@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…
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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.
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Cached at: 07/01/26, 02:08 PM
The Illustrated LLM Book
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@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.
@GitHub_Daily: Want to understand the underlying principles of large language models? Most resources only cover theory or provide source code, leaving you still confused. Stumbled upon this open-source tutorial, EveryonesLLM, which guides us step by step to build a complete large language model from scratch on Google Colab, writing code throughout. The whole tutorial is divided into...
EveryonesLLM is an open-source tutorial that provides 29 chapters of Colab notebooks. It teaches users step by step to build a complete large language model from scratch on Google Colab, including pre-training and instruction fine-tuning, and supports Chinese.
@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.
@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.
@Xx15573208: I've read many articles about Transformers and understand the theory, but when I actually sit down to write code, I have no idea where to start. LLMs-from-scratch is specifically designed to solve this problem: it accompanies the book "Build a Large Language Model" and guides you through implementing GPT from scratch using PyTorch…
LLMs-from-scratch is a GitHub repository that accompanies the book "Build a Large Language Model," providing complete code to implement GPT from scratch with PyTorch, covering the full pipeline including pretraining, fine-tuning, and RLHF. It has gained 93K+ stars and is ideal for developers who want to deeply understand the principles behind large language models.