nndl/llm-beginner resource

Health v2 · maintenance only

Adoption not part of the score

6681 stars · 1356 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A step-by-step beginner tutorial series (with a companion textbook) for learning large language models and agents through six progressive hands-on Python tasks, from implementing a Transformer and mini-GPT to fine-tuning, RAG, and building tool-calling and coding agents. Each task includes data download scripts, self-check evaluation harnesses, and LLM-assisted code review prompts.

Use cases

• learn how LLMs work by implementing a mini-GPT from scratch
• hands-on tutorial for building AI agents
• practice instruction fine-tuning and DPO alignment
• build a RAG pipeline step by step
• learn transformer architecture with exercises
• beginner exercises for tool-calling agents
• self-study curriculum for large language models

When to choose

• you have Python and basic deep learning knowledge and want a structured, hands-on LLM/agent curriculum
• you prefer learning by implementing from scratch before using frameworks
• you want self-checkable exercises with evaluation scripts
• you read Chinese and want a tutorial aligned with the NNDL textbook series

When to avoid

• you need production-ready LLM or agent libraries rather than educational exercises
• you want an English-language tutorial
• you lack a GPU and cannot use quantized models
• you need a quick reference rather than a multi-week course

Facets

learning-resource · maturity active

machine-learning deep-learning llm-training rag agent-framework prompt-engineering large-language-models deep-learning tutorials python cross-platform llm agents hands-on-tutorial step-by-step chinese transformer mini-gpt sft dpo rag tool-calling coding-agent nndl ai-agents natural-language-processing

Member repositories

For agents

markdown · JSON · MCP: product_card(name="nndl/llm-beginner")