CookLLM

CookLLM is a curated, practical course for learning how large language models work by building them yourself—from first principles. Instead of relying on black-box APIs, you implement core components end to end, including tokenization, transformer modeling, GPU kernels for training/inference, and the full training pipeline.

The curriculum is organized around the key decisions involved in training an LLM. It covers fundamentals (Tokenization, Transformer, Optimization), system-level GPU concepts (GPU Kernels, Parallelism, Inference Acceleration), scaling considerations (Scaling Laws and compute budgeting), and the data pipeline (cleaning, filtering, deduplication, and mixing). You also work through evaluation practices and reliability-oriented benchmarking.

A core part of the experience is post-training and alignment. You’ll learn and implement SFT and RLHF-style approaches (including PPO, GRPO, and DPO) as part of bringing a small model to alignment. Then you move into a hands-on training track where you train and align a small model using provided code to run end-to-end.

For learners who want “under the hood” understanding, CookLLM is built for developers and ML practitioners with Python and basic PyTorch familiarity who want to reason about real engineering tradeoffs and troubleshoot issues at the root-cause level. Pricing is a one-time purchase of $39 for lifetime access, including all current and future chapters and runnable source code, with private GitHub repository access.

CookLLM 主要用来学什么？

用从零构建的方式理解并实现大语言模型：从分词器、Transformer 到 GPU 相关系统实现、训练流水线，再到后训练对齐（如 SFT/RLHF）。

课程会不会只教用现成框架？

不会。课程强调不依赖高层封装，对关键模块从第一性原理亲手实现，帮助你在模型出问题时能定位根因并知道怎么改。

我需要什么基础才能开始？

建议具备 Python 以及基础 PyTorch 经验。课程面向希望理解底层实现、并能把公式落到可运行代码的人。

学习包含哪些训练与对齐内容？

会覆盖后训练与对齐方法，包括 SFT，以及 RLHF 路线中的 PPO 等，同时也涉及 DPO、GRPO 等对齐相关内容。

CookLLM 的动手训练部分能做什么？

你会从零训练并对齐一个小模型，端到端跑通所需流程（包含从数据准备、分词器训练、模型搭建到预训练与后训练对齐）。

CookLLM 的价格和访问方式是什么？

提供一次性买断定价：$39 终身访问高级功能，包括所有章节与代码、交互式可视化，以及后续所有更新章节；不包含订阅。