Datawhale Releases Diy-LLM: Chinese Tutorial for Stanford CS336 with 16 Chapters & 6 Hands-on Assignments

The Datawhale community has released Diy-LLM , a comprehensive Chinese-language tutorial series based on Stanford's CS336 course "Language Modeling from Scratch." The project aims to bridge the gap between using LLMs via APIs and understanding their internal mechanics — covering model architecture, training methods, data processing, and systems engineering in an integrated way.

Project Scope and Structure

Diy-LLM currently comprises 16 chapters and 6 practical assignment groups . Core chapters and all assignments are available for study; Chapter 1 (Tool Usage) is still being refined, and Chapter 16 (Extended Content) receives ongoing updates. The tutorial provides Chinese explanations, accompanying code, and hands-on exercises so learners can incrementally build and understand each component.

Access points:

GitHub repository: https://github.com/datawhalechina/diy-llm Online reading: https://datawhalechina.github.io/diy-llm/ Offline PDF: available via the repository's Releases page ( https://github.com/datawhalechina/diy-llm/releases/latest/)

Five-Stage Learning Path

Stage 1: Build Your First Language Model from Basic Components (Chapters 1–5 + Assignment 1)

Starting from tooling, tokenizers, and PyTorch, the tutorial progresses into language model architecture, training details, and Mixture-of-Experts (MoE). Assignment 1: Basic Component Implementation guides learners through a complete hand-written path from BPE tokenization to a Transformer model — implementing RoPE, RMSNorm, SwiGLU, the tokenizer, model structure, and optimizer, culminating in a full small-model training run.

Stage 2: GPU & Distributed Training — Understand Why Models Run Fast or Slow (Chapters 6–9 + Assignments 2 & 3)

After getting a model running, the focus shifts to performance: where time is spent and what consumes VRAM. Chapters 6–9 cover GPU principles and optimization, high-performance programming, distributed training, and Scaling Laws. Topics include memory bandwidth, operator fusion, FlashAttention, Triton, ZeRO, and FSDP. Assignment 2: System Optimization and Assignment 3: Scaling Laws provide experiments in performance profiling, attention computation optimization, and scaling-law fitting so learners can measure what each optimization actually improves and how to estimate compute–data–model ratios before scaling up.

Stage 3: Connect Data, Inference & Evaluation into a Complete Loop (Chapters 10–12 + Assignments 4 & 6)

Chapters 10–12 address inference, data engineering, and evaluation/benchmarking. Assignment 4: Data Processing walks through cleaning, filtering, and deduplication of pre-training data, showing how raw web pages become high-quality corpora. The inference chapter dissects KV Cache, quantization, and batching effects on efficiency. Assignment 6: Model Evaluation uses lm-evaluation-harness and EvalScope to establish evaluation criteria across language understanding, coding, and mathematical reasoning.

Stage 4: From Pre-training to Alignment — Shaping Model Capabilities (Chapters 13–14 + Assignment 5)

Chapters 13–14 outline the full training pipeline and reinforcement learning with verifiable rewards. They cover pre-training, supervised fine-tuning (SFT), DPO, RLHF, and GRPO, emphasizing the distinct objectives, data formats, and optimization methods at each stage. Assignment 5: Model Alignment provides SFT and RL practice using mathematical reasoning tasks, letting learners observe alignment effects on reasoning ability firsthand.

Stage 5: Extend to Multimodal & Frontier Topics (Chapters 15–16)

The final chapters expand to multimodal models — CLIP, vision-language models (VLMs) — and LLM inference directions. The project plans to continuously add architecture breakdowns of mainstream models, linking fundamental building blocks to cutting-edge model designs.

Target Audience & Prerequisites

Diy-LLM suits university students, researchers, and developers who want systematic understanding of LLM training and engineering details. Prerequisites: Python and PyTorch deep-learning basics. CUDA knowledge can be acquired alongside Chapters 6–7; those who have already written kernels can jump straight to performance analysis and distributed training. The recommended approach: start small, run the pipeline end-to-end, then gradually increase scale and complexity.

Community & Contribution

The project invites feedback, issue reports, and experimental contributions via the GitHub repository to co-build future frontier-model architecture interpretations.