CookLLM offers an in-depth educational platform focused on mastering large language models through hands-on, from-scratch implementation. The curriculum is structured into six main sections, covering foundational concepts like tokenization, Transformer architecture, and optimization systems, extending to advanced topics such as GPU kernels, parallel computing, and inference acceleration. It also delves into scaling laws, data preparation (cleaning, filtering, deduplication), and model evaluation benchmarks.
A significant portion is dedicated to post-training alignment techniques, including SFT, RLHF, GRPO, and DPO. The platform provides a practical "hands-on training" track, guiding users to build and align a small LLM (BentoLM 29M→134M) from the ground up. This includes data preparation, tokenizer training (RustBPE, tiktoken), model construction (BentoLM, RoPE, Muon), pre-training, and post-training alignment. The learning experience is enhanced with interactive documentation that combines formulas, diagrams, and runnable code, alongside two complete code repositories (recipes for principles, bento for hands-on training). Users gain lifetime access to a private GitHub repository with continuous updates.
This course is designed for AI/ML engineers, developers, and researchers who aim to deeply understand and build large language models from foundational principles to advanced applications.
CookLLM serves as a robust educational resource for individuals aiming to develop a deep, practical understanding of large language model engineering. It provides a structured path to master LLM architecture, training, and application through comprehensive, hands-on coding exercises and detailed documentation.
$139
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