This repository contains my implementations for all assignments from Stanford's CS336: Language Modeling from Scratch course (Spring 2025). The course covers the fundamentals of building, training, and deploying large language models.
CS336 provides a comprehensive, hands-on approach to understanding language models by implementing them from the ground up. Topics include model architecture, systems optimization, scaling laws, data processing, and alignment techniques.
Core Topics: Tokenization, Transformer Architecture, Language Model Training
Implementation of fundamental language modeling components:
- Byte Pair Encoding (BPE) tokenization
- Multi-head self-attention mechanisms
- Transformer decoder architecture
- Basic training loops and optimization
- Training on TinyStories and OpenWebText datasets
Note: CPU parallel tokenizer training is not implemented in this assignment.
Core Topics: Performance Optimization, Distributed Training, Systems Engineering
Deep dive into efficient LM training systems:
- Flash Attention and kernel-level optimizations
- Mixed precision training (fp16/bf16)
- Memory profiling and optimization
- Distributed data parallelism
- Model parallelism techniques
Note: Distributed parallel training components are not implemented in this assignment.
Core Topics: Scaling Laws, Compute-Optimal Training, Model Scaling
Exploration of scaling behaviors in language models:
- Empirical investigation of scaling laws
- Compute-optimal training strategies
- Model size vs. training data trade-offs
- Performance prediction across scales
Note: Due to the lack of official API access, this assignment could not be fully tested. It is recommended to use custom synthetic datasets or publicly available datasets for verification.
Core Topics: Data Curation, Quality Filtering, Dataset Construction
Building high-quality training datasets:
- Web scraping and data collection
- Content filtering and deduplication
- Quality classification models
- Data pipeline construction
- Training on curated datasets
Note: This assignment was completed using smaller datasets. Large-scale dataset training was not performed.
Core Topics: RLHF, Preference Learning, Model Safety
Aligning language models with human preferences:
- Direct Preference Optimization (DPO)
- Group Relative Policy Optimization (GRPO)
- Reward modeling
- Safety alignment techniques
- Instruction tuning
Note: The supplemental assignment on safety RLHF has not been implemented yet. Stay tuned for future updates!
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├── assignment1-basics/ # Fundamental LM implementation
├── assignment2-systems/ # Systems optimization
├── assignment3-scaling/ # Scaling law investigations
├── assignment4-data/ # Data processing pipeline
├── assignment5-alignment/ # Alignment techniques
└── README.md # This file
Each assignment directory contains:
- Complete source code implementation
- Unit tests
- Assignment handout (PDF)
- Assignment-specific README with setup instructions
- Language: Python 3.11+
- Deep Learning: PyTorch
- Dependency Management: uv
- Testing: pytest
- Key Libraries:
torch- Deep learning frameworkflash-attn- Optimized attention implementationeinops- Tensor operationswandb- Experiment tracking
- Install
uvfor dependency management:
# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# or via pip
pip install uv
# or via Homebrew
brew install uv- Python 3.11 or higher
Each assignment is self-contained. Navigate to the assignment directory and use uv run:
cd assignment1-basics
uv run pytest # Run tests
uv run python scripts/train.py # Run training scriptDependencies are automatically installed based on each assignment's pyproject.toml.
Through these assignments, I gained practical experience in:
- Model Architecture: Implementing Transformers from scratch, understanding attention mechanisms and layer normalization
- Training Infrastructure: Building robust training loops, implementing gradient accumulation and mixed precision training
- Systems Optimization: Profiling memory usage, optimizing compute kernels, implementing distributed training
- Scaling Principles: Understanding how model performance scales with compute, data, and parameters
- Data Engineering: Constructing high-quality datasets through filtering, deduplication, and quality assessment
- Alignment Techniques: Implementing RLHF and preference optimization to align models with human values
- Course: CS336 - Language Modeling from Scratch
- Institution: Stanford University
- Term: Spring 2025
- Course Website: https://stanford-cs336.github.io/spring2025/
- Large files (datasets, model checkpoints, trained models) have been excluded from this repository
- Each assignment includes comprehensive unit tests to verify implementations
- For detailed assignment requirements, refer to the PDF handouts in each assignment directory
This code is provided for educational purposes. Please refer to individual assignment directories for specific licensing information.
本仓库包含我完成的斯坦福大学 CS336《从零开始的语言模型》课程（2025年春季）的所有作业实现。该课程涵盖了构建、训练和部署大型语言模型的基础知识。
CS336 提供了一种全面的实践方法来理解语言模型，通过从头实现来深入掌握。主题包括模型架构、系统优化、缩放定律、数据处理和对齐技术。
核心主题： 分词、Transformer架构、语言模型训练
实现语言建模的基础组件：
- 字节对编码（BPE）分词
- 多头自注意力机制
- Transformer解码器架构
- 基础训练循环和优化
- 在TinyStories和OpenWebText数据集上训练
注意： 本作业未实现CPU并行分词器训练。
核心主题： 性能优化、分布式训练、系统工程
深入研究高效的语言模型训练系统：
- Flash Attention和内核级优化
- 混合精度训练（fp16/bf16）
- 内存分析和优化
- 分布式数据并行
- 模型并行技术
注意： 本作业未实现分布式并行训练组件。
核心主题： 缩放定律、计算最优训练、模型扩展
探索语言模型中的缩放行为：
- 缩放定律的实证研究
- 计算最优训练策略
- 模型大小与训练数据的权衡
- 跨规模的性能预测
注意： 由于缺乏官方API访问权限，本作业无法完全测试。建议使用自定义合成数据集或公开可用的数据集进行验证。
核心主题： 数据管理、质量过滤、数据集构建
构建高质量的训练数据集：
- 网页抓取和数据收集
- 内容过滤和去重
- 质量分类模型
- 数据pipeline构建
- 在精选数据集上训练
注意： 本作业使用较小的数据集完成。未进行大规模数据集训练。
核心主题： RLHF、偏好学习、模型安全
使语言模型与人类偏好对齐：
- 直接偏好优化（DPO）
- 组相对策略优化（GRPO）
- 奖励建模
- 安全对齐技术
- 指令调优
注意： 安全RLHF补充作业尚未实现。敬请期待后续更新！
.
├── assignment1-basics/ # 基础语言模型实现
├── assignment2-systems/ # 系统优化
├── assignment3-scaling/ # 缩放定律研究
├── assignment4-data/ # 数据处理pipeline
├── assignment5-alignment/ # 对齐技术
└── README.md # 本文件
每个作业目录包含：
- 完整的源代码实现
- 单元测试
- 作业说明（PDF）
- 作业专属的README及安装说明
- 编程语言： Python 3.11+
- 深度学习： PyTorch
- 依赖管理： uv
- 测试： pytest
- 关键库：
torch- 深度学习框架flash-attn- 优化的注意力实现einops- 张量操作wandb- 实验跟踪
- 安装
uv进行依赖管理：
# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# 或通过 pip
pip install uv
# 或通过 Homebrew
brew install uv- Python 3.11 或更高版本
每个作业都是独立的。进入作业目录并使用 uv run：
cd assignment1-basics
uv run pytest # 运行测试
uv run python scripts/train.py # 运行训练脚本依赖项会根据每个作业的 pyproject.toml 自动安装。
通过这些作业，我获得了以下实践经验：
- 模型架构： 从零实现Transformers，理解注意力机制和层归一化
- 训练基础设施： 构建健壮的训练循环，实现梯度累积和混合精度训练
- 系统优化： 分析内存使用，优化计算内核，实现分布式训练
- 缩放原理： 理解模型性能如何随计算、数据和参数规模变化
- 数据工程： 通过过滤、去重和质量评估构建高质量数据集
- 对齐技术： 实现RLHF和偏好优化，使模型与人类价值观对齐
- 课程： CS336 - 从零开始的语言模型
- 院校： 斯坦福大学
- 学期： 2025年春季
- 课程网站： https://stanford-cs336.github.io/spring2025/
- 本仓库已排除大型文件（数据集、模型检查点、训练好的模型）
- 每个作业都包含全面的单元测试以验证实现
- 详细的作业要求请参考各作业目录中的PDF文档
本代码仅供教育目的使用。具体许可信息请参考各作业目录。