Datawhale Rebuilds Stanford's CS336 Into a Chinese Open-Source LLM Course
A Chinese-language adaptation of Stanford's CS336 bundles 16 chapters and 6 hands-on assignments covering the full LLM stack, from BPE to GRPO.
- Datawhale released diy-llm, a Chinese adaptation of Stanford CS336 with 16 chapters and 6 coding assignments.
- The repo passed 1.7k stars and 180 forks, trending on GitHub this week.
- Assignments walk from hand-written BPE tokenizers to FlashAttention-2 in Triton to GRPO alignment.
- Systems chapters cover FSDP, ZeRO, PagedAttention, speculative decoding, GPTQ and AWQ quantization.
- Localization adds Qwen and DeepSeek case studies plus a downloadable PDF of lecture notes.
- Licensed CC BY-NC-SA 4.0, with full source at datawhalechina.github.io/diy-llm.
Stanford’s CS336, Language Modeling from Scratch, teaches students to build the components behind a language model. Datawhale’s open-source project repository adapts that material for Chinese learners through localized notes, examples based on Qwen and DeepSeek, and six coding assignments that cover the model pipeline from tokenization to alignment.
The repository has attracted more than 1,700 GitHub stars and appeared in GitHub Trending. Its code-heavy structure gives developers a guided route through model architecture, training systems, data preparation, scaling, alignment, and evaluation.
From tokenizer to GRPO in six assignments
Across 16 chapters, each assignment builds on infrastructure introduced earlier in the course. Learners finish with a readable codebase that touches every major stage of language-model development.
| Assignment | What developers build | Core concepts |
|---|---|---|
| 1. Basics | Write a BPE tokenizer, model architecture, and optimizer, then train a minimal language model. | Tokenization, transformer components, optimization, and training loops |
| 2. Systems | Profile training, implement FlashAttention-2 in Triton, and add distributed training. | GPU performance, custom kernels, memory use, and parallelism |
| 3. Scaling | Fit scaling laws and use them to predict model behavior at larger compute budgets. | Compute allocation, empirical prediction, and experiment design |
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