@NFTCPS: Brothers, doing AI without large models is like doing nothing! Today I have to recommend an open-source masterpiece 'Foundations of LLMs' to you. Don't wait, just read it! This book doesn't beat around the bush—it goes deep from the start! From getting started with large language models to architectural evolution, and then it breaks down Prompt engineering, parameter-efficient fine-tuning, model editing, RAG (Retrieval-Augmented Generation) and other hardcore techniques in one go—a one-stop service.
Summary
This article promotes the open-source book 'Foundations of LLMs', which systematically explains knowledge about large language models, and introduces the multi-agent development framework Agent-Kernel.
View Cached Full Text
Cached at: 05/08/26, 03:36 PM
Large Model Fundamentals
Similar Articles
@wsl8297: Sharing an easy-to-read open-source book 'Foundations of Large Models'. From an introduction to large language models to architectural evolution, then to key technologies such as Prompt engineering, parameter-efficient fine-tuning, model editing, retrieval-augmented generation (RAG), all in one book. GitHub: https://github.com/ZJU-LLMs/…
The Zhejiang University team open-sourced an easy-to-understand textbook on large models 'Foundations of Large Models', covering from architectural evolution to key technologies like RAG, accompanied by the Agent-Kernel multi-agent framework.
@XAMTO_AI: Stop bookmarking those flashy but useless AI tutorials. This 'Hands-On Large Models' is what you really need—open source, free, and code that runs. The book covers 12 chapters, guiding you step by step through the complete workflow of deploying large models: ① Language Model Basics ② Prompt Engineering ③ Semantic Search ④ Model Fine-Tuning ⑤ Multimodal…
Recommend an open-source free tutorial 'Hands-On Large Models', covering 12 chapters including language model basics, prompt engineering, semantic search, model fine-tuning, multimodal applications, etc. All code can be run directly in Colab.
@Jolyne_AI: I found a solid hands-on tutorial for large language models on GitHub: the "Hands-On Large Model Series." It takes you from zero to mastering the entire tech stack. Using a combination of videos, documents, and code, it links key capabilities like fine-tuning, deployment, RAG, and Agent into a reproducible learning path — each knowledge point can be directly practiced...
Recommend the "Hands-on Large Model Series" tutorial on GitHub, which systematically explains practical techniques such as fine-tuning, deployment, RAG, and Agent through videos, documents, and code, suitable for AI developers to improve their engineering skills.
@tanzhengmc97: https://x.com/tanzhengmc97/status/2066531753762656730
Explained the operating principles of large models in easy-to-understand language, including word vectors, Transformer attention mechanism, next-word prediction training, and emergent abilities, suitable for beginners to understand basic AI concepts.
@Jolyne_AI: An open-source hands-on book: "Hands-On Large Language Models". The book has 12 chapters, progressing from language model fundamentals to prompt engineering, semantic search, model fine-tuning, and multimodal applications, covering the key paths to deploying large models in practice. GitHub: h…
An open-source hands-on book "Hands-On Large Language Models", with 12 chapters covering language model fundamentals, prompt engineering, semantic search, model fine-tuning, and multimodal applications. It provides runnable code examples, ideal for practical learning.