diy-llm
Covers pre-training data, Tokenizer, Transformer, MoE,distributed training, Scaling Laws, inference & alignment .6 progressive code assignments for full-stack LLM learning | 涵盖预训练数据、分词器、Transformer、MoE、分布式训练、缩放定律、推理与对齐，6 项渐进代码作业，掌握 LLM 全栈知识
Learn to build and understand large language models through hands-on coding assignments.
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Free · no card · unsubscribe anytimeCovers pre-training data, Tokenizer, Transformer, MoE,distributed training, Scaling Laws, inference & alignment .6 progressive code assignments for full-stack LLM learning | 涵盖预训练数据、分词器、Transformer、MoE、分布式训练、缩放定律、推理与对齐，6 项渐进代码作业，掌握 LLM 全栈知识
diy-llm has 2.9k stars on GitHub. It has been forked 276 times. diy-llm is written mainly in Jupyter Notebook. It has been in active development since 2025. Its main topics are cs336, datawhale, gpu-programming, grpo.
What is diy-llm?
Covers pre-training data, Tokenizer, Transformer, MoE,distributed training, Scaling Laws, inference & alignment .6 progressive code assignments for full-stack LLM learning | 涵盖预训练数据、分词器、Transformer、MoE、分布式训练、缩放定律、推理与对齐，6 项渐进代码作业，掌握 LLM 全栈知识
Is diy-llm open source?
diy-llm is an open-source project.
Is diy-llm free?
Yes. diy-llm is free and open source — you can use, modify and self-host it.
What language is diy-llm written in?
diy-llm is written mainly in Jupyter Notebook.
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