cs336

动手学大模型全栈：CS336 中文精讲 · PyTorch 手搓 Transformer · 单卡复现 Pretrain/SFT/LoRA/DPO/GRPO/RLVR · PEFT/Agent/RAG 落地（含真实实验、曲线与复现脚本）

Building a small LLM in PyTorch from scratch - a step-by-step journey inspired by Stanford CS336.

The NoteBook and Assignments implemention via Learning CS336 Spring 2026😛

Comprehensive illustrated notes for Stanford CS336 (Spring 2025) — Language Modeling from Scratch. Live: qihongruan.github.io/cs336/

实现CS336的作业1，并从头开始构建一个transformer模型。Implement CS336's job 1 and build a transformer from scratch

CS336 Final Project - Vietnamese Legal Text Retrieval

Learning CS336...

Rutgers CS:336 | Principles of Info and Data Management Spring 2022

Implementations, assignments, and experiment notes for Stanford CS336: Language Modeling from Scratch.

CS336 作业 5：基于 Qwen2.5 模型的 LLM 对齐与推理强化学习。完整实现了监督微调（SFT）与组相对策略优化（GRPO）算法，并在 GSM8K 数据集上完成零样本、在策与离策的训练与评估对比。

Self-study notes and labs for Stanford CS336: language modeling, transformers, GRPO, BPE

Implementation of a Decoder-only Transformer language model from scratch for CS336, featuring a byte-level BPE tokenizer, RoPE, Multi-Head Self-Attention and SwiGLU FFN. Trained on TinyStories with 1.39 Val Loss.

160+ pages of handwritten notes from Stanford CS336: Language Modeling from Scratch, covering language modeling, LLM systems, and full-stack AI engineering from data to deployment and evaluation and RL alignment

Living comparison table of LLM architectural choices (norm, attention, MoE, positional encoding, and more) from the Original Transformer (2017) to frontier models (2026). Based on Harm de Vries's figure, Sebastian Raschka's Big LLM Architecture Comparison, and Tatsunori Hashimoto's Stanford CS 336 lecture.

Project for Information Retrieval Courses

Turn technical lecture videos into timestamped, enriched chapter-book learning handouts.

中文 CS336 学习教练 Codex Skill：从概念诊断与实现辅导，到解释、推导、实现和迁移四项证据考核，支持项目级学习记录。

My solutions to Stanford CS336: Language Modeling from Scratch, Spring 2026

Implementation-first Stanford CS336 study repo covering tokenization, transformer/language-modeling fundamentals, training loops, and evaluation notes.