diff --git a/app/docs/ai/multimodal/video-mm-todo/index.mdx b/app/docs/ai/multimodal/video-mm-todo/index.mdx new file mode 100644 index 0000000..d84d248 --- /dev/null +++ b/app/docs/ai/multimodal/video-mm-todo/index.mdx @@ -0,0 +1,7 @@ +--- +title: "多模态视频大模型" +description: "视频多模态资料与方向" +status: todo +--- + +资料后续补充。 diff --git a/app/docs/ai/multimodal/vit/index.mdx b/app/docs/ai/multimodal/vit/index.mdx new file mode 100644 index 0000000..96c0161 --- /dev/null +++ b/app/docs/ai/multimodal/vit/index.mdx @@ -0,0 +1,4 @@ +--- +title: "ViT 视觉编码器" +description: "Vision Transformer 资料" +--- diff --git a/app/docs/ai/recommender-systems/index.mdx b/app/docs/ai/recommender-systems/index.mdx new file mode 100644 index 0000000..dba4ab4 --- /dev/null +++ b/app/docs/ai/recommender-systems/index.mdx @@ -0,0 +1,171 @@ +--- +title: "推荐系统" +description: "推荐系统学习路径、实战项目、论文资源和相关比赛" +date: "2025-01-27" +tags: + - recommender-systems + - recsys + - llm4rec + - collaborative-filtering + - deep-learning +--- + +推荐系统是AI在工业界最重要的应用之一，从传统的协同过滤到现代的大模型推荐，本节涵盖推荐系统的完整学习路径。 + +## 8.1 推荐系统学习路径（WIP） + +### 8.1.1 学习业务 + +搜索&广告&推荐的scope不太一样： + +- **搜索**: 关注query召回，理解用户搜索意图 +- **广告**: 关注广告主&广告平台&用户体验三方博弈 +- **推荐**: 关注长期生态，用户长期价值最大化 + +**核心差异**: + +- 推荐只看item融合得分的序是否准确 +- 广告还要保证得分的距离（涉及到出价机制） +- 一般来说，推荐系统item得分可以简单概括为：**recall → ctr × cvr** +- 广告系统还要乘上bid(出价）和deep_cvr得分（如付费金额） + +**业务理解建议**: 不管是搜索广告还是推荐，都建议买一本《计算广告》把业务读懂。 + +### 8.1.2 学习模型 + +推荐系统模型发展历程： + +1. **传统方法**: 协同过滤、矩阵分解 +2. **深度学习**: DeepFM、Wide&Deep、DIN等 +3. **预训练模型**: BERT4Rec、SASRec等 +4. **大模型时代**: LLM4Rec、ChatRec等 + +## 8.2 推荐学习资料 + +### 8.2.1 王树森推荐系统课程 + +- **视频地址**: [B站推荐系统课程](https://www.bilibili.com/video/BV1pS4y1a7QT) +- **特色**: 理论与实践并重，工业界经验丰富 +- **内容**: 从基础的协同过滤到深度学习推荐模型 + +### 8.2.2 Datawhale大模型推荐系统组队学习 + +- **教程地址**: [GitHub仓库](https://github.com/datawhalechina/fun-rec) +- **在线网站**: [https://datawhalechina.github.io/fun-rec/](https://datawhalechina.github.io/fun-rec/#/) +- **特色**: 组队学习模式，适合初学者入门 + +### 8.2.3 《互联网大厂推荐算法实战》 + +- **资源链接**: [知乎文章介绍](https://zhuanlan.zhihu.com/p/623814354) +- **特色**: 工业界实战经验分享 +- **内容**: 大厂推荐算法的实际应用案例 + +## 8.3 初学者实战项目 + +### 8.3.1 阿里天池新闻推荐系统 + +- **比赛地址**: [天池新闻推荐](https://tianchi.aliyun.com/competition/entrance/531842/introduction) +- **特色**: 真实业务场景，数据质量高 +- **学习价值**: 完整的推荐系统开发流程 + +**推荐系统实战项目合集**: 整理更多实战项目资源 + +## 8.4 推荐系统学习笔记 + +📊 **推荐系统学习笔记表格**: 查看完整的学习笔记整理 + +**核心内容包括**: + +- 经典推荐算法原理 +- 深度学习推荐模型 +- 工业界部署经验 +- 评估指标和优化策略 + +## 8.5 推荐系统项目论文（待筛选） + +GitHub论文合集，持续更新。内容比较贴近业务，不太涉及生成式推荐。 + +📊 **推荐系统论文筛选表格**: 查看完整的论文筛选列表 + +**筛选状态**: + +- 未开始：待筛选 +- 评估中：正在评估 +- 已完成：已审理完成 +- 不推荐：质量不达标 +- 未开源：代码未开源 + +### LLM4REC + +大模型在推荐系统中的应用，包括： + +- **预训练推荐模型**: 使用大规模数据预训练 +- **生成式推荐**: 将推荐转化为生成任务 +- **多模态推荐**: 结合文本、图像等多模态信息 +- **对话式推荐**: 基于自然语言交互的推荐 + +## 8.6 相关比赛 + +### 腾讯广告大赛 + +- **官网**: [https://algo.qq.com](https://algo.qq.com) +- **赛题特点**: 参赛者需基于脱敏处理后用户的协同、文本、视觉等全模态历史行为数据，预测其下一次可能交互的广告 +- **技术要求**: 每个行为包含广告的ID类特征，以及多模态信息(如图像、文本等) +- **创新方向**: 通过提供基线模型，增加方案评审环节等方式，引导选手突破传统判别式推荐算法框架，探索生成式推荐 + +### 多模态短视频点击预测项目 - 马栏山杯 + +- **官网**: [https://challenge.ai.mgtv.com/](https://challenge.ai.mgtv.com/) +- **赛题**: 基于芒果TV的多模态特征，结合用户特征和行为数据，预测用户观看的视频和完播率 +- **技术亮点**: 2025 MGTV多模态视频推荐0.256+baseline完整分享 + +### Kaggle Playground - FlightRank2025 + +- **赛题**: 2025旅客个性化航班推荐 +- **目标**: 构建智能航班排名模型，预测商务旅客将从搜索结果中选择哪个航班选项 +- **参考方案**: [CatBoost Ranker Baseline](https://www.kaggle.com/code/ka1242/catboost-ranker-baseline-flightrank-2025) + +## 技术发展趋势 + +### 传统推荐 → 大模型推荐 + +1. **表示学习**: 从稀疏特征到dense embedding +2. **序列建模**: 从静态特征到动态序列 +3. **多模态融合**: 从单一模态到多模态信息 +4. **生成式推荐**: 从判别式模型到生成式模型 + +### 工业界部署考虑 + +1. **延迟要求**: 毫秒级响应时间 +2. **throughput**: 高并发请求处理 +3. **存储优化**: 模型压缩和量化 +4. **A/B测试**: 在线效果评估 + +### 评估指标体系 + +**离线指标**: + +- 准确率类：Precision, Recall, F1 +- 排序类：NDCG, MAP, MRR +- 多样性：Coverage, Diversity + +**在线指标**: + +- 点击率 (CTR) +- 转化率 (CVR) +- 停留时间 +- 用户留存 + +## 学习建议 + +1. **业务理解优先**: 深入理解推荐业务的本质和目标 +2. **算法进阶**: 从经典算法开始，逐步深入深度学习方法 +3. **实战导向**: 通过项目和比赛积累实际经验 +4. **工程能力**: 重视系统设计和工程实现能力 +5. **持续学习**: 关注大模型等前沿技术在推荐中的应用 + +## 来自群文档摘录 + +- LLM4REC（大模型推荐） +- 学习路径（WIP）：学习业务、学习模型 +- 推荐学习资料：王树森、Datawhale、实战项目 diff --git a/app/docs/ai/reinforcement-learning/index.mdx b/app/docs/ai/reinforcement-learning/index.mdx new file mode 100644 index 0000000..ca5c9a0 --- /dev/null +++ b/app/docs/ai/reinforcement-learning/index.mdx @@ -0,0 +1,149 @@ +--- +title: "强化学习" +description: "强化学习基础理论、思维链COT、GRPO等在大模型中的应用" +date: "2025-01-27" +tags: + - reinforcement-learning + - rlhf + - cot + - grpo + - ppo +--- + +强化学习在大模型时代发挥着重要作用，特别是在推理和对齐方面。从RLHF到思维链推理，强化学习为大模型的能力提升提供了关键技术支撑。 + +## 推荐学习资料 + +### 西湖大学赵世钰强化学习课程 + +**课程特色**: 强化学习的数学原理，从零开始到透彻理解 + +**学习资源**: + +- **书籍与PPT**: [GitHub仓库](https://github.com/MathFoundationRL/Book-Mathmatical-Foundation-of-Reinforcement-Learning) +- **视频课程**: [B站完整课程](https://www.bilibili.com/video/BV1sd4y167NS/) +- **特色**: 数学推导严谨，理论基础扎实 + +### RethinkFun强化学习系列 + +**推荐UP主**: [@RethinkFun](https://space.bilibili.com/18235884/) + +**核心视频**: + +- [强化学习RL基本原理](https://www.bilibili.com/video/BV1rooaYVEk8/) - 大白话解释，原理图解+公式推导 +- [PPO和GRPO算法流程](https://www.bilibili.com/video/BV15cZYYvEhz/) - 算法原理图解 + +### 入门教程 + +- **强化学习极简入门** - 通俗理解MDP、DP MC TD和Q学习、策略梯度、PPO + +## 强化学习项目论文（待筛选） + +> 📊 **论文筛选表格**: 点击查看完整的强化学习论文待筛选列表 +> +> 状态分类：未开始、评估中、已完成、不推荐、未开源 + +## GRPO复现参考 + +### TRL框架 + +- **项目**: [TRL (Transformer Reinforcement Learning)](https://github.com/huggingface/trl) +- **特色**: HuggingFace官方强化学习框架 +- **支持**: GRPO、PPO、DPO等多种算法 + +## 思维链COT + +### 核心概念 + +思维链推理是让大模型展示推理过程的重要技术，提高了模型的可解释性和推理能力。 + +### 重要论文与项目 + +#### CoT-Valve: Length-Compressible Chain-of-Thought Tuning + +- **特色**: 可压缩长度的思维链调优技术 +- **来源**: [HuggingFace Daily Papers](https://huggingface.co/papers/date/2025-07-25) + +#### MCOT (Multi-Chain-of-Thought) + +- **项目**: [Awesome-MCoT](https://github.com/yaotingwangofficial/Awesome-MCoT) +- **特色**: 多思维链推理，提升复杂推理任务性能 + +#### Latent CoT (潜在思维链) + +- **项目**: [Awesome-Latent-CoT](https://github.com/awesome-latent-cot) +- **核心理念**: 将推理过程从语言符号转移到潜在空间中，捕捉更丰富和复杂的思维过程 + +### 多模态COT + +结合视觉和文本信息的思维链推理，在多模态任务中展现强大能力。 + +### 潜空间推理综述 + +- **重要综述**: [哈工大首篇潜空间推理综述](https://zhuanlan.zhihu.com/p/1930639357087298531) +- **核心观点**: 重构大模型推理能力边界，探索潜在空间中的推理机制 + +## DEEPSEEK-R1解读 + +DeepSeek-R1作为推理能力突出的大模型，其技术细节值得深入研究： + +- 推理机制设计 +- 训练策略分析 +- 性能评估方法 + +## 学习路径建议 + +### 基础阶段 + +1. **数学基础**: 概率论、动态规划、优化理论 +2. **基本概念**: MDP、值函数、策略、回报 +3. **经典算法**: Q-learning、策略梯度、Actor-Critic + +### 进阶阶段 + +1. **现代算法**: PPO、TRPO、SAC、TD3 +2. **大模型应用**: RLHF、Constitutional AI +3. **思维链技术**: CoT、MCOT、Latent CoT + +### 实践阶段 + +1. **框架使用**: OpenAI Gym、Stable Baselines3、TRL +2. **项目实战**: 游戏AI、对话系统优化 +3. **论文复现**: 关键算法的复现与改进 + +## 应用领域 + +### 大模型对齐 + +- **RLHF**: 从人类反馈中学习，提升模型输出质量 +- **Constitutional AI**: 基于原则的AI对齐方法 +- **DPO**: 直接偏好优化，简化RLHF流程 + +### 推理能力提升 + +- **思维链推理**: 提升复杂推理任务性能 +- **工具使用**: 训练模型使用外部工具 +- **代码生成**: 提升编程能力 + +### 多智能体系统 + +- **协作学习**: 多个智能体协同解决问题 +- **竞争学习**: 通过竞争提升个体能力 +- **社会学习**: 从其他智能体行为中学习 + +## 前沿趋势 + +1. **离线强化学习**: 从静态数据集中学习策略 +2. **元学习**: 快速适应新任务的学习算法 +3. **安全强化学习**: 确保学习过程和结果的安全性 +4. **可解释强化学习**: 提升决策过程的可解释性 + +## 来自 UNSW IT-AI内卷地狱 文档摘录 + +- 思维链 COT / Multi-step COT（MCOT）/ Latent CoT + +- GRPO 学习资料： + - B站合集：https://space.bilibili.com/18235884/search?keyword=GRPO + - PPO/GRPO 算法讲解：https://www.bilibili.com/video/BV15cZYYvEhz/ + - 论文与资料汇总：https://github.com/yaotingwangofficial/Awesome-MCoT +- 强化学习数学基础书籍：GitHub https://github.com/MathFoundationRL/Book-Mathmatical-Foundation-of-Reinforcement-Learning diff --git a/package-lock.json b/package-lock.json new file mode 100644 index 0000000..aab6eab 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