AIAny
Icon for item

Foundations-of-LLMs

Publishes a structured open textbook on large language model foundations, covering language modeling, LLM architectures, prompt engineering, PEFT, model editing, and RAG.

Introduction

LLM material ages quickly, but foundational concepts still need a stable path. This project treats large language models as a curriculum rather than a pile of papers.

What Sets It Apart

Textbook chapters, related paper lists, and periodic updates from a university LLM group give readers a staged route from language modeling basics to adaptation, editing, and retrieval-augmented generation. The Chinese-first presentation fills an important access gap.

Who Should Use It

Great fit if you want a guided introduction to LLM foundations for study, teaching, or research onboarding. Look elsewhere for implementation-heavy tutorials, leaderboard tracking, or deployment guidance.

Information

  • Websitegithub.com
  • OrganizationsDAILY Lab, Zhejiang University, ZJU-LLMs
  • AuthorsDAILY Lab, Zhejiang University (ZJU-LLMs)
  • Published date2024/06/30

More Items

GitHub

Practical, full-stack tutorial for building Retrieval-Augmented Generation (RAG) systems—covers data preprocessing, vector embedding and indexing, hybrid and multimodal retrieval, generation integration, evaluation and production-ready engineering. Includes hands-on projects and examples for developers with Python experience.

GitHub

Open textbook for upper-level undergraduates that explains computational principles behind autonomous robots — mechanisms, sensors, actuators, perception, and planning — with exercises and simulation assets. Distributed as LaTeX source under a CC-BY-NC-ND license and accompanied by course materials and Webots examples.

GitHub

Provides 150+ executed Jupyter notebooks and code that reproduce the book 'Machine Learning for Algorithmic Trading (2nd ed.)' — covers feature engineering, alternative-data signal extraction, backtesting, NLP, deep learning and reinforcement learning for trading; best for quant researchers and practitioners.